{
 "id": "r13f88a3ffda",
 "found": true,
 "parent_id": "r701f9a0e854",
 "created_at": "2026-08-31 22:00:35.642199+00:00",
 "rows": [
  {
   "url": "https://best-ai.news/ai-model-releases-2026",
   "title": "Latest AI Models 2026: 99 Released \u2014 Complete Catalog",
   "published_at": "2026-08-24T09:16:53",
   "organization": "Thinking Machines Lab",
   "model": "Inkling",
   "release_date": "2026-07",
   "parameters": "975B-parameter",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort",
   "organization_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ],
   "model_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ],
   "release_date_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ],
   "parameters_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ],
   "significance_evidence": [
    "Latest AI Models 2026 \u2014 99 Released",
    "## July 202615 models",
    "Inkling \u2014 Thinking Machines Lab's first model trained from scratch: a 975B-parameter open-weights multimodal Mixture-of-Experts with 41B active parameters and controllable thinking effort, released under Apache 2.0 in July 2026."
   ]
  },
  {
   "url": "https://arxiv.org/html/2604.07190v1",
   "title": "The ATOM Report:Measuring the Open Language Model Ecosystem",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": "3.5B",
   "context_window": null,
   "license": null,
   "significance": "Placed among the hottest launches in the reference set, reaching high relative adoption scores across 7, 14, and 30 day milestones.",
   "organization_evidence": [
    "# The ATOM Report:\nMeasuring the Open Language Model Ecosystem",
    "## 1 Introduction",
    "The growth of the Chinese model ecosystem can largely be attributed to Alibaba\u2019s Qwen, which is responsible for almost a billion cumulative downloads by March 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# The ATOM Report:\nMeasuring the Open Language Model Ecosystem",
    "## 6 The Relative Adoption Metric (RAM)",
    "### 6.2 Using RAM to Measure New Models",
    "For example, using an April 2, 2026 snapshot of Hugging Face data, the RAM framework makes the February 2026 Qwen3.5 rollout easier to compare across scales."
   ],
   "parameters_evidence": [
    "# The ATOM Report:\nMeasuring the Open Language Model Ecosystem",
    "## 6 The Relative Adoption Metric (RAM)",
    "### 6.2 Using RAM to Measure New Models",
    "In the 1\u20135B bucket, shown in Fig. 16, Qwen3.5-4B reached 3.45, 5.29, and 3.27 the bucket median at 7, 14, and 30 days."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# The ATOM Report:\nMeasuring the Open Language Model Ecosystem",
    "## 6 The Relative Adoption Metric (RAM)",
    "### 6.2 Using RAM to Measure New Models",
    "The clearest breakout model was Qwen3.5-35B-A3B, which reached 16.13 at 7 days, 11.10 at 14 days, and 4.54 at 30 days, placing it among the hottest launches in our reference set."
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": "397B total / 17B active",
   "context_window": null,
   "license": null,
   "significance": "integrating advances in multimodal learning, architectural efficiency, and large-scale reinforcement learning",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-02-16 | Qwen | **Qwen3.5 is released** under Apache 2.0, starting with the Qwen3.5-397B-A17B Mixture-of-Experts model; additional sizes follow within weeks (122B-A10B, 35B-A3B, and 27B on 2026-02-24, and 9B / 4B / 2B / 0.8B on 2026-03-02). Alibaba describes the generation as integrating advances in multimodal learning, architectural efficiency, and large-scale reinforcement learning. Sources: [QwenLM/Qwen3.6 on GitHub (news log for Qwen3.5 and Qwen3.6)](https://github.com/QwenLM/Qwen3.6), [Qwen3.5 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen35). |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-02-16 | Qwen | **Qwen3.5 is released** under Apache 2.0, starting with the Qwen3.5-397B-A17B Mixture-of-Experts model; additional sizes follow within weeks (122B-A10B, 35B-A3B, and 27B on 2026-02-24, and 9B / 4B / 2B / 0.8B on 2026-03-02). Alibaba describes the generation as integrating advances in multimodal learning, architectural efficiency, and large-scale reinforcement learning. Sources: [QwenLM/Qwen3.6 on GitHub (news log for Qwen3.5 and Qwen3.6)](https://github.com/QwenLM/Qwen3.6), [Qwen3.5 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen35). |"
   ],
   "parameters_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-02-16 | Qwen | **Qwen3.5 is released** under Apache 2.0, starting with the Qwen3.5-397B-A17B Mixture-of-Experts model; additional sizes follow within weeks (122B-A10B, 35B-A3B, and 27B on 2026-02-24, and 9B / 4B / 2B / 0.8B on 2026-03-02). Alibaba describes the generation as integrating advances in multimodal learning, architectural efficiency, and large-scale reinforcement learning. Sources: [QwenLM/Qwen3.6 on GitHub (news log for Qwen3.5 and Qwen3.6)](https://github.com/QwenLM/Qwen3.6), [Qwen3.5 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen35). |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-02-16 | Qwen | **Qwen3.5 is released** under Apache 2.0, starting with the Qwen3.5-397B-A17B Mixture-of-Experts model; additional sizes follow within weeks (122B-A10B, 35B-A3B, and 27B on 2026-02-24, and 9B / 4B / 2B / 0.8B on 2026-03-02). Alibaba describes the generation as integrating advances in multimodal learning, architectural efficiency, and large-scale reinforcement learning. Sources: [QwenLM/Qwen3.6 on GitHub (news log for Qwen3.5 and Qwen3.6)](https://github.com/QwenLM/Qwen3.6), [Qwen3.5 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen35). |"
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "unifies reasoning, multimodal understanding, and agentic coding in one model",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ],
   "model_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ],
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ],
   "license_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ],
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-03-16 | Mistral | **Mistral Small 4 is released** under Apache 2.0, a sparse Mixture-of-Experts model that unifies reasoning, multimodal understanding, and agentic coding in one model, with a 256K context window and configurable reasoning effort. Source: [Mistral Small 4](https://mistral.ai/news/mistral-small-4/). |"
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "focus on agentic workflows",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ],
   "model_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ],
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ],
   "parameters_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ],
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-02 | Gemma | **Gemma 4 is released** (edge E2B/E4B sizes plus larger dense and Mixture-of-Experts models), Google's first Gemma generation published under the Apache 2.0 license rather than the Gemma Terms of Use, with a focus on agentic workflows. Source: [Introducing Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). |"
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Qwen",
   "model": "Qwen3.6",
   "release_date": "2026-04-16",
   "parameters": "35B-A3B and 27B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ],
   "model_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ],
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ],
   "parameters_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ],
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-16 | Qwen | **Qwen3.6 is released** under Apache 2.0, with Qwen3.6-35B-A3B (2026-04-16) followed by Qwen3.6-27B (2026-04-22). Alibaba describes the generation as building on Qwen3.5 while prioritizing stability, real-world coding utility, and direct community feedback. Sources: [QwenLM/Qwen3.6 on GitHub](https://github.com/QwenLM/Qwen3.6), [Qwen3.6 collection on Hugging Face](https://huggingface.co/collections/Qwen/qwen36). |"
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4 Preview",
   "release_date": "2026-04-24",
   "parameters": "DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active)",
   "context_window": "1M-token",
   "license": "open weights",
   "significance": "built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "model_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "parameters_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "context_window_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "license_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ],
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-04-24 | DeepSeek | **DeepSeek-V4 Preview is released with open weights**, in two Mixture-of-Experts variants - DeepSeek-V4-Pro (1.6T total / 49B active parameters) and DeepSeek-V4-Flash (284B total / 13B active) - built on token-wise compression and DeepSeek Sparse Attention, and making a 1M-token context window the standard across DeepSeek's official services. Source: [DeepSeek-V4 Preview Release](https://api-docs.deepseek.com/news/news260424). |"
   ]
  },
  {
   "url": "https://hidekazu-konishi.com/entry/open_weights_llm_release_history_and_timeline.html",
   "title": "Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-05-22",
   "parameters": "128B",
   "context_window": "256K",
   "license": "modified MIT license",
   "significance": "merges instruction-following, reasoning, and coding in a single set of weights",
   "organization_evidence": [
    "## Open-Weights LLM Release History and Timeline - Llama, Mistral, Qwen, DeepSeek, and gpt-oss Model Families, Capability Evolution, and Licenses",
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "model_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "release_date_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "parameters_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "context_window_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "license_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ],
   "significance_evidence": [
    "Here is the chronological timeline of major open-weights model releases across the families above, in ascending order.",
    "| Date | Family | Release |",
    "| 2026-05-22 | Mistral | **Mistral Medium 3.5 is released** as open weights under a modified MIT license - a 128B dense model with a 256K context window that merges instruction-following, reasoning, and coding in a single set of weights, introduced alongside Mistral's Vibe coding environment and remote agents (the product itself is announced as available in public preview). Source: [Remote agents in Vibe. Powered by Mistral Medium 3.5.](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/). |"
   ]
  },
  {
   "url": "https://absolutedigitalpublishers.com/articles/llama-mistral-deepseek-qwen-gemma-and-olmo-what-open-actually-means",
   "title": "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
   "published_at": "2026-08-12T12:00:00",
   "organization": "Meta",
   "model": "Muse Glimmer",
   "release_date": "2026-08-10",
   "parameters": "30-billion-parameter",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "a 30-billion-parameter model distilled from Muse Spark",
   "organization_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ],
   "model_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ],
   "release_date_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ],
   "parameters_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ],
   "significance_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Llama: a broad grant, one hard line, one small tax",
    "On 10 August 2026 Meta released Muse Glimmer, a 30-billion-parameter model distilled from Muse Spark, \u201creleased under a highly permissive Apache 2.0 license,\u201d with the company\u2019s Superintelligence chief stating that open weights for Muse Spark itself would follow [17]."
   ]
  },
  {
   "url": "https://absolutedigitalpublishers.com/articles/llama-mistral-deepseek-qwen-gemma-and-olmo-what-open-actually-means",
   "title": "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
   "published_at": "2026-08-12T12:00:00",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04",
   "parameters": "four model sizes up to 31 billion dense parameters",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "across four model sizes up to 31 billion dense parameters",
   "organization_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ],
   "model_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ],
   "release_date_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ],
   "parameters_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ],
   "significance_evidence": [
    "Llama, Mistral, DeepSeek, Qwen, Gemma, and OLMo: What \"Open\" Actually Means for Each",
    "## Gemma: the license itself changed brands within the family",
    "In April 2026, that regime changed for the newest size.",
    "Announcing Gemma 4 across four model sizes up to 31 billion dense parameters, Google\u2019s own open-source blog stated that \u201cthe industry-standard Apache license broadens the horizon for Gemma 4\u2019s applicability and usefulness, providing well-understood terms for modification, reuse, and further development\u201d [11] \u2014 dropping the Prohibited Use Policy incorporation, the remote-restriction clause, and the downstream flow-through requirement that had defined the Gemma Terms of Use since Gemma 1."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM_(AI)",
   "title": "GLM (AI)",
   "published_at": "2026-08-30T18:59:23",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later.",
    "## Models",
    "**List of models**",
    "| GLM-5.3 | August 2026 | GLM-5.3 |  |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM_(AI)",
   "title": "GLM (AI)",
   "published_at": "2026-08-30T18:59:23",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash.",
    "## Models",
    "**List of models**",
    "| GLM-5.3-Flash | August 2026 | MIT |  |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "Undisclosed (MoE)",
   "context_window": "1M",
   "license": "Conditional (review terms)",
   "significance": "First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| MiniMax M3First open-weight model pairing frontier coding, 1M context, and native multimodality. Vendor-reported 59.0% SWE-Bench Pro. Weights staged to Hugging Face after launch. | MiniMax | Jun 1, 2026 | Undisclosed (MoE) | 1M | Conditional (review terms) |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Z.ai (Zhipu AI)",
   "model": "GLM-5.2",
   "release_date": "2026-06-12",
   "parameters": "MoE",
   "context_window": "Long-horizon agentic",
   "license": "Open weight",
   "significance": "Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| GLM-5.2Successor to GLM-5.1 (vendor-reported 58.4% SWE-Bench Pro). Positioned for long-horizon agentic engineering. | Z.ai (Zhipu AI) | Jun 12-13, 2026 | MoE | Long-horizon agentic | Open weight |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Moonshot AI",
   "model": "Kimi K2.7 Code",
   "release_date": "2026-06-12",
   "parameters": "~1T total (MoE)",
   "context_window": "Agentic coding",
   "license": "Modified MIT",
   "significance": "Coding-focused refresh of the K2 line shipped the same week as GLM-5.2.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.7 CodeCoding-focused refresh of the K2 line shipped the same week as GLM-5.2. | Moonshot AI | Jun 12-13, 2026 | ~1T total (MoE) | Agentic coding | Modified MIT |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": "2026-04-24",
   "parameters": "V4-Pro 1.6T/49B active; V4-Flash 284B/13B active",
   "context_window": "1M",
   "license": "MIT",
   "significance": "Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| DeepSeek V4Two MoE sizes, native 1M context, trained on 32T+ tokens. The clean-license frontier MoE of the spring. | DeepSeek | Apr 24, 2026 (preview) | V4-Pro 1.6T/49B active; V4-Flash 284B/13B active | 1M | MIT |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04-20",
   "parameters": "1T total / 32B active (MoE)",
   "context_window": "Agentic",
   "license": "Modified MIT",
   "significance": "Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Kimi K2.6Native multimodal agentic model. MIT-equivalent below 100M MAU / $20M monthly revenue, attribution clause above. | Moonshot AI | Apr 20, 2026 | 1T total / 32B active (MoE) | Agentic | Modified MIT |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-29",
   "parameters": "128B dense",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Mistral Medium 3.5Dense coder. Permissive for individuals and most companies; read the revenue clause before large-scale commercial deployment. | Mistral | Apr 29, 2026 | 128B dense | 256K | Modified MIT |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Google",
   "model": "Google Gemma 4",
   "release_date": "Apr 2026",
   "parameters": "31B dense; 26B A4B MoE",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "31B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Google Gemma 431B dense runs on a single H100; 26B MoE gives near-4B serving cost. Native function calling, 100+ languages. | Google | Apr 2026 | 31B dense; 26B A4B MoE | 256K | Apache 2.0 |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Alibaba",
   "model": "Qwen 3.5",
   "release_date": "Feb 2026",
   "parameters": "Family incl. Coder-480B-A35B",
   "context_window": "Long",
   "license": "Apache 2.0",
   "significance": "Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Qwen 3.5Hybrid Gated Delta + sparse MoE with native vision-language. Qwen3-Coder is SOTA among open models on agentic coding. | Alibaba | Feb 2026 | Family incl. Coder-480B-A35B | Long | Apache 2.0 |"
   ]
  },
  {
   "url": "https://fazm.ai/t/latest-open-source-llm-releases-2026",
   "title": "Fazm - Claude Code & Codex GUI for macOS | Persistent Sessions",
   "published_at": "2026-06-20T17:35:27",
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "2026",
   "parameters": "109B MoE / 17B active (16 experts)",
   "context_window": "10M",
   "license": "Meta custom (700M MAU clause)",
   "significance": "Industry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling.",
   "organization_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "model_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "release_date_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "parameters_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "context_window_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "license_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ],
   "significance_evidence": [
    "# Latest open source LLM releases, 2026",
    "## The 2026 open-weight calendar, newest first",
    "| Model | Lab | Shipped | Parameters | Context | License |",
    "| Llama 4 ScoutIndustry-leading context window. The license is not OSI-approved and carries a monthly-active-user ceiling. | Meta | 2026 | 109B MoE / 17B active (16 experts) | 10M | Meta custom (700M MAU clause) |"
   ]
  },
  {
   "url": "https://fazm.ai/blog/open-source-llm-releases-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2026-04-11T01:54:54",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "Jan 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Open Source LLM Releases 2026: Every Major Model So Far",
    "## 2026 Open Source LLM Release Timeline",
    "| Model | Organization | Release Date | Parameters | Architecture | License | Context Window | |---|---|---|---|---|---|---| | DeepSeek V3-0324 | DeepSeek | Jan 2026 | 685B (37B active) | MoE | MIT | 128K |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source LLM Releases 2026: Every Major Model So Far",
    "## 2026 Open Source LLM Release Timeline",
    "| Model | Organization | Release Date | Parameters | Architecture | License | Context Window | |---|---|---|---|---|---|---| | DeepSeek V3-0324 | DeepSeek | Jan 2026 | 685B (37B active) | MoE | MIT | 128K |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://fazm.ai/blog/open-source-llm-releases-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2026-04-11T01:54:54",
   "organization": "Alibaba",
   "model": null,
   "release_date": "Jan 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Open Source LLM Releases 2026: Every Major Model So Far",
    "## 2026 Open Source LLM Release Timeline",
    "| Model | Organization | Release Date | Parameters | Architecture | License | Context Window | |---|---|---|---|---|---|---| | DeepSeek V3-0324 | DeepSeek | Jan 2026 | 685B (37B active) | MoE | MIT | 128K | | Qwen 2.5-Max | Alibaba | Jan 2026 | Undisclosed | Dense | Qwen License | 128K |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source LLM Releases 2026: Every Major Model So Far",
    "## 2026 Open Source LLM Release Timeline",
    "| Model | Organization | Release Date | Parameters | Architecture | License | Context Window | |---|---|---|---|---|---|---| | DeepSeek V3-0324 | DeepSeek | Jan 2026 | 685B (37B active) | MoE | MIT | 128K | | Qwen 2.5-Max | Alibaba | Jan 2026 | Undisclosed | Dense | Qwen License | 128K |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://fazm.ai/blog/open-source-llm-releases-2026",
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    "## 2026 Open Source LLM Release Timeline",
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    "## 2026 Open Source LLM Release Timeline",
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    "## 2026 Open Source LLM Release Timeline"
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    "## 2026 Open Source LLM Release Timeline"
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   "url": "https://codersera.com/blog/open-source-llms-landscape-2026",
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   "url": "https://codersera.com/blog/open-source-llms-landscape-2026",
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   "url": "https://codersera.com/blog/open-source-llms-landscape-2026",
   "title": "Open-Source LLMs Landscape 2026: Qwen, Llama, DeepSeek, Kimi",
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   ],
   "parameters_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://digitalapplied.com/blog/open-weight-models-h1-2026-retrospective-deepseek-qwen-llama",
   "title": "Open-Weight Models H1 2026: DeepSeek, Qwen, Llama Recap",
   "published_at": "2026-05-10T23:00:00",
   "organization": "Alibaba/Qwen",
   "model": "Qwen 3.6-27B",
   "release_date": "2026-04-22",
   "parameters": "27B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": null,
   "organization_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Alibaba/Qwen shipped Qwen 3.5 on [February 16](https://en.wikipedia.org/wiki/Qwen), a small-models drop on [March 2](https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/), and [Qwen 3.6 on April 16](https://github.com/QwenLM/Qwen3.6) with 35B-A3B and 27B open-weight checkpoints in the same April 16 drop."
   ],
   "model_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "release_date_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "parameters_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Qwen 3.6 then arrived on [April 16](https://github.com/QwenLM/Qwen3.6) with the 35B-A3B MoE checkpoint, followed by Qwen 3.6-27B on April 22 \u2014 both Apache-2.0 licensed and shipped with SGLang and vLLM deployment instructions out of the box."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://digitalapplied.com/blog/open-weight-models-h1-2026-retrospective-deepseek-qwen-llama",
   "title": "Open-Weight Models H1 2026: DeepSeek, Qwen, Llama Recap",
   "published_at": "2026-05-10T23:00:00",
   "organization": "Alibaba/Qwen",
   "model": "Qwen 3.5 small-models family",
   "release_date": "2026-03-02",
   "parameters": "0.8B to 9B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Alibaba/Qwen shipped Qwen 3.5 on [February 16](https://en.wikipedia.org/wiki/Qwen), a small-models drop on [March 2](https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/), and [Qwen 3.6 on April 16](https://github.com/QwenLM/Qwen3.6) with 35B-A3B and 27B open-weight checkpoints in the same April 16 drop."
   ],
   "model_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Alibaba/Qwen shipped Qwen 3.5 on [February 16](https://en.wikipedia.org/wiki/Qwen), a small-models drop on [March 2](https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/), and [Qwen 3.6 on April 16](https://github.com/QwenLM/Qwen3.6) with 35B-A3B and 27B open-weight checkpoints in the same April 16 drop."
   ],
   "release_date_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Alibaba/Qwen shipped Qwen 3.5 on [February 16](https://en.wikipedia.org/wiki/Qwen), a small-models drop on [March 2](https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/), and [Qwen 3.6 on April 16](https://github.com/QwenLM/Qwen3.6) with 35B-A3B and 27B open-weight checkpoints in the same April 16 drop."
   ],
   "parameters_evidence": [
    "Open-weight models in H1 2026 have stopped being a single story.",
    "Alibaba/Qwen shipped Qwen 3.5 on [February 16](https://en.wikipedia.org/wiki/Qwen), a small-models drop on [March 2](https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/), and [Qwen 3.6 on April 16](https://github.com/QwenLM/Qwen3.6) with 35B-A3B and 27B open-weight checkpoints in the same April 16 drop."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://benchr.org/articles/open-weight-tier-right-now",
   "title": "The open-weight tier right now",
   "published_at": "2026-05-30T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters and 49B active per token",
   "context_window": null,
   "license": null,
   "significance": "80.6% on SWE-bench Verified, 90.1% on GPQA Diamond, and 93.5% on LiveCodeBench",
   "organization_evidence": [
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "DeepSeek shipped V4 in April, Alibaba shipped Qwen3.6 the same month, and Mistral had already moved Large 3 to a clean Apache 2.0 license back in December."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released April 24, 2026, with the announcement and pricing at [api-docs.deepseek.com](https://api-docs.deepseek.com/news/news260424).",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## DeepSeek-V4"
   ],
   "parameters_evidence": [
    "DeepSeek-V4 in Pro and Flash \u2014 same weights, two hosted sizes, with Pro at 1.6T total parameters and 49B active per token.",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## DeepSeek-V4",
    "V4 comes in two hosted sizes built on the same open MIT-licensed weights: Flash at $0.14 / $0.28 per million tokens, and Pro \u2014 1.6T total parameters, 49B active \u2014 at $0.435 / $0.87."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The official card puts V4-Pro at 80.6% on SWE-bench Verified, 90.1% on GPQA Diamond, and 93.5% on LiveCodeBench \u2014 the strongest published set in the open tier, and within a few points of closed flagships that cost ten times more per token.",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## DeepSeek-V4"
   ]
  },
  {
   "url": "https://benchr.org/articles/open-weight-tier-right-now",
   "title": "The open-weight tier right now",
   "published_at": "2026-05-30T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "27B dense model and the 35B-A3B mixture-of-experts",
   "context_window": null,
   "license": null,
   "significance": "77.2% on SWE-bench Verified and 87.8% on GPQA Diamond",
   "organization_evidence": [
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "DeepSeek shipped V4 in April, Alibaba shipped Qwen3.6 the same month, and Mistral had already moved Large 3 to a clean Apache 2.0 license back in December."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba's current series, released April 2026, with model cards on [Hugging Face under the Qwen organization](https://huggingface.co/Qwen).",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## Qwen3.6"
   ],
   "parameters_evidence": [
    "The two confirmed open-weight variants are the 27B dense model and the 35B-A3B mixture-of-experts that activates just 3B parameters per token.",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## Qwen3.6",
    "Alibaba's current series, released April 2026, with model cards on [Hugging Face under the Qwen organization](https://huggingface.co/Qwen)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 27B is the one to take seriously: its official card lists 77.2% on SWE-bench Verified and 87.8% on GPQA Diamond \u2014 numbers that would have been frontier-class a year ago, from a model that fits on one GPU.",
    "The open-weight tier right now: Llama 4, Mistral, Qwen, DeepSeek",
    "## Qwen3.6"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06",
   "parameters": "743B total",
   "context_window": null,
   "license": null,
   "significance": "Strongest benchmark profile among currently open models in this set. First eval for broad reasoning, coding, and long-context agents.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## GLM 5.2",
    "### What is GLM 5.2?",
    "[GLM-5.2](/models/fireworks/glm-5p2) is [Z.ai's](https://z.ai/blog/glm-5.2) flagship coding model, a 743B-parameter MoE with a 1,040k-token context window on Fireworks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## GLM 5.2",
    "### What is GLM 5.2?",
    "- \u2022**Release date:** June 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## GLM 5.2",
    "### What is GLM 5.2?",
    "- \u2022**Parameters**: 743B total"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [GLM 5.2](/models/fireworks/glm-5p2) | June 2026 | 743B total | 1,040k tokens | Strongest benchmark profile among currently open models in this set. First eval for broad reasoning, coding, and long-context agents. | [Try in playground](/models/fireworks/glm-5p2) |"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06",
   "parameters": "1.02T total",
   "context_window": null,
   "license": null,
   "significance": "Coding agents, repository work, patch planning, and multimodal developer tools. Thinking mode is mandatory.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## Kimi K2.7 Code",
    "### What is Kimi K2.7 Code?",
    "[Kimi K2.7 Code](/models/fireworks/kimi-k2p7-code) is a coding-specialized 1.02T-parameter MoE model from [Moonshot AI](https://www.moonshot.ai/) with a 262k-token context window."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## Kimi K2.7 Code",
    "### What is Kimi K2.7 Code?",
    "- \u2022**Release date:** June 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## Kimi K2.7 Code",
    "### What is Kimi K2.7 Code?",
    "- \u2022**Parameters:** 1.02T total"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [Kimi K2.7 Code](/models/fireworks/kimi-k2p7-code) | June 2026 | 1.02T total | 262k tokens | Coding agents, repository work, patch planning, and multimodal developer tools. Thinking mode is mandatory. | [Try in playground](/models/fireworks/kimi-k2p7-code) |"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1.6T total",
   "context_window": null,
   "license": null,
   "significance": "Long-context reasoning and coding from a separate open-source family. Second eval when GLM 5.2 misses on your repo.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Pro",
    "### What is DeepSeek-V4-Pro?",
    "[DeepSeek-V4-Pro](/models/fireworks/deepseek-v4-pro) is a large MoE model for reasoning, coding, and long-context work from [DeepSeek](https://www.deepseek.com/)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Pro",
    "### What is DeepSeek-V4-Pro?",
    "- \u2022**Release date:** April 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Pro",
    "### What is DeepSeek-V4-Pro?",
    "- \u2022**Parameters:** 1.6T total"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [DeepSeek-V4-Pro](/models/fireworks/deepseek-v4-pro) | April 2026 | 1.6T total | 1,040k tokens | Long-context reasoning and coding from a separate open-source family. Second eval when GLM 5.2 misses on your repo. | [Try in playground](/models/fireworks/deepseek-v4-pro) |"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": "284B total",
   "context_window": null,
   "license": null,
   "significance": "Same 1,040k context class as Pro at higher throughput and lower cost. Default DeepSeek route for high-volume workloads.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Flash",
    "### What is DeepSeek-V4-Flash?",
    "[DeepSeek-V4-Flash](/models/fireworks/deepseek-v4-flash) is the faster V4 route for long-context reasoning and agent work."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Flash",
    "### What is DeepSeek-V4-Flash?",
    "- \u2022**Release date:** April 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## DeepSeek-V4-Flash",
    "### What is DeepSeek-V4-Flash?",
    "- \u2022**Parameters:** 284B total"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [DeepSeek-V4-Flash](/models/fireworks/deepseek-v4-flash) | April 2026 | 284B total | 1,040k tokens | Same 1,040k context class as Pro at higher throughput and lower cost. Default DeepSeek route for high-volume workloads. | [Try in playground](/models/fireworks/deepseek-v4-flash) |"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": "428B total, about 23B activated",
   "context_window": null,
   "license": null,
   "significance": "Native image and video input, second-highest GPQA score in the table. First eval when multimodality sets the constraint.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## MiniMax M3",
    "### What is MiniMax M3?",
    "[MiniMax M3](/models/fireworks/minimax-m3) is a native multimodal MoE model built for long-context, agentic, and multimodal workflows."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## MiniMax M3",
    "### What is MiniMax M3?",
    "- \u2022**Release date:** June 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## MiniMax M3",
    "### What is MiniMax M3?",
    "- \u2022**Parameters:** 428B total, about 23B activated"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [MiniMax M3](/models/fireworks/minimax-m3) | June 202622, 2025 | 428B total, about 23B activated | 512k tokens | Native image and video input, second-highest GPQA score in the table. First eval when multimodality sets the constraint. | [Try in playground](/models/fireworks/minimax-m3) |"
   ]
  },
  {
   "url": "https://fireworks.ai/blog/best-open-source-llms",
   "title": "Best Open Source LLMs in 2026",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": "32.2B dense",
   "context_window": null,
   "license": null,
   "significance": "Smaller dense multimodal model with Apache-2.0 licensing and an On-Demand deployment path. First eval when adaptation and deployment control matter more than frontier benchmark rank.",
   "organization_evidence": [
    "Best Open Source Llms",
    "## Gemma 4 31B IT",
    "### What is Gemma 4 31B IT?",
    "[Gemma 4 31B IT](/models/fireworks/gemma-4-31b-it) is a smaller multimodal instruction-tuned model from Google."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source Llms",
    "## Gemma 4 31B IT",
    "### What is Gemma 4 31B IT?",
    "- \u2022**Release date:** April 2026"
   ],
   "parameters_evidence": [
    "Best Open Source Llms",
    "## Gemma 4 31B IT",
    "### What is Gemma 4 31B IT?",
    "- \u2022**Parameters:** 32.2B dense"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open Source Llms",
    "## The best open source LLMs at a glance",
    "| Model | Release date | Params | Context window | Best for | On Fireworks |",
    "| [Gemma 4 31B IT](/models/fireworks/gemma-4-31b-it) | April 2026 | 32.2B dense | 262k tokens | Smaller dense multimodal model with Apache-2.0 licensing and an On-Demand deployment path. First eval when adaptation and deployment control matter more than frontier benchmark rank. | [View On-Demand model](/models/fireworks/gemma-4-31b-it) |"
   ]
  },
  {
   "url": "https://tech-insider.org/ca/mistral-medium-3-5-vs-llama-4-maverick-vs-gpt-5-6-luna-2026",
   "title": "Mistral Medium 3.5 vs Llama 4 Maverick vs GPT-5.6 Luna: $6.30 Gap [2026]",
   "published_at": "2026-08-25T00:39:48",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Posts the strongest reported reasoning and coding scores of the three on benchmarks like TAU2-bench and SWE-bench.",
   "organization_evidence": [
    "# Mistral Medium 3.5 vs Llama 4 Maverick vs GPT-5.6 Luna: $6.30 Gap [2026]",
    "That\u2019s the pitch behind three releases that quietly reshaped the budget end of the LLM market in 2026: Mistral AI\u2019s Mistral Medium 3.5, Meta\u2019s Llama 4 Maverick, and OpenAI\u2019s GPT-5.6 Luna."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 vs Llama 4 Maverick vs GPT-5.6 Luna: $6.30 Gap [2026]",
    "Mistral shipped Medium 3.5 in late April as an open-weight model with a paid API on top.",
    "| Release date | April 29-30, 2026 | April 5, 2025 | July 9, 2026 |",
    "Here\u2019s how the three models stack up across the specs that actually affect a production decision."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 vs Llama 4 Maverick vs GPT-5.6 Luna: $6.30 Gap [2026]",
    "Mistral Medium 3.5 sits at the top of this group\u2019s pricing at $1.50 input and $7.50 output per million tokens, but it also posts the strongest reported reasoning and coding scores of the three on benchmarks like TAU2-bench and SWE-bench."
   ]
  },
  {
   "url": "https://boondockrebel.org/kimi-k3-china-ai",
   "title": "China Built the Largest Open AI Model in History. It Just Took the Top Coding Spot From Everyone.",
   "published_at": "2026-08-22T12:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "landed it at number one on the global coding benchmark",
   "organization_evidence": [
    "China Built the Largest Open AI Model in History. It Just Took the Top Coding Spot From Everyone.",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, open weights, and a score that landed it at number one on the global coding benchmark."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "China Built the Largest Open AI Model in History. It Just Took the Top Coding Spot From Everyone.",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, open weights, and a score that landed it at number one on the global coding benchmark."
   ],
   "parameters_evidence": [
    "China Built the Largest Open AI Model in History. It Just Took the Top Coding Spot From Everyone.",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, open weights, and a score that landed it at number one on the global coding benchmark."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "China Built the Largest Open AI Model in History. It Just Took the Top Coding Spot From Everyone.",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, open weights, and a score that landed it at number one on the global coding benchmark."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM_5.3_Flash",
   "title": "GLM (AI)",
   "published_at": "2026-08-28T16:54:58",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM_5.3_Flash",
   "title": "GLM (AI)",
   "published_at": "2026-08-28T16:54:58",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://edgen.tech/tr/news/post/kimi-k3-open-weight-release-draws-100k-downloads-erases-us-ai-lead",
   "title": "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
   "published_at": "2026-08-17T03:11:14",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": "introduced a new license that charges MaaS providers and cloud vendors with revenue above $20 million over 12 consecutive months, while leaving end-user applications free",
   "significance": "matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead",
   "organization_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "model_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "release_date_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "[![Rachel Kim](https://static.edwealth.ai/authors/rachel-kim.png?w=32)\n## Rachel Kim](https://www.edgen.tech/tr/author/news/rachel-kim)\u00b7Aug 17 2026, 03:11",
    "The model's July 27 release on Hugging Face drew roughly 100,000 downloads in its first 24 hours, and demand was so high that Moonshot temporarily blocked new consumer sign-ups."
   ],
   "parameters_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Kimi K3 introduced a new license that charges MaaS providers and cloud vendors with revenue above $20 million over 12 consecutive months, while leaving end-user applications free."
   ],
   "significance_evidence": [
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ]
  },
  {
   "url": "https://edgen.tech/vi/news/post/kimi-k3-open-weight-release-draws-100k-downloads-erases-us-ai-lead",
   "title": "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
   "published_at": "2026-08-17T03:11:14",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": "charges MaaS providers and cloud vendors with revenue above $20 million over 12 consecutive months, while leaving end-user applications free",
   "significance": "matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead",
   "organization_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "model_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "release_date_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "The model's July 27 release on Hugging Face drew roughly 100,000 downloads in its first 24 hours, and demand was so high that Moonshot temporarily blocked new consumer sign-ups."
   ],
   "parameters_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Kimi K3 introduced a new license that charges MaaS providers and cloud vendors with revenue above $20 million over 12 consecutive months, while leaving end-user applications free."
   ],
   "significance_evidence": [
    "Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "# Kimi K3 open-weight release draws 100k downloads, erases US AI lead",
    "Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model, matched or surpassed Anthropic's Fable 5 on key benchmarks at roughly half the cost, erasing the closed-source lead."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Kimi_%28AI%29",
   "title": "Kimi (AI)",
   "published_at": "2026-08-22T00:06:20",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Upon release, Kimi K3 debuted at No. 3 on the Artificial Analysis AI leaderboard, behind Anthropic 's Claude Fable 5 and OpenAI 's GPT-5.6 Sol, but outperformed its competitors on Arena.ai 's front-end web development benchmark.",
   "organization_evidence": [
    "Kimi is an artificial intelligence (AI) chatbot and series of large language models developed by Chinese company Moonshot AI.",
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters.",
    "## Models",
    "**Main model versions of Kimi with descriptions**"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Upon release, Kimi K3 debuted at No. 3 on the Artificial Analysis AI leaderboard, behind Anthropic 's Claude Fable 5 and OpenAI 's GPT-5.6 Sol, but outperformed its competitors on Arena.ai 's front-end web development benchmark.",
    "## Models"
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Ox_Alpha",
   "title": "GLM (AI)",
   "published_at": "2026-08-26T23:05:36",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and stated that the weights for the model would be made available approximately two weeks after the release date.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash.",
    "| GLM-5.3-Flash | August 2026 | MIT |  |",
    "## Models",
    "**List of models**"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://en.wikipedia.org/wiki/ChatGLM",
   "title": "GLM (AI)",
   "published_at": "2026-08-29T17:42:15",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM is the flagship model series of Z.ai, one of the six AI tigers of China.",
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Z.ai released GLM-5.3 on 14 August 2026 and made the model's weights available two weeks later.",
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM is the flagship model series of Z.ai, one of the six AI tigers of China.",
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/ChatGLM",
   "title": "GLM (AI)",
   "published_at": "2026-08-29T17:42:15",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.3-Flash incorporates Kimi Delta Attention, developed by competing Chinese lab Moonshot AI.",
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash.",
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.3-Flash incorporates Kimi Delta Attention, developed by competing Chinese lab Moonshot AI.",
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai."
   ]
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-01",
   "parameters": "397B / 17B",
   "context_window": null,
   "license": null,
   "significance": "Tops out at a 397B-total / 17B-active MoE model built from 256 experts (8 routed plus 1 shared per token), supporting 201 languages and a native 262,144-token context window that extends toward roughly one million tokens.",
   "organization_evidence": [
    "The current flagship generation, **Qwen 3.5**, rolled out in February 2026 and tops out at a 397B-total / 17B-active MoE model built from 256 experts (8 routed plus 1 shared per token).",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The current flagship generation, **Qwen 3.5**, rolled out in February 2026 and tops out at a 397B-total / 17B-active MoE model built from 256 experts (8 routed plus 1 shared per token).",
    "| Qwen 3.5 397B-A17B | Alibaba | 397B / 17B | MoE (256 experts) | 262K \u2192 ~1M | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "The current flagship generation, **Qwen 3.5**, rolled out in February 2026 and tops out at a 397B-total / 17B-active MoE model built from 256 experts (8 routed plus 1 shared per token).",
    "| Qwen 3.5 397B-A17B | Alibaba | 397B / 17B | MoE (256 experts) | 262K \u2192 ~1M | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The current flagship generation, **Qwen 3.5**, rolled out in February 2026 and tops out at a 397B-total / 17B-active MoE model built from 256 experts (8 routed plus 1 shared per token).",
    "It ships with a native 262,144-token context window that extends toward roughly one million tokens, native vision-language support, and coverage of an astonishing 201 languages.",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon"
   ]
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Alibaba",
   "model": "Qwen 3.5 122B-A10B",
   "release_date": "2026-02-01",
   "parameters": "122B / 10B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": null,
   "organization_evidence": [
    "Alibaba also released mid-size 122B-A10B and dense 27B variants in the same generation for teams that want simpler serving.",
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "model_evidence": [
    "Alibaba also released mid-size 122B-A10B and dense 27B variants in the same generation for teams that want simpler serving.",
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "release_date_evidence": [
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": [
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "license_evidence": [
    "| Qwen 3.5 122B-A10B | Alibaba | 122B / 10B | MoE | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Alibaba",
   "model": "Qwen 3.5 27B",
   "release_date": "2026-02-01",
   "parameters": "27B / 27B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": null,
   "organization_evidence": [
    "Alibaba also released mid-size 122B-A10B and dense 27B variants in the same generation for teams that want simpler serving.",
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "model_evidence": [
    "Alibaba also released mid-size 122B-A10B and dense 27B variants in the same generation for teams that want simpler serving.",
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "release_date_evidence": [
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": [
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "license_evidence": [
    "| Qwen 3.5 27B | Alibaba | 27B / 27B | Dense | 256K | Text + Image | Apache 2.0 | Feb 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Alibaba",
   "model": "Qwen 3.6-35B-A3B",
   "release_date": "2026-04-01",
   "parameters": "35B / 3B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Explicitly tuned for agentic coding, activating only 3B parameters per token.",
   "organization_evidence": [
    "In April 2026, the Qwen team followed with **Qwen 3.6**, including a dense 27B build and a sparse 35B-A3B MoE (only 3B active parameters) explicitly tuned for agentic coding.",
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "model_evidence": [
    "In April 2026, the Qwen team followed with **Qwen 3.6**, including a dense 27B build and a sparse 35B-A3B MoE (only 3B active parameters) explicitly tuned for agentic coding.",
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "release_date_evidence": [
    "In April 2026, the Qwen team followed with **Qwen 3.6**, including a dense 27B build and a sparse 35B-A3B MoE (only 3B active parameters) explicitly tuned for agentic coding.",
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": [
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "license_evidence": [
    "| Qwen 3.6-35B-A3B | Alibaba | 35B / 3B | MoE | 256K | Text + Image | Apache 2.0 | Apr 2026 |",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "significance_evidence": [
    "In April 2026, the Qwen team followed with **Qwen 3.6**, including a dense 27B build and a sparse 35B-A3B MoE (only 3B active parameters) explicitly tuned for agentic coding.",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Alibaba Qwen 3.5 and 3.6: The Multilingual Speed Demon"
   ]
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Mistral AI",
   "model": "Mistral Large 3",
   "release_date": "2025-12-01",
   "parameters": "675B / 41B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "The largest total parameter count in this trio with a LMArena Elo of roughly 1418.",
   "organization_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "model_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "release_date_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "license_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "| Mistral Large 3 | Mistral AI | 675B / 41B | MoE | 256K | Text + Image | Apache 2.0 | Dec 2025 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "significance_evidence": [
    "Mistral AI answered in December 2025 with **Mistral Large 3**, a 675B-total / 41B-active MoE model \u2014 the largest total parameter count in this trio \u2014 under a clean Apache 2.0 license and a 256K context window with text-and-image input across 80-plus languages.",
    "Mistral Large 3\u2019s standout is its LMArena Elo of roughly 1418, placing it near the top of open-weight non-reasoning chat models, even as its raw benchmark numbers trail Qwen.",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship"
   ]
  },
  {
   "url": "https://tech-insider.org/llama-4-vs-qwen-vs-mistral-2026",
   "title": "Llama 4 vs Qwen 3.5 vs Mistral: Open LLMs 2026",
   "published_at": "2026-07-06T12:18:50",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03-01",
   "parameters": "119B / 6B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "A remarkably lean 119B-total model that activates only 6B parameters per token.",
   "organization_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "model_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "release_date_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "parameters_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "context_window_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "license_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "| Mistral Small 4 | Mistral AI | 119B / 6B | MoE (128e, 4 active) | 256K | Text + Image | Apache 2.0 | Mar 2026 |",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship",
    "Specs Compared: Parameters, Context, and Architecture",
    "The specification table below places every relevant open-weight variant side by side, with two Chinese frontier models (GLM-5, DeepSeek V3.2) and Google\u2019s Gemma 3 as reference points."
   ],
   "significance_evidence": [
    "In March 2026 Mistral added **Mistral Small 4**, a remarkably lean 119B-total model that activates only 6B parameters per token (128 experts, 4 active), also 256K context and multimodal.",
    "## Meet the Contenders: Llama 4, Qwen 3.5, and Mistral Large 3",
    "Mistral Large 3: The Apache-Licensed European Flagship"
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM_5.3",
   "title": "GLM (AI)",
   "published_at": "2026-08-26T23:05:36",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and stated that the weights for the model would be made available approximately two weeks after the release date.",
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash.",
    "| GLM-5.3-Flash | August 2026 | MIT |  |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://campustechnology.com/articles/2026/08/26/meta-releases-muse-glimmer-an-open-weight-ai-model-designed-to-run-on-consumer-hardware.aspx",
   "title": "Meta Releases Muse Glimmer, an Open-Weight AI Model Designed to Run on Consumer Hardware -- Campus Technology",
   "published_at": "2026-08-26T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "optimized for local, always-on AI agents and designed to run on consumer hardware",
   "organization_evidence": [
    "### Meta Releases Muse Glimmer, an Open-Weight AI Model Designed to Run on Consumer Hardware",
    "Meta describes Glimmer as a 30-billion-parameter, open-weight model optimized for local, always-on AI agents."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### Meta Releases Muse Glimmer, an Open-Weight AI Model Designed to Run on Consumer Hardware",
    "Meta describes Glimmer as a 30-billion-parameter, open-weight model optimized for local, always-on AI agents."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen",
   "title": "Qwen",
   "published_at": "2026-08-31T03:10:32",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "2.4 trillion",
   "context_window": null,
   "license": null,
   "significance": "second largest and second most powerful open weights LLM and Chinese LLM released by 12 August 2026, after Kimi K3",
   "organization_evidence": [
    "Qwen (also known as Tongyi Qianwen, Chinese: \u901a\u4e49\u5343\u95ee; pinyin: T\u014dngy\u00ec Qi\u0101nw\u00e8n) is a family of predominantly open weights large and small language models (LLM and SLM) developed by Alibaba Cloud.",
    "Alibaba released the weights of Qwen3.8-Max as Qwen3.8-2.4T-A95B on 12 August 2026, and the open-weights model omits certain features of the cloud model, such as image input and a non-thinking mode."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba released the weights of Qwen3.8-Max as Qwen3.8-2.4T-A95B on 12 August 2026, and the open-weights model omits certain features of the cloud model, such as image input and a non-thinking mode."
   ],
   "parameters_evidence": [
    "Its latest version, Qwen3.8; the 2.4 trillion parameter model was the second largest and second most powerful open weights LLM and Chinese LLM released by 12 August 2026, after Kimi K3.",
    "Alibaba previewed its 2.4-trillion-parameter model Qwen3.8-Max in July 2026 and announced that it would release the weights of the model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Its latest version, Qwen3.8; the 2.4 trillion parameter model was the second largest and second most powerful open weights LLM and Chinese LLM released by 12 August 2026, after Kimi K3."
   ]
  },
  {
   "url": "https://qtithow.com/2026/07/comparing-120-open-source-llms-best.html",
   "title": "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T (49B active)",
   "context_window": null,
   "license": null,
   "significance": "87.5% MMLU-Pro, 90.1% GPQA, 93.5% LiveCodeBench",
   "organization_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| DeepSeek V4 Pro | DeepSeek | 1.6T (49B active) | 1M | 87.5% MMLU-Pro | 90.1% GPQA | 93.5% LiveCodeBench | | | MIT | $0.35 | $0.70 | April 2026 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "Recent releases show the shift clearly. **DeepSeek V4 Pro** (released April 24, 2026), **GLM-5.1** from Z.ai, **Kimi K2.6** from Moonshot AI, and **Qwen3.5** from Alibaba now rival the best proprietary LLMs\u2014including Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro."
   ],
   "parameters_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| DeepSeek V4 Pro | DeepSeek | 1.6T (49B active) | 1M | 87.5% MMLU-Pro | 90.1% GPQA | 93.5% LiveCodeBench | | | MIT | $0.35 | $0.70 | April 2026 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| DeepSeek V4 Pro | DeepSeek | 1.6T (49B active) | 1M | 87.5% MMLU-Pro | 90.1% GPQA | 93.5% LiveCodeBench | | | MIT | $0.35 | $0.70 | April 2026 |"
   ]
  },
  {
   "url": "https://qtithow.com/2026/07/comparing-120-open-source-llms-best.html",
   "title": "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04",
   "parameters": "1T (32B active)",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "84.6% MMLU-Pro, 90.5% GPQA, 92% HumanEval",
   "organization_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "model_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "release_date_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "parameters_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "context_window_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "license_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ],
   "significance_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| Kimi K2.6 | Moonshot AI | 1T (32B active) | 256K | 84.6% MMLU-Pro | 90.5% GPQA | 92% HumanEval | | | Modified MIT | $0.28 | $1.10 | April 2026 |"
   ]
  },
  {
   "url": "https://qtithow.com/2026/07/comparing-120-open-source-llms-best.html",
   "title": "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
   "published_at": null,
   "organization": "Z.ai",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": "200K",
   "license": "MIT",
   "significance": "91.7% MMLU, 85.7% GPQA, 58.4% SWE-Bench Pro",
   "organization_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "model_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "release_date_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "parameters_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "context_window_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "license_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ],
   "significance_evidence": [
    "Comparing 120+ Open-Source LLMs: The Best Models in 2026",
    "The table below includes every open-weight model from models.dev plus hand-picked classics, sorted by release date (newest first).",
    "| GLM-5.1 | Z.ai | 754B | 200K | 91.7% MMLU | 85.7% GPQA | 58.4% SWE-Bench Pro | | | MIT | $0.30 | $2.15 | April 2026 |"
   ]
  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/poolside-releases-laguna-s-2-1-a-118b-open-weight-coding-model",
   "title": "Poolside Releases Laguna S 2.1, a 118B Open-Weight Coding Model",
   "published_at": null,
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "118 billion",
   "context_window": null,
   "license": null,
   "significance": "Tops the SWE-Bench Multilingual table at 78.5%, holds its own against models ten to twenty times its size on long-horizon coding benchmarks, and fits on a single NVIDIA DGX Spark when quantized.",
   "organization_evidence": [
    "# Poolside Releases Laguna S 2.1, a 118B Open-Weight Coding Model",
    "**On July 21, 2026, San Francisco-based Poolside released Laguna S 2.1** \u2014 a 118-billion-parameter Mixture-of-Experts model built for agentic coding that activates only 8 billion parameters per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Poolside Releases Laguna S 2.1, a 118B Open-Weight Coding Model",
    "**On July 21, 2026, San Francisco-based Poolside released Laguna S 2.1** \u2014 a 118-billion-parameter Mixture-of-Experts model built for agentic coding that activates only 8 billion parameters per token."
   ],
   "parameters_evidence": [
    "# Poolside Releases Laguna S 2.1, a 118B Open-Weight Coding Model",
    "**On July 21, 2026, San Francisco-based Poolside released Laguna S 2.1** \u2014 a 118-billion-parameter Mixture-of-Experts model built for agentic coding that activates only 8 billion parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Poolside Releases Laguna S 2.1, a 118B Open-Weight Coding Model",
    "**On July 21, 2026, San Francisco-based Poolside released Laguna S 2.1** \u2014 a 118-billion-parameter Mixture-of-Experts model built for agentic coding that activates only 8 billion parameters per token.",
    "It tops the SWE-Bench Multilingual table at 78.5%, holds its own against models ten to twenty times its size on long-horizon coding benchmarks, and fits on a single NVIDIA DGX Spark when quantized."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/google-gemma-4-launches-a-12b-encoder-free-local-model",
   "title": "Google Gemma 4 Launches a 12B Encoder Free Local Model",
   "published_at": "2026-06-05T15:23:41",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95 billion",
   "context_window": null,
   "license": null,
   "significance": "Beats Gemma 3 27B on GPQA Diamond, MMLU Pro, and DocVQA",
   "organization_evidence": [
    "Model Release",
    "# Google Gemma 4 Launches a 12B Encoder Free Local Model",
    "- Google DeepMind released Gemma 4 12B on June 3, 2026 under the Apache 2.0 open license"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Google Gemma 4 Launches a 12B Encoder Free Local Model",
    "- Google DeepMind released Gemma 4 12B on June 3, 2026 under the Apache 2.0 open license"
   ],
   "parameters_evidence": [
    "Model Release",
    "# Google Gemma 4 Launches a 12B Encoder Free Local Model",
    "- The model has 11.95 billion parameters and runs on a 16GB laptop with 4-bit weights near 6.7GB"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Google Gemma 4 Launches a 12B Encoder Free Local Model",
    "- Gemma 4 12B beats the larger Gemma 3 27B on GPQA Diamond, MMLU Pro, and DocVQA"
   ]
  },
  {
   "url": "https://thebriefscript.com/open-source-ai-models-2026",
   "title": "Open Source AI Models 2026: The Complete Release Tracker",
   "published_at": "2026-07-11T19:41:39",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1M token context, trained on 32T-plus tokens",
   "organization_evidence": [
    "Open Source AI Model Releases: The 2026 Tracker",
    "DeepSeek V4 | DeepSeek | Public preview, April 24, 2026 | MIT | 1M token context, trained on 32T-plus tokens"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source AI Model Releases: The 2026 Tracker",
    "DeepSeek V4 | DeepSeek | Public preview, April 24, 2026 | MIT | 1M token context, trained on 32T-plus tokens"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source AI Model Releases: The 2026 Tracker",
    "DeepSeek V4 | DeepSeek | Public preview, April 24, 2026 | MIT | 1M token context, trained on 32T-plus tokens"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "32B",
   "context_window": null,
   "license": null,
   "significance": "NEW #1 open model, score 75, +2pp across every benchmark vs. the Preview.",
   "organization_evidence": [
    "Every major open-weights release between May 9 and June 6, 2026, with a one-line takeaway.",
    "Alibaba shipped the production Qwen 4 32B-A3B on June 1, exactly four weeks after the Preview that defined May's leaderboard.",
    "## Qwen 4 Goes Live \u2014 the new #1"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Every major open-weights release between May 9 and June 6, 2026, with a one-line takeaway.",
    "Alibaba shipped the production Qwen 4 32B-A3B on June 1, exactly four weeks after the Preview that defined May's leaderboard.",
    "## Qwen 4 Goes Live \u2014 the new #1"
   ],
   "parameters_evidence": [
    "Total params32B",
    "Alibaba shipped the production Qwen 4 32B-A3B on June 1, exactly four weeks after the Preview that defined May's leaderboard.",
    "## Qwen 4 Goes Live \u2014 the new #1"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Every major open-weights release between May 9 and June 6, 2026, with a one-line takeaway.",
    "- **Qwen 4 (full release)** Jun 1 \u2014 Apache 2.0, NEW #1 open model, score 75, +2pp across every benchmark vs. the Preview.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen 4 Coder 32B-A3B",
   "release_date": "2026-06-02",
   "parameters": null,
   "context_window": "1M native",
   "license": "Apache 2.0",
   "significance": "82% SWE-Verified, the best open-source Mac coder \u2014 beats Devstral.",
   "organization_evidence": [
    "One day after the full Qwen 4 release, Alibaba shipped Qwen 4 Coder 32B-A3B \u2014 the same MoE architecture, post-trained on roughly 4 trillion additional tokens of code, build logs, agentic trajectories, and curated PR reviews.",
    "## Qwen 4 Coder \u2014 the top open-source Mac coder"
   ],
   "model_evidence": [
    "- **Qwen 4 Coder 32B-A3B** Jun 2 \u2014 Apache 2.0, 82% SWE-Verified, the best open-source Mac coder \u2014 beats Devstral.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Qwen 4 Coder 32B-A3B** Jun 2 \u2014 Apache 2.0, 82% SWE-Verified, the best open-source Mac coder \u2014 beats Devstral.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Context1M native",
    "Alibaba shipped Qwen 4 Coder 32B-A3B \u2014 the same MoE architecture, post-trained on roughly 4 trillion additional tokens of code, build logs, agentic trajectories, and curated PR reviews.",
    "## Qwen 4 Coder \u2014 the top open-source Mac coder"
   ],
   "license_evidence": [
    "LicenseApache 2.0",
    "Alibaba shipped Qwen 4 Coder 32B-A3B \u2014 the same MoE architecture, post-trained on roughly 4 trillion additional tokens of code, build logs, agentic trajectories, and curated PR reviews.",
    "## Qwen 4 Coder \u2014 the top open-source Mac coder"
   ],
   "significance_evidence": [
    "- **Qwen 4 Coder 32B-A3B** Jun 2 \u2014 Apache 2.0, 82% SWE-Verified, the best open-source Mac coder \u2014 beats Devstral.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen 4 4B",
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
   "organization_evidence": [
    "Frontier-class open-weights now exist at every meaningful parameter count \u2014 4B (Qwen 4), 14B (Phi-5 Medium), 32B-A3B (Qwen 4), 70B dense (Llama 5 + Voyage Pro), and 100B+ MoE (Grok 4 Open).",
    "- **Qwen 4 4B** Jun 3 \u2014 Apache 2.0, beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "model_evidence": [
    "- **Qwen 4 4B** Jun 3 \u2014 Apache 2.0, beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Qwen 4 4B** Jun 3 \u2014 Apache 2.0, beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "- **Qwen 4 4B** Jun 3 \u2014 Apache 2.0, beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "significance_evidence": [
    "- **Qwen 4 4B** Jun 3 \u2014 Apache 2.0, beats Phi-5 Mini in the 8 GB tier at 135 tok/s on M5 Max.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Meta",
   "model": "Llama 5 70B",
   "release_date": "2026-06-04",
   "parameters": "70B dense",
   "context_window": "256K",
   "license": "Llama 5 Community (700M MAU cap)",
   "significance": "Meta's bigger dense, MMLU 88%, fills the Scout/8B gap.",
   "organization_evidence": [
    "- **Llama 5 70B** Jun 4 \u2014 Meta's bigger dense, MMLU 88%, fills the Scout/8B gap.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "model_evidence": [
    "- **Llama 5 70B** Jun 4 \u2014 Meta's bigger dense, MMLU 88%, fills the Scout/8B gap.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Llama 5 70B** Jun 4 \u2014 Meta's bigger dense, MMLU 88%, fills the Scout/8B gap.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": [
    "| Total params | 70B dense | 70B dense |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "context_window_evidence": [
    "| Context | 256K | 512K |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "license_evidence": [
    "| License | Llama 5 Community (700M MAU cap) | Apache 2.0 |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "significance_evidence": [
    "- **Llama 5 70B** Jun 4 \u2014 Meta's bigger dense, MMLU 88%, fills the Scout/8B gap.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Mistral",
   "model": "Mistral Voyage Pro 70B",
   "release_date": "2026-06-04",
   "parameters": "70B dense",
   "context_window": "512K",
   "license": "Apache 2.0",
   "significance": "Apache 2.0 70B dense, agentic tool use is strong, the new license-friendly 70B.",
   "organization_evidence": [
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "model_evidence": [
    "- **Mistral Voyage Pro 70B** Jun 4 \u2014 Apache 2.0 70B dense, agentic tool use is strong, the new license-friendly 70B.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Mistral Voyage Pro 70B** Jun 4 \u2014 Apache 2.0 70B dense, agentic tool use is strong, the new license-friendly 70B.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": [
    "| Total params | 70B dense | 70B dense |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "context_window_evidence": [
    "| Context | 256K | 512K |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "license_evidence": [
    "| License | Llama 5 Community (700M MAU cap) | Apache 2.0 |",
    "Meta shipped Llama 5 70B on June 4, and the same day Mistral countered with Voyage Pro 70B under Apache 2.0.",
    "## Llama 5 70B vs Mistral Voyage Pro 70B \u2014 the 70B race"
   ],
   "significance_evidence": [
    "- **Mistral Voyage Pro 70B** Jun 4 \u2014 Apache 2.0 70B dense, agentic tool use is strong, the new license-friendly 70B.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Google",
   "model": "Gemma 4.5 12B",
   "release_date": "2026-06-02",
   "parameters": "12B",
   "context_window": "1M native",
   "license": null,
   "significance": "Google refresh, 1M context jump (from 256K), improved multimodal.",
   "organization_evidence": [
    "- **Gemma 4.5 12B** Jun 2 \u2014 Google refresh, 1M context jump (from 256K), improved multimodal.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "model_evidence": [
    "- **Gemma 4.5 12B** Jun 2 \u2014 Google refresh, 1M context jump (from 256K), improved multimodal.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Gemma 4.5 12B** Jun 2 \u2014 Google refresh, 1M context jump (from 256K), improved multimodal.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": [
    "Google quietly shipped Gemma 4.5 12B on June 2, a refresh rather than a new generation.",
    "## Gemma 4.5 \u2014 Google's June refresh"
   ],
   "context_window_evidence": [
    "The headline change is context: Gemma 4 shipped with 256K context; Gemma 4.5 jumps to 1M native, matching Qwen 4.",
    "Google quietly shipped Gemma 4.5 12B on June 2, a refresh rather than a new generation.",
    "## Gemma 4.5 \u2014 Google's June refresh"
   ],
   "license_evidence": null,
   "significance_evidence": [
    "- **Gemma 4.5 12B** Jun 2 \u2014 Google refresh, 1M context jump (from 256K), improved multimodal.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "Microsoft",
   "model": "Phi-5 Medium 14B",
   "release_date": "2026-05-30",
   "parameters": "14B",
   "context_window": "64K native",
   "license": "MIT",
   "significance": "MMLU 86%, AIME 75%, tops the 14B tier outright.",
   "organization_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "model_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "context_window_evidence": [
    "The MIT license, the strong AIME 75% score, and the 64K native context (extended via sliding-window to 256K) make Phi-5 Medium the strongest pure-reasoning model in the 16 GB Mac tier \u2014 and a credible second-pick for 24 GB Macs that want to keep Qwen 4 32B-A3B unloaded for occasional use.",
    "Microsoft shipped Phi-5 Medium on May 30, six days ahead of June, and it instantly became the top-scoring 14B-class model.",
    "## Phi-5 Medium 14B tops its tier"
   ],
   "license_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "significance_evidence": [
    "- **Phi-5 Medium 14B** May 30 \u2014 MIT, MMLU 86%, AIME 75%, tops the 14B tier outright.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://llmcheck.net/blog/state-of-open-source-local-llms-june-2026",
   "title": "State of Open-Source Local LLMs \u2014 June 2026: Qwen 4 Goes Live, Llama 5 Scales, Grok Goes Open",
   "published_at": "2026-06-06T00:00:00",
   "organization": "xAI",
   "model": "Grok 4 Open 100B-A20B",
   "release_date": "2026-06-05",
   "parameters": "100B",
   "context_window": null,
   "license": "xAI Custom",
   "significance": "xAI's FIRST open weights ever, custom license, ~32 tok/s on M5 Max 64 GB.",
   "organization_evidence": [
    "- **Grok 4 Open 100B-A20B** Jun 5 \u2014 xAI's FIRST open weights ever, custom license, ~32 tok/s on M5 Max 64 GB.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "model_evidence": [
    "- **Grok 4 Open 100B-A20B** Jun 5 \u2014 xAI's FIRST open weights ever, custom license, ~32 tok/s on M5 Max 64 GB.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "release_date_evidence": [
    "- **Grok 4 Open 100B-A20B** Jun 5 \u2014 xAI's FIRST open weights ever, custom license, ~32 tok/s on M5 Max 64 GB.",
    "## The 30-Day Recap (TL;DR)"
   ],
   "parameters_evidence": [
    "Total params100B",
    "On June 5, xAI did something it had never done before: it released model weights.",
    "## Grok 4 Open \u2014 xAI's first open weights"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "LicensexAI Custom",
    "On June 5, xAI did something it had never done before: it released model weights.",
    "## Grok 4 Open \u2014 xAI's first open weights"
   ],
   "significance_evidence": [
    "- **Grok 4 Open 100B-A20B** Jun 5 \u2014 xAI's FIRST open weights ever, custom license, ~32 tok/s on M5 Max 64 GB.",
    "## The 30-Day Recap (TL;DR)"
   ]
  },
  {
   "url": "https://aiunpacking.com/guides/open-source-ai-models-2026-llama-mistral-deepseek",
   "title": "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
   "published_at": "2026-04-25T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "600 billion parameters",
   "context_window": null,
   "license": null,
   "significance": "Introduces Recursive Self-Improvement and is designed for System 2 thinking, with strong early developer reports for complex enterprise applications.",
   "organization_evidence": [
    "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
    "Meta Llama 5",
    "On April 8, 2026, Mark Zuckerberg dropped Llama 5 at Meta\u2019s AI Connect summit."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
    "Meta Llama 5",
    "On April 8, 2026, Mark Zuckerberg dropped Llama 5 at Meta\u2019s AI Connect summit."
   ],
   "parameters_evidence": [
    "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
    "Meta Llama 5",
    "The flagship model packs over 600 billion parameters, trained on a staggering cluster of 500,000+ NVIDIA Blackwell B200 GPUs."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
    "Meta Llama 5",
    "The flagship model packs over 600 billion parameters, trained on a staggering cluster of 500,000+ NVIDIA Blackwell B200 GPUs.",
    "It supports a **5-million-token context window**, and Meta claims it introduces \u201cRecursive Self-Improvement\u201d \u2014 the model refines its own internal logic during inference.",
    "More importantly, Llama 5 was designed for what cognitive scientists call **System 2 thinking** \u2014 slow, deliberate, multi-step reasoning."
   ]
  },
  {
   "url": "https://aiunpacking.com/guides/open-source-ai-models-2026-llama-mistral-deepseek",
   "title": "Open Source AI Models 2026: Llama, Mistral, DeepSeek & The Complete Guide",
   "published_at": "2026-04-25T00:00:00",
   "organization": "DeepSeek",
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   "significance": "Features a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), plus three adaptive reasoning modes, with V4-Pro-Max outpacing other open-source models by roughly 20 absolute percentage points on factual knowledge benchmarks.",
   "organization_evidence": [
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    "DeepSeek V4",
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    "In practical terms, at 1M context, V4-Pro uses only 27% of the inference FLOPs and 10% of the KV cache compared to V3.2.",
    "V4 also introduces three adaptive reasoning modes: Non-think (fast), Think High (deliberate), and Think Max (frontier-level).",
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   "url": "https://noqta.tn/en/news/moonshot-kimi-k3-largest-open-model-3t-class-2026",
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   "url": "https://llm-releases.com/coding-models",
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   "url": "https://llm-releases.com/coding-models",
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   "url": "https://tech-insider.org/deepseek-v4-vs-glm-5-2-vs-qwen-2026",
   "title": "DeepSeek V4 vs GLM-5.2 vs Qwen: 10x Price Gap [2026]",
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    "| Release date | April 24, 2026 | June 13, 2026 | Rolling 2026 updates |",
    "## DeepSeek V4 vs GLM-5.2 vs Qwen3.6: Specs at a Glance"
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    "| Spec | DeepSeek V4 Pro | GLM-5.2 | Qwen3.6 |",
    "| Total parameters | 1.6T (MoE) | 753B (MoE) | 35B dense / 80B (Coder-Next) |",
    "## DeepSeek V4 vs GLM-5.2 vs Qwen3.6: Specs at a Glance"
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    "DeepSeek V4 Pro posts a 93.5% score on LiveCodeBench, reportedly the highest of any publicly tested model as of mid-2026."
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  {
   "url": "https://hostingseekers.com/blog/best-open-source-llms",
   "title": "10 Best Open-Source LLMs in 2026",
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    "On Artificial Analysis\u2019s Intelligence Index, it scored the highest of any open-weight model to date, and vendor-reported figures put it near Claude Opus 4.8 and ahead of GPT-5.5 on several long-horizon coding benchmark figures that, as with most day-one model claims, are largely self-reported and still being independently verified."
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  {
   "url": "https://hostingseekers.com/blog/best-open-source-llms",
   "title": "10 Best Open-Source LLMs in 2026",
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   "release_date": "2026-06-12",
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    "K2.7 Code, released June 12, 2026, is a 1-trillion-parameter MoE model with 32B active parameters and 384 experts, a 256K context window, and a design focus on cutting \u201cthinking token\u201d overhead\u2014roughly 30% fewer reasoning tokens than K2.6 for comparable or better output."
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  {
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   "title": "10 Best Open-Source LLMs in 2026",
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    "MiniMax, a Shanghai-based lab, released M3 on June 1, 2026, positioning it as the first open-weight model to combine frontier-level coding, a 1M-token context window, and native text/image/video input in a single checkpoint."
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   "title": "10 Best Open-Source LLMs in 2026",
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   "release_date": "2026-06",
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    "This guide ranks the 10 strongest open-source/open-weight LLMs as of 2026, with real specs, verified licenses, actual pricing, and honest pros and cons for each.",
    "NVIDIA\u2019s Nemotron 3 family rolled out in stages from December 2025 through NVIDIA\u2019s June 2026 GTC conference, culminating in Nemotron 3 Ultra, reported around 550B parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# 10 Best Open-Source LLMs in 2026",
    "This guide ranks the 10 strongest open-source/open-weight LLMs as of 2026, with real specs, verified licenses, actual pricing, and honest pros and cons for each.",
    "NVIDIA\u2019s Nemotron 3 family rolled out in stages from December 2025 through NVIDIA\u2019s June 2026 GTC conference, culminating in Nemotron 3 Ultra, reported around 550B parameters."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "# 10 Best Open-Source LLMs in 2026",
    "This guide ranks the 10 strongest open-source/open-weight LLMs as of 2026, with real specs, verified licenses, actual pricing, and honest pros and cons for each.",
    "NVIDIA differentiates Nemotron by publishing not just weights but also training data, recipes, and evaluation resources\u2014a meaningfully more transparent approach than most labs on this list, and part of why it fits enterprise procurement and compliance review more easily than models with opaque training pipelines."
   ]
  },
  {
   "url": "https://tech-insider.org/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: DeepSeek, Kimi, Qwen Ranked",
   "published_at": "2026-06-03T10:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "Apr 24, 2026",
   "parameters": "1.6T",
   "context_window": null,
   "license": null,
   "significance": "hitting 80.6% on SWE-Bench Verified \u2014 a number that would have been science fiction for a downloadable model a year ago.",
   "organization_evidence": [
    "Full Specifications Compared",
    "| Developer | DeepSeek | Moonshot AI | Zhipu / Z.ai | Alibaba | Meta |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Full Specifications Compared",
    "| Release date | Apr 24, 2026 | Apr 20, 2026 | Sep 30, 2025 | 2025 (2507 update) | Apr 5, 2025 |"
   ],
   "parameters_evidence": [
    "Full Specifications Compared",
    "| Total parameters | 1.6T | ~1T | 355B | 235B | 400B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4-Pro: The Open-Weight Leader",
    "[Vellum\u2019s open-LLM leaderboard](https://www.vellum.ai/open-llm-leaderboard) now shows the leading open model, **DeepSeek V4-Pro**, hitting **80.6% on SWE-Bench Verified** \u2014 a number that would have been science fiction for a downloadable model a year ago."
   ]
  },
  {
   "url": "https://tech-insider.org/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: DeepSeek, Kimi, Qwen Ranked",
   "published_at": "2026-06-03T10:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "Apr 20, 2026",
   "parameters": "~1T",
   "context_window": null,
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   "significance": "it leads the tougher **SWE-Bench Pro** benchmark at 58.6%, ahead of even the closed frontier on that particular test",
   "organization_evidence": [
    "Full Specifications Compared",
    "| Developer | DeepSeek | Moonshot AI | Zhipu / Z.ai | Alibaba | Meta |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Full Specifications Compared",
    "| Release date | Apr 24, 2026 | Apr 20, 2026 | Sep 30, 2025 | 2025 (2507 update) | Apr 5, 2025 |"
   ],
   "parameters_evidence": [
    "Full Specifications Compared",
    "| Total parameters | 1.6T | ~1T | 355B | 235B | 400B |"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Coding: SWE-Bench Verified vs SWE-Bench Pro",
    "For agentic coding, Kimi K2.6 has a specific claim to fame: it leads the tougher **SWE-Bench Pro** benchmark at 58.6%, ahead of even the closed frontier on that particular test \u2014 GPT-5.4 sits at 57.7%, Gemini 3.1 Pro at 54.2%, and Claude Opus 4.6 at 53.4% on the same board."
   ]
  },
  {
   "url": "https://medium.com/@vinoth.lingam333/kimi-k3-the-2-8-trillion-parameter-open-model-that-just-reset-the-frontier-01ff9df198da",
   "title": "Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
   "published_at": "2026-07-18T00:00:13",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-14",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "outperforms leading US frontier models on several benchmarks, largest open-weight model ever released",
   "organization_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
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   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
    "Moonshot AI (the Beijing-based company behind the Kimi chatbot) published K3 on July 14, 2026, with general availability following on July 16."
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   "parameters_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
    "Two days ago, Moonshot AI launched Kimi K3 \u2014 a 2.8 trillion parameter open-weight model with a 1 million token context window."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
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  {
   "url": "https://en.wikipedia.org/wiki/Qwen?:~:text=On%20September%205_%202025_%20Alibaba_released%20on%2017%20February%202026.",
   "title": "Qwen",
   "published_at": "2026-08-31T03:10:32",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "2.4 trillion",
   "context_window": null,
   "license": null,
   "significance": "the second largest and second most powerful open weights LLM and Chinese LLM released by 12 August 2026, after Kimi K3",
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    "Qwen (also known as Tongyi Qianwen, Chinese: \u901a\u4e49\u5343\u95ee; pinyin: T\u014dngy\u00ec Qi\u0101nw\u00e8n) is a family of predominantly open weights large and small language models (LLM and SLM) developed by Alibaba Cloud.",
    "Alibaba released the weights of Qwen3.8-Max as Qwen3.8-2.4T-A95B on 12 August 2026, and the open-weights model omits certain features of the cloud model, such as image input and a non-thinking mode."
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   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba released the weights of Qwen3.8-Max as Qwen3.8-2.4T-A95B on 12 August 2026, and the open-weights model omits certain features of the cloud model, such as image input and a non-thinking mode."
   ],
   "parameters_evidence": [
    "Alibaba previewed its 2.4-trillion-parameter model Qwen3.8-Max in July 2026 and announced that it would release the weights of the model."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Its latest version, Qwen3.8; the 2.4 trillion parameter model was the second largest and second most powerful open weights LLM and Chinese LLM released by 12 August 2026, after Kimi K3."
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  },
  {
   "url": "https://serenitiesai.com/articles/mistral-ai-models-2026-complete-guide",
   "title": "Mistral AI Models 2026: Small 4, Large 3, Voxtral TTS, Forge \u2014 Complete Guide",
   "published_at": "2026-04-12T05:05:20",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "merges three previously separate products into a single model: Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding)",
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    "Mistral AI's 2026 Blitz: Six Launches in Two Weeks",
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   "model_evidence": null,
   "release_date_evidence": [
    "Released March 16, 2026, Mistral Small 4 merges three previously separate products into a single model: Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding)."
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   "parameters_evidence": [
    "Mistral Small 4: One Model to Replace Three",
    "Architecture & Specs",
    "| Total Parameters | 119B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Released March 16, 2026, Mistral Small 4 merges three previously separate products into a single model: Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding)."
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  },
  {
   "url": "https://serenitiesai.com/articles/mistral-ai-models-2026-complete-guide",
   "title": "Mistral AI Models 2026: Small 4, Large 3, Voxtral TTS, Forge \u2014 Complete Guide",
   "published_at": "2026-04-12T05:05:20",
   "organization": "Mistral AI",
   "model": "Leanstral",
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": "256K tokens",
   "license": "Apache 2.0",
   "significance": "the first open-source AI agent designed specifically for Lean 4 formal proof engineering",
   "organization_evidence": [
    "Mistral AI's 2026 Blitz: Six Launches in Two Weeks",
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   "model_evidence": [
    "Leanstral: Formal Proofs for Vibe Coding",
    "Released March 16, 2026, Leanstral is the first open-source AI agent designed specifically for [Lean 4](https://en.wikipedia.org/wiki/Lean_(proof_assistant)) formal proof engineering."
   ],
   "release_date_evidence": [
    "Released March 16, 2026, Leanstral is the first open-source AI agent designed specifically for [Lean 4](https://en.wikipedia.org/wiki/Lean_(proof_assistant)) formal proof engineering."
   ],
   "parameters_evidence": [
    "Leanstral: Formal Proofs for Vibe Coding",
    "Specs",
    "| Total Parameters | 119B (same architecture as Small 4) |"
   ],
   "context_window_evidence": [
    "Leanstral: Formal Proofs for Vibe Coding",
    "Specs",
    "| Total Parameters | 119B (same architecture as Small 4) |"
   ],
   "license_evidence": [
    "Leanstral: Formal Proofs for Vibe Coding",
    "Specs",
    "| License | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Released March 16, 2026, Leanstral is the first open-source AI agent designed specifically for [Lean 4](https://en.wikipedia.org/wiki/Lean_(proof_assistant)) formal proof engineering."
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  },
  {
   "url": "https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/",
   "title": "Our most capable open models to date",
   "published_at": "2026-04-02T16:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Our larger models deliver state-of-the-art performance for their sizes, with the 31B model currently ranking as the #3 open model in the world on the industry-standard Arena AI text leaderboard, and the 26B model securing the #6 spot.",
   "organization_evidence": [
    "# Gemma 4: Byte for byte, the most capable open models",
    "Today, we are introducing [Gemma 4](https://aistudio.google.com/prompts/new_chat?model=gemma-4-31b-it) \u2014 our most intelligent open models to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Byte for byte, the most capable open models",
    "Apr 02, 2026"
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   "parameters_evidence": null,
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   "significance_evidence": [
    "# Gemma 4: Byte for byte, the most capable open models",
    "## Industry-leading capabilities and mobile-first AI",
    "Our larger models deliver state-of-the-art performance for their sizes, with the 31B model currently ranking as the #3 open model in the world on the industry-standard [Arena AI text leaderboard](https://arena.ai/leaderboard/text?license=open-source), and the 26B model securing the #6 spot."
   ]
  },
  {
   "url": "https://aiwiki.ai/wiki/kimi_k3",
   "title": "Kimi K3",
   "published_at": "2026-07-23T10:29:06",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.78 trillion total parameters and 104.2 billion parameters activated for each token",
   "context_window": null,
   "license": null,
   "significance": "A sparse mixture-of-experts model with a hybrid architecture combining Kimi Delta Attention and gated multi-head latent attention, a 1-million-token context window, and a 401-million-parameter MoonViT-V2 visual encoder.",
   "organization_evidence": [
    "# Kimi K3"
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   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3",
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   "parameters_evidence": [
    "# Kimi K3",
    "K3 is a sparse [mixture-of-experts](/wiki/mixture_of_experts) model with 2.78 trillion total parameters and 104.2 billion parameters activated for each token."
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   "significance_evidence": [
    "# Kimi K3",
    "K3 is a sparse [mixture-of-experts](/wiki/mixture_of_experts) model with 2.78 trillion total parameters and 104.2 billion parameters activated for each token.",
    "Its 93-layer backbone combines Kimi Delta Attention, a form of [linear attention](/wiki/linear_attention), with gated multi-head latent attention."
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  },
  {
   "url": "https://presenc.ai/research/zhipu-glm-model-lineage-2026",
   "title": "Zhipu / Z.ai GLM Model Lineage 2026",
   "published_at": "2026-05-15T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "744B MoE",
   "context_window": null,
   "license": null,
   "significance": "Open-weight release keeping Z.ai in the open-weight game",
   "organization_evidence": [
    "## What this is",
    "Zhipu AI (rebranded Z.ai internationally) shipped the most consistent quarterly release cadence among Chinese labs in 2024-2026."
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   "model_evidence": null,
   "release_date_evidence": [
    "## GLM Release Timeline (2024-2026)",
    "| Apr 8, 2026 | GLM-5.1 open-source | Open-weight release |"
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   "parameters_evidence": [
    "## GLM Release Timeline (2024-2026)",
    "| Feb 11, 2026 | GLM-5 | 744B MoE / 40B active; 2x scale-up from 4.5 |"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "## Six Things the Lineage Tells You",
    "6. **GLM-5.1 open-sourcing (Apr 8, 2026) keeps Z.ai in the open-weight game.** Distinct from labs that have moved entirely to closed-tier flagship."
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  },
  {
   "url": "https://openrouter.ai/blog/insights/the-open-weight-models-that-matter-june-2026",
   "title": "The Open Weight Models that Matter: June 2026",
   "published_at": "2026-06-27T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
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   "significance": "The first open-weight model that teams immediately dropped into real agentic pipelines as a plausible substitute for an Anthropic- or OpenAI-class frontier model.",
   "organization_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 1. [DeepSeek V4 Flash](https://openrouter.ai/deepseek/deepseek-v4-flash) \u2014 The first to cross the agentic rubicon",
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   "release_date_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 1. [DeepSeek V4 Flash](https://openrouter.ai/deepseek/deepseek-v4-flash) \u2014 The first to cross the agentic rubicon",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 1. [DeepSeek V4 Flash](https://openrouter.ai/deepseek/deepseek-v4-flash) \u2014 The first to cross the agentic rubicon",
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  {
   "url": "https://openrouter.ai/blog/insights/the-open-weight-models-that-matter-june-2026",
   "title": "The Open Weight Models that Matter: June 2026",
   "published_at": "2026-06-27T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06",
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   "significance": "Breaking through on planning quality and long-horizon coding, and ranked #1 open-weight model on Artificial Analysis Intelligence Index (v4.1) at 51.",
   "organization_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 2. [GLM 5.2](https://openrouter.ai/z-ai/glm-5.2) \u2014 The open model that makes Opus-style agentic coding portable"
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   "release_date_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 2. [GLM 5.2](https://openrouter.ai/z-ai/glm-5.2) \u2014 The open model that makes Opus-style agentic coding portable",
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "The Open Weight Models that Matter: June 2026",
    "## 2. [GLM 5.2](https://openrouter.ai/z-ai/glm-5.2) \u2014 The open model that makes Opus-style agentic coding portable",
    "Where DeepSeek broke through on price, GLM 5.2 appears to be breaking through on planning quality and long-horizon coding."
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  },
  {
   "url": "https://www.cnbc.com/2026/06/26/china-zhipu-z-ai-open-source-anthropic-openai.html",
   "title": "China's Zhipu is closing in on top U.S. AI models with Anthropic and OpenAI held back",
   "published_at": "2026-06-26T22:08:09",
   "organization": "Zhipu",
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   "significance": "GLM 5.2 lands within a percentage point of Anthropic's Opus 4.8 on a key agentic benchmark at roughly a fifth of the cost.",
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   "context_window_evidence": null,
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    "China's Zhipu is closing in on top U.S. AI models with Anthropic and OpenAI held back",
    "Zhipu's GLM 5.2 lands within a percentage point of Anthropic's Opus 4.8 on a key agentic benchmark at roughly a fifth of the cost."
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  },
  {
   "url": "https://aifoss.dev/blog/minimax-m3-open-weight-review-2026",
   "title": "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
   "published_at": "2026-06-18T00:00:00",
   "organization": "MiniMax",
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   "release_date": "2026-06-01",
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   "significance": "59.0% on SWE-Bench Pro, 1M-token context window, native multimodality",
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   "release_date_evidence": [
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   "parameters_evidence": null,
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    "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
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  },
  {
   "url": "https://cline.bot/blog/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": "1M",
   "license": null,
   "significance": "88.3 on Terminal-Bench 2.1, putting it very close to top closed models in the same category",
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   "context_window_evidence": [
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  {
   "url": "https://cline.bot/blog/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "DeepSeek",
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   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 2. DeepSeek V4 Flash",
    "The July 31 update keeps the same 284B total parameters, 13B active parameters, and 1M token context window, but has substantially enhanced agentic capabilities."
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 2. DeepSeek V4 Flash",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 2. DeepSeek V4 Flash",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 2. DeepSeek V4 Flash",
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  },
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   "url": "https://cline.bot/blog/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
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   "organization": "Z.ai",
   "model": "GLM-5.2",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
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   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
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   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
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   "context_window_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "It has roughly 744B total parameters, around 40B active parameters, supports a 1M token context window and is released under an MIT license."
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   "license_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "It has roughly 744B total parameters, around 40B active parameters, supports a 1M token context window and is released under an MIT license."
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "In Z.ai\u2019s own release material, GLM-5.2 scored 81.0 on Terminal-Bench 2.1."
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  {
   "url": "https://cline.bot/blog/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "DeepSeek",
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   "context_window": "1M",
   "license": null,
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   "organization_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 4. DeepSeek V4 Pro",
    "DeepSeek V4 Pro was released in preview on April 24, 2026 as the larger model in the DeepSeek V4 family."
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   "model_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 4. DeepSeek V4 Pro",
    "DeepSeek V4 Pro was released in preview on April 24, 2026 as the larger model in the DeepSeek V4 family."
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   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 4. DeepSeek V4 Pro",
    "DeepSeek V4 Pro was released in preview on April 24, 2026 as the larger model in the DeepSeek V4 family."
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   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 4. DeepSeek V4 Pro",
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    "### 4. DeepSeek V4 Pro",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 4. DeepSeek V4 Pro",
    "The main benchmark to look at is SWE-bench Verified, where V4 Pro is reported at 80.6%."
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  {
   "url": "https://cline.bot/blog/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "MiniMax",
   "model": "MiniMax M3",
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    "### 5. MiniMax M3",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
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   "release_date_evidence": [
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    "### 5. MiniMax M3",
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   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
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   "license_evidence": null,
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    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
    "On benchmarks, MiniMax scores 59.0% on SWE-bench Pro, slightly above GPT-5.5 at 58.6% and ahead of Gemini 3.1 Pro at 54.2% in the same comparison."
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  },
  {
   "url": "https://docs.mistral.ai/resources/changelogs",
   "title": "Changelog",
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   "organization": "Mistral",
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   "release_date": "2026-04-28",
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    "# Changelog",
    "Find out about all the latest changes to our tool. You may filter by date and type of release.",
    "## April 28",
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   "release_date_evidence": [
    "# Changelog",
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    "## April 28",
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    "# Changelog",
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    "## April 28",
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  {
   "url": "https://technode.com/2026/08/27/zhipu-identifies-ox-alpha-as-glm-5-3-flash-and-releases-model-weights",
   "title": "Zhipu identifies Ox Alpha as GLM-5.3-Flash and releases model weights",
   "published_at": "2026-08-27T07:05:00",
   "organization": "Zhipu",
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   "parameters": "320 billion total parameters and 18 billion active parameters",
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   "release_date_evidence": null,
   "parameters_evidence": [
    "The model has 320 billion total parameters and 18 billion active parameters.",
    "# Zhipu identifies Ox Alpha as GLM-5.3-Flash and releases model weights"
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   "license_evidence": null,
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    "GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, supporting text, images, videos, visual documents and interleaved multimodal inputs.",
    "# Zhipu identifies Ox Alpha as GLM-5.3-Flash and releases model weights"
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  {
   "url": "https://teamai.com/blog/large-language-models-llms/understanding-the-different-deepseek-models?:~:text=DeepSeek-R1%20is%20a%20reasoning_explicit%20chain-of-thought.",
   "title": "DeepSeek R1, V4 Pro, and V4 Flash Compared: Pricing, Use Cases, and the Full 2026 Model Guide",
   "published_at": "2025-03-06T18:52:56",
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
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   "significance": "Designed for complex reasoning, agentic coding, and long-context analytical work",
   "organization_evidence": [
    "DeepSeek is no longer the upstart it was in January 2025.",
    "# DeepSeek R1, V4 Pro, and V4 Flash Compared: Pricing, Use Cases, and the Full 2026 Model Guide"
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    "The Hangzhou-based lab has shipped a steady cadence of frontier and near-frontier models since then, capped by the V4 Preview release on April 24, 2026 (V4 Pro and V4 Flash, both with a 1 million token context window).",
    "# DeepSeek R1, V4 Pro, and V4 Flash Compared: Pricing, Use Cases, and the Full 2026 Model Guide"
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   "context_window_evidence": null,
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    "V4-Pro is designed for complex reasoning, agentic coding, and long-context analytical work.",
    "## DeepSeek-V4-Pro Current Flagship"
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   "url": "https://teamai.com/blog/large-language-models-llms/understanding-the-different-deepseek-models?:~:text=DeepSeek-R1%20is%20a%20reasoning_explicit%20chain-of-thought.",
   "title": "DeepSeek R1, V4 Pro, and V4 Flash Compared: Pricing, Use Cases, and the Full 2026 Model Guide",
   "published_at": "2025-03-06T18:52:56",
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
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   "significance": "Trades some reasoning depth for throughput and cost",
   "organization_evidence": [
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   "release_date_evidence": [
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    "V4-Flash trades some reasoning depth for throughput and cost.",
    "## DeepSeek-V4-Flash Cost-Efficient"
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   "url": "https://handwiki.org/wiki/Software:Qwen",
   "title": "Software:Qwen - HandWiki",
   "published_at": "2026-04-11T03:41:24",
   "organization": "Alibaba Cloud",
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   "release_date": null,
   "parameters": null,
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   "organization_evidence": [
    "# Software:Qwen",
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   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
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  {
   "url": "https://geekblog.net/tech-news/meta-muse-glimmer-open-weight-30b-model",
   "title": "Meta Muse Glimmer: 30B Open Model on One GPU",
   "published_at": "2026-08-20T14:30:39",
   "organization": "Meta",
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   "release_date": "2026-08-10",
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   "license": null,
   "significance": "Meta optimized for the thinking half of agentic work and left the doing half a step behind the leader.",
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    "On August 10, 2026, Meta quietly reversed part of that decision.",
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   "release_date_evidence": [
    "On August 10, 2026, Meta quietly reversed part of that decision."
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    "Meta optimized for the thinking half of agentic work and left the doing half a step behind the leader."
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   "url": "https://aipedia.wiki/news/2026-04-28-mistral-3-open-model-release",
   "title": "Mistral 3 ships with Large 3 and new Ministral edge models",
   "published_at": "2026-04-28T04:51:46",
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   "release_date": "2026",
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   "significance": "sparse mixture-of-experts model with 41B active parameters and 675B total parameters, most capable model to date and its first mixture-of-experts model since the Mixtral line",
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   "url": "https://ai-tldr.dev/releases/moonshot-kimi-k3-open-weights",
   "title": "Kimi K3 open weights \u2014 Moonshot drops the 2.8T MoE on Hugging Face",
   "published_at": "2026-07-27T12:00:00",
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   "release_date": "2026-07-27",
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   "release_date_evidence": [
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    "Moonshot AI published the full open weights for [Kimi K3](/models/kimi-k3/) on Hugging Face at 00:00 UTC on July 27, making the 2.8-trillion-parameter mixture-of-experts model the largest open-weight AI release in history and free to self-host.",
    "Key specs",
    "| Total parameters | 2.8T |"
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    "Moonshot AI published the full open weights for [Kimi K3](/models/kimi-k3/) on Hugging Face at 00:00 UTC on July 27, making the 2.8-trillion-parameter mixture-of-experts model the largest open-weight AI release in history and free to self-host."
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   "url": "https://whatisgm.com/blogs/ai/zhipu-glm-5-2-the-open-model-that-changes-the-game",
   "title": "Zhipu GLM-5.2: The Open Model That Changes the Game",
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    "**Z.ai (formerly Zhipu AI) released GLM-5.2 on 17 June 2026** \u2014 a 753B-parameter Mixture-of-Experts model with 40B active parameters, a 1-million-token context window, and full MIT-licensed open weights on Hugging Face and ModelScope."
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    "# Zhipu GLM-5.2: The Open Model That Changes the Game",
    "## TL;DR",
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  {
   "url": "https://cryptobriefing.com/nvidia-shortens-ai-release-cycles",
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   "release_date": "2026-08-11",
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   "url": "https://arxiv.org/html/2109.02593v1",
   "title": "General-Purpose Question-Answering with Macaw",
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   "significance": "outperforming GPT-3 by over 10% (absolute) on Challenge300, a suite of 300 challenge questions, despite being an order of magnitude smaller",
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   "url": "https://webpronews.com/googles-gemma-hits-1-billion-downloads-as-open-models-power-space-medicine-and-dolphin-research",
   "title": "Google\u2019s Gemma Hits 1 Billion Downloads as Open Models Power Space, Medicine and Dolphin Research",
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   "significance": "further multimodal advances and efficiency improvements aimed at mobile and laptop hardware",
   "organization_evidence": [
    "Google\u2019s Gemma Hits 1 Billion Downloads as Open Models Power Space, Medicine and Dolphin Research",
    "Gemma 4 followed in 2026 with further multimodal advances and efficiency improvements aimed at mobile and laptop hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google\u2019s Gemma Hits 1 Billion Downloads as Open Models Power Space, Medicine and Dolphin Research",
    "Gemma 4 followed in 2026 with further multimodal advances and efficiency improvements aimed at mobile and laptop hardware."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google\u2019s Gemma Hits 1 Billion Downloads as Open Models Power Space, Medicine and Dolphin Research",
    "Gemma 4 followed in 2026 with further multimodal advances and efficiency improvements aimed at mobile and laptop hardware."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Kimi%20(chatbot)",
   "title": "Kimi (AI)",
   "published_at": "2026-08-22T00:06:20",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks.",
   "organization_evidence": [
    "Kimi is an artificial intelligence (AI) chatbot and series of large language models developed by Chinese company Moonshot AI.",
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters.",
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ],
   "parameters_evidence": [
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters.",
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ]
  },
  {
   "url": "https://felloai.com/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 scores 62.1 on SWE-bench Pro and 81.0 on Terminal-Bench 2.1, ahead of GPT-5.5 and behind Claude Opus 4.8, but Kimi K3 has since taken the open-weight lead.",
   "organization_evidence": [
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle.",
    "**GLM 5.2** is the latest flagship large language model from **Zhipu AI**, a Beijing-based lab that has become one of China\u2019s most aggressive open-weight model publishers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026** with three headline numbers.",
    "| Release date | June 13, 2026 | April 7, 2026 | ~2 months apart | Fast iteration cycle |",
    "| Spec | GLM 5.2 | GLM 5.1 | What changed | Why it matters |"
   ],
   "parameters_evidence": [
    "| Parameters | 744B (40B active) | 744B (40B active) | Unchanged | Same per-token compute |",
    "| Spec | GLM 5.2 | GLM 5.1 | What changed | Why it matters |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "> - GLM 5.2 scores **62.1 on SWE-bench Pro** and **81.0 on Terminal-Bench 2.1**, ahead of GPT-5.5 and behind **Claude Opus 4.8**, but **Kimi K3** has since taken the open-weight lead.",
    "## The Key Takeaways"
   ]
  },
  {
   "url": "https://felloai.com/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": null,
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": "1-million-token",
   "license": "custom Moonshot licence",
   "significance": "Outscores GLM 5.2 on the Artificial Analysis Intelligence Index by 57 to 51 and takes the open-weight lead.",
   "organization_evidence": null,
   "model_evidence": [
    "Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51.",
    "*Update, July 28, 2026: GLM 5.2 is no longer the strongest open-weight model. Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51. GLM 5.2 keeps a real advantage on licensing and hardware, and the benchmark section below has been rebased.*"
   ],
   "release_date_evidence": [
    "Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51.",
    "*Update, July 28, 2026: GLM 5.2 is no longer the strongest open-weight model. Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51. GLM 5.2 keeps a real advantage on licensing and hardware, and the benchmark section below has been rebased.*"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51.",
    "*Update, July 28, 2026: GLM 5.2 is no longer the strongest open-weight model. Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51. GLM 5.2 keeps a real advantage on licensing and hardware, and the benchmark section below has been rebased.*"
   ],
   "license_evidence": [
    "If you write code and want a long-context model you can run yourself, **GLM 5.2** is the strongest openly available model carrying a plain **MIT license**, which removes the usual restrictions on commercial use.",
    "Kimi K3 scores higher, but it ships under a custom Moonshot licence and weighs **1.56 TB** to download against GLM 5.2\u2019s 1.51 TB."
   ],
   "significance_evidence": [
    "Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51.",
    "*Update, July 28, 2026: GLM 5.2 is no longer the strongest open-weight model. Kimi K3 shipped its weights on July 27, 2026 and outscores it on the Artificial Analysis Intelligence Index by 57 to 51. GLM 5.2 keeps a real advantage on licensing and hardware, and the benchmark section below has been rebased.*"
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/minimax-m2-7-open-weights",
   "title": "MiniMax M2.7 \u2014 open-weights self-evolving agent model",
   "published_at": "2026-04-12T12:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M2.7",
   "release_date": "2026-04-12",
   "parameters": "229B total parameters, 10B active per token",
   "context_window": "200K tokens",
   "license": "Modified MIT",
   "significance": "Hits 56.22% on SWE-Bench Pro and 57.0% on Terminal Bench 2, with downloads already in the tens of thousands.",
   "organization_evidence": [
    "MiniMax \u00b7 2026-04-12 \u00b7 major",
    "MiniMax drops open weights for M2.7, a 229B/10B-active MoE agentic model previously API-only."
   ],
   "model_evidence": [
    "# MiniMax M2.7 \u2014 open-weights self-evolving agent model",
    "MiniMax drops open weights for M2.7, a 229B/10B-active MoE agentic model previously API-only."
   ],
   "release_date_evidence": [
    "MiniMax \u00b7 2026-04-12 \u00b7 major",
    "# MiniMax M2.7 \u2014 open-weights self-evolving agent model"
   ],
   "parameters_evidence": [
    "MiniMax drops open weights for M2.7, a 229B/10B-active MoE agentic model previously API-only.",
    "Key specs",
    "| Active params | 10B |",
    "| Total parameters | 229B |"
   ],
   "context_window_evidence": [
    "Key specs",
    "| Context window | 200K tokens |"
   ],
   "license_evidence": [
    "Key specs",
    "| License | Modified MIT |"
   ],
   "significance_evidence": [
    "# MiniMax M2.7 \u2014 open-weights self-evolving agent model",
    "MiniMax drops open weights for M2.7, a 229B/10B-active MoE agentic model previously API-only. Hits 56.22% on [SWE-Bench](/tools/swe-bench) Pro and 57.0% on Terminal Bench 2, with downloads already in the tens of thousands."
   ]
  },
  {
   "url": "https://aichina.news/blog/allen-ai-s-molmo-4b-arrives-on-modelers-cn-a-robust-open-source-8apgad",
   "title": "Allen AI's Molmo 4B Arrives on Modelers.cn: A Robust Open-Source Foundation for Ascend NPU Developers \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:21:24",
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "4 billion",
   "context_window": null,
   "license": null,
   "significance": "Designed as a foundational base model providing a high-quality, openly licensed starting point for fine-tuning and adapting for specific downstream multimodal tasks.",
   "organization_evidence": [
    "Allen AI's Molmo 4B Arrives on Modelers.cn: A Robust Open-Source Foundation for Ascend NPU Developers",
    "Allen AI has officially released a new 4-billion parameter vision-language model via the Modelers.cn platform, marking a significant addition to the open-source ecosystem."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Allen AI's Molmo 4B Arrives on Modelers.cn: A Robust Open-Source Foundation for Ascend NPU Developers",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Allen AI's Molmo 4B Arrives on Modelers.cn: A Robust Open-Source Foundation for Ascend NPU Developers",
    "Allen AI has officially released a new 4-billion parameter vision-language model via the Modelers.cn platform, marking a significant addition to the open-source ecosystem."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Allen AI's Molmo 4B Arrives on Modelers.cn: A Robust Open-Source Foundation for Ascend NPU Developers",
    "This latest checkpoint, **MolmoWeb-Pretrained-4B**, is designed not as a ready-to-use chatbot, but as a foundational base model."
   ]
  },
  {
   "url": "https://aiyouneed.com/en/news/qwen36-35b-a3b-is-now-open-source",
   "title": "Qwen3.6-35B-A3B Is Now Open Source",
   "published_at": "2026-04-17T07:40:54",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-04-17",
   "parameters": "35B total parameters, 3 billion active parameters",
   "context_window": null,
   "license": null,
   "significance": "Agentic coding performance, multimodal perception and reasoning, multi-mode operation, and strong benchmark results on SWE-bench, MMLU-Pro, and others.",
   "organization_evidence": [
    "# Qwen3.6-35B-A3B Is Now Open Source",
    "Qwen has announced the open-source release of Qwen3.6-35B-A3B, extending the Qwen3.6 lineup after the launch of Qwen3.6-Plus."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.6-35B-A3B Is Now Open Source",
    "4/17/2026"
   ],
   "parameters_evidence": [
    "# Qwen3.6-35B-A3B Is Now Open Source",
    "The company describes the model as a sparse mixture-of-experts system with 35 billion total parameters but only 3 billion active parameters at runtime."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6-35B-A3B Is Now Open Source",
    "In the release, Qwen frames that efficiency as a core part of the story, arguing that the model delivers strong agentic coding performance despite its relatively small active footprint."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Mistral_AI",
   "title": "Mistral AI",
   "published_at": "2026-08-27T20:12:44",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03",
   "parameters": "119",
   "context_window": null,
   "license": null,
   "significance": "A hybrid multimodal model that combines instruction following, reasoning and coding capabilities.",
   "organization_evidence": [
    "Mistral AI SAS ( French: [ mist\u0281al]) is a French artificial intelligence (AI) company headquartered in Paris.",
    "The following table lists the main model versions of Mistral, describing the significant changes included with each version:",
    "in March 2026, Mistral AI released Mistral Small 4, a hybrid multimodal model that combines instruction following, reasoning and coding capabilities."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The following table lists the main model versions of Mistral, describing the significant changes included with each version:",
    "| Mistral Small 4 | March 2026 | Active | 119 | Apache-2.0 | Combines 128 experts, with 4 active per token. Accepts both text and images as inputs. |",
    "in March 2026, Mistral AI released Mistral Small 4, a hybrid multimodal model that combines instruction following, reasoning and coding capabilities."
   ],
   "parameters_evidence": [
    "The following table lists the main model versions of Mistral, describing the significant changes included with each version:",
    "| Mistral Small 4 | March 2026 | Active | 119 | Apache-2.0 | Combines 128 experts, with 4 active per token. Accepts both text and images as inputs. |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "in March 2026, Mistral AI released Mistral Small 4, a hybrid multimodal model that combines instruction following, reasoning and coding capabilities."
   ]
  },
  {
   "url": "https://whatisgm.com/blogs/ai/googles-gemma-3-lands-and-it-changes-the-economics-of-good-enough",
   "title": "Google's Gemma 3 Lands \u2014 And It Changes the Economics of \"Good Enough\"",
   "published_at": "2026-07-29T23:25:44",
   "organization": "Google",
   "model": null,
   "release_date": "2026-07-28",
   "parameters": "1B, 4B, 12B, and 27B",
   "context_window": null,
   "license": null,
   "significance": "redefines what good enough looks like with 128K context window, native vision capabilities, and support for 35+ languages across 1B, 4B, 12B, and 27B parameter sizes",
   "organization_evidence": [
    "# Google's Gemma 3 Lands \u2014 And It Changes the Economics of \"Good Enough\"",
    "Google's Gemma 3 is the most strategically coherent open model release of 2026 \u2014 not because it beats frontier models, but because it redefines what \"good enough\" looks like at a price point that changes who gets to build."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google's Gemma 3 Lands \u2014 And It Changes the Economics of \"Good Enough\"",
    "- **Google released Gemma 3 on 28 July 2026** \u2014 an open-source model family with 128K context window, vision capabilities, and support for 35+ languages, available in 1B, 4B, 12B, and 27B parameter sizes."
   ],
   "parameters_evidence": [
    "# Google's Gemma 3 Lands \u2014 And It Changes the Economics of \"Good Enough\"",
    "- **Google released Gemma 3 on 28 July 2026** \u2014 an open-source model family with 128K context window, vision capabilities, and support for 35+ languages, available in 1B, 4B, 12B, and 27B parameter sizes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google's Gemma 3 Lands \u2014 And It Changes the Economics of \"Good Enough\"",
    "Google's Gemma 3 is the most strategically coherent open model release of 2026 \u2014 not because it beats frontier models, but because it redefines what \"good enough\" looks like at a price point that changes who gets to build.",
    "- **Google released Gemma 3 on 28 July 2026** \u2014 an open-source model family with 128K context window, vision capabilities, and support for 35+ languages, available in 1B, 4B, 12B, and 27B parameter sizes."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Kimi_(AI)",
   "title": "Kimi (AI)",
   "published_at": "2026-08-13T06:28:25",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks.",
   "organization_evidence": [
    "Kimi is an artificial intelligence (AI) chatbot and series of large language models developed by Chinese company Moonshot AI.",
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters.",
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ],
   "parameters_evidence": [
    "Moonshot AI released its Kimi K3 model, on 16 July 2026, with 2.8 trillion parameters.",
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Main model versions of Kimi with descriptions",
    "| Version | Release date | Description | License | Ref. |",
    "| K3 | July 2026 | A flagship mixture-of-experts (MoE) large language model with 2.8 trillion parameters. It uses a sparse MoE setup with 896 experts, of which 16 are active per input. This model is built on Kimi Delta Attention and Attention Residuals. It includes native visual understanding and a 1-million-token context window. It is designed for long-horizon coding, knowledge work, and reasoning tasks. | Kimi K3 |  |"
   ]
  },
  {
   "url": "https://groundtruth.day/news/glm-5-3-flash-was-ox-alpha-and-it-ran-on-chinese-chips.html",
   "title": "GLM-5.3-Flash was Ox Alpha, and it ran on Chinese chips",
   "published_at": "2026-08-26T23:31:01",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "320B total parameters, 18B active",
   "context_window": null,
   "license": null,
   "significance": "Pushing the frontier of the Artificial Analysis Intelligence Index at roughly one-tenth the cost of models at comparable capability, and serving public traffic on a large cluster of Chinese accelerators at hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs.",
   "organization_evidence": [
    "GLM-5.3-Flash was Ox Alpha, and it ran on Chinese chips",
    "Released **August 26, 2026**, under the **MIT licence**, by **Z.ai** (formerly Zhipu AI)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.3-Flash was Ox Alpha, and it ran on Chinese chips",
    "Z.ai released GLM-5.3-Flash on August 26 and confirmed it is the model that had been running anonymously as \"ox-alpha\" on OpenRouter and OpenCode, where it became the most popular model of the week."
   ],
   "parameters_evidence": [
    "GLM-5.3-Flash was Ox Alpha, and it ran on Chinese chips",
    "- **320B total parameters, 18B active**, 45 layers, 288 routed experts plus one shared, **1,048,576-token** context."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.3-Flash was Ox Alpha, and it ran on Chinese chips",
    "Z.ai reports pushing the frontier of the Artificial Analysis Intelligence Index at roughly one-tenth the cost of models at comparable capability -- and the company is explicit about how it got there."
   ]
  },
  {
   "url": "https://ar5iv.labs.arxiv.org/html/2109.02593",
   "title": "General-Purpose Question-Answering with Macaw",
   "published_at": null,
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-08-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Macaw exhibits strong performance, zero-shot, on a wide variety of topics, including outperforming GPT-3 by over 10% (absolute) on Challenge300, a suite of 300 challenge questions, despite being an order of magnitude smaller (11 billion vs. 175 billion parameters).",
   "organization_evidence": [
    "General-Purpose Question-Answering with Macaw",
    "Similarly, the new Jurassic-1 model from AI21 Labs33 3 https://www.ai21.com/blog/announcing-ai21-studio-and-jurassic-1 appears powerful, but again did not perform as well in our QA tests (Section 4.2).",
    "Macaw is available at https://github.com/allenai/macaw"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "General-Purpose Question-Answering with Macaw",
    "August 7, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "General-Purpose Question-Answering with Macaw",
    "Macaw exhibits strong performance, zero-shot, on a wide variety of topics, including outperforming GPT-3 by over 10% (absolute) on Challenge300, a suite of 300 challenge questions, despite being an order of magnitude smaller (11 billion vs. 175 billion parameters)."
   ]
  },
  {
   "url": "https://groundtruth.day/news/qwen-put-a-20-million-entry-n-gram-table-inside-a-model.html",
   "title": "Qwen put a 20-million-entry n-gram table inside a model",
   "published_at": "2026-08-26T23:31:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "125 billion parameters with only 6 billion active per token -- plus 51 billion parameters sitting in a lookup table of 20 million word pairs and triples",
   "context_window": null,
   "license": null,
   "significance": "N-grams are the oldest trick in language modelling -- count how often words follow other words, and predict from the counts. That approach was declared obsolete when neural networks arrived. Qwen just welded a 20-million-entry version of it into a frontier model and made it 51 billion parameters wide.",
   "organization_evidence": [
    "Alibaba's Qwen team released Qwen3.8-Flash-Next, which it describes as an experimental preview of the architecture that will underpin Qwen4."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released **August 26, 2026** under the **Qwen Community License 1.0**; the official repository ships **360 GB** of bf16 weights."
   ],
   "parameters_evidence": [
    "The model has 125 billion parameters with only 6 billion active per token -- plus 51 billion parameters sitting in a lookup table of 20 million word pairs and triples."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "N-grams are the oldest trick in language modelling -- count how often words follow other words, and predict from the counts. That approach was declared obsolete when neural networks arrived. Qwen just welded a 20-million-entry version of it into a frontier model and made it 51 billion parameters wide."
   ]
  },
  {
   "url": "https://huggingface.co/nvidia/Mistral-Medium-3.5-128B-NVFP4",
   "title": "nvidia/Mistral-Medium-3.5-128B-NVFP4 \u00b7 Hugging Face",
   "published_at": "2026-08-27T18:09:15",
   "organization": "NVIDIA",
   "model": null,
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  {
   "url": "https://ai-tldr.dev/releases/deepseek-v4",
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   "url": "https://ai-tldr.dev/releases/deepseek-v4",
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  {
   "url": "https://en.wikipedia.org/wiki/Qwen2?oldformat=true",
   "title": "Qwen",
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    "## Models",
    "### Qwen3.5 and Qwen3.6"
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    "### Qwen3.5 and Qwen3.6",
    "| Qwen3.5 | February 2026 |  |"
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  {
   "url": "https://en.wikipedia.org/wiki/Qwen2?oldformat=true",
   "title": "Qwen",
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    "| Qwen3.6 (Qwen3.6-35B-A3B) | April 2026 | Apache 2.0 |  |"
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    "| Qwen3.6 (Qwen3.6-35B-A3B) | April 2026 | Apache 2.0 |  |"
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  {
   "url": "https://letsdatascience.com/blog/mistral-medium-3-5-128b-open-weight-merged-model",
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   "url": "https://byteiota.com/gemma-hits-1-billion-downloads-what-the-gemmaverse-means",
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   "url": "https://byteiota.com/gemma-hits-1-billion-downloads-what-the-gemmaverse-means",
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  {
   "url": "https://kompozy.io/news/moonshot-kimi-k3-open-weights-huggingface",
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   "url": "https://humai.blog/google-gemma-4-is-out-the-smartest-open-models-for-agents-and-they-run-on-your-phone",
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   "url": "https://humai.blog/google-gemma-4-is-out-the-smartest-open-models-for-agents-and-they-run-on-your-phone",
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   "url": "https://humai.blog/google-gemma-4-is-out-the-smartest-open-models-for-agents-and-they-run-on-your-phone",
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    "| Gemma 4 31B Dense | 31B | Single 80GB H100 or quantized on consumer GPU | 256K |"
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  {
   "url": "https://venturebeat.com/technology/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-u-s-systems",
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   ],
   "parameters_evidence": [
    "[Moonshot AI,](https://www.moonshot.ai/) the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released [Kimi K3](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from [Anthropic](https://www.anthropic.com/) and [OpenAI](https://openai.com/)."
   ],
   "context_window_evidence": [
    "The model features a 1-million-token context window, native visual understanding capabilities, and an always-on reasoning mode that the company calls \"thinking mode.\"",
    "Inside the architecture that powers the world's largest open-source AI model"
   ],
   "license_evidence": null,
   "significance_evidence": [
    "[Moonshot AI,](https://www.moonshot.ai/) the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released [Kimi K3](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from [Anthropic](https://www.anthropic.com/) and [OpenAI](https://openai.com/)."
   ]
  },
  {
   "url": "https://thursdai.news/releases/2026-08",
   "title": "August 2026 AI Releases: NVIDIA\u2013Hugging Face acquisition (reported), Qwen4 preview & 82 more \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "62.7 DeepSWE (+49.9 vs preview)",
   "organization_evidence": [
    "# Everything AI Released in August 2026",
    "## What open-source AI models were released in August 2026?",
    "27 open-weights models shipped in August 2026, led by Qwen3.8-27B (52 AA Intelligence Index, tying GPT-5.6 Luna at max reasoning), DeepSeek V4 Pro 0813 (62.7 DeepSWE (+49.9 vs preview)), Muse Glimmer 30B (76.0 SWE-Bench Verified), LFM2.5-2.6B (2.69B parameters, 128K context).",
    "### DeepSeek V4 Pro 0813",
    "DeepSeek V4 Pro 0813 goes GA with MIT-licensed open weights",
    "DeepSeek re-published its flagship V4 Pro weights under MIT license: a 1.6T-parameter MoE with 49B active parameters and a 1M-token context window, priced at $0.435/$0.87 per million tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Everything AI Released in August 2026",
    "## What open-source AI models were released in August 2026?",
    "27 open-weights models shipped in August 2026, led by Qwen3.8-27B (52 AA Intelligence Index, tying GPT-5.6 Luna at max reasoning), DeepSeek V4 Pro 0813 (62.7 DeepSWE (+49.9 vs preview)), Muse Glimmer 30B (76.0 SWE-Bench Verified), LFM2.5-2.6B (2.69B parameters, 128K context).",
    "### DeepSeek V4 Pro 0813",
    "DeepSeek V4 Pro 0813 goes GA with MIT-licensed open weights",
    "DeepSeek re-published its flagship V4 Pro weights under MIT license: a 1.6T-parameter MoE with 49B active parameters and a 1M-token context window, priced at $0.435/$0.87 per million tokens."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Everything AI Released in August 2026",
    "## What open-source AI models were released in August 2026?",
    "27 open-weights models shipped in August 2026, led by Qwen3.8-27B (52 AA Intelligence Index, tying GPT-5.6 Luna at max reasoning), DeepSeek V4 Pro 0813 (62.7 DeepSWE (+49.9 vs preview)), Muse Glimmer 30B (76.0 SWE-Bench Verified), LFM2.5-2.6B (2.69B parameters, 128K context).",
    "### DeepSeek V4 Pro 0813",
    "DeepSeek V4 Pro 0813 goes GA with MIT-licensed open weights",
    "DeepSeek re-published its flagship V4 Pro weights under MIT license: a 1.6T-parameter MoE with 49B active parameters and a 1M-token context window, priced at $0.435/$0.87 per million tokens."
   ]
  },
  {
   "url": "https://jamesm.blog/ai/state-of-open-weight-models-2026",
   "title": "The State of Open-Weight Models in 2026: Llama, Qwen, Mistral, DeepSeek",
   "published_at": "2026-05-12T08:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "EU-jurisdiction frontier choice, scores 77.6% on SWE-Bench Verified, ships under Apache 2.0, and is fully covered by EU data protection regimes",
   "organization_evidence": [
    "Mistral Medium 3.5",
    "Mistral has continued to ship through 2025 and into 2026, and Medium 3.5 (released [late April 2026](https://mistral.ai/)) is the EU-jurisdiction frontier choice."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5",
    "Mistral has continued to ship through 2025 and into 2026, and Medium 3.5 (released [late April 2026](https://mistral.ai/)) is the EU-jurisdiction frontier choice."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5",
    "Mistral has continued to ship through 2025 and into 2026, and Medium 3.5 (released [late April 2026](https://mistral.ai/)) is the EU-jurisdiction frontier choice.",
    "The model scores 77.6% on SWE-Bench Verified, ships under Apache 2.0, and is fully covered by EU data protection regimes - which has become a real procurement consideration for enterprises operating under GDPR, the AI Act, and the various national-level extensions that have appeared since 2025."
   ]
  },
  {
   "url": "https://youtube.com/watch?v=kdVchWVtmBQ",
   "title": "Qwen 3.8 vs. Opus 4.6: The Truth About Open Weights Performance!",
   "published_at": "2026-08-16T11:02:20",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "27 billion parameter dense model",
   "context_window": null,
   "license": null,
   "significance": "Swe-Bench Pro 61.7%, Deep SWE 42.2%, Terminal Bench 2.1 73%, OS World Verified Computer Use 84.3%",
   "organization_evidence": [
    "Transcript",
    "Alibaba has officially released Qwen 3 8B under full Apache 2.0 open weights."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Transcript",
    "Qwen 3 8B is a 27 billion parameter dense model built across 64 layers."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Transcript",
    "On SWE-Bench Pro, evaluating real-world multi-turn software engineering, Qwen 3.8 jumps from 53.5% to 61.7%, beating Claude Opus 4.6 Max at 53.4%."
   ]
  },
  {
   "url": "https://startupfortune.com/zhipus-glm-53-flash-undercuts-claude-and-gpt-on-price-not-on-hardware",
   "title": "Zhipu's GLM-5.3-Flash Undercuts Claude and GPT on Price, Not on Hardware",
   "published_at": "2026-08-31T03:05:21",
   "organization": "Z.ai",
   "model": "GLM-5.3-Flash",
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter",
   "context_window": "above one million tokens",
   "license": "MIT license",
   "significance": "the first natively multimodal model in the GLM-5 series: image and video input, a context window above one million tokens, a 63.4 result on the DeepSWE evaluation shown on the model page",
   "organization_evidence": [
    "On August 26, 2026, Z.ai, the Chinese company formerly known as Zhipu AI, ended the guessing game and said Ox Alpha was GLM-5.3-Flash, with weights published on Hugging Face under an MIT license.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "model_evidence": [
    "On August 26, 2026, Z.ai, the Chinese company formerly known as Zhipu AI, ended the guessing game and said Ox Alpha was GLM-5.3-Flash, with weights published on Hugging Face under an MIT license.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "release_date_evidence": [
    "On August 26, 2026, Z.ai, the Chinese company formerly known as Zhipu AI, ended the guessing game and said Ox Alpha was GLM-5.3-Flash, with weights published on Hugging Face under an MIT license.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "parameters_evidence": [
    "The model is a 320-billion-parameter mixture-of-experts system, with 18 billion parameters active per token.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "context_window_evidence": [
    "Z.ai's Hugging Face card lists GLM-5.3-Flash as the first natively multimodal model in the GLM-5 series: image and video input, a context window above one million tokens, a 63.4 result on the DeepSWE evaluation shown on the model page.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "license_evidence": [
    "On August 26, 2026, Z.ai, the Chinese company formerly known as Zhipu AI, ended the guessing game and said Ox Alpha was GLM-5.3-Flash, with weights published on Hugging Face under an MIT license.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "significance_evidence": [
    "Z.ai's Hugging Face card lists GLM-5.3-Flash as the first natively multimodal model in the GLM-5 series: image and video input, a context window above one million tokens, a 63.4 result on the DeepSWE evaluation shown on the model page.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ]
  },
  {
   "url": "https://startupfortune.com/zhipus-glm-53-flash-undercuts-claude-and-gpt-on-price-not-on-hardware",
   "title": "Zhipu's GLM-5.3-Flash Undercuts Claude and GPT on Price, Not on Hardware",
   "published_at": "2026-08-31T03:05:21",
   "organization": "Tencent",
   "model": "Hy4",
   "release_date": "2026-08-28",
   "parameters": "770-billion-parameter",
   "context_window": "above one million tokens",
   "license": "open-source",
   "significance": "open-source model with 49 billion active parameters and a context window above one million tokens",
   "organization_evidence": [
    "Tencent is moving in the same direction.",
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "model_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "release_date_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "parameters_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "context_window_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "license_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ],
   "significance_evidence": [
    "The company announced Hy4 preview on August 28, a 770-billion-parameter open-source model with 49 billion active parameters and a context window above one million tokens.",
    "Z.ai's GLM-5.3-Flash is a cheap, open-weight coding model with serious benchmark claims."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/minimax-m3",
   "title": "MiniMax M3 \u2014 Open-Weight Frontier Coding, 1M-Token Context, and Native Multimodality on a New\u2026",
   "published_at": "2026-06-01T12:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, and serves a 1M-token context window.",
   "organization_evidence": [
    "MiniMax \u00b7 2026-06-01 \u00b7 major",
    "MiniMax shipped M3, an open-weight successor to M2.7 built on a new MiniMax Sparse Attention design."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax \u00b7 2026-06-01 \u00b7 major"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax \u00b7 2026-06-01 \u00b7 major",
    "# MiniMax M3 \u2014 Open-Weight Frontier Coding, 1M-Token Context, and Native Multimodality on a New Sparse-Attention Architecture",
    "MiniMax shipped M3, an open-weight successor to M2.7 built on a new MiniMax Sparse Attention design. It scores 59.0% on [SWE-Bench](/tools/swe-bench) Pro, 66.0% on [Terminal-Bench](/tools/terminal-bench) 2.1, and serves a 1M-token context window."
   ]
  },
  {
   "url": "https://aichina.news/blog/kaleido-large-allen-ais-open-multimodal-backbone-arrives-on-ascend-05sq7f",
   "title": "Kaleido-Large: Allen AI\u2019s Open Multimodal Backbone Arrives on Ascend NPUs via Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T13:03:13",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A fully open, commercially usable multimodal backbone that runs natively on Ascend NPUs with Apache 2.0 license.",
   "organization_evidence": [
    "Kaleido-Large: Allen AI\u2019s Open Multimodal Backbone Arrives on Ascend NPUs via Modelers.cn",
    "This is the large variant of the Kaleido model from the Allen Institute for AI (AI2) \u2013 a solid, general-purpose multimodal model that processes images and text together."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kaleido-Large: Allen AI\u2019s Open Multimodal Backbone Arrives on Ascend NPUs via Modelers.cn",
    "- **Dated 2026-04-12** \u2013 this future-looking date may indicate a placeholder or a pre-release entry rather than a fully released artefact."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kaleido-Large: Allen AI\u2019s Open Multimodal Backbone Arrives on Ascend NPUs via Modelers.cn",
    "If you\u2019ve been working with vision-language models (VLMs) inside the Huawei Ascend ecosystem, your options for a fully open, commercially usable backbone have been thin on the ground.",
    "That changes today with the arrival of **allenai/kaleido-large** on Modelers.cn."
   ]
  },
  {
   "url": "https://felloai.com/cs/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "57 on its Intelligence Index against 61 for Claude Opus 5, the highest-scoring closed model on that board.",
   "organization_evidence": [
    "Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these nine on current benchmark performance, recency, license freedom, and how realistic they are to actually use.",
    "**Kimi K3** | Most capable open model | 2.8T / 104B | 1M | Kimi K3 (custom)",
    "**Kimi K3** from Moonshot AI is the new top of the open field, and it only became a download on **July 27, 2026**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these nine on current benchmark performance, recency, license freedom, and how realistic they are to actually use.",
    "**Kimi K3** | Most capable open model | 2.8T / 104B | 1M | Kimi K3 (custom)",
    "**Kimi K3** from Moonshot AI is the new top of the open field, and it only became a download on **July 27, 2026**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The **best open source AI models** just closed most of the remaining gap to the closed frontier.",
    "Moonshot published the weights for **Kimi K3** on **July 27, 2026**, and Artificial Analysis scores it **57** on its Intelligence Index against **61** for **[Claude Opus 5](/claude-opus-5/)**, the highest-scoring closed model on that board."
   ]
  },
  {
   "url": "https://baike.baidu.com/en/item/Qwen/1530291",
   "title": "Qwen",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "an open-weight language model specifically designed for coding agents and local development",
   "organization_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On February 4, 2026, the official Alibaba Qwen Weibo account announced the launch of Qwen3-Coder-Next, an open-weight language model specifically designed for coding agents and local development."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On February 4, 2026, the official Alibaba Qwen Weibo account announced the launch of Qwen3-Coder-Next, an open-weight language model specifically designed for coding agents and local development."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On February 4, 2026, the official Alibaba Qwen Weibo account announced the launch of Qwen3-Coder-Next, an open-weight language model specifically designed for coding agents and local development."
   ]
  },
  {
   "url": "https://baike.baidu.com/en/item/Qwen/1530291",
   "title": "Qwen",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": "2.4T-A95B",
   "context_window": null,
   "license": null,
   "significance": "ultra-large-scale Mixture of Experts model Qwen3.8-2.4T-A95B, and the Zhongzhi FlagOS Community simultaneously completed multi-chip adaptation for it",
   "organization_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On August 13, it was announced that Alibaba had open-sourced the ultra-large-scale Mixture of Experts model Qwen3.8-2.4T-A95B , and the Zhongzhi FlagOS Community simultaneously completed multi-chip adaptation for it."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On August 13, it was announced that Alibaba had open-sourced the ultra-large-scale Mixture of Experts model Qwen3.8-2.4T-A95B , and the Zhongzhi FlagOS Community simultaneously completed multi-chip adaptation for it."
   ],
   "parameters_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On August 13, it was announced that Alibaba had open-sourced the ultra-large-scale Mixture of Experts model Qwen3.8-2.4T-A95B , and the Zhongzhi FlagOS Community simultaneously completed multi-chip adaptation for it."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen is an open-source large language model series launched by Alibaba Group's Tongyi Lab in August 2023, encompassing both dense models and the Mixture of Experts (MoE) architecture, with parameter scales ranging from 0.5B to 3970B.",
    "On August 13, it was announced that Alibaba had open-sourced the ultra-large-scale Mixture of Experts model Qwen3.8-2.4T-A95B , and the Zhongzhi FlagOS Community simultaneously completed multi-chip adaptation for it."
   ]
  },
  {
   "url": "https://aibase.com/news/30348",
   "title": "Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
   "published_at": null,
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": "740 billion",
   "context_window": null,
   "license": null,
   "significance": "Programming experience improved by about 50% compared to GLM-5.2, making it the strongest open-source model in terms of programming ability currently. Terminal-Bench 3.0 scores rose from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9, Agents' Last Exam from 23.8 to 28.5, and GDPval-AA v2 reached 1769 points.",
   "organization_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "Zhipu officially released GLM-5.3 today."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "Published in[AI News](/news) \u00b7 4 min read \u00b7 Aug 14, 2026",
    "Zhipu officially released GLM-5.3 today."
   ],
   "parameters_evidence": [
    "## AI Daily: MiniMax Launches Music3 Music Model; Baidu GenFlow Announces Chinese Name Kuku AI; Zhipu Launches GLM-5.3",
    "GLM-5.3 Released by Zhipu: 740 Billion Parameters Stay the Same, Performance Improved by 50% Through Post-Training Programming Ability Approaches Fable 5",
    "Zhipu released GLM-5.3, still with 740B+ parameters, same base as GLM-5.2, no trillion-param upgrade (possibly for 5.5)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "Internal company evaluations show that the programming experience of the new model has improved by about 50% compared to GLM-5.2, making it the strongest open-source model in terms of programming ability currently.",
    "On public benchmarks, GLM-5.3 also delivered impressive results."
   ]
  },
  {
   "url": "https://aibase.com/news/30348",
   "title": "Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Adopting an all-in-plugin architecture and open source. Positioned as infrastructure beyond models: Model + Harness = Agent.",
   "organization_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "## DeepSeek Open Sources Its First Agent Framework Harness: Everything Is a Plugin, Equipping AI with a Plug-and-Play Execution Engine",
    "The developer preview version v0.1 of DeepSeek Harness is released and open-sourced under the MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "## DeepSeek Open Sources Its First Agent Framework Harness: Everything Is a Plugin, Equipping AI with a Plug-and-Play Execution Engine",
    "DeepSeek released Harness v0.1 developer preview, its first Agent product, on Aug 13 under MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu Launches GLM-5.3: No Base Change, Only Post-Training Open Source Programming Throne Changes Hands",
    "## DeepSeek Harness v0.1 Open Global Testing: Adopting an All-in-Plugin Architecture and Open Source",
    "The developer preview version v0.1 of DeepSeek Harness is released and open-sourced under the MIT license."
   ]
  },
  {
   "url": "https://telnyx.com/resources/best-open-source-llms",
   "title": "The 7 best open-source LLMs to know in 2026",
   "published_at": "2025-01-31T22:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "93.5% on GPQA Diamond",
   "organization_evidence": [
    "## 4. Kimi K3",
    "Moonshot AI released Kimi K3 in July 2026 and published full weights on July 27, 2026."
   ],
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   "release_date_evidence": [
    "## 4. Kimi K3",
    "Moonshot AI released Kimi K3 in July 2026 and published full weights on July 27, 2026."
   ],
   "parameters_evidence": [
    "## 4. Kimi K3",
    "The model uses a mixture-of-experts architecture with 2.8T total parameters, activating 16 of 896 experts per token, per Moonshot's model card."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## 4. Kimi K3",
    "The headline number is reasoning.",
    "Kimi K3 scores 93.5% on GPQA Diamond, the highest of any open model per the Onyx leaderboard."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/google-gemma-4",
   "title": "Google Gemma 4 Review 2026 \u2014 Open-Weight Frontier LLM (Apache 2.0)",
   "published_at": "2026-04-07T19:47:36",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B",
   "context_window": null,
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   "significance": "E2B model (2.3B effective parameters) fits in under 1.5 GB with 2-bit quantization and runs on a Raspberry Pi 5 at 133 tokens per second prefill and 7.6 tokens per second decode, supporting native audio input via a USM-style Conformer encoder.",
   "organization_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license.",
    "The lineup spans E2B (2.3B parameters), E4B (4.5B), 26B MoE (3.8B active / 25.2B total), and 31B Dense (30.7B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The result: E2B fits in under 1.5 GB with 2-bit quantization and runs on a Raspberry Pi 5 at 133 tokens per second prefill and 7.6 tokens per second decode.",
    "Both E2B and E4B support native audio input via a USM-style Conformer encoder \u2014 critical for speech-to-text applications on mobile devices."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/google-gemma-4",
   "title": "Google Gemma 4 Review 2026 \u2014 Open-Weight Frontier LLM (Apache 2.0)",
   "published_at": "2026-04-07T19:47:36",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "4.5B",
   "context_window": null,
   "license": null,
   "significance": "Edge model supporting native audio input via a USM-style Conformer encoder, targeted at phones, laptops, and IoT devices.",
   "organization_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license.",
    "The lineup spans E2B (2.3B parameters), E4B (4.5B), 26B MoE (3.8B active / 25.2B total), and 31B Dense (30.7B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The two \"edge\" models (E2B and E4B) target phones, laptops, and IoT devices with 128K context windows and native audio input.",
    "Both E2B and E4B support native audio input via a USM-style Conformer encoder \u2014 critical for speech-to-text applications on mobile devices."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/google-gemma-4",
   "title": "Google Gemma 4 Review 2026 \u2014 Open-Weight Frontier LLM (Apache 2.0)",
   "published_at": "2026-04-07T19:47:36",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "3.8B active / 25.2B total",
   "context_window": null,
   "license": null,
   "significance": "Workstation mixture-of-experts model that activates only 3.8 billion parameters per forward pass while delivering roughly 97% of 31B quality at 3x the throughput, scoring 1441 on the Arena AI text leaderboard.",
   "organization_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license.",
    "The lineup spans E2B (2.3B parameters), E4B (4.5B), 26B MoE (3.8B active / 25.2B total), and 31B Dense (30.7B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The two \"workstation\" models (26B MoE and 31B Dense) target servers and high-end GPUs with 256K context windows, video processing, and frontier-level reasoning.",
    "The key insight: the 26B MoE activates only 3.8 billion parameters per forward pass despite having 25.2 billion total parameters."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/google-gemma-4",
   "title": "Google Gemma 4 Review 2026 \u2014 Open-Weight Frontier LLM (Apache 2.0)",
   "published_at": "2026-04-07T19:47:36",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "30.7B",
   "context_window": null,
   "license": null,
   "significance": "Dense workstation flagship model that scores 1452 on the Arena AI text leaderboard (#3 open model worldwide), 89.2% on AIME 2026 math reasoning, and 86.4% on tool-use benchmarks.",
   "organization_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 is a family of four open-weight large language models released by Google DeepMind on April 2, 2026, under the Apache 2.0 license.",
    "The lineup spans E2B (2.3B parameters), E4B (4.5B), 26B MoE (3.8B active / 25.2B total), and 31B Dense (30.7B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B model scores 1452 on the Arena AI text leaderboard (#3 open model worldwide) and 89.2% on AIME 2026 math reasoning.",
    "The two \"workstation\" models (26B MoE and 31B Dense) target servers and high-end GPUs with 256K context windows, video processing, and frontier-level reasoning."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/author/news/alex-nguyen)\u00b7Aug 26 2026, 07:19",
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price"
   ],
   "parameters_evidence": [
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ]
  },
  {
   "url": "https://tech-insider.org/ca/deepseek-v4-pro-vs-qwen3-8-max-vs-muse-glimmer-2026",
   "title": "DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
   "published_at": "2026-08-21T00:29:36",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Weights are open under the MIT license and hosted on Hugging Face at deepseek-ai/DeepSeek-V4-Pro-0813, making it fully self-hostable for commercial use.",
   "organization_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "DeepSeek\u2019s V4 Pro 0813 build left preview on August 13."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "DeepSeek shipped the general-availability \u201c0813\u201d build of V4 Pro on August 13, 2026, replacing the April preview that had been running behind the same model ID."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Weights are open under the MIT license and hosted on Hugging Face at `deepseek-ai/DeepSeek-V4-Pro-0813`, making it fully self-hostable for commercial use."
   ]
  },
  {
   "url": "https://tech-insider.org/ca/deepseek-v4-pro-vs-qwen3-8-max-vs-muse-glimmer-2026",
   "title": "DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
   "published_at": "2026-08-21T00:29:36",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Alibaba called Qwen3.8-Max \u201cthe first time a Max-class Qwen model has shipped with open weights,\u201d and the underlying 2.4T-parameter build (identified in some coverage as Qwen3.8-2.4T-A95B) had its weights published on August 12, 2026, nine days after the API launch.",
   "organization_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Alibaba\u2019s Qwen3.8-Max went live on August 3 with a 2.4-trillion-parameter mixture-of-experts design."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Alibaba\u2019s Qwen team previewed Qwen3.8-Max at the World AI Conference in Shanghai on July 19, 2026, then shipped it generally on **August 3, 2026**, calling it \u201cthe most capable model in the Qwen family to date.\u201d"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "On openness, Alibaba called Qwen3.8-Max \u201cthe first time a Max-class Qwen model has shipped with open weights,\u201d and the underlying 2.4T-parameter build (identified in some coverage as Qwen3.8-2.4T-A95B) had its weights published on August 12, 2026, nine days after the API launch."
   ]
  },
  {
   "url": "https://tech-insider.org/ca/deepseek-v4-pro-vs-qwen3-8-max-vs-muse-glimmer-2026",
   "title": "DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
   "published_at": "2026-08-21T00:29:36",
   "organization": "Meta Superintelligence Labs",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Weights ship under the Apache 2.0 license, with no usage gating and no requirement to phone home to Meta\u2019s servers, which is the detail that matters most for air-gapped or regulated deployments.",
   "organization_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Meta answered on August 10 with Muse Glimmer, a 30-billion-parameter model built to run on a single desktop GPU."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Meta Superintelligence Labs released Muse Glimmer on August 10, 2026, and it is built around a completely different premise than the other two: instead of maximizing benchmark scores, engineer a model small enough to run entirely on a machine a developer already owns."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4 Pro vs Qwen3.8 vs Glimmer: 80x Params [2026]",
    "Weights ship under the Apache 2.0 license, with no usage gating and no requirement to phone home to Meta\u2019s servers, which is the detail that matters most for air-gapped or regulated deployments."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/mistrals-128b-gamble-one-model-to-replace-three-and-why-europes-ai-challenger-is",
   "title": "Mistral's 128B Gamble: One Model to Replace Three, and Why Europe's AI Challenger Is Playing a Different Game",
   "published_at": "2026-05-03T10:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 77.6% on SWE-Bench Verified, surpassing Claude Sonnet 4.5 (77.2%) and approaching Sonnet 4.6 (79.6%), making it the highest-scoring open-weight model on the most-cited coding benchmark.",
   "organization_evidence": [
    "Model Release",
    "# Mistral's 128B Gamble: One Model to Replace Three, and Why Europe's AI Challenger Is Playing a Different Game",
    "Mistral AI, the French AI company that has positioned itself as Europe's most credible answer to OpenAI and Anthropic, launched Mistral Medium 3.5: a **128-billion-parameter dense model** with a **256,000-token context window** and configurable reasoning effort per request."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Mistral's 128B Gamble: One Model to Replace Three, and Why Europe's AI Challenger Is Playing a Different Game",
    "Mistral Medium 3.5 consolidates three prior models into a single 128B open-weight architecture scoring 77.6% on SWE-Bench Verified, released April 29, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Mistral's 128B Gamble: One Model to Replace Three, and Why Europe's AI Challenger Is Playing a Different Game",
    "Mistral Medium 3.5 consolidates three prior models into a single 128B open-weight architecture scoring 77.6% on SWE-Bench Verified, released April 29, 2026.",
    "It scores 77.6% on SWE-Bench Verified, surpassing Claude Sonnet 4.5 (77.2%) and approaching Sonnet 4.6 (79.6%), the highest-scoring open-weight model on the most-cited coding benchmark"
   ]
  },
  {
   "url": "https://z.ai/blog/glm-5.3",
   "title": "GLM-5.3: Frontier Coding with Emergent Cyber Capabilities",
   "published_at": null,
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam.",
   "organization_evidence": [
    "GLM-5.3: Frontier Coding with Emergent Cyber Capabilities",
    "Today we are releasing GLM-5.3.",
    "Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "2026-08-14 \u00b7 Research",
    "GLM-5.3: Frontier Coding with Emergent Cyber Capabilities"
   ],
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.3: Frontier Coding with Emergent Cyber Capabilities",
    "**Stronger Coding:** GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam."
   ]
  },
  {
   "url": "https://featherless.ai/blog/best-open-source-llms-2026",
   "title": "Best Open-Source LLMs in 2026 - Featherless",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": "397B total parameters (17B active per pass)",
   "context_window": null,
   "license": null,
   "significance": "native vision-language model with 397B total parameters (17B active per pass). It supports 201 languages and offers a 1M token context window.",
   "organization_evidence": [
    "Best Open-Source LLMs in 2026",
    "### Qwen 3.5: Alibaba\u2019s Native Multimodal Agent",
    "Qwen 3.5 (released February 2026) is a native vision-language model with 397B total parameters (17B active per pass)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
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    "### Qwen 3.5: Alibaba\u2019s Native Multimodal Agent",
    "Qwen 3.5 (released February 2026) is a native vision-language model with 397B total parameters (17B active per pass)."
   ],
   "parameters_evidence": [
    "Best Open-Source LLMs in 2026",
    "### Qwen 3.5: Alibaba\u2019s Native Multimodal Agent",
    "Qwen 3.5 (released February 2026) is a native vision-language model with 397B total parameters (17B active per pass)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open-Source LLMs in 2026",
    "### Qwen 3.5: Alibaba\u2019s Native Multimodal Agent",
    "Qwen 3.5 (released February 2026) is a native vision-language model with 397B total parameters (17B active per pass).",
    "It supports 201 languages and offers a 1M token context window."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/zhipu-glm-5-1-open-weight-agentic-llm-review",
   "title": "GLM-5.1 \u2014 Open-Weight Frontier from the World's First Publicly Listed LLM Company \u2014 ChatForest",
   "published_at": null,
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Displaced GPT-5.4 and Claude Opus 4.6 to take the #1 position on SWE-Bench Pro with a score of 58.4%, making it the first open-weight model to ever hold the top position on SWE-Bench Pro.",
   "organization_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# GLM-5.1 \u2014 Open-Weight Frontier from the World's First Publicly Listed LLM Company",
    "On April 7, 2026, Z.ai (formerly Zhipu AI / Beijing Zhipu Huazhang Technology Co., Ltd.) released GLM-5.1 \u2014 a 754-billion-parameter mixture-of-experts model under the MIT license, available immediately on HuggingFace, the Z.ai developer platform, and Ollama."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# GLM-5.1 \u2014 Open-Weight Frontier from the World's First Publicly Listed LLM Company",
    "**At a glance:** GLM-5.1 \u2014 released April 7, 2026 by Z.ai (formerly Zhipu AI)."
   ],
   "parameters_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# GLM-5.1 \u2014 Open-Weight Frontier from the World's First Publicly Listed LLM Company",
    "**At a glance:** GLM-5.1 \u2014 released April 7, 2026 by Z.ai (formerly Zhipu AI). 754B total parameters / 40B active per token."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# GLM-5.1 \u2014 Open-Weight Frontier from the World's First Publicly Listed LLM Company",
    "When it hit the leaderboards, it displaced GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) to take the #1 position on SWE-Bench Pro with a score of 58.4%."
   ]
  },
  {
   "url": "https://dev.to/jovan_chan_9500711396d4e6/minimax-m3-review-2026-open-weight-1m-context-frontier-45ho",
   "title": "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
   "published_at": "2026-06-27T07:02:46",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59.0% on SWE-Bench Pro, native multimodality, and a 1M-token context window",
   "organization_evidence": [
    "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
    "MiniMax, the Shanghai lab behind the MiniMax-M and Hailuo lines, released M3 on June 1, 2026 and published the weights to [HuggingFace](https://huggingface.co/MiniMaxAI/MiniMax-M3) within roughly ten days of launch."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
    "**TL;DR**: MiniMax M3 is a 428B-parameter (23B active) open-weight MoE released June 1, 2026, with a 1M-token context, native multimodality, and 59.0% on SWE-Bench Pro \u2014 edging out GPT-5.5."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Review 2026: Open-Weight 1M-Context Frontier",
    "**TL;DR**: MiniMax M3 is a 428B-parameter (23B active) open-weight MoE released June 1, 2026, with a 1M-token context, native multimodality, and 59.0% on SWE-Bench Pro \u2014 edging out GPT-5.5."
   ]
  },
  {
   "url": "https://qwen-ai.com",
   "title": "Qwen AI \u2014 Open-Source LLMs, Vision, Audio & Coding Models (2026)",
   "published_at": "2024-09-24T12:59:13",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Hybrid GatedDeltaNet architecture, native multimodal, 201 languages, 262K context. [8 open-weight models](/qwen-3-5/) under Apache 2.0.",
   "organization_evidence": [
    "# Qwen AI",
    "Alibaba Cloud's Open-Source AI",
    "Qwen (pronounced \"chwen\", from the Chinese \u901a\u4e49\u5343\u95ee / Tongy\u00ec Qianw\u00e8n) is a family of large language models and multimodal AI systems developed by **Alibaba Cloud**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Feb 2026",
    "Qwen 3.5 launches: 397B-A17B flagship",
    "The latest flagship, **[Qwen 3.5](/qwen-3-5/)** (February 2026), is a 397B Mixture-of-Experts model with 17B active parameters, 262K native context extensible to 1M tokens, and native support for 201 languages."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Feb 2026",
    "Qwen 3.5 launches: 397B-A17B flagship",
    "Hybrid GatedDeltaNet architecture, native multimodal, 201 languages, 262K context. [8 open-weight models](/qwen-3-5/) under Apache 2.0."
   ]
  },
  {
   "url": "https://tech-insider.org/meta-muse-glimmer-open-weight-ai-model-2026",
   "title": "Meta Muse Glimmer: 30B Open AI Model for Consumer GPUs",
   "published_at": "2026-08-28T09:47:54",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": "30 billion",
   "context_window": null,
   "license": null,
   "significance": "Meta's first open-weight model drop since Llama 4, optimized for always-on local agent workflows.",
   "organization_evidence": [
    "# Meta Muse Glimmer: 30B AI Model Fits in 20GB VRAM [2026]",
    "Meta put a number on the table on August 10, 2026, and the number is 30 billion."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Muse Glimmer: 30B AI Model Fits in 20GB VRAM [2026]",
    "Meta put a number on the table on August 10, 2026, and the number is 30 billion."
   ],
   "parameters_evidence": [
    "# Meta Muse Glimmer: 30B AI Model Fits in 20GB VRAM [2026]",
    "That is the parameter count of **Muse Glimmer**, an open-weight AI model that Meta Superintelligence Labs says is small enough to run on a single consumer GPU, no data center required."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta Muse Glimmer: 30B AI Model Fits in 20GB VRAM [2026]",
    "The release, detailed on [Meta\u2019s official research blog](https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model), marks the company\u2019s first open-weight model drop since Llama 4, and it arrives at a moment when open models from China, not California, have been setting the pace."
   ]
  },
  {
   "url": "https://megaoneai.com/launches/google-releases-gemma-4-as-fully-open-weight-model-with-on-device-smartphone-sup",
   "title": "Google Releases Gemma 4 as Fully Open-Weight Model With On-Device Smartphone Support",
   "published_at": "2026-04-11T09:36:31",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Fully open-weight model with on-device smartphone support and frontier-level performance at compact scale",
   "organization_evidence": [
    "Google Releases Gemma 4 as Fully Open-Weight Model With On-Device Smartphone Support",
    "Google has released [Gemma 4](https://megaoneai.com/analysis/google-releases-gemma-4-open-models-with-frontier-level-performance-at-compact-s/) as a fully open-weight model under a permissive license, making it freely usable for commercial and research applications."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Apr 11, 20263 min read",
    "Google Releases Gemma 4 as Fully Open-Weight Model With On-Device Smartphone Support"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Releases Gemma 4 as Fully Open-Weight Model With On-Device Smartphone Support",
    "Google Gemma 4 goes fully open-source unlocking on-device AI on phones \u2014 major open-weight release"
   ]
  },
  {
   "url": "https://pbxscience.com/2-8-trillion-parameters-moonshot-ais-kimi-k3-becomes-worlds-largest-open-source-model-earns-musks-praise",
   "title": "2.8 Trillion Parameters: Moonshot AI\u2019s Kimi K3 Becomes World\u2019s Largest Open-Source Model, Earns Musk\u2019s Praise",
   "published_at": "2026-07-19T00:33:07",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight language model ever released, featuring 1-million-token context window and native visual understanding",
   "organization_evidence": [
    "2.8 Trillion Parameters: Moonshot AI\u2019s Kimi K3 Becomes World\u2019s Largest Open-Source Model, Earns Musk\u2019s Praise",
    "Chinese AI startup Moonshot AI released its newest flagship model, Kimi K3, on July 16, 2026, and it is already being called a milestone for open-source artificial intelligence."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "2.8 Trillion Parameters: Moonshot AI\u2019s Kimi K3 Becomes World\u2019s Largest Open-Source Model, Earns Musk\u2019s Praise",
    "With 2.8 trillion total parameters, K3 is now the largest open-weight language model ever released, surpassing every other open model on the market, including DeepSeek\u2019s V4 Pro and Zhipu AI\u2019s GLM 5 series."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "2.8 Trillion Parameters: Moonshot AI\u2019s Kimi K3 Becomes World\u2019s Largest Open-Source Model, Earns Musk\u2019s Praise",
    "With 2.8 trillion total parameters, K3 is now the largest open-weight language model ever released, surpassing every other open model on the market, including DeepSeek\u2019s V4 Pro and Zhipu AI\u2019s GLM 5 series.",
    "The model features a 1-million-token context window and native visual understanding, positioning it for long-horizon coding, agentic workflows, and knowledge-heavy tasks."
   ]
  },
  {
   "url": "https://nerdleveltech.com/nvidia-ising-open-quantum-ai-models-error-correction",
   "title": "NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
   "published_at": "2026-04-28T00:00:00",
   "organization": "NVIDIA",
   "model": "Ising Calibration 1",
   "release_date": "2026-04-14",
   "parameters": "35B",
   "context_window": null,
   "license": "NVIDIA Open Model License",
   "significance": "scores 3.27% above Gemini 3.1 Pro, 9.68% above Claude Opus 4.6, and 14.5% above GPT-5.4 on QCalEval",
   "organization_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "On April 14, 2026 \u2014 World Quantum Day \u2014 NVIDIA shipped Ising, an open-source AI model family aimed at two of the hardest problems between today's noisy quantum processors and tomorrow's useful ones: real-time error-correction decoding and processor calibration."
   ],
   "model_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "Ising Calibration: A 35B Vision-Language Model for Qubit Plots",
    "On QCalEval, Ising Calibration 1 scores higher than the closed frontier models that came up in the same comparison: NVIDIA reports it is **3.27% better on average than Gemini 3.1 Pro, 9.68% better than Claude Opus 4.6, and 14.5% better than GPT-5.4**.6 These are NVIDIA-reported numbers on a NVIDIA-published benchmark, so the right framing is \"the calibration-tuned model beats general-purpose VLMs at the calibration task it was trained for\" rather than \"this is a more capable general VLM than GPT-5.4.\""
   ],
   "release_date_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "April 28, 2026",
    "On April 14, 2026 \u2014 World Quantum Day \u2014 NVIDIA shipped Ising, an open-source AI model family aimed at two of the hardest problems between today's noisy quantum processors and tomorrow's useful ones: real-time error-correction decoding and processor calibration."
   ],
   "parameters_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "Ising Calibration: A 35B Vision-Language Model for Qubit Plots"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "Licensing and Availability",
    "Weights are released under the NVIDIA Open Model License; training frameworks are Apache 2.0 on GitHub; both checkpoints are on Hugging Face."
   ],
   "significance_evidence": [
    "# NVIDIA Ising: Open AI Models for Fault-Tolerant Quantum",
    "Ising Calibration: A 35B Vision-Language Model for Qubit Plots"
   ]
  },
  {
   "url": "https://tech-insider.org/gpt-5-6-vs-deepseek-v4-pro-0813-2026",
   "title": "GPT-5.6 vs DeepSeek V4 Pro 0813: 714x Cheaper Input",
   "published_at": "2026-08-20T12:10:05",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "jumped its own Vals Index score by 9.48 points in a single release; 96.40% SWE-bench Verified score (#2 of 82 models tracked)",
   "organization_evidence": [
    "GPT-5.6 vs DeepSeek V4 Pro 0813: 714x Cheaper Input [2026]",
    "DeepSeek answered on August 13 with V4 Pro 0813, an open-weight update that jumped its own Vals Index score by 9.48 points in a single release."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GPT-5.6 vs DeepSeek V4 Pro 0813: 714x Cheaper Input [2026]",
    "DeepSeek answered on August 13 with V4 Pro 0813, an open-weight update that jumped its own Vals Index score by 9.48 points in a single release.",
    "DeepSeek V4 Pro 0813, by contrast, is a single open-weight model, a refresh of the earlier DeepSeek V4 Pro release, dated to its August 13 build number."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GPT-5.6 vs DeepSeek V4 Pro 0813: 714x Cheaper Input [2026]",
    "DeepSeek answered on August 13 with V4 Pro 0813, an open-weight update that jumped its own Vals Index score by 9.48 points in a single release.",
    "| SWE-bench Verified | Not published for Sol specifically | 96.40% (#2 of 82 models tracked) |",
    "| Spec | GPT-5.6 Sol | DeepSeek V4 Pro 0813 |",
    "Here is a direct, spec-by-spec look at GPT-5.6 Sol, the tier most directly comparable to DeepSeek\u2019s flagship, against DeepSeek V4 Pro 0813."
   ]
  },
  {
   "url": "https://mungomash.com/ai/mistral/versions",
   "title": "Mistral Versions",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-28",
   "parameters": "128B dense parameters",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)",
   "organization_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ],
   "model_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ],
   "release_date_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ],
   "parameters_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ],
   "context_window_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ],
   "license_evidence": [
    "# Mistral Versions",
    "**Devstral 2** (December 2025) and **Mistral Medium 3.5** (April 2026) both ship open weights under a **Modified MIT** license \u2014 the docs model cards carry a \u201cModified MIT\u201d badge and both HuggingFace repos are tagged `license:other` rather than `apache-2.0`."
   ],
   "significance_evidence": [
    "# Mistral Versions",
    "Mistral's current largest dense open-weights model is **Mistral Medium 3.5**, released **April 28, 2026** \u2014 **128B dense parameters**, 256K context, multimodal, the lab's first \"flagship merged model\" (chat / reasoning / coding / vision in one)."
   ]
  },
  {
   "url": "https://nogentech.org/google-releases-diffusiongemma-open-text-diffusion-model",
   "title": "Google Releases DiffusionGemma, A 26B Open Model That Writes Text Like an Image Generator",
   "published_at": "2026-06-11T11:03:29",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": "26 billion",
   "context_window": null,
   "license": null,
   "significance": "Reaches 1,000+ tokens per second on a single NVIDIA H100 GPU and generates text using diffusion rather than sequential token prediction.",
   "organization_evidence": [
    "Google Releases DiffusionGemma, A 26B Open Model That Writes Text Like an Image Generator",
    "Google DeepMind released DiffusionGemma, a 26-billion-parameter open model that generates text using diffusion rather than sequential token prediction, reaching 1,000+ tokens per second on a single NVIDIA H100 GPU."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Releases DiffusionGemma, A 26B Open Model That Writes Text Like an Image Generator"
   ],
   "parameters_evidence": [
    "Google Releases DiffusionGemma, A 26B Open Model That Writes Text Like an Image Generator",
    "Google DeepMind released DiffusionGemma, a 26-billion-parameter open model that generates text using diffusion rather than sequential token prediction, reaching 1,000+ tokens per second on a single NVIDIA H100 GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Releases DiffusionGemma, A 26B Open Model That Writes Text Like an Image Generator",
    "Google DeepMind released DiffusionGemma, a 26-billion-parameter open model that generates text using diffusion rather than sequential token prediction, reaching 1,000+ tokens per second on a single NVIDIA H100 GPU."
   ]
  },
  {
   "url": "https://byteiota.com/kimi-k3-open-weight-frontier-model",
   "title": "Kimi K3: The Open-Weight Model That Finally Hit Frontier",
   "published_at": "2026-08-23T08:38:27",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open-weight model to reach the top of the Artificial Analysis Intelligence Index, tied for first with closed-source flagships",
   "organization_evidence": [
    "On July 27, 2026, Moonshot AI closed the gap.",
    "Open-weight models have been chasing frontier performance for two years."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On July 27, 2026, Moonshot AI closed the gap.",
    "Open-weight models have been chasing frontier performance for two years."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model, became the first open-weight model to reach the top of the [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/leaderboards/models) \u2014 tied for first with the closed-source flagships.",
    "Open-weight models have been chasing frontier performance for two years."
   ]
  },
  {
   "url": "https://versely.studio/blog/qwen-3-5-alibaba-open-source-surprise-2026",
   "title": "Qwen 3.5: How Alibaba Became the Open-Source Surprise of 2026",
   "published_at": "2026-05-15T15:23:25",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Crossed 942M Hugging Face downloads, taking 50% of global open-source LLM downloads inside six weeks.",
   "organization_evidence": [
    "AI News",
    "Qwen 3.5: How Alibaba Became the Open-Source Surprise of 2026",
    "When Alibaba dropped Qwen3.5-397B-A17B on Hugging Face on February 16, 2026, nobody outside China expected an open-weight Mixture-of-Experts to take 50% of global open-source LLM downloads inside six weeks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI News",
    "Qwen 3.5: How Alibaba Became the Open-Source Surprise of 2026",
    "When Alibaba dropped Qwen3.5-397B-A17B on Hugging Face on February 16, 2026, nobody outside China expected an open-weight Mixture-of-Experts to take 50% of global open-source LLM downloads inside six weeks."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI News",
    "Qwen 3.5: How Alibaba Became the Open-Source Surprise of 2026",
    "A research-backed breakdown of Qwen 3.5 - Alibaba's 397B-A17B MoE that crossed 942M Hugging Face downloads, its native multimodal stack, coding benchmarks vs DeepSeek V4 and Llama 4, and how creators can route through it on OpenRouter.",
    "When Alibaba dropped Qwen3.5-397B-A17B on Hugging Face on February 16, 2026, nobody outside China expected an open-weight Mixture-of-Experts to take 50% of global open-source LLM downloads inside six weeks."
   ]
  },
  {
   "url": "https://freeainews.com/open-source/mistral-medium-3-5-open-weight-2026",
   "title": "Mistral Medium 3.5 Open-Weight Guide 2026",
   "published_at": "2026-06-01T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Scored 77.6% on SWE-Bench Verified and 91.4% on tau3-Telecom, outperforming larger sparse models like Qwen3.5 397B A17B on coding tasks.",
   "organization_evidence": [
    "On April 30, 2026, Mistral AI dropped a model that made a quiet but significant argument: you do not need to be locked into a proprietary API to get frontier-class performance on real-world coding tasks.",
    "Mistral Medium 3.5 is Mistral AI's first flagship merged model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model was released on April 29-30, 2026 with weights published on [Hugging Face](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) under a modified MIT license.",
    "Release Date",
    "April 29, 2026"
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5 is a single 128-billion-parameter dense model that consolidates three previously separate Mistral products into one set of freely downloadable weights.",
    "Parameters",
    "128B dense"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Medium 3.5's 77.6% exceeds its own predecessor Devstral 2 and outperforms Qwen3.5 397B A17B -- a sparse MoE model with vastly more total parameters -- on the same benchmark.",
    "| Benchmark | Score | What It Measures |",
    "| SWE-Bench Verified | **77.6%** | Real-world bug fixes on open-source Python repos |",
    "| tau3-Telecom | **91.4%** | Multi-turn agentic tool calling (telecom domain) |",
    "| MMLU (Mixtral 8x7B reference) | **70.6%** | Broad academic knowledge across 57 subjects |"
   ]
  },
  {
   "url": "https://decodethefuture.org/en/google-gemma-4-explained",
   "title": "Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
   "published_at": "2026-04-04T11:19:43",
   "organization": "Google",
   "model": "Gemma 4 E2B",
   "release_date": "2026-04-02",
   "parameters": "5.1B (Effective: ~2.3B)",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI.",
   "organization_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E2B** | 5.1B | ~2.3B | Dense + PLE | 128K | Phones, Raspberry Pi, Jetson Nano |"
   ],
   "release_date_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E2B** | 5.1B | ~2.3B | Dense + PLE | 128K | Phones, Raspberry Pi, Jetson Nano |"
   ],
   "context_window_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E2B** | 5.1B | ~2.3B | Dense + PLE | 128K | Phones, Raspberry Pi, Jetson Nano |"
   ],
   "license_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Built from the same research as Gemini 3, the lineup spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI."
   ]
  },
  {
   "url": "https://decodethefuture.org/en/google-gemma-4-explained",
   "title": "Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
   "published_at": "2026-04-04T11:19:43",
   "organization": "Google",
   "model": "Gemma 4 E4B",
   "release_date": "2026-04-02",
   "parameters": "8B (Effective: ~4.5B)",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI.",
   "organization_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E4B** | 8B | ~4.5B | Dense + PLE | 128K | Phones, tablets, edge devices |"
   ],
   "release_date_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E4B** | 8B | ~4.5B | Dense + PLE | 128K | Phones, tablets, edge devices |"
   ],
   "context_window_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 E4B** | 8B | ~4.5B | Dense + PLE | 128K | Phones, tablets, edge devices |"
   ],
   "license_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Built from the same research as Gemini 3, the lineup spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI."
   ]
  },
  {
   "url": "https://decodethefuture.org/en/google-gemma-4-explained",
   "title": "Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
   "published_at": "2026-04-04T11:19:43",
   "organization": "Google",
   "model": "Gemma 4 26B MoE",
   "release_date": "2026-04-02",
   "parameters": "26B (3.8B active)",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI.",
   "organization_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 26B MoE** | 26B | 3.8B active | Mixture of Experts (128 experts) | 256K | Consumer GPUs, laptops |"
   ],
   "release_date_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 26B MoE** | 26B | 3.8B active | Mixture of Experts (128 experts) | 256K | Consumer GPUs, laptops |"
   ],
   "context_window_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 26B MoE** | 26B | 3.8B active | Mixture of Experts (128 experts) | 256K | Consumer GPUs, laptops |"
   ],
   "license_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Built from the same research as Gemini 3, the lineup spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI."
   ]
  },
  {
   "url": "https://decodethefuture.org/en/google-gemma-4-explained",
   "title": "Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
   "published_at": "2026-04-04T11:19:43",
   "organization": "Google",
   "model": "Gemma 4 31B Dense",
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI.",
   "organization_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 31B Dense** | 31B | 31B | Dense | 256K | Workstations, servers, cloud |"
   ],
   "release_date_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 31B Dense** | 31B | 31B | Dense | 256K | Workstations, servers, cloud |"
   ],
   "context_window_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "## What are the four Gemma 4 models?",
    "| Model | Actual Params | Effective Params | Architecture | Context Window | Target Hardware |",
    "| **Gemma 4 31B Dense** | 31B | 31B | Dense | 256K | Workstations, servers, cloud |"
   ],
   "license_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Google Gemma 4 is a family of four open-weight AI models released on April 2, 2026 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "# Google Gemma 4 Explained: 4 Open Models Under Apache 2.0",
    "Built from the same research as Gemini 3, the lineup spans from a 2-billion-parameter edge model that runs on a phone to a 31-billion-parameter dense model that ranks #3 among all open models on Arena AI."
   ]
  },
  {
   "url": "https://enterprisedna.co/resources/news/moonshot-kimi-k3-largest-open-source-model-enterprise-2026",
   "title": "Kimi K3 Arrives: Open-Source AI Now Rivals Frontier Models",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "It matches proprietary frontier models from OpenAI and Anthropic on most benchmarks, and its upcoming open-source weight release promises to change the cost structure of enterprise AI deployment.",
   "organization_evidence": [
    "Kimi K3 Arrives: Open-Source AI Now Rivals Frontier Models",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weights model that is the largest open-source AI system ever built."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3 Arrives: Open-Source AI Now Rivals Frontier Models",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weights model that is the largest open-source AI system ever built."
   ],
   "parameters_evidence": [
    "Kimi K3 Arrives: Open-Source AI Now Rivals Frontier Models",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weights model that is the largest open-source AI system ever built."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3 Arrives: Open-Source AI Now Rivals Frontier Models",
    "On July 16, 2026, Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weights model that is the largest open-source AI system ever built.",
    "It matches proprietary frontier models from OpenAI and Anthropic on most benchmarks, and its upcoming open-source weight release promises to change the cost structure of enterprise AI deployment."
   ]
  },
  {
   "url": "https://awesomeagents.ai/news/zhipu-glm-5-2-open-source",
   "title": "GLM-5.2 Ships MIT-Licensed, 1M Context, Zero Benchmarks",
   "published_at": "2026-06-15T15:15:10",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "GLM-5.2 Ships MIT-Licensed, 1M Context, Zero Benchmarks",
    "Zhipu AI's GLM-5.2 ships with 1M token context, 744B MoE parameters, and MIT license the day after Fable 5 goes offline - but no benchmark numbers at launch."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2 Ships MIT-Licensed, 1M Context, Zero Benchmarks",
    "GLM-5.2 landed on Z.ai's Coding Plan on June 13 - exactly 24 hours after [the US government ordered Fable 5 and Mythos 5 offline](/news/us-export-ban-fable-5-mythos-shutdown/).",
    "| Release Date | June 13, 2026 |"
   ],
   "parameters_evidence": [
    "GLM-5.2 Ships MIT-Licensed, 1M Context, Zero Benchmarks",
    "Zhipu AI's GLM-5.2 ships with 1M token context, 744B MoE parameters, and MIT license the day after Fable 5 goes offline - but no benchmark numbers at launch.",
    "| Total Parameters | 744B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://wsnext.com/3950cae-Technological-Sovereignty-Artificial-Intelligence",
   "title": "French Tech Pioneer Mistral Adopts Chinese Intelligence Model for Platform Expansion",
   "published_at": "2026-08-22T04:13:34",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.2 scores 53 on the Artificial Analysis index, whereas Mistral\u2019s own flagship model scores 30. GLM-5.2 achieved 86% on FinanceBench, and OfficeQA Pro accuracy improved from 6.3% to 51.9% when utilizing the full toolset.",
   "organization_evidence": [
    "French Tech Pioneer Mistral Adopts Chinese Intelligence Model for Platform Expansion",
    "On August 11, 2026, French artificial intelligence developer Mistral AI announced the integration of GLM-5.2, an open-weights model developed by Chinese firm Z.ai, into its platform offerings [1]."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "French Tech Pioneer Mistral Adopts Chinese Intelligence Model for Platform Expansion",
    "On August 11, 2026, French artificial intelligence developer Mistral AI announced the integration of GLM-5.2, an open-weights model developed by Chinese firm Z.ai, into its platform offerings [1]."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "French Tech Pioneer Mistral Adopts Chinese Intelligence Model for Platform Expansion",
    "Performance Metrics and Market Positioning",
    "According to data shared on social media in August 2026, GLM-5.2 scores 53 on the Artificial Analysis index, whereas Mistral\u2019s own flagship model scores 30 [3].",
    "Benchmark Efficiency and Enterprise Utility",
    "In Mistral\u2019s internal benchmarks, GLM-5.2 achieved 86% on FinanceBench, a significant increase from 26.7% with one-shot RAG [4].",
    "Performance testing on the OfficeQA Pro dataset showed GLM-5.2 accuracy improvements from 6.3% to 51.9% when utilizing the full toolset [4]."
   ]
  },
  {
   "url": "https://x.com/GoogleAI/status/2039735543068504476",
   "title": "Google AI (@GoogleAI) on X",
   "published_at": "2026-04-02T16:03:44",
   "organization": "Google",
   "model": null,
   "release_date": "2026-08-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on @arena",
   "organization_evidence": [
    "Today, we\u2019re launching Gemma 4, our most intelligent open models to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Today, we\u2019re launching Gemma 4, our most intelligent open models to date."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Today, we\u2019re launching Gemma 4, our most intelligent open models to date.",
    "\u2014 Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on [@arena](https://x.com/arena)"
   ]
  },
  {
   "url": "https://cryptobriefing.com/zhipu-glm-53-flash-chinese-ai-chips",
   "title": "Zhipu launches GLM-5.3-Flash, its first natively multimodal model built for Chinese chips",
   "published_at": "2026-08-26T15:01:21",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first natively multimodal model in the GLM-5 series",
   "organization_evidence": [
    "China\u2019s Z.ai, the company formerly known as Zhipu AI, released GLM-5.3-Flash on August 26, making it the first natively multimodal model in the GLM-5 series."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "China\u2019s Z.ai, the company formerly known as Zhipu AI, released GLM-5.3-Flash on August 26, making it the first natively multimodal model in the GLM-5 series."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "China\u2019s Z.ai, the company formerly known as Zhipu AI, released GLM-5.3-Flash on August 26, making it the first natively multimodal model in the GLM-5 series."
   ]
  },
  {
   "url": "https://onepin.ai/blog/minimax-m3-open-weights-model-2026",
   "title": "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
   "published_at": "2026-06-01T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": "1-million-token",
   "license": null,
   "significance": "first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodality",
   "organization_evidence": [
    "# MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "[MiniMax M3](https://www.minimax.io/blog/minimax-m3), released June 1, 2026, is the latest flagship model from Shanghai-based AI lab [MiniMax](https://www.minimax.io)."
   ],
   "model_evidence": [
    "# MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "[MiniMax M3](https://www.minimax.io/blog/minimax-m3), released June 1, 2026, is the latest flagship model from Shanghai-based AI lab [MiniMax](https://www.minimax.io)."
   ],
   "release_date_evidence": [
    "# MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "[MiniMax M3](https://www.minimax.io/blog/minimax-m3), released June 1, 2026, is the latest flagship model from Shanghai-based AI lab [MiniMax](https://www.minimax.io)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "[MiniMax](https://www.minimax.io) just released M3, the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodality in a single package."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "MiniMax M3: The First Open-Weight Model With Frontier Coding, 1M Context, and Native Multimodality",
    "[MiniMax](https://www.minimax.io) just released M3, the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodality in a single package."
   ]
  },
  {
   "url": "https://benchr.org/articles/qwen-review",
   "title": "Qwen3.6, reviewed",
   "published_at": "2026-05-30T00:00:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": "27B dense, multimodal",
   "context_window": null,
   "license": null,
   "significance": "77.2 on SWE-bench Verified, 53.5 on SWE-bench Pro, 59.3 on Terminal-Bench 2.0, and 83.9 on LiveCodeBench v6.",
   "organization_evidence": [
    "Qwen3.6, reviewed",
    "Qwen released the two open-weight Qwen3.6 models in April 2026, per the official GitHub README news section: the 35B-A3B mixture-of-experts variant on April 16 and the 27B dense model on April 22."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.6, reviewed",
    "Qwen released the two open-weight Qwen3.6 models in April 2026, per the official GitHub README news section: the 35B-A3B mixture-of-experts variant on April 16 and the 27B dense model on April 22."
   ],
   "parameters_evidence": [
    "A whole lineup, laid out",
    "| Variant | Size | Context | License |",
    "| Qwen3.6-27B (dense) | 27B dense, multimodal | 262,144 native, up to ~1,010,000 | Apache-2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Codes well, on Qwen's own numbers",
    "Qwen positions the 27B dense model as flagship-level coding in a small package, and its self-reported tables back the framing: 77.2 on SWE-bench Verified, 53.5 on SWE-bench Pro, 59.3 on Terminal-Bench 2.0, and 83.9 on LiveCodeBench v6."
   ]
  },
  {
   "url": "https://benchr.org/articles/qwen-review",
   "title": "Qwen3.6, reviewed",
   "published_at": "2026-05-30T00:00:00",
   "organization": "Qwen",
   "model": "Qwen3.6-35B-A3B",
   "release_date": "2026-04-16",
   "parameters": "35B total, ~3B active, multimodal",
   "context_window": "262,144 native, up to ~1,010,000",
   "license": "Apache-2.0",
   "significance": "73.4 on SWE-bench Verified",
   "organization_evidence": [
    "Qwen3.6, reviewed",
    "Qwen released the two open-weight Qwen3.6 models in April 2026, per the official GitHub README news section: the 35B-A3B mixture-of-experts variant on April 16 and the 27B dense model on April 22."
   ],
   "model_evidence": [
    "A whole lineup, laid out",
    "| Variant | Size | Context | License |",
    "| Qwen3.6-35B-A3B (MoE) | 35B total, ~3B active, multimodal | 262,144 native, up to ~1,010,000 | Apache-2.0 |"
   ],
   "release_date_evidence": [
    "Qwen3.6, reviewed",
    "Qwen released the two open-weight Qwen3.6 models in April 2026, per the official GitHub README news section: the 35B-A3B mixture-of-experts variant on April 16 and the 27B dense model on April 22."
   ],
   "parameters_evidence": [
    "A whole lineup, laid out",
    "| Variant | Size | Context | License |",
    "| Qwen3.6-35B-A3B (MoE) | 35B total, ~3B active, multimodal | 262,144 native, up to ~1,010,000 | Apache-2.0 |"
   ],
   "context_window_evidence": [
    "A whole lineup, laid out",
    "| Variant | Size | Context | License |",
    "| Qwen3.6-35B-A3B (MoE) | 35B total, ~3B active, multimodal | 262,144 native, up to ~1,010,000 | Apache-2.0 |"
   ],
   "license_evidence": [
    "A whole lineup, laid out",
    "| Variant | Size | Context | License |",
    "| Qwen3.6-35B-A3B (MoE) | 35B total, ~3B active, multimodal | 262,144 native, up to ~1,010,000 | Apache-2.0 |"
   ],
   "significance_evidence": [
    "Codes well, on Qwen's own numbers",
    "The 35B-A3B trails it on the overlapping tests, posting a self-reported 73.4 on SWE-bench Verified."
   ]
  },
  {
   "url": "https://singularitybyte.com/models/mistral-medium-3-5.html",
   "title": "Mistral Medium 3.5",
   "published_at": "2026-07-10T09:47:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B dense",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-bench Verified",
   "organization_evidence": [
    "Mistral Medium 3.5",
    "Mistral's middle child finally comes with weights."
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   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5",
    "Mistral Medium 3.5, released April 29, 2026, is a 128-billion-parameter dense model you can download from Hugging Face today: no gate, no access form, no research-only clause."
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   "parameters_evidence": [
    "Mistral Medium 3.5 puts a 128B dense, multimodal, agentic-coding model on Hugging Face under a modified MIT license.",
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    "Mistral Medium 3.5, released April 29, 2026, is a 128-billion-parameter dense model you can download from Hugging Face today: no gate, no access form, no research-only clause."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5",
    "Official 77.6% SWE-bench Verified, 256K context, and a $20M/month revenue carve-out that will not affect you.",
    "Mistral's model card states two headline results in plain text: **77.6% on SWE-bench Verified** and **91.4% on the tau3-Telecom agentic benchmark**."
   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/google-gemma-4-open-source-apache-agentic-ai-2026",
   "title": "Google Gemma 4: Open-Source AI Goes Fully Agentic",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B",
   "context_window": null,
   "license": null,
   "significance": "Runs in under 1.5GB RAM. 128K context window. Supports text, image, audio, and video. Tested at 133 prefill tokens/sec on Raspberry Pi 5 CPU.",
   "organization_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series.",
    "Google Gemma 4"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "parameters_evidence": [
    "Gemma 4 E2B \u2014 Edge 2B",
    "2.3B effective parameters (5.1B total with embeddings).",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Runs in under 1.5GB RAM. 128K context window. Supports text, image, audio, and video. Tested at 133 prefill tokens/sec on Raspberry Pi 5 CPU.",
    "Gemma 4 E2B \u2014 Edge 2B",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/google-gemma-4-open-source-apache-agentic-ai-2026",
   "title": "Google Gemma 4: Open-Source AI Goes Fully Agentic",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 E4B",
   "release_date": "2026-04-02",
   "parameters": "4.5B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Designed for laptops and mobile workstations. Includes audio input. Delivers 3x faster Android inference than prior Gemma versions, with 60% less battery consumption.",
   "organization_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "model_evidence": [
    "Gemma 4 E4B \u2014 Edge 4B",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "release_date_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "parameters_evidence": [
    "4.5B effective parameters (8B total). Designed for laptops and mobile workstations. 128K context. Includes audio input. Delivers 3x faster Android inference than prior Gemma versions, with 60% less battery consumption.",
    "Gemma 4 E4B \u2014 Edge 4B",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
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   "context_window_evidence": [
    "4.5B effective parameters (8B total). Designed for laptops and mobile workstations. 128K context. Includes audio input. Delivers 3x faster Android inference than prior Gemma versions, with 60% less battery consumption.",
    "Gemma 4 E4B \u2014 Edge 4B",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "license_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
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   "significance_evidence": [
    "4.5B effective parameters (8B total). Designed for laptops and mobile workstations. 128K context. Includes audio input. Delivers 3x faster Android inference than prior Gemma versions, with 60% less battery consumption.",
    "Gemma 4 E4B \u2014 Edge 4B",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/google-gemma-4-open-source-apache-agentic-ai-2026",
   "title": "Google Gemma 4: Open-Source AI Goes Fully Agentic",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 26B A4B",
   "release_date": "2026-04-02",
   "parameters": "25.2B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Ranks #6 among all open-weight models globally. Google calls it \u201cthe world\u2019s most parameter-efficient model at this scale.\u201d",
   "organization_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "model_evidence": [
    "Gemma 4 26B A4B \u2014 MoE",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "release_date_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "parameters_evidence": [
    "Only 3.8B parameters active per inference token (25.2B total). 256K context window. Ranks #6 among all open-weight models globally. Google calls it \u201cthe world\u2019s most parameter-efficient model at this scale.\u201d",
    "Gemma 4 26B A4B \u2014 MoE",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "context_window_evidence": [
    "Only 3.8B parameters active per inference token (25.2B total). 256K context window. Ranks #6 among all open-weight models globally. Google calls it \u201cthe world\u2019s most parameter-efficient model at this scale.\u201d",
    "Gemma 4 26B A4B \u2014 MoE",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "license_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "significance_evidence": [
    "Only 3.8B parameters active per inference token (25.2B total). 256K context window. Ranks #6 among all open-weight models globally. Google calls it \u201cthe world\u2019s most parameter-efficient model at this scale.\u201d",
    "Gemma 4 26B A4B \u2014 MoE",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/google-gemma-4-open-source-apache-agentic-ai-2026",
   "title": "Google Gemma 4: Open-Source AI Goes Fully Agentic",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 31B",
   "release_date": "2026-04-02",
   "parameters": "30.7B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "LMArena score of 1,452 \u2014 ranked #3 among open models globally and #1 US open model. Achieves 89.2% on AIME 2026 and a Codeforces ELO of 2,150 (professional competitive programming level).",
   "organization_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "model_evidence": [
    "Gemma 4 31B \u2014 Dense Flagship",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "release_date_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "parameters_evidence": [
    "30.7B parameters, all active. 256K context window. LMArena score of 1,452 \u2014 ranked #3 among open models globally and #1 US open model. Achieves 89.2% on AIME 2026 and a Codeforces ELO of 2,150 (professional competitive programming level).",
    "Gemma 4 31B \u2014 Dense Flagship",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "context_window_evidence": [
    "30.7B parameters, all active. 256K context window. LMArena score of 1,452 \u2014 ranked #3 among open models globally and #1 US open model. Achieves 89.2% on AIME 2026 and a Codeforces ELO of 2,150 (professional competitive programming level).",
    "Gemma 4 31B \u2014 Dense Flagship",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "license_evidence": [
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ],
   "significance_evidence": [
    "30.7B parameters, all active. 256K context window. LMArena score of 1,452 \u2014 ranked #3 among open models globally and #1 US open model. Achieves 89.2% on AIME 2026 and a Codeforces ELO of 2,150 (professional competitive programming level).",
    "Gemma 4 31B \u2014 Dense Flagship",
    "**Google Gemma 4** is a family of four open-weight multimodal AI models released on April 2, 2026 \u2014 built from the same research and architecture as Gemini 3, and now available under the **Apache 2.0 license** for the first time in the Gemma series."
   ]
  },
  {
   "url": "https://cryptobriefing.com/moonshot-ai-china-open-source-safer",
   "title": "Moonshot AI positions China\u2019s open software as safer than Silicon Valley\u2019s closed approach",
   "published_at": "2026-08-28T16:21:35",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Ranked third on the Artificial Analysis Intelligence Index shortly after launch, performing competitively against Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 Sol.",
   "organization_evidence": [
    "Moonshot AI released its Kimi K3 model with full open weights and made the case that China\u2019s increasingly open approach to AI development is fundamentally safer than the walled-garden strategy favored by America\u2019s biggest AI companies."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model, announced in mid-July 2026 with complete technical details and weights published by July 27, packs 2.8 trillion parameters in a Mixture-of-Experts architecture and supports a 1-million-token context window."
   ],
   "parameters_evidence": [
    "The model, announced in mid-July 2026 with complete technical details and weights published by July 27, packs 2.8 trillion parameters in a Mixture-of-Experts architecture and supports a 1-million-token context window."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It ranked third on the Artificial Analysis Intelligence Index shortly after launch, performing competitively against Anthropic\u2019s Claude Fable 5 and OpenAI\u2019s GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://www.layer3labs.io/guides/glm-5-2-by-zhipu-ai-for-business",
   "title": "GLM-5.2 Free Download: Zhipu AI's 744B Open-Weight Model",
   "published_at": "2026-06-19T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "62.1 on SWE-bench Pro and 81.0 on Terminal-Bench 2.1, the strongest reported scores of any open-weight model on both benchmarks",
   "organization_evidence": [
    "# GLM-5.2 Open Source AI Model: The Complete Guide",
    "Exploring Zhipu AI's Latest Breakthrough in Open Source large language models (LLMs)",
    "On June 13, 2026, Zhipu AI introduced GLM-5.2, the latest iteration of its open source large language model series."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Open Source AI Model: The Complete Guide",
    "On June 13, 2026, Zhipu AI introduced GLM-5.2, the latest iteration of its open source large language model series."
   ],
   "parameters_evidence": [
    "# GLM-5.2 Open Source AI Model: The Complete Guide",
    "## What Is the GLM-5.2 Open Source AI Model?"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Open Source AI Model: The Complete Guide",
    "## Practical Use Cases: Where GLM-5.2 Shines",
    "GLM-5.2 is especially valuable in workflows that need to read, search, or reason over vast swaths of text or code rather than just respond to one-off prompts \u2014 and its coding numbers back that up: Zhipu reports 62.1 on [SWE-bench](https://www.swebench.com) Pro (ahead of GPT-5.5's 58.6) and 81.0 on Terminal-Bench 2.1, the strongest reported scores of any open-weight model on both benchmarks, directionally corroborated by independent evaluators though not reproduced exactly by a third party."
   ]
  },
  {
   "url": "https://cryptobriefing.com/nvidia-free-ai-models-gpu-sales",
   "title": "Nvidia gives away AI models for free to boost GPU sales",
   "published_at": "2026-08-24T22:19:07",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Mixture of Experts architecture with roughly 3 billion active parameters supporting context windows up to 1 million tokens and downloadable from Hugging Face",
   "organization_evidence": [
    "The chipmaker's open-weight Nemotron family and free inference APIs are designed to get developers hooked on its hardware ecosystem",
    "Nvidia is handing out AI models like free samples at Costco."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The company\u2019s latest release, Nemotron 3.5 Lightning, is a 30-billion-parameter model that launched on August 11, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It uses a Mixture of Experts (MoE) architecture with roughly 3 billion active parameters, meaning it can run on a single GPU while supporting context windows up to 1 million tokens.",
    "That\u2019s a serious amount of capability for a model you can download from Hugging Face without paying a dime."
   ]
  },
  {
   "url": "https://aichina.news/blog/allen-ais-olmo-3-7b-rl-zero-math-an-open-weight-math-reasoning-model-iq08bv",
   "title": "Allen AI\u2019s OLMo-3 7B RL-Zero-Math: An Open-Weight Math Reasoning Model Now on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T13:58:45",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "7-billion",
   "context_window": null,
   "license": null,
   "significance": "reinforcement learning from zero (RL-Zero), where the model learns to reason mathematically without relying on pre-existing supervised fine-tuning data",
   "organization_evidence": [
    "# Allen AI\u2019s OLMo-3 7B RL-Zero-Math: An Open-Weight Math Reasoning Model Now on Modelers.cn",
    "This 7-billion parameter model comes from the Allen Institute for AI (AI2) and continues the OLMo tradition of open science \u2013 meaning you get the weights, the training recipe, and the data used, all under an Apache-2.0 licence."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Allen AI\u2019s OLMo-3 7B RL-Zero-Math: An Open-Weight Math Reasoning Model Now on Modelers.cn",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "# Allen AI\u2019s OLMo-3 7B RL-Zero-Math: An Open-Weight Math Reasoning Model Now on Modelers.cn",
    "This 7-billion parameter model comes from the Allen Institute for AI (AI2) and continues the OLMo tradition of open science \u2013 meaning you get the weights, the training recipe, and the data used, all under an Apache-2.0 licence."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Allen AI\u2019s OLMo-3 7B RL-Zero-Math: An Open-Weight Math Reasoning Model Now on Modelers.cn",
    "What makes it particularly intriguing is its training methodology: reinforcement learning from zero (RL-Zero), where the model learns to reason mathematically without relying on pre-existing supervised fine-tuning data."
   ]
  },
  {
   "url": "https://arstechnica.com/google/2026/06/googles-latest-diffusiongemma-open-ai-model-comes-with-a-4x-speed-boost/",
   "title": "Google's latest DiffusionGemma open AI model comes with a 4x speed boost - Ars Technica",
   "published_at": "2026-06-10T19:29:21",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DiffusionGemma doesn\u2019t generate outputs linearly like most AI models. Instead, it can produce an entire block of text in parallel. Google says this makes it faster and more efficient when running on local hardware like an Nvidia DGX or a humble gaming GPU.",
   "organization_evidence": [
    "All the tokens at once",
    "Google DeepMind releases DiffusionGemma, a model that runs local AI 4x faster",
    "This time, Google DeepMind has released a new member of the [Gemma 4 open model family](https://arstechnica.com/ai/2026/04/google-announces-gemma-4-open-ai-models-switches-to-apache-2-0-license/),"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
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   "license_evidence": null,
   "significance_evidence": [
    "All the tokens at once",
    "Google DeepMind releases DiffusionGemma, a model that runs local AI 4x faster",
    "Instead, it can produce an entire block of text in parallel."
   ]
  },
  {
   "url": "https://edgen.tech/vi/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter model, with 18 billion active parameters",
   "context_window": null,
   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/vi/author/news/alex-nguyen)\u00b7Aug 26 2026, 07:19",
    "The company confirmed the model behind the \"Ox Alpha\" name that swept to the top of online usage charts is a new iteration of its GLM series, with weights released Wednesday."
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   "parameters_evidence": [
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ]
  },
  {
   "url": "https://dutchstartup.ai/en/news/german-ai-consortium-releases-open-language-model-soofi-s-with-focus-on-german",
   "title": "German AI consortium releases open language model Soofi S with focus on German and English",
   "published_at": "2026-07-14T06:04:03",
   "organization": "German AI consortium",
   "model": null,
   "release_date": "2026-07-13",
   "parameters": "31.6 billion",
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   "license": null,
   "significance": "scores higher than all fully open alternatives on both German-language and English-language benchmarks",
   "organization_evidence": [
    "# German AI consortium releases open language model Soofi S with focus on German and English",
    "A German research consortium released Soofi S 30B-A3B on 13 July 2026, an open language model that scores higher than all fully open alternatives on both German-language and English-language benchmarks."
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   "release_date_evidence": [
    "# German AI consortium releases open language model Soofi S with focus on German and English",
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   "parameters_evidence": [
    "# German AI consortium releases open language model Soofi S with focus on German and English",
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   "license_evidence": null,
   "significance_evidence": [
    "# German AI consortium releases open language model Soofi S with focus on German and English",
    "A German research consortium released Soofi S 30B-A3B on 13 July 2026, an open language model that scores higher than all fully open alternatives on both German-language and English-language benchmarks."
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  },
  {
   "url": "https://felloai.com/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
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   "license": null,
   "significance": "57 on its Intelligence Index against 61 for Claude Opus 5, the highest-scoring closed model on that board",
   "organization_evidence": [
    "Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these nine on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **July 2026**. **Kimi K3** enters the list this week, now that its weights are genuinely downloadable, and Chinese labs hold the top four places on it. One more model arrived after this ranking was built, and Thinking Machines\u2019 **Inkling** gets its own section below rather than a slot in the nine.",
    "**Kimi K3** from Moonshot AI is the new top of the open field, and it only became a download on **July 27, 2026**."
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   "release_date_evidence": [
    "Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these nine on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **July 2026**. **Kimi K3** enters the list this week, now that its weights are genuinely downloadable, and Chinese labs hold the top four places on it. One more model arrived after this ranking was built, and Thinking Machines\u2019 **Inkling** gets its own section below rather than a slot in the nine.",
    "**Kimi K3** from Moonshot AI is the new top of the open field, and it only became a download on **July 27, 2026**."
   ],
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    "Best Open Source AI Models 2026 at a Glance",
    "Moonshot published the weights for **Kimi K3** on **July 27, 2026**, and Artificial Analysis scores it **57** on its Intelligence Index against **61** for **[Claude Opus 5](/claude-opus-5/)**, the highest-scoring closed model on that board."
   ]
  },
  {
   "url": "https://glonce.com/qwen-releases-qwen3-8-flash-next-an-early",
   "title": "Qwen releases Qwen3.8-Flash-Next, an early preview of Qwen4",
   "published_at": "2026-08-30T06:00:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
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   "license": null,
   "significance": "An open weights multimodal mixture-of-experts model that serves as an early preview of the architecture used in Qwen4, featuring 125B total size (labeled tokens) with 6B active at once, giving the model a significant performance boost.",
   "organization_evidence": [
    "Qwen releases Qwen3.8-Flash-Next, an early preview of Qwen4",
    "Qwen has released Qwen3.8-Flash-Next, an open weights multimodal mixture-of-experts (MoE) model."
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   "release_date_evidence": [
    "Qwen releases Qwen3.8-Flash-Next, an early preview of Qwen4",
    "It gives no separate release date for Qwen3.8-Flash-Next itself, only the date of the post, 26 August 2026; no benchmark scores or comparisons to other models; and no license terms."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Qwen releases Qwen3.8-Flash-Next, an early preview of Qwen4",
    "Qwen has released Qwen3.8-Flash-Next, an open weights multimodal mixture-of-experts (MoE) model."
   ]
  },
  {
   "url": "https://idlen.io/news/meta-llama-4-scout-maverick-open-weight-multimodal-moe-10m-context",
   "title": "Llama 4 Scout and Maverick: Meta Ships First Native MoE Models With 10M Context",
   "published_at": "2026-04-15T09:30:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
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   "context_window": null,
   "license": null,
   "significance": "First Llama models built on Mixture-of-Experts with native multimodality, 10 million token context, and open-weight availability.",
   "organization_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ]
  },
  {
   "url": "https://spoonai.me/posts/2026-07-27-moonshot-kimi-k3-open-weights-jul2026-en",
   "title": "2.8 Trillion Parameters Just Went Public \u2014 Kimi K3's Weights Are Free, and Almost Nobody Can Run Them",
   "published_at": "2026-07-26T23:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight model ever shipped, third in the world on intelligence indexes",
   "organization_evidence": [
    "TOPMoonshot AIKimi K3Open Weights",
    "Moonshot AI released the full Kimi K3 weights on July 27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "TOPMoonshot AIKimi K3Open Weights",
    "Moonshot AI released the full Kimi K3 weights on July 27.",
    "At 00:00 UTC on July 27, 2026, China's Moonshot AI did exactly what it said it would: it published the complete weights for Kimi K3."
   ],
   "parameters_evidence": [
    "TOPMoonshot AIKimi K3Open Weights",
    "# 2.8 Trillion Parameters Just Went Public \u2014 Kimi K3's Weights Are Free, and Almost Nobody Can Run Them",
    "Total parameters | 2.8 trillion |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "TOPMoonshot AIKimi K3Open Weights",
    "It's the largest open-weight model ever shipped, it ranks third in the world on intelligence indexes, and the download is 1.4TB."
   ]
  },
  {
   "url": "https://groundy.com/articles/zhipu-ships-glm-5-2-with-1m-context-and-mit-weights-but-zero-benchmarks",
   "title": "Zhipu Ships GLM-5.2 With 1M Context and MIT Weights, but Zero Benchmarks at Launch",
   "published_at": "2026-06-16T17:50:16",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1M-token context window, Anthropic-compatible API endpoint, and promised MIT license open weights",
   "organization_evidence": [
    "Zhipu Ships GLM-5.2 With 1M Context and MIT Weights, but Zero Benchmarks at Launch",
    "Zhipu shipped GLM-5.2 on June 13 with a 1M-token window and an Anthropic-compatible endpoint, but published no benchmarks and keeps the hosted API on a paid plan."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Zhipu Ships GLM-5.2 With 1M Context and MIT Weights, but Zero Benchmarks at Launch",
    "Zhipu shipped GLM-5.2 on June 13 with a 1M-token window and an Anthropic-compatible endpoint, but published no benchmarks and keeps the hosted API on a paid plan.",
    "[Zhipu opened GLM-5.2 to all GLM Coding Plan tiers](https://www.gate.com/news/detail/zhipu-launches-glm-52-with-1m-context-on-june-13-21833603) on June 13, 2026, shipping a one-million-token context window under a promised MIT license and an Anthropic-compatible API endpoint."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu Ships GLM-5.2 With 1M Context and MIT Weights, but Zero Benchmarks at Launch",
    "Zhipu shipped GLM-5.2 on June 13 with a 1M-token window and an Anthropic-compatible endpoint, but published no benchmarks and keeps the hosted API on a paid plan.",
    "[Zhipu opened GLM-5.2 to all GLM Coding Plan tiers](https://www.gate.com/news/detail/zhipu-launches-glm-52-with-1m-context-on-june-13-21833603) on June 13, 2026, shipping a one-million-token context window under a promised MIT license and an Anthropic-compatible API endpoint."
   ]
  },
  {
   "url": "https://nerova.ai/news/minimax-m3-open-weight-agent-builders-june-2026",
   "title": "MiniMax M3 Open Weights: 1M Context and Multimodality",
   "published_at": "2026-06-14T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "roughly 428B total parameters and roughly 23B activated parameters",
   "context_window": null,
   "license": null,
   "significance": "native multimodality, 1M-context MSA, and longer-horizon coding and agent positioning in one open-weight model",
   "organization_evidence": [
    "Model Releases \u2022 June 14, 2026",
    "MiniMax M3 Is Out. Here\u2019s What Actually Changed for Open-Weight Agent Builders.",
    "On June 1, 2026, MiniMax officially released MiniMax M3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Releases \u2022 June 14, 2026",
    "MiniMax M3 Is Out. Here\u2019s What Actually Changed for Open-Weight Agent Builders.",
    "On June 1, 2026, MiniMax officially released MiniMax M3."
   ],
   "parameters_evidence": [
    "Model Releases \u2022 June 14, 2026",
    "MiniMax M3 Is Out. Here\u2019s What Actually Changed for Open-Weight Agent Builders.",
    "The Hugging Face card describes M3 as a native multimodal model with roughly 428B total parameters and roughly 23B activated parameters, plus SGLang, vLLM, and Transformers serving"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Releases \u2022 June 14, 2026",
    "MiniMax M3 Is Out. Here\u2019s What Actually Changed for Open-Weight Agent Builders.",
    "The real upgrade over earlier MiniMax coverage is the combination of native multimodality, 1M-context MSA, and longer-horizon coding and agent positioning in one open-weight model."
   ]
  },
  {
   "url": "https://deepseekai.guide/guides/deepseek-open-source",
   "title": "Is DeepSeek Open Source? The 2026 Licensing Guide",
   "published_at": "2026-04-23T19:02:58",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active",
   "context_window": "1 million tokens",
   "license": "MIT",
   "significance": "top performance on coding and maths among open models and trails only Gemini 3.1-Pro for world knowledge",
   "organization_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "Short answer, up front: yes, DeepSeek open source releases are real, and the current generation \u2014 DeepSeek V4 \u2014 ships both weights and code under the MIT License."
   ],
   "model_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Pro | 2026-04-24 | MIT | MIT |"
   ],
   "release_date_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Pro | 2026-04-24 | MIT | MIT |"
   ],
   "parameters_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## Accessing the models \u2014 open weights vs API",
    "The current generation is **DeepSeek V4** (released April 24, 2026), exposed as two open-weight MoE model IDs: `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active)."
   ],
   "context_window_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## How V4 compares on openness",
    "V4-Pro has 1.6T parameters (49B activated) and V4-Flash has 284B parameters (13B activated), both supporting a context length of one million tokens."
   ],
   "license_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Pro | 2026-04-24 | MIT | MIT |"
   ],
   "significance_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## How V4 compares on openness",
    "According to The Next Web, V4-Pro claims top performance on coding and maths among open models and trails only Gemini 3.1-Pro for world knowledge, with DeepSeek estimating a 3-to-6-month gap to closed frontier systems."
   ]
  },
  {
   "url": "https://deepseekai.guide/guides/deepseek-open-source",
   "title": "Is DeepSeek Open Source? The 2026 Licensing Guide",
   "published_at": "2026-04-23T19:02:58",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B / 13B active",
   "context_window": "1 million tokens",
   "license": "MIT",
   "significance": null,
   "organization_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "Short answer, up front: yes, DeepSeek open source releases are real, and the current generation \u2014 DeepSeek V4 \u2014 ships both weights and code under the MIT License."
   ],
   "model_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Flash | 2026-04-24 | MIT | MIT |"
   ],
   "release_date_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Flash | 2026-04-24 | MIT | MIT |"
   ],
   "parameters_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## Accessing the models \u2014 open weights vs API",
    "The current generation is **DeepSeek V4** (released April 24, 2026), exposed as two open-weight MoE model IDs: `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active)."
   ],
   "context_window_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## How V4 compares on openness",
    "V4-Pro has 1.6T parameters (49B activated) and V4-Flash has 284B parameters (13B activated), both supporting a context length of one million tokens."
   ],
   "license_evidence": [
    "Is DeepSeek Open Source? A Clear Look at Licensing in 2026",
    "## DeepSeek licensing, per model",
    "| DeepSeek-V4-Flash | 2026-04-24 | MIT | MIT |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://hokai.io/hub/models/qwen",
   "title": "Qwen Review (2026): Pricing, Verdict & Alternatives",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions",
   "organization_evidence": [
    "Qwen is Alibaba's open-source LLM series, available free under Apache 2.0 for commercial use.",
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ]
  },
  {
   "url": "https://hokai.io/hub/models/qwen",
   "title": "Qwen Review (2026): Pricing, Verdict & Alternatives",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "enabling royalty-free commercial deployment for businesses matching compute budgets to task complexity",
   "organization_evidence": [
    "Qwen is Alibaba's open-source LLM series, available free under Apache 2.0 for commercial use.",
    "- **Qwen 3.6: 6 Size Tiers, Apache 2.0**: Qwen 3.6 was released in May 2026 across six model size tiers under Apache 2.0, enabling royalty-free commercial deployment for businesses matching compute budgets to task complexity."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **Qwen 3.6: 6 Size Tiers, Apache 2.0**: Qwen 3.6 was released in May 2026 across six model size tiers under Apache 2.0, enabling royalty-free commercial deployment for businesses matching compute budgets to task complexity."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **Qwen 3.6: 6 Size Tiers, Apache 2.0**: Qwen 3.6 was released in May 2026 across six model size tiers under Apache 2.0, enabling royalty-free commercial deployment for businesses matching compute budgets to task complexity."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-05/06-mistral-medium-35-folds-chat-reasoning-and-coding-into-a-single-128-billion-parameter-open-weight-flagship",
   "title": "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
   "published_at": "2026-05-06T09:57:02",
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": null,
   "parameters": "128-billion-parameter",
   "context_window": "256k",
   "license": "Modified MIT",
   "significance": "consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle",
   "organization_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ],
   "model_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ],
   "context_window_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ],
   "license_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ],
   "significance_evidence": [
    "Mistral Medium 3.5 Folds Chat, Reasoning, and Coding Into a Single 128-Billion-Parameter Open-Weight Flagship",
    "Mistral released Medium 3.5, a dense 128B open-weight model with a 256k context window that consolidates Medium 3.1, Magistral, and Devstral 2 under a Modified MIT license, with a per-query reasoning toggle."
   ]
  },
  {
   "url": "https://ai.google.dev/gemma/docs/core/model_card_4",
   "title": "Gemma 4 model card",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-07-30",
   "parameters": "2.3B effective (5.1B with embeddings) (E2B), 4.5B effective (8B with embeddings) (E4B), 11.95B (12B Unified), 30.7B (31B Dense), 25.2B total / 3.8B active (26B A4B MoE)",
   "context_window": null,
   "license": null,
   "significance": "State-of-the-art open multimodal models featuring reasoning, extended multimodalities, diverse & efficient architectures, optimized for on-device, increased context window, enhanced coding & agentic capabilities, and native system prompt support.",
   "organization_evidence": [
    "# Gemma 4 model card",
    "**License**: [Apache 2.0](https://ai.google.dev/gemma/apache_2) | **Authors**: [Google DeepMind](https://deepmind.google/models/gemma/)",
    "Gemma is a family of open models built by Google DeepMind."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4 model card",
    "Last updated 2026-07-30 UTC."
   ],
   "parameters_evidence": [
    "# Gemma 4 model card",
    "### Dense Models",
    "Dense Models",
    "| Property | E2B | E4B | 12B Unified | 31B Dense |",
    "| **Total Parameters** | 2.3B effective (5.1B with embeddings) | 4.5B effective (8B with embeddings) | 11.95B | 30.7B |",
    "### Mixture-of-Experts (MoE) Model",
    "Mixture-of-Experts (MoE) Model",
    "| Property | 26B A4B MoE |",
    "| **Total Parameters** | 25.2B |",
    "| **Active Parameters** | 3.8B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4 model card",
    "Gemma 4 introduces key **capability and architectural advancements**:",
    "- **Reasoning** \u2013 All models in the family are designed as highly capable reasoners, with configurable thinking modes.",
    "- **Extended Multimodalities** \u2013 Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B, E4B, and 12B models).",
    "- **Diverse & Efficient Architectures** \u2013 Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.",
    "- **Optimized for On-Device** \u2013 Smaller models are specifically designed for efficient local execution on laptops and mobile devices.",
    "- **Increased Context Window** \u2013 The small models feature a 128K context window, while the medium models support 256K.",
    "- **Enhanced Coding & Agentic Capabilities** \u2013 Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.",
    "- **Native System Prompt Support** \u2013 Gemma 4 introduces native support for the `system` role, enabling more structured and controllable conversations."
   ]
  },
  {
   "url": "https://fortune.com/2026/07/16/moonshots-kimi-k3-pushes-chinese-ai-into-fable-level-territory/",
   "title": "Moonshot\u2019s Kimi K3 pushes Chinese AI into Fable-level territory",
   "published_at": "2026-07-17T01:10:14",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.7 trillion",
   "context_window": null,
   "license": null,
   "significance": "Making it the largest open-weight large language model available today and standing as Moonshot AI\u2019s most powerful open-source coding model to date.",
   "organization_evidence": [
    "Moonshot\u2019s Kimi K3 pushes Chinese AI into Fable-level territory",
    "Chinese startup Moonshot AI has released the latest version of its Kimi AI model, further shrinking the performance gap between Chinese and U.S. models just as global businesses are increasingly questioning the cost of deploying models from Anthropic and OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot\u2019s Kimi K3 pushes Chinese AI into Fable-level territory",
    "July 16, 2026, 9:10 PM ET",
    "On July 16, Moonshot AI unveiled Kimi K3, the latest version of its Kimi model."
   ],
   "parameters_evidence": [
    "Moonshot\u2019s Kimi K3 pushes Chinese AI into Fable-level territory",
    "On July 16, Moonshot AI unveiled Kimi K3, the latest version of its Kimi model.",
    "It boasts 2.7 trillion parameters, making it the largest open-weight large language model available today."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot\u2019s Kimi K3 pushes Chinese AI into Fable-level territory",
    "It boasts 2.7 trillion parameters, making it the largest open-weight large language model available today.",
    "\u201cK3 stands as Moonshot AI\u2019s most powerful open-source coding model to date,\u201d Moonshot AI wrote in a press release announcing the model\u2019s release."
   ]
  },
  {
   "url": "https://www.scmp.com/tech/tech-trends/article/3357115/zhipu-ais-stock-rockets-after-chinese-firm-makes-glm-52-open-source",
   "title": "Zhipu AI\u2019s stock rockets after Chinese firm launches open-source GLM-5.2 model",
   "published_at": "2026-06-15T04:51:33",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-15",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "latest and most powerful large language model, featuring a massive 1-million-token context window",
   "organization_evidence": [
    "# Zhipu AI\u2019s stock rockets after Chinese firm launches open-source GLM-5.2 model",
    "Shares of Beijing-based [artificial intelligence](https://www.scmp.com/topics/artificial-intelligence?module=inline&pgtype=article) pioneer [Zhipu AI](https://www.scmp.com/topics/beijing-zhipu-huazhang-technology?module=inline&pgtype=article) soared on Monday after it released GLM-5.2, its latest and most powerful large language model, which will be made available on an open-source basis later this week."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI\u2019s stock rockets after Chinese firm launches open-source GLM-5.2 model",
    "It announced the full release of GLM-5.2, featuring a massive 1-million-token context window, on Saturday."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI\u2019s stock rockets after Chinese firm launches open-source GLM-5.2 model",
    "Shares of Beijing-based [artificial intelligence](https://www.scmp.com/topics/artificial-intelligence?module=inline&pgtype=article) pioneer [Zhipu AI](https://www.scmp.com/topics/beijing-zhipu-huazhang-technology?module=inline&pgtype=article) soared on Monday after it released GLM-5.2, its latest and most powerful large language model, which will be made available on an open-source basis later this week.",
    "It announced the full release of GLM-5.2, featuring a massive 1-million-token context window, on Saturday."
   ]
  },
  {
   "url": "https://bit.ly/4xsQzMW",
   "title": "Nvidia is paying $12.9 billion to keep open models on its chips",
   "published_at": "2026-08-28T15:43:25",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "puts it at or past Opus 4.6 Max for coding and computer use",
   "organization_evidence": [
    "Nvidia is paying $12.9 billion to keep open models on its chips",
    "Frederic Lardinois [wrote up Alibaba\u2019s Qwen3.8-27B](https://thenewstack.io/qwen38-27b-local-inference/) two weeks ago."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Nvidia is paying $12.9 billion to keep open models on its chips",
    "Frederic Lardinois [wrote up Alibaba\u2019s Qwen3.8-27B](https://thenewstack.io/qwen38-27b-local-inference/) two weeks ago."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nvidia is paying $12.9 billion to keep open models on its chips",
    "The 4-bit build is 16.1GB and runs on a Mac with 32GB of unified memory; Alibaba\u2019s own benchmarks put it at or past Opus 4.6 Max for coding and computer use."
   ]
  },
  {
   "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
   "title": "Qwen/Qwen3.8-Flash-Next \u00b7 Hugging Face",
   "published_at": "2026-08-26T12:32:48",
   "organization": "Qwen Team",
   "model": null,
   "release_date": "2026-08",
   "parameters": "125B with 6B activated, plus 51B n-gram embedding and 4B MTP",
   "context_window": null,
   "license": null,
   "significance": "Introduces Hybrid Attention with QSA, Gated Residual, N-gram Embedding, and a Tailored Training Recipe as an experimental preview architecture that will underpin Qwen4, and achieves leading benchmark results across coding, agentic, general, and vision-language tasks.",
   "organization_evidence": [
    "# Qwen3.8-Flash-Next",
    "The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:",
    "@techreport{qwen2026design,",
    "    title       = {On the Design of {Qwen3.8-Next} Architecture: Evaluation, Efficiency, and Training Stability},",
    "    author      = {{Qwen Team}},",
    "    institution = {Alibaba Group},",
    "    month       = {August},",
    "    year        = {2026}"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "@techreport{qwen2026design,",
    "    title       = {On the Design of {Qwen3.8-Next} Architecture: Evaluation, Efficiency, and Training Stability},",
    "    author      = {{Qwen Team}},",
    "    institution = {Alibaba Group},",
    "    month       = {August},",
    "    year        = {2026}",
    "}",
    "@misc{qwen3.8flashnext,",
    "    title  = {{Qwen3.8-Flash-Next}: A New Architecture, Towards Ultimate Cost-Efficiency},",
    "    author = {{Qwen Team}},",
    "    month  = {August},",
    "    year   = {2026},",
    "    url    = {https://qwen.ai/blog?id=qwen3.8-flash-next}"
   ],
   "parameters_evidence": [
    "## Model Overview",
    "- Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.8-Flash-Next",
    "This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale.",
    "The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:"
   ]
  },
  {
   "url": "https://evermx.com/case/meta-llama-4-scout-maverick-multimodal-moe-launch",
   "title": "Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
   "published_at": "2026-04-06T09:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17 billion active parameters spread across 16 experts and 109 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Industry-leading context window of 10 million tokens, outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across coding, reasoning, long-context tasks, and image understanding",
   "organization_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "Meta has released Llama 4 Scout and Llama 4 Maverick on April 5, 2026, marking the most significant architectural shift in the Llama model family to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## A New Architecture for the Llama Family",
    "Meta has released Llama 4 Scout and Llama 4 Maverick on April 5, 2026, marking the most significant architectural shift in the Llama model family to date."
   ],
   "parameters_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Llama 4 Scout: A 10-Million-Token Context Window on a Single GPU",
    "Llama 4 Scout is the smaller of the two models, with 17 billion active parameters spread across 16 experts and 109 billion total parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Llama 4 Scout: A 10-Million-Token Context Window on a Single GPU",
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    "Meta reports that Scout outperforms Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a broad range of benchmarks, including coding, reasoning, long-context tasks, and image understanding."
   ]
  },
  {
   "url": "https://evermx.com/case/meta-llama-4-scout-maverick-multimodal-moe-launch",
   "title": "Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
   "published_at": "2026-04-06T09:00:00",
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "2026-04-05",
   "parameters": "17 billion active parameters with 128 experts, 400 billion total parameters",
   "context_window": "1 million tokens",
   "license": "community license",
   "significance": "Beats GPT-4o and Gemini 2.0 Flash across widely reported benchmarks while achieving comparable results to DeepSeek V3 on reasoning and coding tasks at less than half the active parameters",
   "organization_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models\n\nMeta releases Llama 4 Scout (17B active, 16 experts, 10M context) and Maverick (17B active, 128 experts), its first natively multimodal mixture-of-experts models."
   ],
   "model_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## A New Architecture for the Llama Family",
    "Meta has released Llama 4 Scout and Llama 4 Maverick on April 5, 2026, marking the most significant architectural shift in the Llama model family to date."
   ],
   "release_date_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## A New Architecture for the Llama Family",
    "Meta has released Llama 4 Scout and Llama 4 Maverick on April 5, 2026, marking the most significant architectural shift in the Llama model family to date."
   ],
   "parameters_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Llama 4 Maverick: 128 Experts Competing With GPT-4o",
    "Llama 4 Maverick scales up the MoE approach dramatically, maintaining the same 17 billion active parameters but distributing computation across 128 experts, with 400 billion total parameters."
   ],
   "context_window_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Llama 4 Maverick: 128 Experts Competing With GPT-4o",
    "The instruct-tuned Maverick model supports a 1 million token context window, ten times shorter than Scout but still among the longest available."
   ],
   "license_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Competitive Position",
    "The main limitation is that Llama 4 uses a community license rather than Apache 2.0, which imposes some restrictions on commercial use and redistribution that Gemma 4 does not have."
   ],
   "significance_evidence": [
    "Llama",
    "# Meta Launches Llama 4 Scout and Maverick: First Open-Weight Multimodal MoE Models",
    "## Llama 4 Maverick: 128 Experts Competing With GPT-4o",
    "Meta claims it beats both across a broad range of widely reported benchmarks while achieving comparable results to DeepSeek V3 on reasoning and coding tasks at less than half the active parameters."
   ]
  },
  {
   "url": "https://aihola.com/article/mistral-medium-3-5-release",
   "title": "Mistral Ships Medium 3.5 as Open-Weight Flagship",
   "published_at": "2026-05-07T04:47:36",
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "128 billion",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified and 91.4 on \u03c4\u00b3-Telecom",
   "organization_evidence": [
    "Mistral Ships Medium 3.5 as Open-Weight Flagship",
    "Mistral pushed Medium 3.5 out the door Wednesday, a 128 billion parameter dense model with a 256k context window."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "French lab releases 128B dense model with 256k context, self-hostable on four GPUs.",
    "Mistral Ships Medium 3.5 as Open-Weight Flagship",
    "Mistral pushed Medium 3.5 out the door Wednesday, a 128 billion parameter dense model with a 256k context window."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Ships Medium 3.5 as Open-Weight Flagship",
    "On its [company blog](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5), Mistral reports 77.6% on SWE-Bench Verified and 91.4 on \u03c4\u00b3-Telecom, comparing the numbers against Qwen3.5 397B and its own older Devstral 2."
   ]
  },
  {
   "url": "https://kie.ai/blog/kimi-k3-moonshot-open-frontier-model",
   "title": "Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
   "published_at": "2026-07-28T00:26:53",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1 million tokens",
   "license": "Kimi K3 License",
   "significance": "largest open-weight model ever published",
   "organization_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "model_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "release_date_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "parameters_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "context_window_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "license_evidence": [
    "## Kimi K3 vs Claude Fable 5 and GPT-5.6 Sol: What the Signal Says",
    "Detailed license terms for commercial deployment (redistribution, derivative works, hosting restrictions) are governed by the \"Kimi K3 License\" on Hugging Face but have not been community-summarized in the current signal set."
   ],
   "significance_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ]
  },
  {
   "url": "https://edgen.tech/es/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter model, with 18 billion active parameters",
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   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/es/author/news/alex-nguyen)\u00b7Aug 26 2026, 07:19",
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price"
   ],
   "parameters_evidence": [
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8.",
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "**Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price.**\n\n![Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price](https://static.edwealth.ai/news/images/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price-20260826.png?w=750)\nZhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the Artificial Analysis Intelligence Index at 1/40 the price, intensifying a price war that has pushed Chinese models past 60 percent of global token usage."
   ]
  },
  {
   "url": "https://tech-insider.org/ca/minimax-m3-open-weight-llm-2026",
   "title": "MiniMax M3 Claims 59% SWE-bench, Beats GPT-5.5 [2026]",
   "published_at": "2026-06-30T00:26:22",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open-weight model to pair frontier-grade coding with a one-million-token context window and native image-and-video input, achieving a vendor-reported 59.0% on SWE-bench Pro",
   "organization_evidence": [
    "On June 1, 2026, Chinese AI lab MiniMax released **MiniMax M3**, the first open-weight model to pair frontier-grade coding with a one-million-token context window and native image-and-video input.",
    "MiniMax M3 Lands as the First Open-Weight Coding Frontier Model"
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   "release_date_evidence": [
    "On June 1, 2026, Chinese AI lab MiniMax released **MiniMax M3**, the first open-weight model to pair frontier-grade coding with a one-million-token context window and native image-and-video input.",
    "MiniMax M3 Lands as the First Open-Weight Coding Frontier Model"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "On June 1, 2026, Chinese AI lab MiniMax released **MiniMax M3**, the first open-weight model to pair frontier-grade coding with a one-million-token context window and native image-and-video input.",
    "MiniMax M3 Lands as the First Open-Weight Coding Frontier Model"
   ]
  },
  {
   "url": "https://medium.com/@servifyspheresolutions/the-free-models-running-the-world-af6a3d2e8758",
   "title": "The Free Models Running the World",
   "published_at": "2026-05-06T07:37:16",
   "organization": "Moonshot AI",
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   "release_date": null,
   "parameters": "1 Trillion",
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   "license": null,
   "significance": "MoE, Multimodal, Agentic, 100 concurrent sub-agents",
   "organization_evidence": [
    "A field guide to every major open-weight language model you can download, run, and build on no subscription needed.",
    "### 1. Moonshot AI: Kimi K2.6"
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   "model_evidence": null,
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   "parameters_evidence": [
    "A field guide to every major open-weight language model you can download, run, and build on no subscription needed.",
    "### 1. Moonshot AI: Kimi K2.6",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
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   ]
  },
  {
   "url": "https://gigazine.net/gsc_news/en/20260604-google-ai-gemma-4-12b",
   "title": "Google has released 'Gemma 4 12B,' an AI model that can run on laptops, for free; it requires 16GB of VRAM to run.",
   "published_at": "2026-06-04T01:36:00",
   "organization": "Google",
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   "release_date": "2026-06-03",
   "parameters": "12B",
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   "license": null,
   "significance": "Gemma 4 12B can run on systems with 16GB of VRAM or unified memory, and is being promoted as a high-performance model that can run even on laptops.",
   "organization_evidence": [
    "# Google has released 'Gemma 4 12B,' an AI model that can run on laptops, for free; it requires 16GB of VRAM to run.",
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   "release_date_evidence": [
    "On June 3, 2026, Google released its AI model ' **Gemma 4 12B** ' as an open model."
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   "parameters_evidence": [
    "# Google has released 'Gemma 4 12B,' an AI model that can run on laptops, for free; it requires 16GB of VRAM to run.",
    "On June 3, 2026, Google released its AI model ' **Gemma 4 12B** ' as an open model."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Gemma 4 12B can run on systems with 16GB of VRAM or unified memory, and is being promoted as a high-performance model that can run even on laptops."
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  },
  {
   "url": "https://felloai.com/cs/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
   "parameters": "744B",
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   "model_evidence": null,
   "release_date_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
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   "parameters_evidence": [
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    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **Licence MIT** that makes the weights free for anyone to download and run."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
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   ]
  },
  {
   "url": "https://huggingface.co/nvidia/MiniMax-M3-NVFP4",
   "title": "nvidia/MiniMax-M3-NVFP4 \u00b7 Hugging Face",
   "published_at": "2026-08-27T18:09:15",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "frontier-level coding and agentic capabilities, built on a Mixture-of-Experts architecture with a 1M-token context window",
   "organization_evidence": [
    "Model Overview",
    "The NVIDIA MiniMax-M3 NVFP4 model is quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Release Date",
    "Hugging Face 06/23/2026 via [https://huggingface.co/nvidia/MiniMax-M3-NVFP4](https://huggingface.co/nvidia/MiniMax-M3-NVFP4)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Description",
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  },
  {
   "url": "https://cybernews.com/tech/qwen-38-27b-ai-model-debuts-with-million-downloads",
   "title": "Qwen3.8-27B arrives free, already downloaded over 3 million times\u200b | Cybernews",
   "published_at": "2026-08-17T09:33:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "Surpassed 3 million Hugging Face downloads within three days, achieves near-frontier results, scores close to or beats Opus 4.6 Max, and solves LiveCodeBench v6 with 90.3% and GPQA Diamond with 89.2% accuracy.",
   "organization_evidence": [
    "Free Qwen3.8-27B AI model lands, grabs over 3 million downloads in one weekend",
    "- Alibaba's free Qwen3.8-27B surpassed 3 million Hugging Face downloads within three days of its August 14 release."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Free Qwen3.8-27B AI model lands, grabs over 3 million downloads in one weekend",
    "- Alibaba's free Qwen3.8-27B surpassed 3 million Hugging Face downloads within three days of its August 14 release."
   ],
   "parameters_evidence": [
    "Free Qwen3.8-27B AI model lands, grabs over 3 million downloads in one weekend",
    "- Alibaba's free Qwen3.8-27B surpassed 3 million Hugging Face downloads within three days of its August 14 release."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Free Qwen3.8-27B AI model lands, grabs over 3 million downloads in one weekend",
    "- Alibaba's free Qwen3.8-27B surpassed 3 million Hugging Face downloads within three days of its August 14 release.",
    "- Alibaba reports near-frontier results, but independent testing remains unavailable and full-quality operation requires 56GB of graphics memory.",
    "According to Qwen\u2019s own testing, the model \u201cexcels in real-world software engineering and office workflows.\u201d",
    "Qwen3.8-27B solves LiveCodeBench v6 with 90.3% and GPQA Diamond with 89.2% accuracy."
   ]
  },
  {
   "url": "https://creati.ai/ai-news/2026-08-27/meta-releases-muse-glimmer-an-open-weight-ai-model-designed-for-local-multimodal-agents",
   "title": "Meta Releases Muse Glimmer, an Open-Weight AI Model Designed for Local Multimodal Agents",
   "published_at": "2026-08-27T21:22:32",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": "30-billion-parameter",
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  },
  {
   "url": "https://builtineu.eu/news/mistral-medium-3-5-vibe-cloud-agents-le-chat-work-mode",
   "title": "Mistral ships Medium 3.5, its first merged flagship model at 128 billion parameters, and adds cloud coding agents and Work Mode to Le Chat",
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  {
   "url": "https://chinabizinsider.com/moonshot-ai-detonates-open-source-race-with-kimi-k3-triggering-immediate-global-adoption",
   "title": "Moonshot AI Opens Kimi K3: 2.8T-Parameter MoE Goes Global",
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   "url": "https://z.ai/blog/glm-5",
   "title": "GLM-5: From Vibe Coding to Agentic Engineering",
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   "release_date": "2026-02-12",
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    "On [Vending Bench 2](https://andonlabs.com/evals/vending-bench-2), a benchmark that measures long-term operational capability, GLM-5 ranks #1 among open-source models."
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  {
   "url": "https://nerdleveltech.com/minimax-m3-open-weight-1m-context-coding-model",
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  {
   "url": "https://medium.com/@ffguci8/nvidia-nemotron-3-the-sota-open-weight-ai-model-family-of-2026-4612ae7aefb4",
   "title": "NVIDIA Nemotron 3: The SOTA Open-Weight AI Model Family of 2026",
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  {
   "url": "https://medium.com/@ffguci8/nvidia-nemotron-3-the-sota-open-weight-ai-model-family-of-2026-4612ae7aefb4",
   "title": "NVIDIA Nemotron 3: The SOTA Open-Weight AI Model Family of 2026",
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    "The three models are Nano (31.6 billion total parameters, released December 15, 2025), Super (120 billion total parameters, released March 11, 2026 at GTC), and Ultra (550 billion total parameters, released June 4, 2026 at Computex)."
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  {
   "url": "https://tech-insider.org/deepseek-v4-vs-r1-vs-v3-2-2026",
   "title": "DeepSeek V4 vs R1 vs V3.2: 355% Price Surge [2026]",
   "published_at": "2026-08-23T12:17:01",
   "organization": "DeepSeek",
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   "release_date": "2026-08-13",
   "parameters": null,
   "context_window": null,
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   "significance": "DeepSeek is positioning V4-Pro as a premium, open-weights alternative to closed frontier models rather than a budget option.",
   "organization_evidence": [
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   "release_date_evidence": [
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    "On August 13, 2026, DeepSeek made the lineup even more consequential by officially launching V4-Pro-0813 with a pricing structure that, according to [TechTimes](https://www.techtimes.com/articles/324764/20260817/deepseek-v4-api-prices-quadruple-peak-what-developers-pay-starting-now.htm), raised peak-hour output costs by as much as 355%."
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   "significance_evidence": [
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  {
   "url": "https://blog.prompt20.com/posts/open-weights-ultimate-guide",
   "title": "Open Weights: The Ultimate Guide (2026 Edition)",
   "published_at": "2026-05-22T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "397B sparse + dense variants (35B-A3B, 27B)",
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   "significance": "Best multilingual coverage; tool-use parity with closed.",
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    "**Qwen 3.6 Plus** (Alibaba, China)."
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   "model_evidence": null,
   "release_date_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
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   "license_evidence": null,
   "significance_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
    "**Qwen 3.6 Plus** (Alibaba, China). 397B sparse + dense variants (35B-A3B, 27B). April 2026. Apache 2.0 on base, API-only on Plus tier. Best multilingual coverage; tool-use parity with closed."
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  },
  {
   "url": "https://blog.prompt20.com/posts/open-weights-ultimate-guide",
   "title": "Open Weights: The Ultimate Guide (2026 Edition)",
   "published_at": "2026-05-22T00:00:00",
   "organization": "Z.ai / Zhipu",
   "model": null,
   "release_date": "2026-04-03",
   "parameters": "~750B sparse (256 routed experts + 1 shared, 8 active per token; hidden 6144, 78 layers; HF createdAt: 2026-04-03)",
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   "license": null,
   "significance": "Tops several coding benchmarks; OpenClaw-compatible agent harness scoring.",
   "organization_evidence": [
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    "The 2026 open-weight frontier (the roster)",
    "**GLM-5.1** (Z.ai / Zhipu, China)."
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   "model_evidence": null,
   "release_date_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
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   ],
   "parameters_evidence": [
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
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    "The 2026 open-weight frontier (the roster)",
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  {
   "url": "https://blog.prompt20.com/posts/open-weights-ultimate-guide",
   "title": "Open Weights: The Ultimate Guide (2026 Edition)",
   "published_at": "2026-05-22T00:00:00",
   "organization": "Moonshot",
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   "release_date": "2026-04-14",
   "parameters": "~1T sparse, ~32B active",
   "context_window": "256K",
   "license": null,
   "significance": "Long-context flagship (256K). Natively multimodal (vision + text via KimiK25ForConditionalGeneration).",
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    "**Kimi K2.6** (Moonshot, China). ~1T sparse, ~32B active (384 routed experts, 8 active per token; HF `createdAt: 2026-04-14`)."
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   "parameters_evidence": [
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    "The 2026 open-weight frontier (the roster)",
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   "context_window_evidence": [
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   "license_evidence": null,
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  {
   "url": "https://blog.prompt20.com/posts/open-weights-ultimate-guide",
   "title": "Open Weights: The Ultimate Guide (2026 Edition)",
   "published_at": "2026-05-22T00:00:00",
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   "release_date": "2026-03",
   "parameters": "230B sparse, 10B active",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
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    "**MiniMax M2.7** (MiniMax, China)."
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   "model_evidence": null,
   "release_date_evidence": [
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    "The 2026 open-weight frontier (the roster)",
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    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
    "**MiniMax M2.7** (MiniMax, China). 230B sparse, 10B active."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://blog.prompt20.com/posts/open-weights-ultimate-guide",
   "title": "Open Weights: The Ultimate Guide (2026 Edition)",
   "published_at": "2026-05-22T00:00:00",
   "organization": "InclusionAI / Ant Group",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1T MoE",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
    "**Ling-2.6** (InclusionAI / Ant Group)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
    "**Ling-2.6** (InclusionAI / Ant Group). Apache 2.0. 1T MoE. April 2026."
   ],
   "parameters_evidence": [
    "Open Weights: The Ultimate Guide (2026 Edition)",
    "The 2026 open-weight frontier (the roster)",
    "**Ling-2.6** (InclusionAI / Ant Group). Apache 2.0. 1T MoE."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://hokai.io/hub/tools/qwen",
   "title": "Qwen Review (2026): Pricing, Verdict & Alternatives",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions",
   "organization_evidence": [
    "Qwen is Alibaba's open-source LLM series, available free under Apache 2.0 for commercial use.",
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba released Qwen 3.6-Plus on April 2, 2026, adding stronger coding and agent capabilities, continuing a rapid release cadence that has kept Qwen competitive against Western frontier models despite US chip export restrictions."
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/google-gemma-4-open-model",
   "title": "Google Gemma 4: Best Open AI Model in 2026?",
   "published_at": "2026-04-03T06:42:05",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B MoE, and 31B Dense",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "The 31B Dense model currently ranks #3 on the Arena AI text leaderboard, beating models 20x its size.",
   "organization_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
    "Google DeepMind's **Gemma 4** launched on April 2, 2026, as a family of four open-weight AI models designed to run on everything from Android smartphones to developer workstations."
   ],
   "model_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
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   "release_date_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
    "Google DeepMind's **Gemma 4** launched on April 2, 2026, as a family of four open-weight AI models designed to run on everything from Android smartphones to developer workstations."
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   "parameters_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
    "Gemma 4 ships in four sizes: E2B, E4B, 26B MoE, and 31B Dense."
   ],
   "context_window_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
    "On multi-needle retrieval tests, the 31B model went from 13.5% accuracy with Gemma 3 to 66.4% with Gemma 4 at a 256K context window."
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   "license_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
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   "significance_evidence": [
    "Google Gemma 4: Best Open AI Model in 2026?",
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  },
  {
   "url": "https://docs.api.nvidia.com/nim/reference/moonshotai-kimi-k3",
   "title": "moonshotai / kimi-k3",
   "published_at": "2026-08-27T23:18:12",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8T",
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   "significance": "Evaluated across reasoning and knowledge, coding, agentic, and multimodal benchmarks with strong results such as GPQA Diamond 93.5, DeepSWE 67.5, and MMVU 82.1.",
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   "model_evidence": null,
   "release_date_evidence": [
    "##\nRelease Date:",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "##\nEvaluation Dataset",
    "**Evaluation Benchmark Score:** Kimi-K3 was evaluated across reasoning and knowledge, coding, agentic, and multimodal benchmarks. Selected partner-reported results are shown below.",
    "**Selected Benchmark Results**\n| Category | Benchmark | Kimi-K3 Result |\n|---|---|---|\n| Reasoning and Knowledge | GPQA Diamond | 93.5 |\n| Reasoning and Knowledge | AA-LCR | 74.7 |\n| Coding | DeepSWE | 67.5 |\n| Coding | ProgramBench | 77.8 |\n| Coding | Terminal-Bench 2.1 | 88.3 |\n| Coding | FrontierSWE | 81.2 |\n| Multimodal | MMVU | 82.1 |"
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  },
  {
   "url": "https://kucoin.com/news/flash/zhipu-launches-and-opens-source-glm-5-3-flash-multimodal-model",
   "title": "Zhipu Launches and Open-Sources the GLM-5.3-Flash Multimodal Model",
   "published_at": "2026-08-26T14:33:45",
   "organization": "Zhipu",
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   "significance": "The model outperforms GLM-5.2, achieving programming and Agent scores near Claude Opus 4.8 at 10% lower cost.",
   "organization_evidence": [
    "# Zhipu Launches and Open-Sources the GLM-5.3-Flash Multimodal Model",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu Launches and Open-Sources the GLM-5.3-Flash Multimodal Model",
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  },
  {
   "url": "https://freeainews.com/open-source/minimax-m3-open-source-2026",
   "title": "MiniMax M3: First Open-Weight AI Model With 1M Context",
   "published_at": "2026-06-01T00:00:00",
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   "organization_evidence": [
    "MiniMax M3 is a multimodal large language model developed by MiniMax, a Shanghai-based AI lab.",
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   "model_evidence": [
    "MiniMax M3 is the first model to bring all three frontier capabilities -- frontier-level coding, a genuine 1-million-token context window, and native image and video understanding -- under a single open-weight release.",
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   "release_date_evidence": [
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    "MiniMax M3 is the first model to bring all three frontier capabilities -- frontier-level coding, a genuine 1-million-token context window, and native image and video understanding -- under a single open-weight release.",
    "What is MiniMax M3 and why does it matter for open-source AI?"
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  {
   "url": "https://tech-insider.org/nvidia-nemotron-3-5-lightning-gpu-demand-2026",
   "title": "NVIDIA Nemotron 3.5 Lightning: Free 30B GPU AI Model",
   "published_at": "2026-08-27T15:48:21",
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   "release_date": "2026-08-11",
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   "significance": "Nemotron 3.5 Lightning is a 30-billion-parameter mixture-of-experts (MoE) model with only 3 billion parameters active per token, a design choice that keeps inference costs down while preserving the reasoning capacity of a larger network.",
   "organization_evidence": [
    "NVIDIA Nemotron 3.5: Free 30B Model, $89B GPU Bet [2026]",
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   "release_date_evidence": [
    "NVIDIA Nemotron 3.5: Free 30B Model, $89B GPU Bet [2026]",
    "NVIDIA gave away a frontier-class AI model on August 11, 2026, and investors are treating it as a GPU sales pitch."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Nemotron 3.5: Free 30B Model, $89B GPU Bet [2026]",
    "Nemotron 3.5 Lightning is a 30-billion-parameter mixture-of-experts (MoE) model with only 3 billion parameters active per token, a design choice that keeps inference costs down while preserving the reasoning capacity of a larger network."
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  {
   "url": "https://aichatdaily.com/llama",
   "title": "Llama: everything to know about Meta's open-weights AI",
   "published_at": "2023-02-24T00:00:00",
   "organization": "Meta",
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   "release_date": "2026-02-28",
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   "significance": "improved reasoning",
   "organization_evidence": [
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   "release_date_evidence": [
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  },
  {
   "url": "https://escapeforward.substack.com/p/ai-sovereignty-is-stack-control",
   "title": "AI Sovereignty is Stack Control, not Weight-of-Origin Fetishism",
   "published_at": "2026-08-26T18:18:36",
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   "significance_evidence": [
    "# AI Sovereignty is Stack Control, not Weight-of-Origin Fetishism",
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  {
   "url": "https://escapeforward.substack.com/p/ai-sovereignty-is-stack-control",
   "title": "AI Sovereignty is Stack Control, not Weight-of-Origin Fetishism",
   "published_at": "2026-08-26T18:18:36",
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    "# AI Sovereignty is Stack Control, not Weight-of-Origin Fetishism",
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    "# AI Sovereignty is Stack Control, not Weight-of-Origin Fetishism",
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  {
   "url": "https://ai-tldr.dev/models/gemma-4",
   "title": "Gemma 4: Google's Open-Weight AI Model (2026) | AI/TLDR",
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   "release_date_evidence": [
    "Gemma 4",
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    "Gemma 4",
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  },
  {
   "url": "https://ailearningguides.com/kimi-k3-2026-open-source-model",
   "title": "Kimi K3 2026: The 2.8-Trillion Open-Source AI Giant",
   "published_at": "2026-07-17T18:14:38",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
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    "Kimi K3 2026: The 2.8-Trillion Open-Source AI Giant",
    "By / July 17, 2026"
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    "By / July 17, 2026"
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    "By / July 17, 2026"
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    "By / July 17, 2026"
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  {
   "url": "https://spoonai.me/posts/2026-04-09-glm-51-zhipu-swe-bench-pro-open-source-en",
   "title": "GLM-5.1 Just Topped SWE-Bench Pro \u2013 And It's Fully Open Source",
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   "parameters_evidence": [
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   "url": "https://spoonai.me/posts/2026-04-09-glm-51-zhipu-swe-bench-pro-open-source-en",
   "title": "GLM-5.1 Just Topped SWE-Bench Pro \u2013 And It's Fully Open Source",
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   "url": "https://spoonai.me/posts/2026-04-09-glm-51-zhipu-swe-bench-pro-open-source-en",
   "title": "GLM-5.1 Just Topped SWE-Bench Pro \u2013 And It's Fully Open Source",
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  },
  {
   "url": "https://gate.com/post/status/20529739",
   "title": "Qwen3.6-27B Open Source Release \"Openclaw, Hermes First Choice\": AI Performance Ties with Claude Opus 4.5, Cost Reduced by 14 Times",
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   "significance_evidence": [
    "Alibaba Qwen Series Latest Flagship Qwen3.6-27B Officially Open-Sourced on the Evening of April 22, 2026.",
    "This 27B dense model scored 59.3 on Terminal-Bench 2.0, tying Claude 4.5 Opus, and with less than 1/14 of the parameters, achieved a score of 77.2 on SWE-bench Verified, surpassing the previous 397B MoE flagship's 76.2."
   ]
  },
  {
   "url": "https://gcn.com/meta-opens-muse-glimmer-billion-parameter/21144",
   "title": "Meta opens Muse Glimmer's 30-billion-parameter weights and pledges to release Muse Spark 1.2 openly",
   "published_at": "2026-08-27T12:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": "30-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Muse Glimmer is an open-weight model designed for agentic tasks that can run on a Mac or PC with a single graphics card, aiming to tap demand for AI systems that run directly on people\u2019s devices.",
   "organization_evidence": [
    "Meta opens Muse Glimmer\u2019s 30-billion-parameter weights and pledges to release Muse Spark 1.2 openly",
    "Meta opened the weights of its 30-billion-parameter Muse Glimmer artificial intelligence model on August 10, while CEO Mark Zuckerberg said the company would open the weights for its latest AI model, Muse Spark 1.2, meaning it can be downloaded and used by the public."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta opened the weights of its 30-billion-parameter Muse Glimmer artificial intelligence model on August 10, while CEO Mark Zuckerberg said the company would open the weights for its latest AI model, Muse Spark 1.2, meaning it can be downloaded and used by the public."
   ],
   "parameters_evidence": [
    "Meta opens Muse Glimmer\u2019s 30-billion-parameter weights and pledges to release Muse Spark 1.2 openly",
    "Meta opened the weights of its 30-billion-parameter Muse Glimmer artificial intelligence model on August 10, while CEO Mark Zuckerberg said the company would open the weights for its latest AI model, Muse Spark 1.2, meaning it can be downloaded and used by the public."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta opens Muse Glimmer\u2019s 30-billion-parameter weights and pledges to release Muse Spark 1.2 openly",
    "Muse Glimmer is an open-weight model designed for agentic tasks that can run on a Mac or PC with a single graphics card, aiming to tap demand for AI systems that run directly on people\u2019s devices."
   ]
  },
  {
   "url": "https://subagentic.ai/posts/mistral-medium-3-5-128b-open-weights",
   "title": "Mistral Medium 3.5 Released \u2014 128B Open Model With 256K Context, Work Mode, and Remote Agents in Vibe",
   "published_at": "2026-04-30T15:14:28",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified, 128B dense model, 256K context window",
   "organization_evidence": [
    "Mistral Medium 3.5 Released \u2014 128B Open Model With 256K Context, Work Mode, and Remote Agents in Vibe",
    "Mistral just made a significant move in the open-weights model space."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5 Released \u2014 128B Open Model With 256K Context, Work Mode, and Remote Agents in Vibe",
    "April 30, 2026 \u00b7 4 min \u00b7 731 words \u00b7 Writer Agent (Claude Sonnet 4.6)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5 Released \u2014 128B Open Model With 256K Context, Work Mode, and Remote Agents in Vibe",
    "- **77.6% on SWE-Bench Verified** \u2014 competitive with frontier closed models on real software engineering tasks"
   ]
  },
  {
   "url": "https://insights.trendforce.com/p/kimi-k3-us-china-ai-gap",
   "title": "Kimi K3 and the 7% Gap",
   "published_at": "2026-08-26T05:30:53",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "the largest open-source model in the world at present",
   "organization_evidence": [
    "# Kimi K3 and the 7% Gap",
    "Then, following the release of Moonshot AI\u2019s Kimi K3 in July 2026, the gap between leading Chinese and US models has narrowed significantly, prompting market discussion of a \u201csecond DeepSeek moment\u201d and of the broader US-China AI contest.",
    "On July 16, 2026, Moonshot AI, the Chinese AI startup backed by Alibaba and Tencent, opened the API access for its flagship model Kimi K3, and formally released the full weights on July 27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 and the 7% Gap",
    "On July 16, 2026, Moonshot AI, the Chinese AI startup backed by Alibaba and Tencent, opened the API access for its flagship model Kimi K3, and formally released the full weights on July 27."
   ],
   "parameters_evidence": [
    "# Kimi K3 and the 7% Gap",
    "Kimi K3 is a 2.8T-parameter MoE model, and activates about 104.2B parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 and the 7% Gap",
    "This makes Kimi K3 the largest open-source model in the world at present."
   ]
  },
  {
   "url": "https://insights.trendforce.com/p/kimi-k3-us-china-ai-gap",
   "title": "Kimi K3 and the 7% Gap",
   "published_at": "2026-08-26T05:30:53",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": "2.4T",
   "context_window": null,
   "license": null,
   "significance": "world's second largest open-source model behind Kimi K3",
   "organization_evidence": [
    "# Kimi K3 and the 7% Gap",
    "Alibaba Cloud released the weights for its flagship model Qwen 3.8 Max on August 14."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 and the 7% Gap",
    "Alibaba Cloud released the weights for its flagship model Qwen 3.8 Max on August 14."
   ],
   "parameters_evidence": [
    "# Kimi K3 and the 7% Gap",
    "It carries 2.4T total parameters with 95B active MoE parameters, making it the world\u2019s second largest open-source model behind Kimi K3."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 and the 7% Gap",
    "It carries 2.4T total parameters with 95B active MoE parameters, making it the world\u2019s second largest open-source model behind Kimi K3."
   ]
  },
  {
   "url": "https://techjacksolutions.com/ai-tools/deepseek/deepseek-v4-pro",
   "title": "DeepSeek V4 Pro: GA Release & New Pricing (2026)",
   "published_at": "2026-08-17T00:21:53",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-13",
   "parameters": "1.6-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "V4 Pro left preview and reached general availability, featuring agent-focused workflow improvements, adjustable reasoning effort, and native support for OpenAI's Responses API format.",
   "organization_evidence": [
    "DeepSeek V4 Pro: GA Release, New Pricing, and What Actually Changed",
    "On August 13, 2026, DeepSeek-V4-Pro left preview and reached general availability, per the official [GA announcement](https://api-docs.deepseek.com/news/news260813); the [pricing page](https://api-docs.deepseek.com/quick_start/pricing) now lists the build as DeepSeek-V4-Pro-0813."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Pro: GA Release, New Pricing, and What Actually Changed",
    "On August 13, 2026, DeepSeek-V4-Pro left preview and reached general availability, per the official [GA announcement](https://api-docs.deepseek.com/news/news260813); the [pricing page](https://api-docs.deepseek.com/quick_start/pricing) now lists the build as DeepSeek-V4-Pro-0813."
   ],
   "parameters_evidence": [
    "DeepSeek V4 Pro: GA Release, New Pricing, and What Actually Changed"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Pro: GA Release, New Pricing, and What Actually Changed",
    "On August 13, 2026, DeepSeek-V4-Pro left preview and reached general availability, per the official [GA announcement](https://api-docs.deepseek.com/news/news260813); the [pricing page](https://api-docs.deepseek.com/quick_start/pricing) now lists the build as DeepSeek-V4-Pro-0813.",
    "Three things are new: agent-focused workflow improvements, adjustable reasoning effort, meaning how much hidden deliberation the model spends before answering (low, high, and max, on both Pro and Flash, with the default level not stated on either surface), and native support for OpenAI's Responses API format, so tools built for OpenAI's newer API surface, including Codex-style coding agents, can point at DeepSeek without a rewrite."
   ]
  },
  {
   "url": "https://aumiqx.com/ai-tools/llama-4-scout-maverick-meta-open-source-2026",
   "title": "Aumiqx \u2014 AI Agents That Actually Do Things",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17 billion active parameters and 109 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Shipped with open weights, a commercially friendly license, and a 10 million token context window.",
   "organization_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "On April 5, 2026, Meta released Llama 4 in two variants: **Llama 4 Scout** and **Llama 4 Maverick**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "On April 5, 2026, Meta released Llama 4 in two variants: **Llama 4 Scout** and **Llama 4 Maverick**."
   ],
   "parameters_evidence": [
    "Llama 4 Scout vs Llama 4 Maverick: Which One Is For You?",
    "### Llama 4 Scout \u2014 The Daily Driver",
    "Scout is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 109 billion total parameters across 16 experts."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "Both ship with open weights, a commercially friendly license, and a feature that makes every other open model on the market suddenly feel small \u2014 a **10 million token context window**."
   ]
  },
  {
   "url": "https://aumiqx.com/ai-tools/llama-4-scout-maverick-meta-open-source-2026",
   "title": "Aumiqx \u2014 AI Agents That Actually Do Things",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17 billion active parameters with 400 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Shipped with open weights, a commercially friendly license, and a 10 million token context window.",
   "organization_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "On April 5, 2026, Meta released Llama 4 in two variants: **Llama 4 Scout** and **Llama 4 Maverick**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "On April 5, 2026, Meta released Llama 4 in two variants: **Llama 4 Scout** and **Llama 4 Maverick**."
   ],
   "parameters_evidence": [
    "Llama 4 Scout vs Llama 4 Maverick: Which One Is For You?",
    "### Llama 4 Maverick \u2014 The Flagship",
    "Maverick is the larger sibling: 17 billion active parameters with **400 billion total parameters across 128 experts**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama 4 Scout + Maverick Review: 10M Token Context Tested",
    "Meta Just Dropped Llama 4 \u2014 And It Changes the Open-Weight Game",
    "Both ship with open weights, a commercially friendly license, and a feature that makes every other open model on the market suddenly feel small \u2014 a **10 million token context window**."
   ]
  },
  {
   "url": "https://aipedia.wiki/tools/mistral-ai",
   "title": "Mistral AI: Features, Pricing & Review (May 2026)",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "41B active / 675B total MoE",
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 flagship mixture-of-experts model",
   "organization_evidence": [
    "# Mistral AI",
    "French open-weight LLM lab. Mistral 3 (April 28, 2026) brings Apache 2.0 Large 3 plus Ministral 3 edge models; Medium 3.5 ships agentic coding. EU data sovereignty is the moat."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "French open-weight LLM lab. Mistral 3 (April 28, 2026) brings Apache 2.0 Large 3 plus Ministral 3 edge models; Medium 3.5 ships agentic coding. EU data sovereignty is the moat.",
    "# Mistral AI"
   ],
   "parameters_evidence": [
    "French open-weight LLM lab. Mistral 3 (April 28, 2026) brings Apache 2.0 Large 3 plus Ministral 3 edge models; Medium 3.5 ships agentic coding. EU data sovereignty is the moat.",
    "# Mistral AI"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "French open-weight LLM lab. Mistral 3 (April 28, 2026) brings Apache 2.0 Large 3 plus Ministral 3 edge models; Medium 3.5 ships agentic coding. EU data sovereignty is the moat.",
    "# Mistral AI"
   ]
  },
  {
   "url": "https://abhs.in/blog/google-gemma-4-april-2026-open-models-gemini-3-developer-guide",
   "title": "Gemma 4 (April 2026): #3 Arena Open LLM, Apache 2, Developer Guide",
   "published_at": "2026-04-03T13:14:30",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "#3 Arena open LLM",
   "organization_evidence": [
    "Gemma 4 (April 2026): #3 Arena Open LLM, Apache 2, Developer Guide",
    "Google did not drip-feed another incremental open checkpoint on April 2, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 (April 2026): #3 Arena Open LLM, Apache 2, Developer Guide",
    "Gemma 4 April 2, 2026: Google open weights on Gemini 3, 400M+ Gemma downloads, 31B #3 Arena open LLM, Apache 2. E2B\u201331B sizes, Ollama, vLLM, Vertex."
   ],
   "parameters_evidence": [
    "Gemma 4 (April 2026): #3 Arena Open LLM, Apache 2, Developer Guide",
    "The new family launches in **four** sizes: Effective 2B (E2B), Effective 4B (E4B), a **26B mixture-of-experts** variant, and a **31B dense** model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 (April 2026): #3 Arena Open LLM, Apache 2, Developer Guide",
    "Google states the **31B** model sits at **#3** on the Arena AI open-source text leaderboard as of April 1, 2026, with the **26B MoE** at **#6**, and argues Gemma 4 punches above its weight against models that are far larger on paper."
   ]
  },
  {
   "url": "https://byteiota.com/kimi-k3-open-weights-developer-guide",
   "title": "Kimi K3 Open Weights: What Developers Need to Know",
   "published_at": "2026-08-29T11:38:06",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Puts frontier-level coding performance in the open-weight category for the first time, entering the WebDev Arena at 1,674 Elo and becoming the first open-weight model to crack the global top 3.",
   "organization_evidence": [
    "Moonshot AI released the full weights of Kimi K3 on July 27 \u2014 a 2.8-trillion-parameter model that puts frontier-level coding performance in the open-weight category for the first time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released the full weights of Kimi K3 on July 27 \u2014 a 2.8-trillion-parameter model that puts frontier-level coding performance in the open-weight category for the first time."
   ],
   "parameters_evidence": [
    "Moonshot AI released the full weights of Kimi K3 on July 27 \u2014 a 2.8-trillion-parameter model that puts frontier-level coding performance in the open-weight category for the first time."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI released the full weights of Kimi K3 on July 27 \u2014 a 2.8-trillion-parameter model that puts frontier-level coding performance in the open-weight category for the first time.",
    "The benchmark that matters most: K3 entered the WebDev Arena at 1,674 Elo, becoming the first open-weight model to crack the global top 3."
   ]
  },
  {
   "url": "https://edgen.tech/tr/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter model, with 18 billion active parameters",
   "context_window": null,
   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The company confirmed the model behind the \"Ox Alpha\" name that swept to the top of online usage charts is a new iteration of its GLM series, with weights released Wednesday."
   ],
   "parameters_evidence": [
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ]
  },
  {
   "url": "https://hokai.io/hub/companies/minimax",
   "title": "MiniMax: Funding, Founders & the Hong Kong IPO (2026)",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": "230-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "MiniMax-M2.7 scores 56.2% on SWE-Bench Pro (versus 32.6% for M2.1) and 57.0% on Terminal-Bench 2, and the company describes it as the first MiniMax model that actively participates in its own training and evaluation loop, handling an estimated 30-50% of that work itself.",
   "organization_evidence": [
    "MiniMax: Funding, Founders & the Hong Kong IPO (2026)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax: Funding, Founders & the Hong Kong IPO (2026)"
   ],
   "parameters_evidence": [
    "MiniMax: Funding, Founders & the Hong Kong IPO (2026)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax: Funding, Founders & the Hong Kong IPO (2026)",
    "M2.7 scores 56.2% on SWE-Bench Pro (versus 32.6% for M2.1) and 57.0% on Terminal-Bench 2, and the company describes it as the first MiniMax model that actively participates in its own training and evaluation loop, handling an estimated 30-50% of that work itself."
   ]
  },
  {
   "url": "https://teqvolt.com/tag/open-weights",
   "title": "open-weights \u2014 TeqVolt \u2014 TeqVolt",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-01",
   "parameters": "1.6T total / 49B active",
   "context_window": null,
   "license": null,
   "significance": "Reported ~80.6% on SWE-bench Verified.",
   "organization_evidence": [
    "Tag",
    "# open-weights",
    "DeepSeek V4: 1.6T Open Weights and 1M Context, Now the Default",
    "DeepSeek released V4 as two open-weight mixture-of-experts models: V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B active), both with a 1M-token default context and 384K max output."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Tag",
    "# open-weights",
    "DeepSeek V4: 1.6T Open Weights and 1M Context, Now the Default"
   ],
   "parameters_evidence": [
    "Tag",
    "# open-weights",
    "DeepSeek V4: 1.6T Open Weights and 1M Context, Now the Default",
    "DeepSeek released V4 as two open-weight mixture-of-experts models: V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B active), both with a 1M-token default context and 384K max output."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Tag",
    "# open-weights",
    "DeepSeek V4: 1.6T Open Weights and 1M Context, Now the Default",
    "Reported ~80.6% on SWE-bench Verified."
   ]
  },
  {
   "url": "https://teqvolt.com/tag/open-weights",
   "title": "open-weights \u2014 TeqVolt \u2014 TeqVolt",
   "published_at": null,
   "organization": "Poolside",
   "model": "Laguna S 2.1",
   "release_date": "2026-07-21",
   "parameters": "118B-parameter",
   "context_window": "1M-token",
   "license": "OpenMDW-1.1",
   "significance": "First-party benchmarks show 78.5% on SWE-Bench Multilingual, but independent verification is still pending.",
   "organization_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license."
   ],
   "model_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license."
   ],
   "release_date_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model"
   ],
   "parameters_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license."
   ],
   "context_window_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license."
   ],
   "license_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license."
   ],
   "significance_evidence": [
    "Tag",
    "# open-weights",
    "Laguna S 2.1: Poolside's 118B Open-Weight Coding Model",
    "First-party benchmarks show 78.5% on SWE-Bench Multilingual, but independent verification is still pending."
   ]
  },
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   "url": "https://teqvolt.com/tag/open-weights",
   "title": "open-weights \u2014 TeqVolt \u2014 TeqVolt",
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   "url": "https://teqvolt.com/tag/open-weights",
   "title": "open-weights \u2014 TeqVolt \u2014 TeqVolt",
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   "url": "https://teqvolt.com/tag/open-weights",
   "title": "open-weights \u2014 TeqVolt \u2014 TeqVolt",
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  {
   "url": "https://dev.to/roberts_jakuko_fbc04cb38/qwen38-27b-vs-muse-glimmer-30b-which-permissive-open-weight-model-fits-your-local-gpu-36h1",
   "title": "Qwen3.8-27B vs Muse Glimmer 30B: Which Permissive Open-Weight Model Fits Your Local GPU?",
   "published_at": "2026-08-30T00:41:23",
   "organization": "Meta",
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   "release_date": "2026-08-10",
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   "significance": "distilled from the larger Muse Spark foundation to excel in local autonomous agent workflows",
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    "Qwen3.8-27B vs Muse Glimmer 30B: Which Permissive Open-Weight Model Fits Your Local GPU?",
    "Within a four-day window, two major labs released dense ~30B parameter multimodal models with downloadable weights under pure **Apache 2.0** licensing: Meta\u2019s **Muse Glimmer 30B** (released August 10) and Alibaba\u2019s **Qwen3.8-27B** (released August 14)."
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   "release_date_evidence": [
    "Core specifications and architectural differences",
    "| Feature / Metric | Qwen3.8-27B | Muse Glimmer 30B |",
    "| **Release Date** | August 14, 2026 | August 10, 2026 |"
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   "parameters_evidence": [
    "Core specifications and architectural differences",
    "| Feature / Metric | Qwen3.8-27B | Muse Glimmer 30B |",
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  {
   "url": "https://dev.to/roberts_jakuko_fbc04cb38/qwen38-27b-vs-muse-glimmer-30b-which-permissive-open-weight-model-fits-your-local-gpu-36h1",
   "title": "Qwen3.8-27B vs Muse Glimmer 30B: Which Permissive Open-Weight Model Fits Your Local GPU?",
   "published_at": "2026-08-30T00:41:23",
   "organization": "Alibaba",
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   "release_date": "2026-08-14",
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   "significance": "native image and video understanding with a 262K native context window",
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    "Qwen3.8-27B vs Muse Glimmer 30B: Which Permissive Open-Weight Model Fits Your Local GPU?",
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   "release_date_evidence": [
    "Core specifications and architectural differences",
    "| Feature / Metric | Qwen3.8-27B | Muse Glimmer 30B |",
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   "parameters_evidence": [
    "Core specifications and architectural differences",
    "| Feature / Metric | Qwen3.8-27B | Muse Glimmer 30B |",
    "| **Total Parameters** | 27 Billion (dense) | 29.6 Billion (dense) |"
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    "Qwen3.8-27B is a 27B causal language model with a vision encoder that natively supports image and video understanding, handling complex visual artifacts from STEM diagrams to hour-scale video inputs.",
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  },
  {
   "url": "https://techfastforward.com/articles/kimi-k25-moonshot-ai-1-trillion-parameter-agent-swarm-open-source-2026",
   "title": "Moonshot AI's 1-Trillion-Parameter Open-Source Gamble Is the Most Disruptive Model Release Nobody Is Talking About",
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   "significance": "96.1% on AIME 2025, 76.8% on SWE-Bench Verified, 100-agent swarm with 1,500 coordinated tool calls",
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    "- 96.1% on AIME 2025, 76.8% on SWE-Bench Verified, benchmark performance matches or exceeds many proprietary frontier models",
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  },
  {
   "url": "https://tosea.ai/blog/glm-5-2-complete-guide",
   "title": "How to Use GLM-5.2: Complete Guide to Zhipu AI's 1M-Context Open-Source Coding Model",
   "published_at": "2026-06-17T00:00:00",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
   "parameters": "744 billion",
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   "significance": "GLM-5.2 is the strongest open-weight coding model by a clear margin, it matches Claude Opus 4.8 on agentic tool use and edges GPT-5.5 on resolved-PR work, and it still trails the closed frontier on the hardest from-scratch coding tasks.",
   "organization_evidence": [
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   "release_date_evidence": [
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  },
  {
   "url": "https://earlyterms.com/term/minimax-m3",
   "title": "MiniMax M3 is a 428B-parameter Mixture-of-Experts large language model from Shanghai-based MiniMax (\u7a00\u5b87\u79d1\u6280), act",
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   "organization_evidence": [
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   "release_date_evidence": [
    "# MiniMax M3",
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   "significance_evidence": [
    "# MiniMax M3",
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  },
  {
   "url": "https://news.ycombinator.com/item?id=49493484",
   "title": "> Tencent has released and open-sourced Tencent Hy4 preview, a next-generation l...",
   "published_at": "2026-08-29T21:27:29",
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    "Tencent has released and open-sourced Tencent Hy4 preview, a next-generation large language model with 770B total parameters and 49B active parameters, and a context window exceeding 1M tokens."
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    "Tencent has released and open-sourced Tencent Hy4 preview, a next-generation large language model with 770B total parameters and 49B active parameters, and a context window exceeding 1M tokens."
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    "Tencent has released and open-sourced Tencent Hy4 preview, a next-generation large language model with 770B total parameters and 49B active parameters, and a context window exceeding 1M tokens."
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    "Hy4 is released under OSI approved Apache License 2.0."
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   "significance_evidence": null
  },
  {
   "url": "https://edgen.tech/news/post/alibaba-qwen38-flash-cuts-training-costs-90-with-6b-active-parameters",
   "title": "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
   "published_at": "2026-08-26T13:08:03",
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   "release_date": "2026-08-26",
   "parameters": "6 billion active of 100 billion total",
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   "significance": "Delivers frontier-level performance with 6B active parameters (out of 100B total), cutting training costs 90% and undercutting DeepSeek-V4-Flash pricing by two-thirds.",
   "organization_evidence": [
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    "\"The next Qwen wave is coming,\" the Qwen team at Alibaba said on X, as the company released the model on its Qwen Office platform and Qwen AI API on Aug. 26."
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   "release_date_evidence": [
    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/author/news/alex-nguyen)\u00b7Aug 26 2026, 06:08",
    "\"The next Qwen wave is coming,\" the Qwen team at Alibaba said on X, as the company released the model on its Qwen Office platform and Qwen AI API on Aug. 26."
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   "parameters_evidence": [
    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
    "Alibaba's Qwen3.8-Flash activates just 6 billion of its 100 billion parameters to match frontier performance, cutting training costs 90 percent and undercutting DeepSeek-V4-Flash pricing by two-thirds."
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   "context_window_evidence": null,
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    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
    "Alibaba's Qwen3.8-Flash delivers frontier-level performance with just 6 billion active parameters, cutting training costs 90 percent and resetting the economics of open-weight AI."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/alibaba-qwen38-flash-cuts-training-costs-90-with-6b-active-parameters",
   "title": "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
   "published_at": "2026-08-26T13:08:03",
   "organization": "Alibaba",
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   "release_date": "2026-08-12",
   "parameters": "2.4 trillion",
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   "significance": "Scored 67.7 on SWE-bench Pro, beating OpenAI's GPT-5.6 Sol at 64.6, with 86.6 on Terminal-Bench 2.1 and 92.6 on GPQA Diamond.",
   "organization_evidence": [
    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
    "The release follows Qwen3.8-Max's debut earlier this month, a 2.4-trillion-parameter open-weight model that scored 67.7 on SWE-bench Pro \u2014 beating OpenAI's GPT-5.6 Sol at 64.6 \u2014 while pricing at $2 per million input tokens and $6 per million output tokens."
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   "release_date_evidence": [
    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
    "The open-weight approach \u2014 with Qwen3.8-Max's checkpoint available on Hugging Face and ModelScope since Aug. 12-13 \u2014 competes directly with Meta's Llama series and Mistral for self-hosted deployments."
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    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
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    "Alibaba Qwen3.8-Flash cuts training costs 90% with 6B active parameters",
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   ]
  },
  {
   "url": "https://ai-solutions.wiki/news/grok-4-5-and-qwen-open-models",
   "title": "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Alibaba",
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   "release_date": "2026-02-15",
   "parameters": "roughly 397 billion total parameters with about 17 billion active",
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   "significance": "natively multimodal mixture-of-experts flagship, spanning some 201 languages, with open weights",
   "organization_evidence": [
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   "release_date_evidence": [
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    "Qwen 3.5 (announced 15 February 2026) is a natively multimodal mixture-of-experts flagship, roughly 397 billion total parameters with about 17 billion active, spanning some 201 languages, with open weights."
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   "parameters_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.5 (announced 15 February 2026) is a natively multimodal mixture-of-experts flagship, roughly 397 billion total parameters with about 17 billion active, spanning some 201 languages, with open weights."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.5 (announced 15 February 2026) is a natively multimodal mixture-of-experts flagship, roughly 397 billion total parameters with about 17 billion active, spanning some 201 languages, with open weights."
   ]
  },
  {
   "url": "https://ai-solutions.wiki/news/grok-4-5-and-qwen-open-models",
   "title": "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen 3.6",
   "release_date": "2026-04-15",
   "parameters": "35 billion total, 3 billion active",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "smaller open-weight MoE focused on agentic coding",
   "organization_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Alibaba's open-weight Qwen 3.5 and 3.6 models, natively multimodal and Apache-licensed."
   ],
   "model_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.6 (Qwen3.6-35B-A3B, 15 April 2026) is a smaller open-weight MoE (35 billion total, 3 billion active) focused on agentic coding, released under Apache 2.0."
   ],
   "release_date_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.6 (Qwen3.6-35B-A3B, 15 April 2026) is a smaller open-weight MoE (35 billion total, 3 billion active) focused on agentic coding, released under Apache 2.0."
   ],
   "parameters_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.6 (Qwen3.6-35B-A3B, 15 April 2026) is a smaller open-weight MoE (35 billion total, 3 billion active) focused on agentic coding, released under Apache 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.6 (Qwen3.6-35B-A3B, 15 April 2026) is a smaller open-weight MoE (35 billion total, 3 billion active) focused on agentic coding, released under Apache 2.0."
   ],
   "significance_evidence": [
    "More 2026 Model Releases: Grok 4.5 and Qwen's Open Family",
    "Qwen 3.6 (Qwen3.6-35B-A3B, 15 April 2026) is a smaller open-weight MoE (35 billion total, 3 billion active) focused on agentic coding, released under Apache 2.0."
   ]
  },
  {
   "url": "https://nerdleveltech.com/mistral-medium-3-5-open-weight-128b-frontier-coder",
   "title": "Mistral Medium 3.5: 128B Open-Weight Frontier Coder",
   "published_at": "2026-05-01T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified and 91.4% on the \u03c4\u00b3-Telecom agentic benchmark",
   "organization_evidence": [
    "# Mistral Medium 3.5: 128B Open-Weight Frontier Coder",
    "Mistral AI released **Mistral Medium 3.5** on April 29, 2026 \u2014 a 128 billion-parameter dense model with a 256,000-token context window and configurable reasoning effort, published as open weights on Hugging Face under a Modified MIT license.12"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5: 128B Open-Weight Frontier Coder",
    "May 1, 2026",
    "Mistral AI released **Mistral Medium 3.5** on April 29, 2026 \u2014 a 128 billion-parameter dense model with a 256,000-token context window and configurable reasoning effort, published as open weights on Hugging Face under a Modified MIT license.12"
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5: 128B Open-Weight Frontier Coder",
    "Mistral AI released **Mistral Medium 3.5** on April 29, 2026 \u2014 a 128 billion-parameter dense model with a 256,000-token context window and configurable reasoning effort, published as open weights on Hugging Face under a Modified MIT license.12"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5: 128B Open-Weight Frontier Coder",
    "The model scores **77.6% on SWE-Bench Verified** and **91.4% on the \u03c4\u00b3-Telecom agentic benchmark**, a narrow lead over Anthropic's previous-generation Claude Sonnet 4.5 (77.2% on SWE-Bench Verified) but a step behind the current Anthropic Sonnet flagship, Sonnet 4.6, at 79.6%.134"
   ]
  },
  {
   "url": "https://techriseups.com/articles/google-gemma-4-open-model-for-agents-2026",
   "title": "Gemma 4 Is Google's Bet That Open Agents Run Locally \u2014 Here's What Devs Get",
   "published_at": "2026-06-04T10:43:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B to 31B",
   "context_window": null,
   "license": null,
   "significance": "That 26B MoE ranks #6 on Arena AI's open leaderboard while activating under 4B params, and Google claims it \"outcompetes models 20x its size.\"",
   "organization_evidence": [
    "Dev & Open Source",
    "Gemma 4 Is Google's Bet That Open Agents Run Locally \u2014 Here's What Devs Get",
    "Google's Gemma 4 ships under Apache 2.0 with native function calling, models from 2B to 31B, and edge weights that run on a Raspberry Pi."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Dev & Open Source",
    "Gemma 4 Is Google's Bet That Open Agents Run Locally \u2014 Here's What Devs Get",
    "Google dropped [Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) on April 2, 2026, with the 12B variant following on June 3."
   ],
   "parameters_evidence": [
    "Dev & Open Source",
    "Gemma 4 Is Google's Bet That Open Agents Run Locally \u2014 Here's What Devs Get",
    "Google's Gemma 4 ships under Apache 2.0 with native function calling, models from 2B to 31B, and edge weights that run on a Raspberry Pi."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Dev & Open Source",
    "Gemma 4 Is Google's Bet That Open Agents Run Locally \u2014 Here's What Devs Get",
    "The efficiency story is the genuinely impressive bit.",
    "That 26B MoE **ranks #6 on Arena AI's open leaderboard while activating under 4B params**, and Google claims it \"outcompetes models 20x its size.\""
   ]
  },
  {
   "url": "https://instadatanews.com/articles/2026-07-29-moonshot-kimi-k3-open-weights.html",
   "title": "Moonshot Releases Kimi K3, the Largest Open-Weight AI Model Yet",
   "published_at": "2026-07-29T04:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "World's first open 3-trillion-class system and the largest open-weight AI model released to date, narrowing the gap between open and closed frontier models.",
   "organization_evidence": [
    "# Moonshot Releases Kimi K3, the Largest Open-Weight AI Model Yet",
    "Beijing-based **Moonshot AI** has published the full weights for **Kimi K3**, a **2.8-trillion-parameter** mixture-of-experts model that it describes as the world's first open 3-trillion-class system and the **largest open-weight AI model** released to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot Releases Kimi K3, the Largest Open-Weight AI Model Yet",
    "**InstaDataNews Desk**July 29, 2026 \u00b7 4 min read",
    "The weights dropped on July 26, 2026 \u2014 a day ahead of the company's stated July 27 target \u2014 landing 96 shards, a license and deployment instructions on Hugging Face, with cloud platforms including Together AI and Modal offering day-zero hosted access."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot Releases Kimi K3, the Largest Open-Weight AI Model Yet",
    "Beijing-based **Moonshot AI** has published the full weights for **Kimi K3**, a **2.8-trillion-parameter** mixture-of-experts model that it describes as the world's first open 3-trillion-class system and the **largest open-weight AI model** released to date.",
    "The honest summary is that Kimi K3 has **narrowed the gap** between open and closed frontier models to a slim margin \u2014 an achievement in itself \u2014 without unseating the leaders."
   ]
  },
  {
   "url": "https://linas.substack.com/p/glm-5-3-flash-guide",
   "title": "GLM-5.3-Flash (Ox Alpha): Benchmarks, Pricing, Local Setup",
   "published_at": "2026-08-28T09:59:18",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The first open multimodal AI model that rivals Claude Opus 4.8 and GPT-5.6, runs on a single Mac, and was served without NVIDIA chips resets the math for founders, builders, and investors.",
   "organization_evidence": [
    "# GLM-5.3-Flash: Local AI Goes Multimodal \ud83e\udd16",
    "On August 26, Bloomberg confirmed it. **Ox Alpha was GLM-5.3-Flash from Zhipu AI**, the Beijing lab that operates internationally as Z.ai (we will use Zhipu & ZAI for simplicity's sake going forward):"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.3-Flash: Local AI Goes Multimodal \ud83e\udd16",
    "On August 26, Bloomberg confirmed it. **Ox Alpha was GLM-5.3-Flash from Zhipu AI**, the Beijing lab that operates internationally as Z.ai (we will use Zhipu & ZAI for simplicity's sake going forward):"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.3-Flash: Local AI Goes Multimodal \ud83e\udd16",
    "Why the first open multimodal AI model that rivals Claude Opus 4.8 and GPT-5.6, runs on a single Mac, and was served without NVIDIA chips resets the math for founders, builders, and investors."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases",
   "title": "Latest AI Model Releases: July 2026 Timeline \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-weights flagship that topped Hugging Face trending and runs on surprisingly modest hardware.",
   "organization_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## June 2026",
    "3. Jun 16, 2026 \u00b7 Zhipu AI"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## June 2026",
    "3. Jun 16, 2026 \u00b7 Zhipu AI"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## June 2026",
    "Open-weights flagship that topped Hugging Face trending and runs on surprisingly modest hardware."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases",
   "title": "Latest AI Model Releases: July 2026 Timeline \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Successor to Qwen3.5 (March 2026), continuing Alibaba's fast open-weights release cadence.",
   "organization_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "2. Apr 2026 \u00b7 Alibaba"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "2. Apr 2026 \u00b7 Alibaba"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "Successor to Qwen3.5 (March 2026), continuing Alibaba's fast open-weights release cadence."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases",
   "title": "Latest AI Model Releases: July 2026 Timeline \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Compact update to the Small line, following the Voxtral TTS release in March 2026.",
   "organization_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "3. Apr 2026 \u00b7 Mistral AI"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "3. Apr 2026 \u00b7 Mistral AI"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "Compact update to the Small line, following the Voxtral TTS release in March 2026."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases",
   "title": "Latest AI Model Releases: July 2026 Timeline \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Meta",
   "model": null,
   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts entry in the Llama 4 line.",
   "organization_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "4. Mar 2026 \u00b7 Meta"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "4. Mar 2026 \u00b7 Meta"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "Mixture-of-experts entry in the Llama 4 line."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases",
   "title": "Latest AI Model Releases: July 2026 Timeline \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-weights Gemma refresh with vision capability across every size in the family.",
   "organization_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "5. Feb 2026 \u00b7 Google DeepMind"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "5. Feb 2026 \u00b7 Google DeepMind"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI Model Releases: July 2026 Timeline",
    "## Earlier in 2026",
    "Open-weights Gemma refresh with vision capability across every size in the family."
   ]
  },
  {
   "url": "https://newslocker.com/en-us/news/technology/qwen4s-architecture-is-here-early-firing-6b-parameters-out-of-125b",
   "title": "Qwen4\u2019s architecture is here early, firing 6B parameters out of 125B - Technology news - NewsLocker",
   "published_at": "2026-08-26T19:55:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-27",
   "parameters": "125B total (activating 6B)",
   "context_window": null,
   "license": null,
   "significance": "An open-weight preview of the architecture intended for Qwen4, carrying 125B parameters while activating only 6B per token.",
   "organization_evidence": [
    "Qwen4\u2019s architecture is here early, firing 6B parameters out of 125B",
    "Alibaba\u2019s Qwen team has released Qwen3.8-Flash-Next, an open-weight preview of the architecture it intends to use for Qwen4, carrying 125B parameters but activating only 6B for each token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen4\u2019s architecture is here early, firing 6B parameters out of 125B",
    "Alibaba\u2019s Qwen team has released Qwen3.8-Flash-Next, an open-weight preview of the architecture it intends to use for Qwen4, carrying 125B parameters but activating only 6B for each token."
   ],
   "parameters_evidence": [
    "Qwen4\u2019s architecture is here early, firing 6B parameters out of 125B",
    "Alibaba\u2019s Qwen team has released Qwen3.8-Flash-Next, an open-weight preview of the architecture it intends to use for Qwen4, carrying 125B parameters but activating only 6B for each token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen4\u2019s architecture is here early, firing 6B parameters out of 125B",
    "Alibaba\u2019s Qwen team has released Qwen3.8-Flash-Next, an open-weight preview of the architecture it intends to use for Qwen4, carrying 125B parameters but activating only 6B for each token."
   ]
  },
  {
   "url": "https://byteiota.com/meta-muse-glimmer-30b-run-a-local-ai-agent-for-free",
   "title": "Meta Muse Glimmer 30B: Run a Local AI Agent for Free",
   "published_at": "2026-08-29T04:38:42",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": "29.6-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "94.7% on AIME 2026, 76% on SWE-bench Verified, 75.5% on MCP-Atlas. It beats Gemma4-31B and Qwen3.6-27B across seven agentic benchmarks.",
   "organization_evidence": [
    "Meta just dropped an open-weight 30B model built specifically for multi-step AI agents \u2014 and it runs entirely on your laptop.",
    "**Muse Glimmer**, released August 10 under Apache 2.0, fits on a single consumer GPU, handles function calling and tool use without phoning home, and came with day-zero Ollama support."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Muse Glimmer**, released August 10 under Apache 2.0, fits on a single consumer GPU, handles function calling and tool use without phoning home, and came with day-zero Ollama support.",
    "Zuckerberg confirmed that [Muse Spark 1.2](https://www.lmsys.org/blog/2026-08-10-meta-muse-glimmer/) \u2014 the full-sized model \u2014 will also be open-sourced in the coming weeks.",
    "## What\u2019s Coming Next"
   ],
   "parameters_evidence": [
    "Muse Glimmer is a 29.6-billion-parameter model distilled from Meta\u2019s closed Muse Spark.",
    "## What Is Muse Glimmer?"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Muse Glimmer\u2019s published numbers are strong: 94.7% on AIME 2026, 76% on SWE-bench Verified, 75.5% on MCP-Atlas.",
    "It beats Gemma4-31B and Qwen3.6-27B across seven agentic benchmarks.",
    "## What the Benchmarks Actually Mean"
   ]
  },
  {
   "url": "https://omidsouresrafil.substack.com/p/china-didnt-win-the-agi-race-it-made",
   "title": "China Didn\u2019t Win the AGI Race. It Made the Race Obsolete.",
   "published_at": "2026-08-27T23:43:23",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 2.8-trillion-parameter mixture-of-experts model with a million-token context window and native vision; on composite leaderboards it sat just behind the leading closed American systems and ahead of several flagship U.S. models on specialized tasks.",
   "organization_evidence": [
    "China Didn\u2019t Win the AGI Race. It Made the Race Obsolete.",
    "The shock arrived in mid-July 2026, when Beijing-based Moonshot AI released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model with a million-token context window and native vision."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "China Didn\u2019t Win the AGI Race. It Made the Race Obsolete.",
    "The shock arrived in mid-July 2026, when Beijing-based Moonshot AI released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model with a million-token context window and native vision."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "China Didn\u2019t Win the AGI Race. It Made the Race Obsolete.",
    "The shock arrived in mid-July 2026, when Beijing-based Moonshot AI released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model with a million-token context window and native vision.",
    "On composite leaderboards it sat just behind the leading closed American systems and ahead of several flagship U.S. models on specialized tasks."
   ]
  },
  {
   "url": "https://medium.com/@manoranjan.rajguru/glm-5-2-the-open-weight-model-that-beat-claude-architecture-deep-dive-benchmarks-deployment-6d4dc4f60c76",
   "title": "GLM-5.2: The Open-Weight Model That Beat Claude \u2014 Architecture Deep Dive, Benchmarks & Deployment\u2026",
   "published_at": "2026-06-29T10:40:14",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 39% F1 on IDOR detection, outperforming Claude Code (Opus 4.6) and Claude Code (Opus 4.8/4.7) while achieving top-ranked open-source status across multiple standard coding, security, and reasoning benchmarks.",
   "organization_evidence": [
    "# GLM-5.2: The Open-Weight Model That Beat Claude \u2014 Architecture Deep Dive, Benchmarks & Deployment Guide",
    "GLM-5.2 is the latest flagship model from **Zhipu AI** (operating commercially as **Z.ai**), a Beijing-based AI lab that has developed the General Language Model (GLM) series since 2021."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "2. What Is GLM-5.2? Background & Release",
    "The model rolled out to GLM Coding Plan subscribers on **June 13, 2026**, with open weights and full release notes following on **June 16, 2026**, under an **MIT license with no regional restrictions**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "1. The Day an Open-Weight Model Outsmarted Claude Code",
    "The **GLM-5.2 open-weight model**, with *no endpoint-discovery scaffolding, no guided navigation, nothing but a prompt and a codebase*, scored 39% F1 on IDOR detection."
   ]
  },
  {
   "url": "https://morphllm.com/minimax-m3",
   "title": "MiniMax M3: 428B MoE, 1M Context, Multimodal. Architecture, Benchmarks, API (2026)",
   "published_at": "2026-07-12T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "428B total parameters, ~23B active per token, a 1M-token context window, and native image and video input. It matches Claude Sonnet 4.6 on real-world agentic benchmarks.",
   "organization_evidence": [
    "MiniMax M3 launch report + Artificial Analysis, June 2026",
    "MiniMax M3 is an open-weight mixture-of-experts model from MiniMax, released June 1, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 is an open-weight mixture-of-experts model from MiniMax, released June 1, 2026.",
    "MiniMax M3 is an open-weight mixture-of-experts (MoE) model from the Shanghai lab MiniMax, released June 1, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "428B total parameters, ~23B active per token, a 1M-token context window, and native image and video input.",
    "It matches Claude Sonnet 4.6 on real-world agentic benchmarks."
   ]
  },
  {
   "url": "https://laura-martel.com/blog/open-weights-you-cannot-run",
   "title": "Open Weights You Can't Actually Run",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Open Weights You Can't Actually Run",
    "Kimi K3 (Moonshot, Jul 26)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "2026-07-27",
    "Open Weights You Can't Actually Run",
    "Kimi K3 (Moonshot, Jul 26)"
   ],
   "parameters_evidence": [
    "Open Weights You Can't Actually Run",
    "Kimi K3's weights are free. The license is close to MIT. Anyone can download 2.8 trillion parameters right now.",
    "| Kimi K3 (Moonshot, Jul 26) | 2.8T | Near-MIT, carve-out above $20M MaaS revenue |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://laura-martel.com/blog/open-weights-you-cannot-run",
   "title": "Open Weights You Can't Actually Run",
   "published_at": "2026-07-27T00:00:00",
   "organization": "AMD",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "16B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| Instella-MoE (AMD, Jul 26) | 16B | **ResearchRAIL \u2014 research only, no commercial use** |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| Instella-MoE (AMD, Jul 26) | 16B | **ResearchRAIL \u2014 research only, no commercial use** |"
   ],
   "parameters_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| Instella-MoE (AMD, Jul 26) | 16B | **ResearchRAIL \u2014 research only, no commercial use** |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://laura-martel.com/blog/open-weights-you-cannot-run",
   "title": "Open Weights You Can't Actually Run",
   "published_at": "2026-07-27T00:00:00",
   "organization": "SK Telecom",
   "model": null,
   "release_date": "2026-07-29",
   "parameters": "688B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| [A.X K2](/blog/ax-k2-korea-sovereign-model) (SK Telecom, Jul 29) | 688B | **Apache-2.0, research and commercial, no conditions** |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| [A.X K2](/blog/ax-k2-korea-sovereign-model) (SK Telecom, Jul 29) | 688B | **Apache-2.0, research and commercial, no conditions** |"
   ],
   "parameters_evidence": [
    "Update \u2014 July 29: the licence gradient doesn't track scale",
    "| [A.X K2](/blog/ax-k2-korea-sovereign-model) (SK Telecom, Jul 29) | 688B | **Apache-2.0, research and commercial, no conditions** |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-qwen",
   "title": "DeepSeek vs Qwen (2026): Which Open Model Wins?",
   "published_at": "2026-04-25T14:33:03",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T / 49B (MoE)",
   "context_window": null,
   "license": null,
   "significance": "DeepSeek V4-Pro at 80.6% on SWE-Bench Verified",
   "organization_evidence": [
    "DeepSeek vs Qwen in 2026: An Engineer\u2019s Side-by-Side",
    "DeepSeek V4 was released on April 24, 2026 as a Preview."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4: two tiers, one feature set",
    "DeepSeek V4 was released on April 24, 2026 as a Preview."
   ],
   "parameters_evidence": [
    "DeepSeek vs Qwen in one table",
    "| DeepSeek V4-Pro | 1.6T / 49B (MoE) | 1,000,000 | $0.435 promo / $1.74 list | $0.87 promo / $3.48 list | Yes (MIT) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Coding",
    "DeepSeek\u2019s V4 announcement reports `deepseek-v4-pro` at 80.6% on SWE-Bench Verified \u2014 refer to the V4 technical report for the exact split before you quote it in a customer-facing deck."
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-qwen",
   "title": "DeepSeek vs Qwen (2026): Which Open Model Wins?",
   "published_at": "2026-04-25T14:33:03",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "284B / 13B (MoE)",
   "context_window": null,
   "license": null,
   "significance": "wins on raw price-per-token and on the simplicity of its API surface",
   "organization_evidence": [
    "DeepSeek vs Qwen in 2026: An Engineer\u2019s Side-by-Side",
    "DeepSeek V4 was released on April 24, 2026 as a Preview."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4: two tiers, one feature set",
    "DeepSeek V4 was released on April 24, 2026 as a Preview."
   ],
   "parameters_evidence": [
    "DeepSeek vs Qwen in one table",
    "| DeepSeek V4-Flash | 284B / 13B (MoE) | 1,000,000 | $0.14 | $0.28 | Yes (MIT) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Verdict: which one should you pick?",
    "For most developers building chat, RAG, or agent workloads, DeepSeek V4-Flash wins on raw price-per-token and on the simplicity of its API surface."
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-qwen",
   "title": "DeepSeek vs Qwen (2026): Which Open Model Wins?",
   "published_at": "2026-04-25T14:33:03",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "35B / 3B (MoE)",
   "context_window": null,
   "license": null,
   "significance": "current-generation quality in an Apache-2.0 model small enough to fit on one or two GPUs",
   "organization_evidence": [
    "DeepSeek vs Qwen in 2026: An Engineer\u2019s Side-by-Side",
    "The Qwen3.5-Omni and Qwen3.6-Plus were released in April 2026 as proprietary; access is limited to the chatbots\u2019 websites and the Alibaba cloud platform, while the Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.6: a wider lineup, more fragmentation",
    "The Qwen3.5-Omni and Qwen3.6-Plus were released in April 2026 as proprietary; access is limited to the chatbots\u2019 websites and the Alibaba cloud platform, while the Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month."
   ],
   "parameters_evidence": [
    "DeepSeek vs Qwen in one table",
    "| Qwen3.6-35B-A3B | 35B / 3B (MoE) | 262K (extendable) | Self-host or provider | Self-host or provider | Yes (Apache 2.0) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "When to pick DeepSeek vs Qwen",
    "- **Pick Qwen3.6-35B-A3B** when you want an Apache-2.0 model small enough to fit on one or two GPUs, with current-generation quality."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3",
   "title": "Qwen",
   "published_at": "2026-07-19T16:33:02",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Qwen",
    "## Models",
    "### Qwen3",
    "In February 2026, Alibaba released the open-weights Qwen3.5 and the proprietary Qwen3.5-Plus."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen",
    "## Models",
    "### Qwen3",
    "In February 2026, Alibaba released the open-weights Qwen3.5 and the proprietary Qwen3.5-Plus."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3",
   "title": "Qwen",
   "published_at": "2026-07-19T16:33:02",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Qwen",
    "## Models",
    "### Qwen3",
    "Qwen3.6 model released under the Apache License in the same month."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen",
    "## Models",
    "### Qwen3",
    "Qwen3.5-Omni and Qwen3.6-Plus were released in April 2026 as proprietary; access to these tools is limited to the chatbots' websites and the Alibaba cloud platform.",
    "Qwen3.6 model released under the Apache License in the same month."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://clarqo.com/2026/04/25/mistral-large-3-drops-with-open-weights-and-256k-context-reigniting-the-frontier-vs-open-debate",
   "title": "Mistral Large 3 Drops With Open Weights and 256K Context, Reigniting the Frontier-vs-Open Debate",
   "published_at": "2026-04-25T22:15:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-25",
   "parameters": "470-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "MMLU-Pro: 84.1, GPQA Diamond: 74.6, SWE-Bench Verified: 62.3, AIME 2025: 88.0 (with reasoning traces enabled)",
   "organization_evidence": [
    "Mistral AI released Mistral Large 3 on April 25, 2026, publishing both API access and downloadable open weights under a permissive research-and-commercial license.",
    "# Mistral Large 3 Drops With Open Weights and 256K Context, Reigniting the Frontier-vs-Open Debate"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral AI released Mistral Large 3 on April 25, 2026, publishing both API access and downloadable open weights under a permissive research-and-commercial license.",
    "# Mistral Large 3 Drops With Open Weights and 256K Context, Reigniting the Frontier-vs-Open Debate"
   ],
   "parameters_evidence": [
    "Mistral published a dense 470-billion-parameter mixture-of-experts model with 39 billion parameters active per token.",
    "## What\u2019s in the Release"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The company reports the following headline benchmarks (its own evaluations):",
    "- MMLU-Pro: 84.1",
    "- GPQA Diamond: 74.6",
    "- SWE-Bench Verified: 62.3",
    "- AIME 2025: 88.0 (with reasoning traces enabled)",
    "## What\u2019s in the Release"
   ]
  },
  {
   "url": "https://medium.com/@shubhamnv2/google-gemma-4-the-open-source-ai-model-that-just-ranked-3-in-the-world-and-runs-on-your-phone-a8f160e5cc83",
   "title": "Google Gemma 4: The Open-Source AI Model That Just Ranked #3 in the World (And Runs on Your Phone)",
   "published_at": "2026-04-03T11:08:08",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B effective parameters, 5.1B total",
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 globally among open models; E2B (Effective 2B) runs on smartphones, Raspberry Pi, Jetson Nano with 128K tokens context window.",
   "organization_evidence": [
    "Google Gemma 4: The Open-Source AI Model That Just Ranked #3 in the World (And Runs on Your Phone)",
    "Google\u2019s most powerful open model yet dropped April 2, 2026 \u2014 four sizes, Apache 2.0 license, and benchmark scores that beat models 20\u00d7 its size.",
    "Google just quietly dropped the most significant open-source AI release of 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: The Open-Source AI Model That Just Ranked #3 in the World (And Runs on Your Phone)",
    "Google\u2019s most powerful open model yet dropped April 2, 2026 \u2014 four sizes, Apache 2.0 license, and benchmark scores that beat models 20\u00d7 its size."
   ],
   "parameters_evidence": [
    "Google Gemma 4: The Open-Source AI Model That Just Ranked #3 in the World (And Runs on Your Phone)",
    "E2B (Effective 2B)",
    "- 2.3B effective parameters, 5.1B total"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: The Open-Source AI Model That Just Ranked #3 in the World (And Runs on Your Phone)",
    "Available in E2B, E4B, 26B MoE, and 31B Dense sizes, the 31B ranks #3 globally among open models.",
    "## Edge Tier: E2B and E4B"
   ]
  },
  {
   "url": "https://aidb.digital/blog/2026-07-17-moonshot-ai-releases-kimi-k3-the-world-s-largest-open-weight-model-at",
   "title": "Moonshot AI Releases Kimi K3, the World's Largest Open-Weight Model at 2.8 Trillion Parameters \u00b7 AIDB",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Claims the largest open-weight release in AI history with a 2.8-trillion-parameter Mixture-of-Experts large language model, featuring a 1-million-token context window, native visual understanding capabilities, an always-on reasoning mode (\"thinking mode\"), and performance that benchmarks show ranks second only to Claude Fable 5 and GPT-5.6 Sol among tested models.",
   "organization_evidence": [
    "# Moonshot AI Releases Kimi K3, the World's Largest Open-Weight Model at 2.8 Trillion Parameters",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, released Kimi K3\u2014a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot AI Releases Kimi K3, the World's Largest Open-Weight Model at 2.8 Trillion Parameters",
    "Kimi K3 went live on July 16, 2026, rolling out first on Kimi Code and inside the Kimi app, with two variants surfaced at launch\u2014K3 Max for chat and agent tasks, and K3 Swarm Max for large-scale parallel processing."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI Releases Kimi K3, the World's Largest Open-Weight Model at 2.8 Trillion Parameters",
    "The Beijing-based lab backed by Alibaba launched Kimi K3\u2014a 2.8-trillion-parameter Mixture-of-Experts large language model\u2014days before the 2026 World Artificial Intelligence Conference in Shanghai, claiming the largest open-weight release in AI history.",
    "The model features a 1-million-token context window, native visual understanding capabilities, and an always-on reasoning mode that the company calls \"thinking mode.\"",
    "On benchmarks, Moonshot says K3 delivers frontier-level performance and that among the models it tested, its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://edgen.tech/zh/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": "GLM-5.3-Flash",
   "release_date": "2026-08-26",
   "parameters": "320-billion-parameter model, with 18 billion active parameters",
   "context_window": null,
   "license": "open-source",
   "significance": "scores 57 on the AA Intelligence Index, matching Claude Opus 4.8",
   "organization_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "release_date_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/zh/author/news/alex-nguyen)\u00b7Aug 26 2026, 14:19",
    "The company confirmed the model behind the \"Ox Alpha\" name that swept to the top of online usage charts is a new iteration of its GLM series, with weights released Wednesday."
   ],
   "parameters_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "significance_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "The 320-billion-parameter model, with 18 billion active parameters, is the first native multimodal release in the GLM-5 series and scores 57 on the AA Intelligence Index, matching Claude Opus 4.8."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-06/07-nvidia-open-sources-nemotron-3-ultra-a-550b-mamba-transformer-mixture-of-experts-built-for-long-running-agents",
   "title": "NVIDIA Open-Sources Nemotron 3 Ultra, a 550B Mamba-Transformer Mixture-of-Experts Built for Long-Running Agents",
   "published_at": "2026-06-07T07:46:35",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550 billion",
   "context_window": null,
   "license": null,
   "significance": "Activates 55 billion parameters per token out of 550 billion total parameters using a hybrid Transformer-Mamba Mixture-of-Experts architecture, handles up to 1M tokens, achieves 5x faster inference and up to 30% lower cost for agentic workflows, and scores 94.7 on the RULER long-context benchmark.",
   "organization_evidence": [
    "# NVIDIA Open-Sources Nemotron 3 Ultra, a 550B Mamba-Transformer Mixture-of-Experts Built for Long-Running Agents",
    "NVIDIA has released Nemotron 3 Ultra, a 550-billion-parameter open-weights language model that activates only a fraction of its parameters on each token and is aimed squarely at long-running autonomous agents."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model became available on June 4, 2026, with [AWS](https://aws.amazon.com/blogs/machine-learning/nvidia-nemotron-3-ultra-now-available-on-amazon-sagemaker-jumpstart/) announcing day-zero availability on Amazon SageMaker JumpStart the same day."
   ],
   "parameters_evidence": [
    "# NVIDIA Open-Sources Nemotron 3 Ultra, a 550B Mamba-Transformer Mixture-of-Experts Built for Long-Running Agents",
    "The 550-billion-parameter model activates 55 billion parameters per token, ships under the Linux Foundation's OpenMDW-1.1 license, and trades blows with China's Kimi-K2.6 on coding benchmarks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Open-Sources Nemotron 3 Ultra, a 550B Mamba-Transformer Mixture-of-Experts Built for Long-Running Agents",
    "The 550-billion-parameter model activates 55 billion parameters per token, ships under the Linux Foundation's OpenMDW-1.1 license, and trades blows with China's Kimi-K2.6 on coding benchmarks.",
    "The model handles up to 1M tokens, according to [AWS](https://aws.amazon.com/blogs/machine-learning/nvidia-nemotron-3-ultra-now-available-on-amazon-sagemaker-jumpstart/)."
   ]
  },
  {
   "url": "https://tech-insider.org/au/how-to-use-deepseek-v4-2026",
   "title": "DeepSeek V4 Flash Wins on Price: $0.22 vs $0.66 [2026]",
   "published_at": "2026-08-18T01:00:26",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": null,
   "license": null,
   "significance": "General availability added agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints.",
   "organization_evidence": [
    "How to Use DeepSeek V4: 13 Steps, 90 Min [2026]",
    "According to DeepSeek\u2019s release notes, V4 Pro is distributed under an open, MIT-licensed model, which means the weights are downloadable and self-hostable without a licence fee, though most teams will start with the hosted API rather than running 1.6 trillion parameters on their own hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "Both models shipped first as a preview on April 24, 2026, with open weights and live API access from day one."
   ],
   "parameters_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "**DeepSeek V4 Pro** is the flagship, a mixture-of-experts (MoE) model built around 1.6 trillion total parameters, of which only 49 billion activate for any single token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "General availability landed on July 20, and DeepSeek used the GA release to add agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints."
   ]
  },
  {
   "url": "https://tech-insider.org/au/how-to-use-deepseek-v4-2026",
   "title": "DeepSeek V4 Flash Wins on Price: $0.22 vs $0.66 [2026]",
   "published_at": "2026-08-18T01:00:26",
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": "1,000,000 tokens",
   "license": "open, MIT-licensed",
   "significance": "General availability added agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints.",
   "organization_evidence": [
    "How to Use DeepSeek V4: 13 Steps, 90 Min [2026]",
    "According to DeepSeek\u2019s release notes, V4 Pro is distributed under an open, MIT-licensed model, which means the weights are downloadable and self-hostable without a licence fee, though most teams will start with the hosted API rather than running 1.6 trillion parameters on their own hardware."
   ],
   "model_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "**DeepSeek V4 Pro** is the flagship, a mixture-of-experts (MoE) model built around 1.6 trillion total parameters, of which only 49 billion activate for any single token."
   ],
   "release_date_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "Both models shipped first as a preview on April 24, 2026, with open weights and live API access from day one."
   ],
   "parameters_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "**DeepSeek V4 Pro** is the flagship, a mixture-of-experts (MoE) model built around 1.6 trillion total parameters, of which only 49 billion activate for any single token."
   ],
   "context_window_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "Both variants share a 1-million-token context window and support output generation up to 384,000 tokens in a single response."
   ],
   "license_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "According to DeepSeek\u2019s release notes, V4 Pro is distributed under an open, MIT-licensed model, which means the weights are downloadable and self-hostable without a licence fee, though most teams will start with the hosted API rather than running 1.6 trillion parameters on their own hardware."
   ],
   "significance_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "General availability landed on July 20, and DeepSeek used the GA release to add agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints."
   ]
  },
  {
   "url": "https://tech-insider.org/au/how-to-use-deepseek-v4-2026",
   "title": "DeepSeek V4 Flash Wins on Price: $0.22 vs $0.66 [2026]",
   "published_at": "2026-08-18T01:00:26",
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion total parameters",
   "context_window": "1,000,000 tokens",
   "license": "open, MIT-licensed",
   "significance": "General availability added agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints.",
   "organization_evidence": [
    "How to Use DeepSeek V4: 13 Steps, 90 Min [2026]",
    "According to DeepSeek\u2019s release notes, V4 Pro is distributed under an open, MIT-licensed model, which means the weights are downloadable and self-hostable without a licence fee, though most teams will start with the hosted API rather than running 1.6 trillion parameters on their own hardware."
   ],
   "model_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "**DeepSeek V4 Flash** is the lighter sibling, carrying 284 billion total parameters with 13 billion active per token, tuned for latency-sensitive and high-volume workloads rather than maximum raw reasoning depth."
   ],
   "release_date_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "Both models shipped first as a preview on April 24, 2026, with open weights and live API access from day one."
   ],
   "parameters_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "**DeepSeek V4 Flash** is the lighter sibling, carrying 284 billion total parameters with 13 billion active per token, tuned for latency-sensitive and high-volume workloads rather than maximum raw reasoning depth."
   ],
   "context_window_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "Both variants share a 1-million-token context window and support output generation up to 384,000 tokens in a single response."
   ],
   "license_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "According to DeepSeek\u2019s release notes, V4 Pro is distributed under an open, MIT-licensed model, which means the weights are downloadable and self-hostable without a licence fee, though most teams will start with the hosted API rather than running 1.6 trillion parameters on their own hardware."
   ],
   "significance_evidence": [
    "What Is DeepSeek V4? Pro vs Flash Explained",
    "General availability landed on July 20, and DeepSeek used the GA release to add agentic tool-calling, stronger maths reasoning, and dedicated code-generation improvements on top of the preview checkpoints."
   ]
  },
  {
   "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8",
   "title": "Qwen/Qwen3.8-Flash-Next-FP8 \u00b7 Hugging Face",
   "published_at": "2026-08-26T12:32:48",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-08",
   "parameters": "125B with 6B activated, plus 51B n-gram embedding and 4B MTP",
   "context_window": null,
   "license": null,
   "significance": "Introduces Hybrid Attention with QSA (Qwen Sparse Attention) operating at the micro-block level, Gated Residual for improved training stability and expressiveness, and N-gram embedding for memory-efficient parameter scaling; supports 262,144 tokens natively and extensible up to 1,000,000 tokens.",
   "organization_evidence": [
    "Qwen3.8-Flash-Next-FP8",
    "@techreport{qwen2026design,",
    "    author      = {{Qwen Team}},",
    "@misc{qwen3.8flashnext,",
    "    author = {{Qwen Team}},"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.8-Flash-Next-FP8",
    "    month       = {August},",
    "    year        = {2026}"
   ],
   "parameters_evidence": [
    "# Qwen3.8-Flash-Next-FP8",
    "## Model Overview",
    "- Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.8-Flash-Next-FP8",
    "## Highlights",
    "The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:",
    "- **Hybrid Attention with QSA**: The Gated DeltaNet and Gated Attention pairing has been reworked into Gated DeltaNet and Qwen Sparse Attention (QSA). Rather than selecting individual tokens for processing, QSA operates at the micro-block level. This cuts long-context latency significantly, a critical gain as agentic workloads increasingly dominate real-world usage.",
    "- **Gated Residual**: Residual streams with normalization are what make deep LLM training manageable. Gated Residual modulates information flowing through widened residual streams via an element-wise, data-dependent read gate and a per-branch scalar write gate. This brings finer-grained expressiveness across layers while preserving training stability and keeping inference overhead low.",
    "- **N-gram Embedding**: Embeddings provide a unique axis for parameter scaling that requires less computation and is more amenable to offloading than Mixture-of-Experts (MoE). By indexing with short n-grams, this approach makes parameter scaling highly efficient for memory-constrained accelerators without sacrificing quality."
   ]
  },
  {
   "url": "https://startupfortune.com/googles-gemma-models-just-hit-a-billion-downloads-and-wall-street-shrugged",
   "title": "Google's Gemma Models Just Hit a Billion Downloads and Wall Street Shrugged",
   "published_at": "2026-08-22T20:27:39",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "31 billion parameter",
   "context_window": null,
   "license": null,
   "significance": "beats rivals twenty times its size on the Arena text leaderboard",
   "organization_evidence": [
    "Google's newest entry, Gemma 4, launched back in April 2026 and marked the first time the whole family shipped under a fully permissive Apache 2.0 license instead of Google's older custom terms."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "It comes in four sizes, from a lightweight E2B model built for phones up to a 31 billion parameter dense model that Google says beats rivals twenty times its size on the Arena text leaderboard."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It comes in four sizes, from a lightweight E2B model built for phones up to a 31 billion parameter dense model that Google says beats rivals twenty times its size on the Arena text leaderboard."
   ]
  },
  {
   "url": "https://hackernoon.com/kimi-k3-pushes-open-weight-ai-to-the-frontier-with-a-catch",
   "title": "Kimi K3 Pushes Open-Weight AI to the Frontier, With a Catch",
   "published_at": "2026-08-23T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "K3 lands fourth overall on the Artificial Analysis Index, crushes other models in the Frontend Code Arena and Programming Bench, and costs 50-65% less per task while providing a 1-million-token context window.",
   "organization_evidence": [
    "Kimi K3 Pushes Open-Weight AI to the Frontier, With a Catch",
    "In July 2026, Moonshot AI dropped something remarkable: Kimi K3, a 2.8 trillion-parameter artificial intelligence model released with full open weights."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Kimi K3 Pushes Open-Weight AI to the Frontier, With a Catch",
    "In July 2026, Moonshot AI dropped something remarkable: Kimi K3, a 2.8 trillion-parameter artificial intelligence model released with full open weights."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Head-to-Head Benchmarks",
    "K3 lands fourth overall.",
    "Where Kimi K3 Actually Dominates:",
    "Frontend Code Arena (Real Developers Voting)",
    "Programming Bench",
    "The Cost-Per-Task Reality",
    "K3 costs 50-65% less while delivering frontier-level performance on coding and reasoning.",
    "It can read an entire novel in its 1-million-token context window (that's about 750,000 words\u2014several novels at once)."
   ]
  },
  {
   "url": "https://felloai.com/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Every Version Compared",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded",
    "GLM-5 (February 2026)",
    "**GLM-5** was the family\u2019s first true open frontier model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Every Version Compared",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded",
    "GLM-5 (February 2026)",
    "**GLM-5** was the family\u2019s first true open frontier model."
   ]
  },
  {
   "url": "https://felloai.com/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Every Version Compared",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded",
    "GLM-5.1 (April 2026)",
    "**GLM-5.1** was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around **94% of Claude Opus** while keeping the open weights."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Every Version Compared",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded",
    "GLM-5.1 (April 2026)",
    "**GLM-5.1** was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around **94% of Claude Opus** while keeping the open weights."
   ]
  },
  {
   "url": "https://felloai.com/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Every Version Compared",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship",
    "GLM-5.2 (June 2026)",
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Every Version Compared",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship",
    "GLM-5.2 (June 2026)",
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**."
   ]
  },
  {
   "url": "https://telnyx.com/release-notes/state-of-the-art-open-source-llms",
   "title": "State-of-the-Art Open Source LLMs Now Available",
   "published_at": "2026-03-05T22:00:00",
   "organization": "zai-org",
   "model": null,
   "release_date": "2026-03-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The highest intelligence open source LLM in the world. With weights exceeding a terabyte, GLM-5 delivers unmatched capability for complex reasoning and generation tasks.",
   "organization_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`zai-org/GLM-5` - The highest intelligence open source LLM in the world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "### 5, Mar 2026",
    "All three models were released in 2026 and represent the efficient frontier of cost per intelligence."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`zai-org/GLM-5` - The highest intelligence open source LLM in the world. With weights exceeding a terabyte, GLM-5 delivers unmatched capability for complex reasoning and generation tasks."
   ]
  },
  {
   "url": "https://telnyx.com/release-notes/state-of-the-art-open-source-llms",
   "title": "State-of-the-Art Open Source LLMs Now Available",
   "published_at": "2026-03-05T22:00:00",
   "organization": "moonshotai",
   "model": null,
   "release_date": "2026-03-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Great balance between intelligence and cost. The non-reasoning version is ideal for real-time voice AI. Also recommended for AI Assistants: a significant step up over Qwen 235B in intelligence at effectively the same latency and price point.",
   "organization_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`moonshotai/Kimi-K2.5` - Great balance between intelligence and cost."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "### 5, Mar 2026",
    "All three models were released in 2026 and represent the efficient frontier of cost per intelligence."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`moonshotai/Kimi-K2.5` - Great balance between intelligence and cost. The non-reasoning version is ideal for real-time voice AI. Also recommended for AI Assistants: a significant step up over Qwen 235B in intelligence at effectively the same latency and price point. Many use cases that required complex prompt engineering with Qwen will simply work with Kimi."
   ]
  },
  {
   "url": "https://telnyx.com/release-notes/state-of-the-art-open-source-llms",
   "title": "State-of-the-Art Open Source LLMs Now Available",
   "published_at": "2026-03-05T22:00:00",
   "organization": "MiniMaxAI",
   "model": null,
   "release_date": "2026-03-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Highly intelligent at a lower cost. A practical choice for teams optimizing spend while maintaining strong model performance.",
   "organization_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`MiniMaxAI/MiniMax-M2.5` - Highly intelligent at a lower cost."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "### 5, Mar 2026",
    "All three models were released in 2026 and represent the efficient frontier of cost per intelligence."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# State-of-the-Art Open Source LLMs Now Available",
    "## New Models",
    "`MiniMaxAI/MiniMax-M2.5` - Highly intelligent at a lower cost. A practical choice for teams optimizing spend while maintaining strong model performance."
   ]
  },
  {
   "url": "https://translate.google.com/website?client=srp&hl=th&sl=en&tl=th&u=https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4",
   "title": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 \u00b7 Hugging Face",
   "published_at": "2026-08-27T18:09:15",
   "organization": "NVIDIA Corporation",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-scale large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities, employing a hybrid Latent Mixture-of-Experts architecture.",
   "organization_evidence": [
    "# NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4",
    "**Model Developer:** NVIDIA Corporation"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4",
    "| **Release Date** | June 4, 2026 |",
    "Hugging Face - 06/04/2026 via [Hugging Face](https://huggingface-co.translate.goog/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4?_x_tr_sl=en&_x_tr_tl=th&_x_tr_hl=th&_x_tr_pto=tc)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4",
    "**Nemotron-3-Ultra-550B-A55B-NVFP4** is a frontier-scale large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3.5",
   "title": "Qwen",
   "published_at": "2026-07-19T12:04:07",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-15",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Released under the Apache 2.0 license",
   "organization_evidence": [
    "Qwen (also known as Tongyi Qianwen, Chinese: \u901a\u4e49\u5343\u95ee; pinyin: T\u014dngy\u00ec Qi\u0101nw\u00e8n) is a family of large language models developed by Alibaba Cloud."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Stable release: Qwen3.7 Max /, May 18, 2026, Qwen3.7 Plus /, May 18, 2026, Qwen3.6-35B-A3B /, April 15, 2026, Qwen3.6-27B /, April 22, 2026,",
    "# Infobox"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month.",
    "### Qwen3"
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3.5",
   "title": "Qwen",
   "published_at": "2026-07-19T12:04:07",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Released as Qwen3.6",
   "organization_evidence": [
    "Qwen (also known as Tongyi Qianwen, Chinese: \u901a\u4e49\u5343\u95ee; pinyin: T\u014dngy\u00ec Qi\u0101nw\u00e8n) is a family of large language models developed by Alibaba Cloud."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Stable release: Qwen3.7 Max /, May 18, 2026, Qwen3.7 Plus /, May 18, 2026, Qwen3.6-35B-A3B /, April 15, 2026, Qwen3.6-27B /, April 22, 2026,",
    "# Infobox"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| Qwen3.6 | April 2026 | Apache 2.0 |  |",
    "**List of Qwen models**"
   ]
  },
  {
   "url": "https://medium.com/@uday.devworks/glm-5-2-just-dropped-and-its-the-open-source-model-that-should-worry-closed-ai-labs-b20767f0cd7f",
   "title": "GLM-5.2 Just Dropped \u2014 And It\u2019s the Open-Source Model That Should Worry Closed AI Labs",
   "published_at": "2026-06-22T08:48:59",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "On Artificial Analysis\u2019s Intelligence Index v4.1, GLM-5.2 scored 51 \u2014 ahead of DeepSeek V4 Pro (44), MiniMax-M3 (44), and Kimi K2.6 (43). On this agentic-focused benchmark, it even edged past Google\u2019s Gemini 3.1 Pro Preview, which landed at 46.",
   "organization_evidence": [
    "# GLM-5.2 Just Dropped \u2014 And It\u2019s the Open-Source Model That Should Worry Closed AI Labs",
    "While the AI world keeps debating which closed model deserves the \u201cfrontier\u201d crown, China\u2019s Zhipu AI (now branded globally as Z.ai) quietly released something that deserves a lot more attention: **GLM-5.2**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Exactly Is GLM-5.2?",
    "It launched on June 13, 2026, succeeding GLM-5.1."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Numbers That Matter",
    "On Artificial Analysis\u2019s Intelligence Index v4.1, GLM-5.2 scored 51 \u2014 ahead of DeepSeek V4 Pro (44), MiniMax-M3 (44), and Kimi K2.6 (43)."
   ]
  },
  {
   "url": "https://tradingview.com/news/prnewswire:b636582195c7f:0-minimax-announces-first-half-2026-financial-results",
   "title": "MiniMax Announces First Half 2026 Financial Results",
   "published_at": "2026-08-26T10:51:00",
   "organization": "MiniMax",
   "model": "MiniMax H3",
   "release_date": "2026-07",
   "parameters": null,
   "context_window": null,
   "license": "open weights",
   "significance": "advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation",
   "organization_evidence": [
    "MiniMax Announces First Half 2026 Financial Results",
    "1H2026 Business Review",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ],
   "model_evidence": [
    "MiniMax Announces First Half 2026 Financial Results",
    "1H2026 Business Review",
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  },
  {
   "url": "https://aichina.news/blog/unveiling-allen-ai-s-olmo-3-7b-rlzero-mix-a-strong-contender-for-uejj9s",
   "title": "Unveiling Allen AI's Olmo-3-7B-RLZero-Mix: A Strong Contender for Ascend Ecosystems \u2014 AICHINA.news Blog",
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    "Unveiling Allen AI's Olmo-3-7B-RLZero-Mix: A Strong Contender for Ascend Ecosystems",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
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  {
   "url": "https://ideabosque.com/library/open-weight-models-agentic-frontier",
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  },
  {
   "url": "https://ideabosque.com/library/open-weight-models-agentic-frontier",
   "title": "Open-Weight Models Crossed the Agentic Frontier: DeepSeek V4, GLM 5.2, and the Model-Flexible Build",
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   "significance": "First US-built open-weight model at frontier-adjacent scale.",
   "organization_evidence": [
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    "## Update \u2014 2026-07-23",
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    "## Update \u2014 2026-07-23",
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   "significance_evidence": [
    "Strategy",
    "## Update \u2014 2026-07-23",
    "1. **Thinking Machines Lab shipped Inkling on July 15 \u2014 the first US-built open-weight model at frontier-adjacent scale.** [Thinking Machines Lab](https://thinkingmachines.ai/news/introducing-inkling/) (Mira Murati, $2B seed at $12B valuation) released a 975B total / 41B active MoE model under Apache 2.0, with weights on [Hugging Face](https://huggingface.co) at launch."
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  },
  {
   "url": "https://ideabosque.com/library/open-weight-models-agentic-frontier",
   "title": "Open-Weight Models Crossed the Agentic Frontier: DeepSeek V4, GLM 5.2, and the Model-Flexible Build",
   "published_at": "2026-07-12T00:00:00",
   "organization": "Meituan",
   "model": null,
   "release_date": "2026-06-30",
   "parameters": null,
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   "license": null,
   "significance": "1.6T open-source agentic coding model trained on domestic Chinese chips.",
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    "## Update \u2014 2026-07-28",
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   "release_date_evidence": [
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    "## Update \u2014 2026-07-28",
    "1. **Meituan LongCat-2.0: a 1.6T open-source agentic coding model trained on domestic Chinese chips.** Meituan open-sourced LongCat-2.0 on June 30, 2026 \u2014 a 1.6-trillion-parameter agentic coding model with 48 billion active parameters per token, a 1 million token context window, and 30+ trillion training tokens."
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    "## Update \u2014 2026-07-28",
    "1. **Meituan LongCat-2.0: a 1.6T open-source agentic coding model trained on domestic Chinese chips.** Meituan open-sourced LongCat-2.0 on June 30, 2026 \u2014 a 1.6-trillion-parameter agentic coding model with 48 billion active parameters per token, a 1 million token context window, and 30+ trillion training tokens."
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  },
  {
   "url": "https://tech-insider.org/ie/grok-4-6-vs-meta-muse-spark-vs-deepseek-v4-pro-2026",
   "title": "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
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   "organization": "Meta",
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   "parameters_evidence": [
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  },
  {
   "url": "https://tech-insider.org/ie/grok-4-6-vs-meta-muse-spark-vs-deepseek-v4-pro-2026",
   "title": "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
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   "model_evidence": [
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    "Five days later, on 10 August, Meta shipped **Muse Glimmer**, a 30-billion-parameter open-weight model released under an Apache 2.0 license \u2014 Meta\u2019s first fully open release since it wound down Llama."
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   "release_date_evidence": [
    "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
    "Five days later, on 10 August, Meta shipped **Muse Glimmer**, a 30-billion-parameter open-weight model released under an Apache 2.0 license \u2014 Meta\u2019s first fully open release since it wound down Llama."
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   "parameters_evidence": [
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  },
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   "url": "https://tech-insider.org/ie/grok-4-6-vs-meta-muse-spark-vs-deepseek-v4-pro-2026",
   "title": "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
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   "release_date_evidence": [
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    "DeepSeek\u2019s V4 family spent roughly three months in preview before General Availability landed on 31 July 2026."
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   "parameters_evidence": [
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   "context_window_evidence": [
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   "license_evidence": [
    "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
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   "significance_evidence": [
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    "| Benchmark signal | 54 on AA Intelligence Index | Claimed top-tier MMLU/coding scores vs Maverick | ~83.5% MRCR 1M retrieval |"
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  },
  {
   "url": "https://tech-insider.org/ie/grok-4-6-vs-meta-muse-spark-vs-deepseek-v4-pro-2026",
   "title": "Grok 4.6 vs Meta Muse Spark vs DeepSeek V4 Pro: 21x Price Gap [2026]",
   "published_at": "2026-08-23T11:11:27",
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   "release_date_evidence": [
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   "license_evidence": [
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   "url": "https://renovateqr.com/blog/google-gemma-4-review-2026",
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   "url": "https://renovateqr.com/blog/google-gemma-4-review-2026",
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   "url": "https://renovateqr.com/blog/google-gemma-4-review-2026",
   "title": "Google Gemma 4 Review: 4 Open Models, Apache 2.0, Tested",
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   "url": "https://renovateqr.com/blog/google-gemma-4-review-2026",
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   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Review: 4 Open Models, Apache 2.0, Tested",
    "Gemma 4 launched April 2: four models from phone to workstation, Apache 2.0, Gemini 3 research inside, #3 on Arena AI. Developer guide.",
    "Google announced Gemma 4 on April 2, 2026, and it is the most significant open model release the company has ever made."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Review: 4 Open Models, Apache 2.0, Tested",
    "Gemma 4 launched April 2: four models from phone to workstation, Apache 2.0, Gemini 3 research inside, #3 on Arena AI. Developer guide.",
    "26B Mixture of Experts (26B MoE) and 31B Dense for laptops and workstations."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Review: 4 Open Models, Apache 2.0, Tested",
    "Gemma 4 launched April 2: four models from phone to workstation, Apache 2.0, Gemini 3 research inside, #3 on Arena AI. Developer guide.",
    "The 31B Dense ranks #3 on Arena AI's open model leaderboard."
   ]
  },
  {
   "url": "https://medium.com/@dremfind/kimi-k3-the-2-8-trillion-parameter-open-source-model-that-just-changed-the-ai-landscape-41c7d296bfdb",
   "title": "Kimi K3: The 2.8 Trillion Parameter Open-Source Model That Just Changed the AI Landscape",
   "published_at": "2026-07-17T12:18:20",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 2.8-trillion-parameter Mixture-of-Experts model that goes toe-to-toe with the best closed-source systems from OpenAI and Anthropic, being the largest open-source model ever released.",
   "organization_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open-Source Model That Just Changed the AI Landscape",
    "On July 16, 2026, Beijing-based Moonshot AI dropped what might be the most consequential open-source AI release of the year: Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model that goes toe-to-toe with the best closed-source systems from OpenAI and Anthropic."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open-Source Model That Just Changed the AI Landscape",
    "On July 16, 2026, Beijing-based Moonshot AI dropped what might be the most consequential open-source AI release of the year: Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model that goes toe-to-toe with the best closed-source systems from OpenAI and Anthropic."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: The 2.8 Trillion Parameter Open-Source Model That Just Changed the AI Landscape",
    "On July 16, 2026, Beijing-based Moonshot AI dropped what might be the most consequential open-source AI release of the year: Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model that goes toe-to-toe with the best closed-source systems from OpenAI and Anthropic.",
    "This is the largest open-source model ever released \u2014 roughly 75% larger than DeepSeek\u2019s V4 Pro (1.6T) \u2014 and it arrives with a 1-million-token context window, native vision understanding, and always-on reasoning baked in."
   ]
  },
  {
   "url": "https://voiceofcontext.substack.com/p/glms-mystery-model-revealed-new-revenue",
   "title": "GLM\u2019s Mystery Model Revealed, New Revenue Routes for Chinese Open Models, and Xiaomi\u2019s Expanding Chip Roadmap",
   "published_at": "2026-08-28T15:31:31",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A preview of the architecture behind Qwen4 that activates 6 billion parameters per token, with a training cost roughly one ninth that of Qwen3.7-Plus.",
   "organization_evidence": [
    "GLM\u2019s Mystery Model Revealed, New Revenue Routes for Chinese Open Models, and Xiaomi\u2019s Expanding Chip Roadmap",
    "Zhipu, DeepSeek, Alibaba and Tencent all released new models this week.",
    "Alibaba [released the open weights for Qwen3.8-Flash-Next](https://qwen.ai/blog?id=qwen3.8-flash-next), a preview of the architecture behind Qwen4 that activates **6 billion parameters per token**."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM\u2019s Mystery Model Revealed, New Revenue Routes for Chinese Open Models, and Xiaomi\u2019s Expanding Chip Roadmap",
    "Zhipu, DeepSeek, Alibaba and Tencent all released new models this week.",
    "Alibaba [released the open weights for Qwen3.8-Flash-Next](https://qwen.ai/blog?id=qwen3.8-flash-next), a preview of the architecture behind Qwen4 that activates **6 billion parameters per token**.",
    "Its training cost was roughly **one ninth** that of Qwen3.7-Plus."
   ]
  },
  {
   "url": "https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF",
   "title": "unsloth/DeepSeek-V4-Flash-0731-GGUF \u00b7 Hugging Face",
   "published_at": "2026-08-30T07:28:58",
   "organization": "DeepSeek-AI",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "284B params",
   "context_window": null,
   "license": null,
   "significance": "DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available.",
   "organization_evidence": [
    "#",
    "# DeepSeek-V4-Flash-0731",
    "@misc{deepseekai2026deepseekv4,",
    "      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},",
    "      author={DeepSeek-AI},"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Read our How to [Run DeepSeek-V4-0731 Guide!](https://unsloth.ai/docs/models/deepseek-v4)",
    "- To run DeepSeek-V4-Flash-0731 in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL).",
    "# DeepSeek-V4-Flash-0731",
    "@misc{deepseekai2026deepseekv4,",
    "      year={2026},"
   ],
   "parameters_evidence": [
    "Model size",
    "284B params"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Introduction",
    "DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available."
   ]
  },
  {
   "url": "https://aiwiki.ai/wiki/mistral_large_3",
   "title": "Mistral Large 3",
   "published_at": "2026-05-16T04:33:27",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Hybrid model unifying instruct, reasoning, and coding; 256K context.",
   "organization_evidence": [
    "# Mistral Large 3",
    "## What other Mistral 3 models followed Large 3?",
    "| Dec 2, 2025 | Mistral Large 3 + Ministral 3 family | Flagship MoE + 3B/8B/14B dense models, Apache 2.0[1] |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Large 3",
    "## What other Mistral 3 models followed Large 3?",
    "| Mar 16, 2026 | Mistral Small 4 (`mistral-small-2603`) + Leanstral | Hybrid model unifying instruct, reasoning, and coding; 256K context. Leanstral is Mistral's first open-source code agent for Lean 4 formal proofs[25] |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Large 3",
    "## What other Mistral 3 models followed Large 3?",
    "| Mar 16, 2026 | Mistral Small 4 (`mistral-small-2603`) + Leanstral | Hybrid model unifying instruct, reasoning, and coding; 256K context. Leanstral is Mistral's first open-source code agent for Lean 4 formal proofs[25] |"
   ]
  },
  {
   "url": "https://aiturnpoint.com/google-launches-gemma-4",
   "title": "Google Launches Gemma 4 with Apache 2.0 License and Gemini 3 Architecture",
   "published_at": "2026-04-03T18:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B (Effective 2B), E4B (Effective 4B), 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Debuted at #3 on the Arena AI text leaderboard among open models for the 31B Dense model and #6 for the 26B MoE model.",
   "organization_evidence": [
    "Google launched [Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) on April 2, 2026, releasing four open-weight models built on the same research and architecture as Gemini 3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google launched [Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) on April 2, 2026, releasing four open-weight models built on the same research and architecture as Gemini 3."
   ],
   "parameters_evidence": [
    "The **E2B** (Effective 2B) and **E4B** (Effective 4B) models target edge devices.",
    "The **26B Mixture of Experts** model activates only 3.8 billion of its 26 billion parameters during inference, delivering high tokens-per-second on local hardware while fitting on consumer GPUs in quantized form.",
    "The **31B Dense** model prioritizes raw quality over speed and runs unquantized on a single 80GB NVIDIA H100 GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google claims the 31B Dense model debuted at [#3 on the Arena AI text leaderboard](https://arena.ai/leaderboard) among open models, behind GLM-5 and Kimi 2.5, while the 26B MoE holds the #6 spot."
   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/kimi-k3-moonshot-open-source-ai-model-2026",
   "title": "Kimi K3: Moonshot AI Releases Largest Open-Source Model",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1-million-token",
   "license": "open-weight",
   "significance": "Moonshot describes it as the largest open-source AI model ever released, and early benchmark results place it within striking distance of proprietary frontier systems like Claude Fable 5 and GPT-5.6 Sol \u2014 while being freely downloadable and self-hostable.",
   "organization_evidence": [
    "**Kimi K3** is a 2.8-trillion-parameter open-weight large language model released by Beijing-based Moonshot AI on July 16, 2026.",
    "### What is Kimi K3?"
   ],
   "model_evidence": [
    "**Kimi K3** is a 2.8-trillion-parameter open-weight large language model released by Beijing-based Moonshot AI on July 16, 2026.",
    "### What is Kimi K3?"
   ],
   "release_date_evidence": [
    "**Kimi K3** is a 2.8-trillion-parameter open-weight large language model released by Beijing-based Moonshot AI on July 16, 2026.",
    "### What is Kimi K3?"
   ],
   "parameters_evidence": [
    "**Kimi K3** is a 2.8-trillion-parameter open-weight large language model released by Beijing-based Moonshot AI on July 16, 2026.",
    "### What is Kimi K3?"
   ],
   "context_window_evidence": [
    "The model ships with a **1-million-token context window** and native multimodal support for text, images, and video, positioning it as a serious option for **long-horizon coding** and agentic workloads that need to hold an entire codebase or document set in memory at once.",
    "## What Is Kimi K3?"
   ],
   "license_evidence": [
    "**Kimi K3** is a 2.8-trillion-parameter open-weight large language model released by Beijing-based Moonshot AI on July 16, 2026.",
    "### What is Kimi K3?"
   ],
   "significance_evidence": [
    "Moonshot describes it as the largest open-source AI model ever released, and early benchmark results place it within striking distance of proprietary frontier systems like Claude Fable 5 and GPT-5.6 Sol \u2014 while being freely downloadable and self-hostable.",
    "### What is Kimi K3?"
   ]
  },
  {
   "url": "https://aitoolsradar.org/blog/models/glm-5-2-benchmarks-local-setup-2026",
   "title": "GLM-5.2: Open-Source Frontier Model with 1M Context, Benchmarks, and Local Setup (2026)",
   "published_at": "2026-06-17T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Topped AIME 2026 at 99.2, leading open-weight math and reasoning performance, with 1M-token context window.",
   "organization_evidence": [
    "Models",
    "# GLM-5.2: Open-Source Frontier Model with 1M Context, Benchmarks, and Local Setup (2026)",
    "GLM-5.2 from Zhipu AI is a 744B open-weight model under MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Models",
    "# GLM-5.2: Open-Source Frontier Model with 1M Context, Benchmarks, and Local Setup (2026)",
    "Zhipu AI shipped GLM-5.2 on June 13, 2026, exactly 24 hours after the US government ordered Anthropic\u2019s Fable 5 offline."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Models",
    "# GLM-5.2: Open-Source Frontier Model with 1M Context, Benchmarks, and Local Setup (2026)",
    "**Short answer (June 2026):** GLM-5.2 is Zhipu AI\u2019s **744B open-weight model** under MIT license with **1M-token context** and 384 Mixture of Experts routing ~40B parameters per token."
   ]
  },
  {
   "url": "https://systems-analysis.ru/eng/MiniMax",
   "title": "MiniMax",
   "published_at": "2026-07-11T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "Approximately 428B / 23B",
   "context_window": null,
   "license": null,
   "significance": "Native text, image, and video understanding; coding and agents",
   "organization_evidence": [
    "# MiniMax",
    "MiniMax released M3 on 1 June 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax",
    "MiniMax released M3 on 1 June 2026."
   ],
   "parameters_evidence": [
    "# MiniMax",
    "MiniMax released M3 on 1 June 2026.",
    "| **MiniMax M3** | June 2026 | Approximately 428B / 23B | Up to 1M API | Text, images, and video | General multimodal coding and agent flagship |",
    "| Model | Release | Total / active parameters | Published context | Native input | Principal role |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax",
    "MiniMax released M3 on 1 June 2026.",
    "| **MiniMax M3** | June 2026 | Approximately 428B / 23B | Up to 1M API | Text, images, and video | General multimodal coding and agent flagship |",
    "| Model | Release | Total / active parameters | Published context | Native input | Principal role |"
   ]
  },
  {
   "url": "https://arxiv.org/html/2607.20062v2",
   "title": "Solar Open 2 Technical Report",
   "published_at": null,
   "organization": "Upstage",
   "model": null,
   "release_date": "2026-07-22",
   "parameters": "250B-A15B",
   "context_window": null,
   "license": null,
   "significance": "Against comparably sized open-weight models on English benchmarks, Solar Open 2 leads on MMLU-Pro, LiveCodeBench, and the APEX-Agents agentic suite, and stays competitive with the strongest (DeepSeek-V4-Flash and MiMo-V2.5) elsewhere. On Korean benchmarks, Solar Open 2 records the highest average of any model compared, including fast-tier closed APIs, and on Ko-GDPval, an in-house Korean officework-agent benchmark, it is competitive with DeepSeek-V4-Pro (1.6T) at less than a sixth of its size.",
   "organization_evidence": [
    "Solar Open 2 Technical Report",
    "Every author is affiliated with Upstage, South Korea, unless specified otherwise."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Solar Open 2 Technical Report",
    "Jul 22, 2026"
   ],
   "parameters_evidence": [
    "Solar Open 2 Technical Report",
    "We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Solar Open 2 Technical Report",
    "Against comparably sized open-weight models on English benchmarks, Solar Open 2 leads on MMLU-Pro, LiveCodeBench, and the APEX-Agents agentic suite, and stays competitive with the strongest (DeepSeek-V4-Flash and MiMo-V2.5) elsewhere."
   ]
  },
  {
   "url": "https://open-techstack.com/blog/kimi-k3-moonshot-open-weights-frontier",
   "title": "Kimi K3: Moonshot's 2.8T Open-Weight Model Shakes Up the AI Race",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "Beats Claude Fable 5 on coding, scores 57 on AI Index at $3/M input.",
   "organization_evidence": [
    "# Kimi K3: Moonshot's 2.8T Open-Weight Model Shakes Up the AI Race",
    "Moonshot AI's Kimi K3: 2.8T parameters, 1M context, open weights July 27. Beats Claude Fable 5 on coding, scores 57 on AI Index at $3/M input."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: Moonshot's 2.8T Open-Weight Model Shakes Up the AI Race",
    "Moonshot AI's Kimi K3: 2.8T parameters, 1M context, open weights July 27. Beats Claude Fable 5 on coding, scores 57 on AI Index at $3/M input."
   ],
   "parameters_evidence": [
    "# Kimi K3: Moonshot's 2.8T Open-Weight Model Shakes Up the AI Race",
    "Moonshot AI's Kimi K3: 2.8T parameters, 1M context, open weights July 27. Beats Claude Fable 5 on coding, scores 57 on AI Index at $3/M input."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: Moonshot's 2.8T Open-Weight Model Shakes Up the AI Race",
    "Moonshot AI's Kimi K3: 2.8T parameters, 1M context, open weights July 27. Beats Claude Fable 5 on coding, scores 57 on AI Index at $3/M input."
   ]
  },
  {
   "url": "https://glonce.com/qwen3-8-max-arrives-as-qwen-s-first",
   "title": "Qwen3.8-Max arrives as Qwen's first open-weight Max-class model",
   "published_at": "2026-08-03T06:00:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-08-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "most capable model in the Qwen family to date and the first Qwen-Max-class model to have its weights open-sourced",
   "organization_evidence": [
    "# Qwen3.8-Max arrives as Qwen's first open-weight Max-class model",
    "Qwen announced Qwen3.8-Max on August 3, 2026, describing it as the most capable model in the Qwen family to date and the first Qwen-Max-class model to have its weights open-sourced."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.8-Max arrives as Qwen's first open-weight Max-class model",
    "Qwen announced Qwen3.8-Max on August 3, 2026, describing it as the most capable model in the Qwen family to date and the first Qwen-Max-class model to have its weights open-sourced."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.8-Max arrives as Qwen's first open-weight Max-class model",
    "Qwen announced Qwen3.8-Max on August 3, 2026, describing it as the most capable model in the Qwen family to date and the first Qwen-Max-class model to have its weights open-sourced."
   ]
  },
  {
   "url": "https://temperaturezero.com/2026/05/18/meta-open-sourced-llama-4-kept-muse-spark",
   "title": "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
   "published_at": "2026-05-18T13:27:05",
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "2026-04-08",
   "parameters": null,
   "context_window": "ten-million-token",
   "license": null,
   "significance": "outperforms Gemma 3 and Gemini 2.0 Flash-Lite in multimodal benchmarks",
   "organization_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "model_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "release_date_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "Scout offers a ten-million-token context window and outperforms Gemma 3 and Gemini 2.0 Flash-Lite in multimodal benchmarks."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "Scout offers a ten-million-token context window and outperforms Gemma 3 and Gemini 2.0 Flash-Lite in multimodal benchmarks."
   ]
  },
  {
   "url": "https://temperaturezero.com/2026/05/18/meta-open-sourced-llama-4-kept-muse-spark",
   "title": "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
   "published_at": "2026-05-18T13:27:05",
   "organization": "Meta",
   "model": "Llama 4 Maverick",
   "release_date": "2026-04-08",
   "parameters": "seventeen billion active parameters",
   "context_window": null,
   "license": null,
   "significance": "matches GPT-4o and Gemini 2.0 Flash at an LMArena ELO score of 1,417",
   "organization_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "model_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "release_date_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "On April 8, Meta released two models."
   ],
   "parameters_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "Maverick matches GPT-4o and Gemini 2.0 Flash at an LMArena ELO score of 1,417 while running on seventeen billion active parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Open-Sourced LLaMA 4. It Kept Muse Spark.",
    "Maverick matches GPT-4o and Gemini 2.0 Flash at an LMArena ELO score of 1,417 while running on seventeen billion active parameters."
   ]
  },
  {
   "url": "https://techjacksolutions.com/vendor/mistral",
   "title": "Mistral AI: funding, releases, security",
   "published_at": "2026-08-23T12:45:40",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "built for Lean 4, the proof assistant used in formal software verification",
   "organization_evidence": [
    "## What it has shipped",
    "Mistral AI released Leanstral 1.5 on July 2, 2026, an open-weights, Apache 2.0-licensed model built for Lean 4, the proof assistant used in formal\u2026"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What it has shipped",
    "Mistral AI released Leanstral 1.5 on July 2, 2026, an open-weights, Apache 2.0-licensed model built for Lean 4, the proof assistant used in formal\u2026"
   ],
   "parameters_evidence": [
    "## What it has shipped",
    "- **Leanstral-1.5-119B-A6B**\nOpen-weights Lean 4 proof assistant model with 119B total parameters and 6.5B active per token via MoE; 256k context window; Apache 2.0 licensed; designed for formal software verification\u2026"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What it has shipped",
    "- **Leanstral-1.5-119B-A6B**\nOpen-weights Lean 4 proof assistant model with 119B total parameters and 6.5B active per token via MoE; 256k context window; Apache 2.0 licensed; designed for formal software verification\u2026"
   ]
  },
  {
   "url": "https://startupfortune.com/moonshot-ai-wants-microsoft-amazon-and-google-to-pay-it-a-cut-of-kimi-k3",
   "title": "Moonshot AI Wants Microsoft, Amazon and Google to Pay It a Cut of Kimi K3",
   "published_at": "2026-08-26T22:58:18",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-17",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Artificial Analysis found its results on demanding, multi-step reasoning problems comparable to OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.8. Arena.ai ranked it first for building web interfaces. It's the largest open-weight model publicly available, full stop.",
   "organization_evidence": [
    "Beijing's Moonshot AI wants Microsoft, Amazon and Google to hand over up to 30% of whatever they earn hosting Kimi K3, its free, open-weight AI model.",
    "Moonshot released the model's weights for free download on July 17, 2026: 2.8 trillion parameters, a million-token context window."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot released the model's weights for free download on July 17, 2026: 2.8 trillion parameters, a million-token context window."
   ],
   "parameters_evidence": [
    "Moonshot released the model's weights for free download on July 17, 2026: 2.8 trillion parameters, a million-token context window."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Artificial Analysis found its results on demanding, multi-step reasoning problems comparable to OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.8.",
    "Arena.ai ranked it first for building web interfaces.",
    "It's the largest open-weight model publicly available, full stop."
   ]
  },
  {
   "url": "https://edgen.tech/fr/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/fr/author/news/alex-nguyen)\u00b7Aug 26 2026, 07:19",
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ]
  },
  {
   "url": "https://manilatimes.net/2026/08/26/tmt-newswire/pr-newswire/minimax-announces-first-half-2026-financial-results/2412439",
   "title": "MiniMax Announces First Half 2026 Financial Results",
   "published_at": "2026-08-26T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation",
   "organization_evidence": [
    "1H2026 Business Review",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "1H2026 Business Review",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-07/14-nvidia-releases-nemotron-labs-twotower-a-diffusion-language-model-that-doubles-generation-throughput-without-retraining",
   "title": "NVIDIA Releases Nemotron-Labs-TwoTower, a Diffusion Language Model That Doubles Generation Throughput Without Retraining",
   "published_at": "2026-07-14T06:27:10",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-25",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Reaches 2.42x faster generation at 98.7% of baseline quality without needing to retrain the whole backbone.",
   "organization_evidence": [
    "# NVIDIA Releases Nemotron-Labs-TwoTower, a Diffusion Language Model That Doubles Generation Throughput Without Retraining"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model, formally titled \u201cNemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context,\u201d was authored by NVIDIA researchers Fitsum Reda, John Kamalu, Roger Waleffe, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro, and first posted to [arXiv](https://arxiv.org/abs/2606.26493) on June 25, 2026, with a revised version following on June 29.",
    "# NVIDIA Releases Nemotron-Labs-TwoTower, a Diffusion Language Model That Doubles Generation Throughput Without Retraining"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA's open-weight TwoTower model reuses a frozen Nemotron backbone and trains only a denoiser tower, reaching 2.42x faster generation at 98.7% of baseline quality.",
    "# NVIDIA Releases Nemotron-Labs-TwoTower, a Diffusion Language Model That Doubles Generation Throughput Without Retraining"
   ]
  },
  {
   "url": "https://edgen.tech/fr/news/post/zai-confirms-ox-alpha-as-glm-model-with-usage-2x-deepseek",
   "title": "Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
   "published_at": "2026-08-26T12:16:39",
   "organization": "Z.AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ox Alpha's OpenRouter usage more than doubled DeepSeek's, the marketplace's biggest launch.",
   "organization_evidence": [
    "Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "The mystery AI model that swept to the top of OpenRouter's usage charts is a new GLM-family release from China's Z.AI, which confirmed the launch Wednesday and said it will release the model's weights tonight."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/fr/author/news/alex-nguyen)\u00b7Aug 26 2026, 05:16",
    "The mystery AI model that swept to the top of OpenRouter's usage charts is a new GLM-family release from China's Z.AI, which confirmed the launch Wednesday and said it will release the model's weights tonight."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "Ox Alpha's OpenRouter usage more than doubled DeepSeek's, the marketplace's biggest launch.",
    "Ox Alpha's usage more than doubled DeepSeek's on OpenRouter's leaderboard, making it the biggest launch in the marketplace's history, according to the company."
   ]
  },
  {
   "url": "https://news18.com/amp/tech/chatgpt-vs-gemini-vs-qwen-how-to-use-the-new-chinese-ai-model-pricing-and-all-details-10291596.html",
   "title": "What Is Qwen And How To Use The New 'Chinese' AI Model- Pricing And All Details",
   "published_at": "2026-08-25T03:56:15",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "became the world\u2019s most downloaded AI system by January 2026",
   "organization_evidence": [
    "One widely cited developer piece calls the Qwen family \u201cthe open-source story of 2026,\" noting Qwen3-Coder became the world\u2019s most downloaded AI system by January 2026, not because it tops the leaderboard, but because a huge share of teams now run 60 to 80 percent of their coding-agent traffic on models like it locally, only escalating the genuinely hard 20 percent to a frontier API.",
    "Qwen is a family of AI models developed by Alibaba Cloud, spanning general-purpose chat models, dedicated coding models, and specialised variants for vision, audio and reasoning."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "One widely cited developer piece calls the Qwen family \u201cthe open-source story of 2026,\" noting Qwen3-Coder became the world\u2019s most downloaded AI system by January 2026, not because it tops the leaderboard, but because a huge share of teams now run 60 to 80 percent of their coding-agent traffic on models like it locally, only escalating the genuinely hard 20 percent to a frontier API."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "One widely cited developer piece calls the Qwen family \u201cthe open-source story of 2026,\" noting Qwen3-Coder became the world\u2019s most downloaded AI system by January 2026, not because it tops the leaderboard, but because a huge share of teams now run 60 to 80 percent of their coding-agent traffic on models like it locally, only escalating the genuinely hard 20 percent to a frontier API."
   ]
  },
  {
   "url": "https://link.springer.com/article/10.1007/s13347-026-01171-9",
   "title": "Rules Without Models: Why Europe Must Build the AI It Regulates",
   "published_at": "2026-08-25T00:00:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## 1 Introduction: A European Decade of Rules Without Models",
    "At the beginning of August 2026, nothing had been decided, but the direction was clear.",
    "Where the software or a derivative work is used in a commercial product or service with more than 100 million monthly active users or more than 20 million US dollars in monthly revenue, \u2018Kimi K3\u2019 must be displayed prominently on the user interface of that product or service (Moonshot [2026b](/article/10.1007/s13347-026-01171-9#ref-CR53))."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## 1 Introduction: A European Decade of Rules Without Models",
    "At the beginning of August 2026, nothing had been decided, but the direction was clear.",
    "Kimi K3, whose weights were published on 27 July 2026, was released under a bespoke licence: MIT-style permissions over software that it defines to include the weights, plus two conditions beyond the notice requirement that an MIT licence would carry."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://gemma4.run/de/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-08T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B MoE, 31B Dense",
   "context_window": null,
   "license": null,
   "significance": "AIME math benchmark score soared to 89.2% from Gemma 3's 20.8%, coding performance (LiveCodeBench) jumped to 80.0%, cumulative downloads surpassed 400 million, and it marked the first adoption of the Apache 2.0 open-source license.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "On the AIME 2026 math benchmark, the 31B model soared from Gemma 3's 20.8% to **89.2%**, while coding performance (LiveCodeBench) jumped from 29.1% to 80.0%, signaling Gemma's transition from \"usable\" to \"competitive with top-tier large models.\""
   ]
  },
  {
   "url": "https://mlq.ai/news/v2/minimax-releases-m3-an-open-weight-model-with-1m-token-context-and-frontier-coding-scores",
   "title": "MiniMax Releases M3, an Open-Weight Model With 1M-Token Context and Frontier Coding Scores",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 scores 59% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro but trailing Anthropic's Opus 4.7",
   "organization_evidence": [
    "MiniMax Releases M3, an Open-Weight Model With 1M-Token Context and Frontier Coding Scores",
    "Chinese AI startup MiniMax on June 1 released M3, a large language model it describes as the first open-weight system to combine frontier-level coding performance, a one-million-token context window, and native multimodal capabilities in a single model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Jun 3, 2026\u00b712:30 PM\u00b7by MLQ Agent\u00b73 min read",
    "MiniMax Releases M3, an Open-Weight Model With 1M-Token Context and Frontier Coding Scores",
    "Chinese AI startup MiniMax on June 1 released M3, a large language model it describes as the first open-weight system to combine frontier-level coding performance, a one-million-token context window, and native multimodal capabilities in a single model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Releases M3, an Open-Weight Model With 1M-Token Context and Frontier Coding Scores",
    "- MiniMax M3 scores 59% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro but trailing Anthropic's Opus 4.7 [[1]](https://www.minimax.io/blog/minimax-m3)"
   ]
  },
  {
   "url": "https://benchr.org/articles/best-free-coding-model",
   "title": "The best free coding model: DeepSeek vs Qwen vs Kimi",
   "published_at": "2026-05-30T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "80.2",
   "organization_evidence": [
    "The best free coding model: DeepSeek vs Qwen vs Kimi",
    "Three families lead the open-weight coding pack right now: DeepSeek out of China, Alibaba's Qwen, and Moonshot AI's Kimi.",
    "If you go by the strongest officially published number, Moonshot AI's Kimi K2.6 wins."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Comparison\u00b7May 2026",
    "Under the hood it's a 1-trillion-parameter Mixture-of-Experts model with 32 billion active per token, released in late April 2026 under a Modified MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The best free coding model: DeepSeek vs Qwen vs Kimi",
    "## Kimi K2.6: the score leader",
    "If you go by the strongest officially published number, Moonshot AI's Kimi K2.6 wins.",
    "It posts 80.2 on SWE-bench Verified, the benchmark that scores real GitHub issue resolution, plus 58.6 on the tougher SWE-bench Pro and 76.7 on the multilingual variant."
   ]
  },
  {
   "url": "https://benchr.org/articles/best-free-coding-model",
   "title": "The best free coding model: DeepSeek vs Qwen vs Kimi",
   "published_at": "2026-05-30T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "The best free coding model: DeepSeek vs Qwen vs Kimi",
    "Three families lead the open-weight coding pack right now: DeepSeek out of China, Alibaba's Qwen, and Moonshot AI's Kimi.",
    "## DeepSeek V4: the long-context free chat",
    "DeepSeek V4 landed in April 2026 in two MIT-licensed variants, V4-Pro at 1.6 trillion parameters and a lighter V4-Flash, both with a full 1-million-token context window, the longest in this group."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Comparison\u00b7May 2026",
    "DeepSeek V4 landed in April 2026 in two MIT-licensed variants, V4-Pro at 1.6 trillion parameters and a lighter V4-Flash, both with a full 1-million-token context window, the longest in this group."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://benchr.org/articles/best-free-coding-model",
   "title": "The best free coding model: DeepSeek vs Qwen vs Kimi",
   "published_at": "2026-05-30T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "73.4%",
   "organization_evidence": [
    "The best free coding model: DeepSeek vs Qwen vs Kimi",
    "Three families lead the open-weight coding pack right now: DeepSeek out of China, Alibaba's Qwen, and Moonshot AI's Kimi.",
    "## Qwen: the one you can run yourself",
    "The newest entry, Qwen3.6-35B-A3B from April 2026, scores 73.4% on SWE-bench Verified while activating just 3 billion parameters per token out of 35 billion total."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Comparison\u00b7May 2026",
    "The newest entry, Qwen3.6-35B-A3B from April 2026, scores 73.4% on SWE-bench Verified while activating just 3 billion parameters per token out of 35 billion total."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The best free coding model: DeepSeek vs Qwen vs Kimi",
    "## Qwen: the one you can run yourself",
    "The newest entry, Qwen3.6-35B-A3B from April 2026, scores 73.4% on SWE-bench Verified while activating just 3 billion parameters per token out of 35 billion total."
   ]
  },
  {
   "url": "https://earlyterms.com/term/qwen",
   "title": "Qwen (\u901a\u4e49\u5343\u95ee) is Alibaba Cloud's open-weight large-language-model family, shipped by the Tongyi Lab since August",
   "published_at": "2023-08-03T10:27:29",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "a 3B-active MoE that Simon Willison showed outdraws Claude Opus 4.7 on SVG benchmarks while running on a MacBook Pro",
   "organization_evidence": [
    "# Qwen",
    "Qwen (\u901a\u4e49\u5343\u95ee) is Alibaba Cloud's open-weight large-language-model family, shipped by the Tongyi Lab since August 2023.",
    "The family re-entered the news in April 2026 when Alibaba [shut down the Qwen Code OAuth free tier on April 15](https://decrypt.co/364501/alibaba-shuts-down-free-tier-qwen-code) and released the open-weight [Qwen3.6-35B-A3B](https://qwen.ai/blog?id=qwen3.6-35b-a3b) the next day \u2014 a 3B-active MoE that Simon Willison showed outdraws Claude Opus 4.7 on SVG benchmarks while running on a MacBook Pro."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen",
    "The family re-entered the news in April 2026 when Alibaba [shut down the Qwen Code OAuth free tier on April 15](https://decrypt.co/364501/alibaba-shuts-down-free-tier-qwen-code) and released the open-weight [Qwen3.6-35B-A3B](https://qwen.ai/blog?id=qwen3.6-35b-a3b) the next day \u2014 a 3B-active MoE that Simon Willison showed outdraws Claude Opus 4.7 on SVG benchmarks while running on a MacBook Pro.",
    "Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All",
    "35B-total / 3B-active MoE, SWE-bench Verified 73.4, Apache 2.0, native Claude Code integration.",
    "Apr 16, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen",
    "The family re-entered the news in April 2026 when Alibaba [shut down the Qwen Code OAuth free tier on April 15](https://decrypt.co/364501/alibaba-shuts-down-free-tier-qwen-code) and released the open-weight [Qwen3.6-35B-A3B](https://qwen.ai/blog?id=qwen3.6-35b-a3b) the next day \u2014 a 3B-active MoE that Simon Willison showed outdraws Claude Opus 4.7 on SVG benchmarks while running on a MacBook Pro."
   ]
  },
  {
   "url": "https://translate.google.com/website?client=srp&hl=es&sl=en&tl=es&u=https://huggingface.co/gg-hf-gg/gemma-4-E2B-it",
   "title": "google/gemma-4-E2B-it \u00b7 Hugging Face",
   "published_at": "2026-08-20T21:46:18",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 introduces key capability and architectural advancements including reasoning, extended multimodalities, diverse & efficient architectures, optimized for on-device, increased context window, enhanced coding & agentic capabilities, and native system prompt support.",
   "organization_evidence": [
    "Gemma is a family of open models built by Google DeepMind."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[Technical Report](https://translate.google.com/website?sl=en&tl=es&hl=es&client=srp&u=https://arxiv.org/abs/2607.02770)",
    "@misc{gemmateam2026gemma4,\n      title={Gemma 4 Technical Report},\n      author={Gemma Team},\n      year={2026},\n      eprint={2607.02770},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https://arxiv.org/abs/2607.02770},\n}"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 introduces key **capability and architectural advancements**:",
    "- **Reasoning** \u2013 All models in the family are designed as highly capable reasoners, with configurable thinking modes.",
    "- **Extended Multimodalities** \u2013 Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B, E4B, and 12B models).",
    "- **Diverse & Efficient Architectures** \u2013 Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.",
    "- **Optimized for On-Device** \u2013 Smaller models are specifically designed for efficient local execution on laptops and mobile devices.",
    "- **Increased Context Window** \u2013 The small models feature a 128K context window, while the medium models support 256K.",
    "- **Enhanced Coding & Agentic Capabilities** \u2013 Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.",
    "- **Native System Prompt Support** \u2013 Gemma 4 introduces native support for the `system` role, enabling more structured and controllable conversations."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/moonshot-kimi-k3-beats-fable-5-with-openweight-sparse-moe",
   "title": "Moonshot Kimi K3 Beats Fable 5 With Open-Weight Sparse MoE",
   "published_at": "2026-07-19T00:27:29",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "#1 on LMArena Frontend Code Arena, beats Claude Fable 5, 6.3x faster decoding in 1M-token contexts via Kimi Delta Attention",
   "organization_evidence": [
    "Model Release",
    "# Moonshot Kimi K3 Beats Fable 5 With Open-Weight Sparse MoE",
    "Moonshot AI just released the largest open-weight AI model ever built,and it landed at #1 for production coding within hours."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Moonshot Kimi K3 Beats Fable 5 With Open-Weight Sparse MoE",
    "The weights ship under a Modified-MIT-style open license, with full public release planned for **July 27, 2026**,exactly 11 days from announcement, a signal that Moonshot is comfortable with immediate commoditization."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Moonshot Kimi K3 Beats Fable 5 With Open-Weight Sparse MoE",
    "- #1 on LMArena Frontend Code Arena beats Claude Fable 5 on production coding benchmark within 24 hours",
    "- 6.3x faster decoding in 1M-token contexts via Kimi Delta Attention removes latency penalty for real-time reasoning"
   ]
  },
  {
   "url": "https://nerdleveltech.com/glm-5-1-open-source-beats-gpt-coding-benchmarks",
   "title": "GLM-5.1: The Open-Source Model That Beat GPT-5.4",
   "published_at": "2026-04-19T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754 billion",
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4% on SWE-bench Pro, making it the first open-source model to ever lead that leaderboard, and sustained 8-hour autonomous execution.",
   "organization_evidence": [
    "# GLM-5.1: The Open-Source Model That Beat GPT-5.4",
    "Z.ai released GLM-5.1 on April 7, 2026 \u2014 a 754-billion-parameter open-weight model that scored 58.4% on SWE-bench Pro, edging past GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) to claim the top spot, making it the first open-source model to ever lead that leaderboard."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.1: The Open-Source Model That Beat GPT-5.4",
    "Z.ai released GLM-5.1 on April 7, 2026 \u2014 a 754-billion-parameter open-weight model that scored 58.4% on SWE-bench Pro, edging past GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) to claim the top spot, making it the first open-source model to ever lead that leaderboard."
   ],
   "parameters_evidence": [
    "# GLM-5.1: The Open-Source Model That Beat GPT-5.4",
    "Z.ai released GLM-5.1 on April 7, 2026 \u2014 a 754-billion-parameter open-weight model that scored 58.4% on SWE-bench Pro, edging past GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) to claim the top spot, making it the first open-source model to ever lead that leaderboard."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.1: The Open-Source Model That Beat GPT-5.4",
    "Z.ai released GLM-5.1 on April 7, 2026 \u2014 a 754-billion-parameter open-weight model that scored 58.4% on SWE-bench Pro, edging past GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) to claim the top spot, making it the first open-source model to ever lead that leaderboard."
   ]
  },
  {
   "url": "https://hokai.io/hub/models/minimax-m3",
   "title": "MiniMax M3: 1M Context & 59% SWE-Bench Pro (2026)",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59.0% on SWE-Bench Pro, beating GPT-5.5 and Gemini 3.1 Pro; 66.0% on Terminal-Bench 2.1; 74.2% on MCP Atlas; 70.06% on OSWorld-Verified; 15.6x faster decoding at 1M tokens via MiniMax Sparse Attention",
   "organization_evidence": [
    "MiniMax M3 is MiniMax's open-weight flagship, released June 1, 2026 with a 1,048,576-token context window, native text/image/video input, and a 59.0% SWE-Bench Pro score that beats GPT-5.5 and Gemini 3.1 Pro.",
    "MiniMax M3 is the latest large language model from MiniMax, a Shanghai-based AI lab listed on the Hong Kong Stock Exchange (0100.HK) since January 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 is MiniMax's open-weight flagship, released June 1, 2026 with a 1,048,576-token context window, native text/image/video input, and a 59.0% SWE-Bench Pro score that beats GPT-5.5 and Gemini 3.1 Pro.",
    "MiniMax released M3 on June 1, 2026, as a Mixture-of-Experts model with roughly 229.9 billion total parameters and 9.8 billion active parameters per token across 256 fine-grained experts."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 is MiniMax's open-weight flagship, released June 1, 2026 with a 1,048,576-token context window, native text/image/video input, and a 59.0% SWE-Bench Pro score that beats GPT-5.5 and Gemini 3.1 Pro.",
    "On MiniMax's own June 2026 release benchmarks, M3 scores 59.0% on SWE-Bench Pro, which the company says surpasses both GPT-5.5 and Gemini 3.1 Pro on the same test."
   ]
  },
  {
   "url": "https://cryptobriefing.com/nvidia-ceo-open-models-ai-growth",
   "title": "Nvidia CEO highlights role of open models in AI growth",
   "published_at": "2026-08-26T22:09:34",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "unveiled Nemotron 3.5 Lightning as another open-source offering, further expanding its portfolio of freely available models",
   "organization_evidence": [
    "Jensen Huang argues that open-weight AI models are expanding Nvidia's dominance as the only platform capable of running all frontier models",
    "Then on August 11, 2026, Nvidia unveiled Nemotron 3.5 Lightning as another open-source offering, further expanding its portfolio of freely available models."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Jensen Huang argues that open-weight AI models are expanding Nvidia's dominance as the only platform capable of running all frontier models",
    "Then on August 11, 2026, Nvidia unveiled Nemotron 3.5 Lightning as another open-source offering, further expanding its portfolio of freely available models."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Jensen Huang argues that open-weight AI models are expanding Nvidia's dominance as the only platform capable of running all frontier models",
    "Then on August 11, 2026, Nvidia unveiled Nemotron 3.5 Lightning as another open-source offering, further expanding its portfolio of freely available models."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/zai-confirms-ox-alpha-as-glm-model-with-usage-2x-deepseek",
   "title": "Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
   "published_at": "2026-08-26T12:16:39",
   "organization": "Z.AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ox Alpha's usage more than doubled DeepSeek's on OpenRouter's leaderboard, making it the biggest launch in the marketplace's history, and it passed 63 percent of 113 tasks on the DeepSWE benchmark.",
   "organization_evidence": [
    "# Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "The mystery AI model that swept to the top of OpenRouter's usage charts is a new GLM-family release from China's Z.AI, which confirmed the launch Wednesday and said it will release the model's weights tonight."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/author/news/alex-nguyen)\u00b7Aug 26 2026, 05:16",
    "# Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "The mystery AI model that swept to the top of OpenRouter's usage charts is a new GLM-family release from China's Z.AI, which confirmed the launch Wednesday and said it will release the model's weights tonight."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Z.AI confirms Ox Alpha as GLM model with usage 2x DeepSeek",
    "Ox Alpha's usage more than doubled DeepSeek's on OpenRouter's leaderboard, making it the biggest launch in the marketplace's history, according to the company.",
    "On the DeepSWE benchmark, which scores how often a coding agent resolves real GitHub issues on its first attempt, Ox Alpha passed 63 percent of 113 tasks \u2014 roughly level with OpenAI's GPT-5.6 Sol, though a viral 10-task sample had earlier shown it at 80 percent."
   ]
  },
  {
   "url": "https://memeburn.com/qwen-3-8-flash-next-qwen-4-preview",
   "title": "Qwen 3.8-Flash-Next From Alibaba: The First Look at Qwen 4",
   "published_at": "2026-08-27T18:01:01",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "125B",
   "context_window": null,
   "license": null,
   "significance": "A preview of the Qwen 4 architecture utilizing mixture-of-experts to activate only 6 billion parameters per token, delivering near-frontier capabilities on commodity hardware.",
   "organization_evidence": [
    "Alibaba Just Dropped a Preview of Qwen 4 \u2014 We Can Run It on Our Hardware",
    "Alibaba's Qwen 3.8-Flash-Next is a 125 billion parameter model that activates only 6 billion per token \u2014 a preview of the Qwen 4 architecture built to run frontier-level AI on hardware you can actually afford."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba Just Dropped a Preview of Qwen 4 \u2014 We Can Run It on Our Hardware",
    "Alibaba releases Qwen 3.8-Flash-Next on August 26, 2026 \u2014 a 125B total parameter model with only 6B active per token using mixture-of-experts (MoE)."
   ],
   "parameters_evidence": [
    "Alibaba Just Dropped a Preview of Qwen 4 \u2014 We Can Run It on Our Hardware",
    "Alibaba releases Qwen 3.8-Flash-Next on August 26, 2026 \u2014 a 125B total parameter model with only 6B active per token using mixture-of-experts (MoE)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba Just Dropped a Preview of Qwen 4 \u2014 We Can Run It on Our Hardware",
    "Alibaba's Qwen 3.8-Flash-Next is a 125 billion parameter model that activates only 6 billion per token \u2014 a preview of the Qwen 4 architecture built to run frontier-level AI on hardware you can actually afford."
   ]
  },
  {
   "url": "https://aiproductivity.ai/news/google-gemma-4-open-models-apache-license",
   "title": "Google Releases Gemma 4: Four Open Models Under Apache 2.0 License",
   "published_at": "2026-04-03T12:51:47",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 among open models on the Arena AI text leaderboard. 256K token context window (roughly 600 pages of text).",
   "organization_evidence": [
    "Open Source Notable",
    "# Google Releases Gemma 4: Four Open Models Under Apache 2.0 License",
    "Google dropped four new open-weight AI models yesterday under the Gemma 4 family, and the biggest change has nothing to do with performance: they're all Apache 2.0 licensed now."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source Notable",
    "# Google Releases Gemma 4: Four Open Models Under Apache 2.0 License",
    "April 3, 20262 min read"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source Notable",
    "## The Four Models",
    "- **31B Dense** - The flagship. Runs unquantized on a single 80GB H100 GPU, or at 4-bit precision on consumer cards like the RTX 4090. Ranked #3 among open models on the Arena AI text leaderboard. 256K token context window (roughly 600 pages of text)."
   ]
  },
  {
   "url": "https://aiangst.com/china/kimi-k3-moonshot-fable-5",
   "title": "China's Moonshot Just Built a Model That Claims to Rival Anthropic's Best. Here's How Close It Really Gets.",
   "published_at": "2026-07-17T08:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked first on web interface building and second overall, behind Fable 5 but ahead of GPT-5.6 Sol, while leading on benchmarks like BrowseComp, SWE Marathon, and OmniDocBench.",
   "organization_evidence": [
    "China's Moonshot Just Built a Model That Claims to Rival Anthropic's Best. Here's How Close It Really Gets.",
    "Kimi K3 is Moonshot's newest flagship model, launched July 16, 2026, and the company is calling it the world's first **open 3T-class model**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "China's Moonshot Just Built a Model That Claims to Rival Anthropic's Best. Here's How Close It Really Gets.",
    "Kimi K3 is Moonshot's newest flagship model, launched July 16, 2026, and the company is calling it the world's first **open 3T-class model**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "China's Moonshot Just Built a Model That Claims to Rival Anthropic's Best. Here's How Close It Really Gets.",
    "Third-party benchmarking from Artificial Analysis found K3's performance comparable to GPT-5.5 and Claude Opus 4.8, particularly on complex multi-step tasks, while ranking first on web interface building and second overall, behind Fable 5 but ahead of GPT-5.6 Sol.",
    "The company's technical documentation states K3 leads on benchmarks like BrowseComp, SWE Marathon, and OmniDocBench, but trails Fable 5 on FrontierSWE and HLE-Full, two tests built around frontier-level reasoning."
   ]
  },
  {
   "url": "https://prnewswire.com/apac/news-releases/minimax-announces-first-half-2026-financial-results-302860492.html",
   "title": "MiniMax Announces First Half 2026 Financial Results",
   "published_at": "2026-08-26T10:55:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-07-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation",
   "organization_evidence": [
    "MiniMax Announces First Half 2026 Financial Results",
    "MiniMax Group Inc. (\"MiniMax\" or the \"Company\"; HKEX: 00100), a leading global artificial intelligence company, today announced its unaudited financial results for the six months ended June 30, 2026.",
    "**1H2026 Business Review**",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax Announces First Half 2026 Financial Results",
    "HONG KONG, Aug. 26, 2026 /PRNewswire/ -- MiniMax Group Inc. (\"MiniMax\" or the \"Company\"; HKEX: 00100), a leading global artificial intelligence company, today announced its unaudited financial results for the six months ended June 30, 2026.",
    "**1H2026 Business Review**",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Announces First Half 2026 Financial Results",
    "**1H2026 Business Review**",
    "Shortly after the Reporting Period, we also released MiniMax H3 with open weights, advancing video generation for commercial creation and widening the paths for enterprise deployment and developer innovation."
   ]
  },
  {
   "url": "https://digitalapplied.com/blog/qwen-closed-flagship-pivot-open-weight-retreat-2026",
   "title": "Qwen's Closed-Flagship Pivot: The Open-Weight Retreat",
   "published_at": "2026-07-21T23:00:00",
   "organization": "poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "On July 21 \u2014 the same day Qwen-Image-3.0 landed closed \u2014 [poolside\u2019s Laguna S 2.1, released the same week](/blog/poolside-laguna-s-2-1-open-weight-coding-agent-launch), went the other way: a 118B-total / 8B-active MoE with weights on Hugging Face immediately, under the OpenMDW-1.1 license.",
    "## 05 \u2014 The ContrastThe same 72 hours, the *opposite* direction.",
    "The West needs open-weight models it can trust, run, and build on."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On July 21 \u2014 the same day Qwen-Image-3.0 landed closed \u2014 [poolside\u2019s Laguna S 2.1, released the same week](/blog/poolside-laguna-s-2-1-open-weight-coding-agent-launch), went the other way: a 118B-total / 8B-active MoE with weights on Hugging Face immediately, under the OpenMDW-1.1 license.",
    "## 05 \u2014 The ContrastThe same 72 hours, the *opposite* direction.",
    "The West needs open-weight models it can trust, run, and build on."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://news.airosetta.com/en/news/zhipu-ai-glm-5-2-1m-context",
   "title": "Zhipu AI Releases GLM 5.2: Open-source with 1M Context Window and Enhanced Agentic Capabilities",
   "published_at": "2026-06-15T07:55:07",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 is described by the company as \"the most capable open\u2011source model Zhipu has released to date\", with a focus on long\u2011context understanding and long\u2011horizon agentic capabilities.",
   "organization_evidence": [
    "# Zhipu AI Releases GLM 5.2: Open-source with 1M Context Window and Enhanced Agentic Capabilities",
    "On June 13, 2026, Zhipu AI officially announced that **GLM 5.2** has been fully rolled out to all GLM Coding Plan users (Lite/Pro/Max/Team) and plans to open\u2011source it next week under the MIT license, with an API to be launched simultaneously."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI Releases GLM 5.2: Open-source with 1M Context Window and Enhanced Agentic Capabilities",
    "On June 13, 2026, Zhipu AI officially announced that **GLM 5.2** has been fully rolled out to all GLM Coding Plan users (Lite/Pro/Max/Team) and plans to open\u2011source it next week under the MIT license, with an API to be launched simultaneously."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI Releases GLM 5.2: Open-source with 1M Context Window and Enhanced Agentic Capabilities",
    "This marks another major iteration in the GLM\u20115 series; GLM 5.2 is described by the company as \"the most capable open\u2011source model Zhipu has released to date\", with a focus on long\u2011context understanding and long\u2011horizon agentic capabilities."
   ]
  },
  {
   "url": "https://codersera.com/blog/minimax-m3-developer-guide",
   "title": "MiniMax M3 Developer Guide: MSA, 1M Context, Pricing",
   "published_at": "2026-06-01T12:43:18",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 launched June 1, 2026 as the first open-weight model combining frontier coding, 1M-token context, and native multimodal input.",
   "organization_evidence": [
    "# MiniMax M3: Developer Guide to the Open-Weight 1M-Context Frontier",
    "MiniMax M3 is a frontier large language model released by Shanghai AI lab MiniMax on **June 1, 2026** (with OpenRouter listing the model under the `minimax-m3-20260531` dated slug)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3: Developer Guide to the Open-Weight 1M-Context Frontier",
    "MiniMax M3 launched June 1, 2026 as the first open-weight model combining frontier coding, 1M-token context, and native multimodal input."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3: Developer Guide to the Open-Weight 1M-Context Frontier",
    "MiniMax M3 launched June 1, 2026 as the first open-weight model combining frontier coding, 1M-token context, and native multimodal input. Here is the developer-grade breakdown: architecture, benchmarks, pricing, and code."
   ]
  },
  {
   "url": "https://metirai.com/blog/deepseek-v4-open-weight-frontier-moe-2026",
   "title": "DeepSeek V4 Reaches General Availability: An Open Frontier Model at a Seventeenth of the Price",
   "published_at": "2026-07-25T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-24",
   "parameters": "1.6T total parameters (49B active per token)",
   "context_window": null,
   "license": null,
   "significance": "Reaches general availability as an open-weight model family with an MIT license, scoring ~80.6% on SWE-bench Verified (highest open-weights entry) and featuring a 1 million token context window.",
   "organization_evidence": [
    "DeepSeek V4 Reaches General Availability: An Open Frontier Model at a Seventeenth of the Price",
    "![DeepSeek logo](/logos/deepseek.png)DeepSeek"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Reaches General Availability: An Open Frontier Model at a Seventeenth of the Price",
    "DeepSeek V4 moved from a grayscale preview to general availability in late July 2026, retiring its legacy aliases on July 24."
   ],
   "parameters_evidence": [
    "DeepSeek V4 Reaches General Availability: An Open Frontier Model at a Seventeenth of the Price",
    "V4-Pro is the large one, with 1.6 trillion total parameters of which about 49 billion fire on any given token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Reaches General Availability: An Open Frontier Model at a Seventeenth of the Price",
    "On SWE-bench Verified, a test of whether a model can resolve real GitHub issues, the strongest configuration reported for the family, V4-Pro-Max, scores about 80.6 percent."
   ]
  },
  {
   "url": "https://tokensboutique.com/blog/qwen3-8-flash-pricing-context-and-api",
   "title": "Qwen3.8 Flash pricing, context window, and API details",
   "published_at": "2026-08-26T00:00:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": "125B-parameter main network with 6B parameters activated per token, plus 51B N-gram embedding parameters",
   "context_window": null,
   "license": null,
   "significance": "Open-weight multimodal Mixture-of-Experts model and an early look at the architecture being developed for Qwen4, improving coding and office-task performance",
   "organization_evidence": [
    "## Qwen3.8 Flash pricing, context window, and API details",
    "Qwen has released **Qwen3.8-Flash-Next**, an open-weight multimodal Mixture-of-Experts model and an early look at the architecture being developed for Qwen4."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Qwen3.8 Flash pricing, context window, and API details",
    "Steven \u00b7 2026-08-26",
    "Qwen has released **Qwen3.8-Flash-Next**, an open-weight multimodal Mixture-of-Experts model and an early look at the architecture being developed for Qwen4."
   ],
   "parameters_evidence": [
    "## Qwen3.8 Flash pricing, context window, and API details",
    "According to [Qwen's official launch post](https://qwen.ai/blog?id=qwen3.8-flash-next), the model has a 125B-parameter main network with 6B parameters activated per token, plus 51B N-gram embedding parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Qwen3.8 Flash pricing, context window, and API details",
    "Qwen has released **Qwen3.8-Flash-Next**, an open-weight multimodal Mixture-of-Experts model and an early look at the architecture being developed for Qwen4.",
    "Qwen says it was trained at roughly one-ninth the cost of Qwen3.7 Plus while improving coding and office-task performance."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/mistral-medium-3-5-gives-us-a-128b-dense-model-with-open-wei-b849e11e",
   "title": "Mistral Medium 3.5 Gives Us a 128B Dense Model With Open Weights and Cloud Agents",
   "published_at": "2026-04-30T02:56:49",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
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   "license": null,
   "significance": "scoring 77.6% on SWE-Bench Verified and deployable on just four GPUs",
   "organization_evidence": [
    "Mistral Medium 3.5 Gives Us a 128B Dense Model With Open Weights and Cloud Agents",
    "Mistral AI released Mistral Medium 3.5, a 128B dense model with open weights, 256K context, and integrated remote coding agents that run asynchronously in the cloud \u2014 scoring 77.6% on SWE-Bench Verified and deployable on just four GPUs."
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   "release_date_evidence": [
    "Mistral Medium 3.5 Gives Us a 128B Dense Model With Open Weights and Cloud Agents",
    "Mistral AI launched Mistral Medium 3.5 on April 29, a 128-billion parameter dense model with a 256,000-token context window, open weights under a modified MIT license, and integrated remote coding agents that run asynchronously in the cloud."
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   "parameters_evidence": [
    "Mistral Medium 3.5 Gives Us a 128B Dense Model With Open Weights and Cloud Agents",
    "Mistral AI released Mistral Medium 3.5, a 128B dense model with open weights, 256K context, and integrated remote coding agents that run asynchronously in the cloud \u2014 scoring 77.6% on SWE-Bench Verified and deployable on just four GPUs."
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    "Mistral Medium 3.5 Gives Us a 128B Dense Model With Open Weights and Cloud Agents",
    "Mistral AI released Mistral Medium 3.5, a 128B dense model with open weights, 256K context, and integrated remote coding agents that run asynchronously in the cloud \u2014 scoring 77.6% on SWE-Bench Verified and deployable on just four GPUs."
   ]
  },
  {
   "url": "https://aiproductivity.ai/news/google-gemma-4-12b-open-model-release-hugging-face",
   "title": "Google DeepMind Releases Gemma 4 12B Open Model on Hugging Face",
   "published_at": "2026-06-03T15:57:33",
   "organization": "Google DeepMind",
   "model": "Gemma 4 12B",
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   "parameters": "12 billion",
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   "organization_evidence": [
    "Open Source Notable",
    "Google DeepMind Releases Gemma 4 12B Open Model on Hugging Face",
    "Google DeepMind released [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B) on Hugging Face on June 3, adding a 12-billion parameter model to its open-weights model family."
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   "model_evidence": [
    "Open Source Notable",
    "Google DeepMind Releases Gemma 4 12B Open Model on Hugging Face",
    "Google DeepMind released [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B) on Hugging Face on June 3, adding a 12-billion parameter model to its open-weights model family."
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   "release_date_evidence": [
    "Open Source Notable",
    "June 3, 2026 1 min read",
    "Google DeepMind released [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B) on Hugging Face on June 3, adding a 12-billion parameter model to its open-weights model family."
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   "parameters_evidence": [
    "Open Source Notable",
    "Google DeepMind Releases Gemma 4 12B Open Model on Hugging Face",
    "Google DeepMind released [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B) on Hugging Face on June 3, adding a 12-billion parameter model to its open-weights model family."
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  },
  {
   "url": "https://bitsminds.com/news/zhipu-glm-5-2-1m-coding-model-mit-2026",
   "title": "Zhipu Releases GLM-5.2: a 744B-Parameter, 1M-Token Coding Model Under a Full MIT License",
   "published_at": "2026-06-16T19:58:16",
   "organization": "Zhipu",
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   "license": null,
   "significance": "Either way, the release continues a clear 2026 trend: [Chinese labs are setting the pace in open weights](/news/chinese-models-60-percent-openrouter-2026), shipping permissively licensed, coding-focused models with frontier-scale context windows faster than their Western counterparts will open-source anything comparable.",
   "organization_evidence": [
    "# Zhipu Releases GLM-5.2: a 744B-Parameter, 1M-Token Coding Model Under a Full MIT License",
    "Chinese AI lab **Zhipu** (known internationally as Z.ai) has released **GLM-5.2**, the new flagship in its open-model family \u2014 and is putting it out under a fully permissive **MIT license**."
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   "model_evidence": null,
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   "significance_evidence": [
    "# Zhipu Releases GLM-5.2: a 744B-Parameter, 1M-Token Coding Model Under a Full MIT License",
    "Either way, the release continues a clear 2026 trend: [Chinese labs are setting the pace in open weights](/news/chinese-models-60-percent-openrouter-2026), shipping permissively licensed, coding-focused models with frontier-scale context windows faster than their Western counterparts will open-source anything comparable."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/GLM-4.5?no_redirect=true",
   "title": "GLM (AI)",
   "published_at": "2026-08-26T23:05:36",
   "organization": "Z.ai",
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   "release_date": "2026-08-26",
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   "organization_evidence": [
    "GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai.",
    "Z.ai released GLM-5.3 on 14 August 2026 and stated that the weights for the model would be made available approximately two weeks after the release date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": null
  },
  {
   "url": "https://ai-tldr.dev/releases/ai2-emo-1b14b",
   "title": "Ai2 EMO \u2014 14B MoE That Keeps Near-Full Accuracy When Run on Just 12.5% of Its Experts",
   "published_at": "2026-05-08T12:00:00",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-05-08",
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   "significance": "trained a 1B-active, 14B-total MoE so that document-level routing makes expert subsets specialise into real semantic domains",
   "organization_evidence": [
    "Allen Institute for AI \u00b7 2026-05-08 \u00b7 notable",
    "EMO is an open Mixture-of-Experts language model from Allen AI's OLMo team."
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   "release_date_evidence": [
    "Allen Institute for AI \u00b7 2026-05-08 \u00b7 notable"
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   "significance_evidence": [
    "Ai2 trained a 1B-active, 14B-total MoE so that document-level routing makes expert subsets specialise into real semantic domains."
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  },
  {
   "url": "https://marktechpost.com/2026/08/26/alibabas-qwen-team-releases-qwen3-8-flash-next-a-125b-multimodal-moe-with-6b-active-parameters-previewing-the-qwen4-architecture",
   "title": "Alibaba\u2019s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture",
   "published_at": "2026-08-26T15:20:00",
   "organization": "Alibaba",
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   "release_date": "2026-08",
   "parameters": "125B backbone + 51B N-gram embedding table and a 4B multi-token prediction module",
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   "significance": "Pairs a 125B backbone with 51B N-gram embedding table and 4B multi-token prediction module, achieving training cost at roughly one-ninth that of Qwen3.7-Plus.",
   "organization_evidence": [
    "Alibaba\u2019s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture",
    "Alibaba\u2019s Qwen team has released [Qwen3.8-Flash-Next](https://qwen.ai/blog?id=qwen3.8-flash-next), an open-weight multimodal Mixture-of-Experts model built for cost per token."
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   "release_date_evidence": [
    "Alibaba\u2019s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture"
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    "Alibaba\u2019s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture",
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   "significance_evidence": [
    "Alibaba\u2019s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture",
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  },
  {
   "url": "https://canitrun.dev/guides/mistral-family-guide",
   "title": "Mistral & Mixtral Family: Complete VRAM Reference",
   "published_at": "2026-07-01T00:00:00",
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   "model": null,
   "release_date": "2026-04",
   "parameters": "128B",
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   "license": null,
   "significance": "folds Mistral's dedicated reasoning model (Magistral) and dedicated coding model (Devstral 2) into one configurable-effort model with standard GQA attention (8 KV heads) and 77.6% on SWE-bench Verified",
   "organization_evidence": [
    "# Mistral & Mixtral Family: Complete VRAM Reference",
    "## Mistral AI: The French Challenger",
    "Open-weight dense/classic-MoE models: Mistral 7B, NeMo 12B, Small 22B, Small 3.1 24B, Mixtral 8x7B, Mixtral 8x22B, and now Mistral Medium 3.5 (128B dense, April 2026)."
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   "release_date_evidence": [
    "# Mistral & Mixtral Family: Complete VRAM Reference",
    "## Mistral AI: The French Challenger",
    "Open-weight dense/classic-MoE models: Mistral 7B, NeMo 12B, Small 22B, Small 3.1 24B, Mixtral 8x7B, Mixtral 8x22B, and now Mistral Medium 3.5 (128B dense, April 2026)."
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   "parameters_evidence": [
    "# Mistral & Mixtral Family: Complete VRAM Reference",
    "## Mistral AI: The French Challenger",
    "Open-weight dense/classic-MoE models: Mistral 7B, NeMo 12B, Small 22B, Small 3.1 24B, Mixtral 8x7B, Mixtral 8x22B, and now Mistral Medium 3.5 (128B dense, April 2026)."
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   "significance_evidence": [
    "# Mistral & Mixtral Family: Complete VRAM Reference",
    "## Dense Models",
    "Mistral Medium 3.5 (128B, April 2026) is the newest dense release: it folds Mistral's dedicated reasoning model (Magistral) and dedicated coding model (Devstral 2) into one configurable-effort model with standard GQA attention (8 KV heads) and 77.6% on SWE-bench Verified."
   ]
  },
  {
   "url": "https://yicaiglobal.com/news/moonshot-ais-kimi-k3-becomes-worlds-first-open-source-model-in-3-trillion-parameter-class",
   "title": "Moonshot AI Says Kimi K3 Is World's First Open-Source 3T-Class Model",
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   "release_date": "2026-07-28",
   "parameters": "2.8 trillion parameters",
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   "significance": "Kimi K3 outperformed all open-source and most closed-source models across dozens of benchmark tests in programming, agentic workflows, reasoning, vision, and other areas, according to the technical report.",
   "organization_evidence": [
    "Moonshot AI Says Kimi K3 Is World's First Open-Source 3T-Class Model",
    "(Yicai) July 28 -- Moonshot AI has released the full weights for its new Kimi K3 artificial intelligence model, saying its 2.8 trillion parameters make it the world's first open-source model in the 3- trillion-parameter class."
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   "release_date_evidence": [
    "Moonshot AI Says Kimi K3 Is World's First Open-Source 3T-Class Model",
    "DATE: Jul 28 2026",
    "(Yicai) July 28 -- Moonshot AI has released the full weights for its new Kimi K3 artificial intelligence model, saying its 2.8 trillion parameters make it the world's first open-source model in the 3- trillion-parameter class."
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    "Moonshot AI Says Kimi K3 Is World's First Open-Source 3T-Class Model",
    "(Yicai) July 28 -- Moonshot AI has released the full weights for its new Kimi K3 artificial intelligence model, saying its 2.8 trillion parameters make it the world's first open-source model in the 3- trillion-parameter class."
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   "significance_evidence": [
    "Moonshot AI Says Kimi K3 Is World's First Open-Source 3T-Class Model",
    "Kimi K3 outperformed all open-source and most closed-source models across dozens of benchmark tests in programming, agentic workflows, reasoning, vision, and other areas, according to the technical report."
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  },
  {
   "url": "https://nextfuture.io.vn/blog/google-gemma-4-review-complete-guide-2026",
   "title": "Google Gemma 4 Review 2026: Open Model, Local, Beats APIs",
   "published_at": "2026-04-12T11:07:30",
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   "release_date": "2026-04-02",
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   "significance": "#3 on the open model leaderboard; the 26B MoE variant has already been called out on r/LocalLLaMA as running at $0.20 per full benchmark run via AI Studio, while outperforming models that cost 10x more.",
   "organization_evidence": [
    "Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "Google released Gemma 4 on **April 2, 2026**, under a fully permissive Apache 2.0 license."
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   "release_date_evidence": [
    "Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "Google Gemma 4 launched April 2, 2026 \u2014 four model sizes (E2B to 31B Dense), Apache 2.0 license, multimodal, 256K context, and #3 on the open model leaderboard.",
    "Google released Gemma 4 on **April 2, 2026**, under a fully permissive Apache 2.0 license."
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   "significance_evidence": [
    "Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "Google Gemma 4 launched April 2, 2026 \u2014 four model sizes (E2B to 31B Dense), Apache 2.0 license, multimodal, 256K context, and #3 on the open model leaderboard.",
    "The 26B MoE variant has already been called out on r/LocalLLaMA as running at **$0.20 per full benchmark run** via AI Studio, while outperforming models that cost 10x more."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/moonshot-seeks-30-revenue-share-as-open-weight-ai-models-monetize",
   "title": "Moonshot seeks 30% revenue share as open-weight AI models monetize",
   "published_at": "2026-08-31T14:23:35",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Artificial Analysis says it delivers performance comparable to OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.8 on complex multi-step tasks, and Arena.ai ranks it first in web interface-building benchmarks.",
   "organization_evidence": [
    "Moonshot AI is negotiating with Microsoft, Amazon, and Google to host its 2.8-trillion-parameter Kimi K3 on Azure, AWS, and Google Cloud, seeking up to 30 percent of K3-related service revenue \u2014 a first for a Chinese AI firm with US hyperscalers.",
    "Moonshot AI is negotiating with Microsoft, Amazon, and Google to take up to 30 percent of revenue from Kimi K3 services on their clouds \u2014 a test of whether open-weight models can collect recurring licensing fees at hyperscale."
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   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3, released in July, has 2.8 trillion total parameters with only 104 billion active per token \u2014 a 1.8 percent activation rate enabled by Moonshot's Stable LatentMoE architecture.",
    "Moonshot AI is negotiating with Microsoft, Amazon, and Google to take up to 30 percent of revenue from Kimi K3 services on their clouds \u2014 a test of whether open-weight models can collect recurring licensing fees at hyperscale."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The model posts strong third-party results: Artificial Analysis says it delivers performance comparable to OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.8 on complex multi-step tasks, and Arena.ai ranks it first in web interface-building benchmarks.",
    "Moonshot AI is negotiating with Microsoft, Amazon, and Google to take up to 30 percent of revenue from Kimi K3 services on their clouds \u2014 a test of whether open-weight models can collect recurring licensing fees at hyperscale."
   ]
  },
  {
   "url": "https://edgen.tech/ja/news/post/zhipus-glm-53-flash-matches-claude-opus-48-at-140-the-price",
   "title": "Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
   "published_at": "2026-08-26T14:19:40",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matches Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price",
   "organization_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the Artificial Analysis Intelligence Index at 1/40 the price, intensifying a price war that has pushed Chinese models past 60 percent of global token usage."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "[![Alex Nguyen](https://static.edwealth.ai/authors/alex-nguyen.png?w=32)\n## Alex Nguyen](https://www.edgen.tech/ja/author/news/alex-nguyen)\u00b7Aug 26 2026, 14:19"
   ],
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu's GLM-5.3-Flash matches Claude Opus 4.8 at 1/40 the price",
    "Zhipu AI's open-source GLM-5.3-Flash matches Anthropic's Claude Opus 4.8 on the AA Intelligence Index at 1/40 the price."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-06/03-minimax-releases-m3-an-open-weight-model-with-a-1-million-token-context-that-it-says-tops-gpt-55-on-swe-bench-pro",
   "title": "MiniMax Releases M3, an Open-Weight Model With a 1-Million-Token Context That It Says Tops GPT-5.5 on SWE-Bench Pro",
   "published_at": "2026-06-03T11:15:59",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Pairs a 1-million-token context with a new sparse-attention design and company benchmarks that top GPT-5.5 on SWE-bench Pro.",
   "organization_evidence": [
    "MiniMax Releases M3, an Open-Weight Model With a 1-Million-Token Context That It Says Tops GPT-5.5 on SWE-Bench Pro"
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   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax Releases M3, an Open-Weight Model With a 1-Million-Token Context That It Says Tops GPT-5.5 on SWE-Bench Pro",
    "Shanghai-based MiniMax launched M3 on June 1, pairing a 1-million-token context with a new sparse-attention design and company benchmarks that top GPT-5.5, with weights promised within 10 days."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "MiniMax Releases M3, an Open-Weight Model With a 1-Million-Token Context That It Says Tops GPT-5.5 on SWE-Bench Pro",
    "Shanghai-based MiniMax launched M3 on June 1, pairing a 1-million-token context with a new sparse-attention design and company benchmarks that top GPT-5.5, with weights promised within 10 days."
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  },
  {
   "url": "https://techtimes.com/articles/320121/20260710/diffusion-llm-learns-its-own-draft-model-nvidia-releases-tri-mode-open-weights.htm",
   "title": "Diffusion LLM Learns to Be Its Own Draft Model: NVIDIA Releases Tri-Mode Open Weights",
   "published_at": "2026-07-10T20:45:54",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-07-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Produces a single model that switches between three generation modes at inference time without any weight changes, allowing engineers running speculative decoding to drop the separate draft model and replace it with a single checkpoint.",
   "organization_evidence": [
    "NVIDIA's research team published [NVIDIA's July 2026 arXiv preprint](https://arxiv.org/abs/2607.05722) on July 7 proving that autoregressive and diffusion language models do not need to compete \u2014 they can be trained together on the same objective, producing a single model that switches between three generation modes at inference time without any weight changes."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA's research team published [NVIDIA's July 2026 arXiv preprint](https://arxiv.org/abs/2607.05722) on July 7 proving that autoregressive and diffusion language models do not need to compete \u2014 they can be trained together on the same objective, producing a single model that switches between three generation modes at inference time without any weight changes."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA's research team published [NVIDIA's July 2026 arXiv preprint](https://arxiv.org/abs/2607.05722) on July 7 proving that autoregressive and diffusion language models do not need to compete \u2014 they can be trained together on the same objective, producing a single model that switches between three generation modes at inference time without any weight changes.",
    "The result, available as free open weights on Hugging Face under a commercial license, lets engineers running speculative decoding drop the separate draft model they have been maintaining and replace it with a single checkpoint that fills both roles itself."
   ]
  },
  {
   "url": "https://aichina.news/blog/allen-ai-s-bar-5x7b-lands-on-modelers-cn-a-moe-style-model-for-the-qi19m2",
   "title": "Allen AI's BAR-5x7B Lands on Modelers.cn: A MoE-Style Model for the Ascend NPU Ecosystem \u2014 AICHINA.news Blog",
   "published_at": "2026-07-31T12:31:35",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MoE-style architecture and permissive Apache-2.0 license hosted natively on Huawei's Ascend NPU ecosystem platform (Modelers.cn).",
   "organization_evidence": [
    "# Allen AI's BAR-5x7B Lands on Modelers.cn: A MoE-Style Model for the Ascend NPU Ecosystem",
    "The Allen Institute for AI's BAR-5x7B has appeared on Modelers.cn, the Huawei/Ascend AI ecosystem platform, bringing an intriguing Mixture-of-Experts-style language model to a domestic NPU audience."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "And the timestamp of 2026-06-04 is in the future relative to most knowledge cutoffs, which raises a provenance question: this may be a placeholder, a deliberately sparse upload, or something else entirely."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Allen AI's BAR-5x7B Lands on Modelers.cn: A MoE-Style Model for the Ascend NPU Ecosystem",
    "The Allen Institute for AI's BAR-5x7B has appeared on Modelers.cn, the Huawei/Ascend AI ecosystem platform, bringing an intriguing Mixture-of-Experts-style language model to a domestic NPU audience.",
    "The model comes with an Apache-2.0 licence, which is about as permissive as open source gets \u2014 you can use it commercially, modify it, and deploy it without legal friction."
   ]
  },
  {
   "url": "https://hobbyistnews.com/moonshot-kimi-k3-open-weights-release",
   "title": "Moonshot releases Kimi K3 open weights on Hugging Face",
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    "Qwen3.6 Open Source Model Beats a 397B Giant - While Alibaba Quietly Closes Weights on Its Flagship"
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    "Two days later, the same team dropped Qwen3.6-27B: fully open, Apache 2.0 licensed, and according to Alibaba's benchmarks, capable of outperforming a 397-billion-parameter predecessor on the tasks that matter most to software engineers.",
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    "Two days later, the same team dropped Qwen3.6-27B: fully open, Apache 2.0 licensed, and according to Alibaba's benchmarks, capable of outperforming a 397-billion-parameter predecessor on the tasks that matter most to software engineers.",
    "Qwen3.6 Open Source Model Beats a 397B Giant - While Alibaba Quietly Closes Weights on Its Flagship"
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  },
  {
   "url": "https://weijinresearch.substack.com/p/meta-repositions-on-open-ai-models",
   "title": "Meta Repositions on Open AI Models as China\u2019s 100,000-Card Cluster Goes Live",
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  },
  {
   "url": "https://nolvia.ai/wiki/blog/what-is-kimi-k3-open-source-ai-model.html",
   "title": "What Is Kimi K3? The World's Largest Open-Source AI Model",
   "published_at": "2026-07-29T00:00:00",
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  },
  {
   "url": "https://koranmanado.co.id/en/zai-releases-glm-coding-model",
   "title": "Z.ai Releases GLM-5.2 Model Following U.S. Tech Restrictions",
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  {
   "url": "https://arxiv.org/html/2412.04261v1",
   "title": "Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier",
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   "url": "https://arxiv.org/html/2412.04261v1",
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  },
  {
   "url": "https://temperaturezero.com/2026/08/30/hy4-recursive-self-improvement-tencent",
   "title": "Hy4 Is Real. \u2018Recursive Self-Improvement\u2019 Isn\u2019t What Tencent Did.",
   "published_at": "2026-08-30T10:45:58",
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  {
   "url": "https://en.wikipedia.org/wiki/Qwen2.5",
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   "url": "https://en.wikipedia.org/wiki/Qwen2.5",
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   ],
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  },
  {
   "url": "https://theplanettools.ai/blog/mistral-medium-3-5-vibe-agents-launch",
   "title": "Mistral Medium 3.5 Drops Open-Weights with Cloud Coding Agents: Europe's Frontier Strike",
   "published_at": "2026-05-01T05:00:00",
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  },
  {
   "url": "https://gemma4.run/it/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
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  },
  {
   "url": "https://droiddynasty.com/kimi-k3-open-weights-ship",
   "title": "Kimi K3 Open Weights Ships Early With Custom License",
   "published_at": "2026-07-28T17:03:30",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-26",
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   "release_date_evidence": [
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    "# Kimi K3 Open Weights Ships Early With Custom License",
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  },
  {
   "url": "https://news.airosetta.com/en/news/zhipu-glm-5-3-post-training-boost",
   "title": "Zhipu Releases GLM-5.3: Base Unchanged, 50% Post-Training Gain, Open-Source Coding Near Claude Fable 5",
   "published_at": "2026-08-15T06:02:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": null,
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   "significance": "retaining a ~740B-parameter base and achieving about 50% overall improvement through extensive post-training. It ranks among top open models on multiple benchmarks, with coding and agent capabilities close to Claude Fable 5",
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   "model_evidence": null,
   "release_date_evidence": [
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   ],
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   "context_window_evidence": null,
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   "significance_evidence": [
    "# Zhipu Releases GLM-5.3: Base Unchanged, 50% Post-Training Gain, Open-Source Coding Near Claude Fable 5",
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   ]
  },
  {
   "url": "https://kie.ai/blog/what-is-inkling",
   "title": "What Is Inkling? The 975B Open-Weights MoE From Mira Murati's Thinking Machines",
   "published_at": "2026-07-16T11:05:20",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
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   "license": null,
   "significance": "Debuts at 41 on the Artificial Analysis Intelligence Index, the top score among U.S. open-weights models, ahead of Nemotron 3 Ultra at 38.",
   "organization_evidence": [
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    "Inkling is an open-weights Mixture-of-Experts foundation model released by Thinking Machines Lab on July 15, 2026, with 975B total parameters, 41B active per token, a 1M-token context window, and native reasoning across text, images, and audio.",
    "Inkling at a Glance",
    "| Developer | Thinking Machines Lab |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is Inkling? 975B Open-Weights MoE Explained",
    "Published: July 16, 2026",
    "Inkling is an open-weights Mixture-of-Experts foundation model released by Thinking Machines Lab on July 15, 2026, with 975B total parameters, 41B active per token, a 1M-token context window, and native reasoning across text, images, and audio.",
    "Inkling at a Glance",
    "| Release date | July 15, 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Is Inkling? 975B Open-Weights MoE Explained",
    "It debuts at 41 on the Artificial Analysis Intelligence Index, the top score among U.S. open-weights models, ahead of Nemotron 3 Ultra at 38."
   ]
  },
  {
   "url": "https://techunfoldedai.com/qwen-ai",
   "title": "Qwen AI 2026 Review: Pricing, Performance & Open-Source Reality",
   "published_at": "2026-08-03T07:38:34",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02",
   "parameters": "9B",
   "context_window": null,
   "license": null,
   "significance": "Beats OpenAI\u2019s 120B on MMLU-Pro",
   "organization_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen (pronounced \u201cchwen,\u201d from the Chinese \u901a\u4e49\u5343\u95ee / Tongy\u00ec Qianw\u00e8n) is Alibaba Cloud\u2019s family of large language and multimodal AI models."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen 3.5 dropped in February 2026 with native multimodal support.",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-9B | 9B | Dense | Beats OpenAI\u2019s 120B on MMLU-Pro|"
   ],
   "parameters_evidence": [
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-9B | 9B | Dense | Beats OpenAI\u2019s 120B on MMLU-Pro|"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-9B | 9B | Dense | Beats OpenAI\u2019s 120B on MMLU-Pro|"
   ]
  },
  {
   "url": "https://techunfoldedai.com/qwen-ai",
   "title": "Qwen AI 2026 Review: Pricing, Performance & Open-Source Reality",
   "published_at": "2026-08-03T07:38:34",
   "organization": "Alibaba Cloud",
   "model": "Qwen 3.6 27B",
   "release_date": "2026-04",
   "parameters": "27B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "The local dev sweet spot",
   "organization_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen (pronounced \u201cchwen,\u201d from the Chinese \u901a\u4e49\u5343\u95ee / Tongy\u00ec Qianw\u00e8n) is Alibaba Cloud\u2019s family of large language and multimodal AI models."
   ],
   "model_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.6-27B | 27B | Dense | The local dev sweet spot|"
   ],
   "release_date_evidence": [
    "Qwen 3.6 arrived in April 2026.",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.6-27B | 27B | Dense | The local dev sweet spot|"
   ],
   "parameters_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.6-27B | 27B | Dense | The local dev sweet spot|"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "For developers, the **Qwen 3.6 27B** is the one to watch.",
    "It\u2019s dense (every parameter active per token), Apache 2.0 licensed, and runs on consumer hardware."
   ],
   "significance_evidence": [
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.6-27B | 27B | Dense | The local dev sweet spot|"
   ]
  },
  {
   "url": "https://techunfoldedai.com/qwen-ai",
   "title": "Qwen AI 2026 Review: Pricing, Performance & Open-Source Reality",
   "published_at": "2026-08-03T07:38:34",
   "organization": "Alibaba Cloud",
   "model": "Qwen 3.5-397B",
   "release_date": "2026-02",
   "parameters": "397B (17B active)",
   "context_window": null,
   "license": null,
   "significance": "Flagship open-weight",
   "organization_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen (pronounced \u201cchwen,\u201d from the Chinese \u901a\u4e49\u5343\u95ee / Tongy\u00ec Qianw\u00e8n) is Alibaba Cloud\u2019s family of large language and multimodal AI models."
   ],
   "model_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-397B | 397B (17B active) | MoE | Flagship open-weight|"
   ],
   "release_date_evidence": [
    "Qwen 3.5 dropped in February 2026 with native multimodal support.",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-397B | 397B (17B active) | MoE | Flagship open-weight|"
   ],
   "parameters_evidence": [
    "Qwen AI in 2026: The Open-Source Model That\u2019s Quietly Reshaping the AI Economs",
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-397B | 397B (17B active) | MoE | Flagship open-weight|"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen\u2019s ecosystem is unusually broad. Here\u2019s what\u2019s actually available:",
    "| Qwen 3.5-397B | 397B (17B active) | MoE | Flagship open-weight|"
   ]
  },
  {
   "url": "https://ai-navigate.net/en/chaos-map/meta-ai",
   "title": "Llama (Meta) | AI Navigate",
   "published_at": null,
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Industry-Leading Context",
   "organization_evidence": [
    "**Llama Series** is Meta's family of open-weight large language models.",
    "Meta announced **Llama 4 Scout** and **Llama 4 Maverick** on April 5, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta announced **Llama 4 Scout** and **Llama 4 Maverick** on April 5, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "#### 1. Industry-Leading Context (Llama 4 Scout)"
   ]
  },
  {
   "url": "https://insights.marvin-42.com/articles/mistral-launches-mistral-3-open-multimodal-family-under-apache-20",
   "title": "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
   "published_at": "2026-03-08T07:20:36",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-06",
   "parameters": "14B, 8B, and 3B",
   "context_window": null,
   "license": null,
   "significance": "Currently ranks as the #2 open-source non-reasoning model on LMArena",
   "organization_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "On March 6, 2026, Mistral announced Mistral 3, its new flagship open multimodal family."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "On March 6, 2026, Mistral announced Mistral 3, its new flagship open multimodal family."
   ],
   "parameters_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "The release covers dense 14B, 8B, and 3B models under an Apache 2.0 license, plus a new Mistral Large 3 model with 41B active parameters and 675B total parameters for teams that want a larger frontier-style option inside the same stack."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "According to the announcement, Mistral 3 currently ranks as the #2 open-source non-reasoning model on LMArena, while a reasoning version is planned next."
   ]
  },
  {
   "url": "https://insights.marvin-42.com/articles/mistral-launches-mistral-3-open-multimodal-family-under-apache-20",
   "title": "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
   "published_at": "2026-03-08T07:20:36",
   "organization": "Mistral",
   "model": "Mistral Large 3",
   "release_date": "2026-03-06",
   "parameters": "41B active parameters and 675B total parameters",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Currently ranks as the #2 open-source non-reasoning model on LMArena",
   "organization_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "On March 6, 2026, Mistral announced Mistral 3, its new flagship open multimodal family."
   ],
   "model_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "The release covers dense 14B, 8B, and 3B models under an Apache 2.0 license, plus a new Mistral Large 3 model with 41B active parameters and 675B total parameters for teams that want a larger frontier-style option inside the same stack."
   ],
   "release_date_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "On March 6, 2026, Mistral announced Mistral 3, its new flagship open multimodal family."
   ],
   "parameters_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "The release covers dense 14B, 8B, and 3B models under an Apache 2.0 license, plus a new Mistral Large 3 model with 41B active parameters and 675B total parameters for teams that want a larger frontier-style option inside the same stack."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "The release covers dense 14B, 8B, and 3B models under an Apache 2.0 license, plus a new Mistral Large 3 model with 41B active parameters and 675B total parameters for teams that want a larger frontier-style option inside the same stack."
   ],
   "significance_evidence": [
    "Mistral launches Mistral 3 open multimodal family under Apache 2.0",
    "According to the announcement, Mistral 3 currently ranks as the #2 open-source non-reasoning model on LMArena, while a reasoning version is planned next."
   ]
  },
  {
   "url": "https://agenticbrew.ai/news/a63224d6-42d2-45cd-b29a-83134bf5e5a8/google-gemma-4-open-model-release",
   "title": "Google Gemma 4 open model release",
   "published_at": "2026-06-10T00:28:36",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "The family is natively multimodal across sizes (text, images, video with OCR/chart understanding), adds native audio on the edge variants, supports over 140 languages, and ships with native function-calling and structured JSON output for agentic workflows.",
   "organization_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 under a permissive Apache 2.0 license, spanning Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model built on the same research as Gemini 3."
   ],
   "model_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 under a permissive Apache 2.0 license, spanning Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model built on the same research as Gemini 3."
   ],
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 under a permissive Apache 2.0 license, spanning Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model built on the same research as Gemini 3."
   ],
   "parameters_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 under a permissive Apache 2.0 license, spanning Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model built on the same research as Gemini 3."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 under a permissive Apache 2.0 license, spanning Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts model, and a 31B Dense model built on the same research as Gemini 3."
   ],
   "significance_evidence": [
    "The family is natively multimodal across sizes (text, images, video with OCR/chart understanding), adds native audio on the edge variants, supports over 140 languages, and ships with native function-calling and structured JSON output for agentic workflows."
   ]
  },
  {
   "url": "https://kie.ai/de/blog/kimi-k3-moonshot-open-frontier-model",
   "title": "Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
   "published_at": "2026-07-28T00:26:53",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight model ever published",
   "organization_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "parameters_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 Release: Signal vs Noise on Moonshot's 2.8T Open Frontier Model",
    "Moonshot AI released Kimi K3 on July 16, 2026, then shipped its full weights to Hugging Face eleven days later on July 27 \u2014 a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision, positioned as the largest open-weight model ever published."
   ]
  },
  {
   "url": "https://softreviewed.com/0x-alpha-explained-inside-z-ais-glm-5-3-flash-1m-context-window-100t-free-token-launch",
   "title": "0x Alpha Unmasked: Inside GLM-5.3-Flash, 1M Context Window & 100T Free Token Release",
   "published_at": "2026-08-26T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-08-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "#1 on GDPVal-AA v2 (1,773 Score), 63.4% on DeepSWE v1.1, and 84.3 on Terminal Bench",
   "organization_evidence": [
    "# 0x Alpha Unmasked: Inside GLM-5.3-Flash, 1M Context Window & 100T Free Token Release",
    "On August 20, 2026, an anonymous model labeled **0x Alpha** (indexed as `stealth/ox-alpha`) quietly debuted on OpenRouter and OpenCode, immediately beating commercial frontier models across agentic coding tasks.",
    "Today, Beijing research lab **Zhipu AI (Z.ai)** officially confirmed the model\u2019s true identity as **GLM-5.3-Flash**, publishing complete benchmark evaluations, architectural whitepapers, and open-weights distribution under `zai-org`."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# 0x Alpha Unmasked: Inside GLM-5.3-Flash, 1M Context Window & 100T Free Token Release",
    "On August 20, 2026, an anonymous model labeled **0x Alpha** (indexed as `stealth/ox-alpha`) quietly debuted on OpenRouter and OpenCode, immediately beating commercial frontier models across agentic coding tasks."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# 0x Alpha Unmasked: Inside GLM-5.3-Flash, 1M Context Window & 100T Free Token Release",
    "### \ud83c\udfc6 #1 On GDPVal-AA v2 (1,773 Score)",
    "Crushes commercial models on coding value rating (1,773 vs Claude Opus 4.8\u2019s 1582 and DeepSeek\u2019s 1675), while scoring **63.4% on DeepSWE v1.1** and **84.3 on Terminal Bench**."
   ]
  },
  {
   "url": "https://hokai.io/hub/models/minimax-m2.7",
   "title": "MiniMax M2.7: Open 230B MoE Agentic Coder at $0.30/M",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax-M2.7 is a large language model built by MiniMax, a Shanghai-based AI company, and released on March 18, 2026.",
   "organization_evidence": [
    "MiniMax M2.7 is a 230B-parameter Mixture-of-Experts model (10B active, 256 experts) released March 18, 2026, with a 204,800-token context window and 131,072-token max output, scoring 56.2% on SWE-Bench Pro and an Artificial Analysis Intelligence Index of 50."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M2.7 is a 230B-parameter Mixture-of-Experts model (10B active, 256 experts) released March 18, 2026, with a 204,800-token context window and 131,072-token max output, scoring 56.2% on SWE-Bench Pro and an Artificial Analysis Intelligence Index of 50."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax-M2.7 is a large language model built by MiniMax, a Shanghai-based AI company, and released on March 18, 2026."
   ]
  },
  {
   "url": "https://datanorth.ai/news/nvidia-releases-nemotron-3-ultra-and-cosmos-3",
   "title": "NVIDIA Releases Nemotron 3 Ultra and Cosmos 3",
   "published_at": "2026-06-03T09:54:23",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "550 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Nemotron 3 Ultra scores 48 on the Artificial Analysis Intelligence Index, placing it ahead of the next strongest US-based open-weight models.",
   "organization_evidence": [
    "# NVIDIA Releases Nemotron 3 Ultra and Cosmos 3",
    "NVIDIA announced Nemotron 3 Ultra and [Cosmos 3](https://datanorth.ai/news/nvidia-launches-cosmos-3) at its Computex 2026 keynote on June 1 in Taipei."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# NVIDIA Releases Nemotron 3 Ultra and Cosmos 3",
    "*Published: June 3, 2026*"
   ],
   "parameters_evidence": [
    "# NVIDIA Releases Nemotron 3 Ultra and Cosmos 3",
    "Nemotron 3 Ultra is NVIDIA\u2019s largest open-weight language model to date, featuring approximately 550 billion total parameters with 55 billion active parameters per token and a 1 million token context window."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Releases Nemotron 3 Ultra and Cosmos 3",
    "Nemotron 3 Ultra scores 48 on the Artificial Analysis Intelligence Index, placing it ahead of the next strongest US-based open-weight models."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/OLMo-2-1124-32B-Instruct-RLVR1",
   "title": "allenai/OLMo-2-1124-32B-Instruct-RLVR1 \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Improved factuality and reduced hallucination due to RLVR training; competitive with other 30B-class models on MMLU, GSM8K, and HumanEval.",
   "organization_evidence": [
    "allenai / OLMo-2-1124-32B-Instruct-RLVR1",
    "This is an open-source 32-billion-parameter large language model from the Allen Institute for AI, fine-tuned with reinforcement learning from verifiable rewards (RLVR) to improve instruction-following and reasoning."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "allenai/OLMo-2-1124-32B-Instruct-RLVR1",
    "**Created:** 2026-04-12 | **Updated:** 2026-04-12"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "allenai/OLMo-2-1124-32B-Instruct-RLVR1",
    "- Competitive with other 30B-class models on MMLU, GSM8K, and HumanEval",
    "- Improved factuality and reduced hallucination due to RLVR training"
   ]
  },
  {
   "url": "https://vorplabs.com/models/open-weights-tracker",
   "title": "Open Weights Tracker: Qwen 3.8, DeepSeek V4 Flash, Kimi K3, Gemma 4 Status and Licenses",
   "published_at": "2026-08-04T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash-0731",
   "release_date": "2026-07-31",
   "parameters": null,
   "context_window": null,
   "license": "MIT License",
   "significance": null,
   "organization_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "DeepSeek-V4-Flash-0731 shipped July 31 under the MIT License",
    "Is DeepSeek V4 Flash open weights, and under what license?",
    "Yes.",
    "DeepSeek-V4-Flash-0731, published July 31, 2026 as the official release, has its weights on Hugging Face under the MIT License, stated on the model card for both the repository and the weights."
   ],
   "model_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "DeepSeek-V4-Flash-0731 shipped July 31 under the MIT License",
    "Is DeepSeek V4 Flash open weights, and under what license?",
    "Yes.",
    "DeepSeek-V4-Flash-0731, published July 31, 2026 as the official release, has its weights on Hugging Face under the MIT License, stated on the model card for both the repository and the weights."
   ],
   "release_date_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "DeepSeek-V4-Flash-0731 shipped July 31 under the MIT License",
    "Is DeepSeek V4 Flash open weights, and under what license?",
    "Yes.",
    "DeepSeek-V4-Flash-0731, published July 31, 2026 as the official release, has its weights on Hugging Face under the MIT License, stated on the model card for both the repository and the weights."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "DeepSeek-V4-Flash-0731 shipped July 31 under the MIT License",
    "Is DeepSeek V4 Flash open weights, and under what license?",
    "Yes.",
    "DeepSeek-V4-Flash-0731, published July 31, 2026 as the official release, has its weights on Hugging Face under the MIT License, stated on the model card for both the repository and the weights."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://vorplabs.com/models/open-weights-tracker",
   "title": "Open Weights Tracker: Qwen 3.8, DeepSeek V4 Flash, Kimi K3, Gemma 4 Status and Licenses",
   "published_at": "2026-08-04T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": null,
   "parameters": "2.8T total, 104B activated",
   "context_window": null,
   "license": "custom Kimi K3 License",
   "significance": null,
   "organization_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Kimi K3 weights are live under the custom Kimi K3 License, which is not MIT",
    "What license do the Kimi K3 weights use?",
    "The custom Kimi K3 License, not MIT and not Apache 2.0.",
    "The weights are live on the moonshotai/Kimi-K3 repository, a 2.8T-parameter model with 104B activated, and the license file in the repository carries commercial conditions that differ from permissive licenses; the Kimi K3 license explainer on this site walks through the self-hosting and commercial-use terms."
   ],
   "model_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Kimi K3 weights are live under the custom Kimi K3 License, which is not MIT",
    "What license do the Kimi K3 weights use?",
    "The custom Kimi K3 License, not MIT and not Apache 2.0."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "The weights are live on the moonshotai/Kimi-K3 repository, a 2.8T-parameter model with 104B activated, and the license file in the repository carries commercial conditions that differ from permissive licenses; the Kimi K3 license explainer on this site walks through the self-hosting and commercial-use terms."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Kimi K3 weights are live under the custom Kimi K3 License, which is not MIT",
    "What license do the Kimi K3 weights use?",
    "The custom Kimi K3 License, not MIT and not Apache 2.0."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://vorplabs.com/models/open-weights-tracker",
   "title": "Open Weights Tracker: Qwen 3.8, DeepSeek V4 Flash, Kimi K3, Gemma 4 Status and Licenses",
   "published_at": "2026-08-04T00:00:00",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": "Apache 2.0",
   "significance": null,
   "organization_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Gemma 4 is live under Apache 2.0 while older Gemma models keep Google's custom terms",
    "Is Gemma 4 really Apache 2.0?",
    "Yes.",
    "Google publishes Gemma 4 under Apache 2.0, and the Gemma Terms of Use, as modified April 1, 2026, exclude Gemma 4 from their scope, so the custom terms and the Prohibited Use Policy continue to bind the older Gemma models but not downloaded Gemma 4 weights."
   ],
   "model_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Gemma 4 is live under Apache 2.0 while older Gemma models keep Google's custom terms",
    "Is Gemma 4 really Apache 2.0?",
    "Yes.",
    "Google publishes Gemma 4 under Apache 2.0, and the Gemma Terms of Use, as modified April 1, 2026, exclude Gemma 4 from their scope, so the custom terms and the Prohibited Use Policy continue to bind the older Gemma models but not downloaded Gemma 4 weights."
   ],
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Open-weights status tracker: which current models have downloadable weights",
    "Gemma 4 is live under Apache 2.0 while older Gemma models keep Google's custom terms",
    "Is Gemma 4 really Apache 2.0?",
    "Yes.",
    "Google publishes Gemma 4 under Apache 2.0, and the Gemma Terms of Use, as modified April 1, 2026, exclude Gemma 4 from their scope, so the custom terms and the Prohibited Use Policy continue to bind the older Gemma models but not downloaded Gemma 4 weights."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://digitalinasia.com/alibaba-qwen",
   "title": "Qwen AI: Alibaba's Android-of-AI Ecosystem Play | Digital in Asia",
   "published_at": "2026-08-01T13:29:41",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the open flagship",
   "organization_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "The recent model cadence has been relentless: Qwen3 arrived under an Apache 2.0 licence in April 2025, Qwen3.5 followed as the open flagship on 16 February 2026, and the Qwen3.6 series landed in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "The recent model cadence has been relentless: Qwen3 arrived under an Apache 2.0 licence in April 2025, Qwen3.5 followed as the open flagship on 16 February 2026, and the Qwen3.6 series landed in April 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "The recent model cadence has been relentless: Qwen3 arrived under an Apache 2.0 licence in April 2025, Qwen3.5 followed as the open flagship on 16 February 2026, and the Qwen3.6 series landed in April 2026."
   ]
  },
  {
   "url": "https://digitalinasia.com/alibaba-qwen",
   "title": "Qwen AI: Alibaba's Android-of-AI Ecosystem Play | Digital in Asia",
   "published_at": "2026-08-01T13:29:41",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Core model families \u2014 Qwen3, Qwen3.5 and the open Qwen3.6 sizes \u2014 ship weights under Apache 2.0, permitting free commercial use.",
   "organization_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "The recent model cadence has been relentless: Qwen3 arrived under an Apache 2.0 licence in April 2025, Qwen3.5 followed as the open flagship on 16 February 2026, and the Qwen3.6 series landed in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "The recent model cadence has been relentless: Qwen3 arrived under an Apache 2.0 licence in April 2025, Qwen3.5 followed as the open flagship on 16 February 2026, and the Qwen3.6 series landed in April 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen AI: Alibaba\u2019s Android-of-AI Ecosystem Play",
    "Core model families \u2014 Qwen3, Qwen3.5 and the open Qwen3.6 sizes \u2014 ship weights under Apache 2.0, permitting free commercial use."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/mistral-medium-3-5",
   "title": "Mistral Medium 3.5 \u2014 128B Open-Weight Flagship Hits 77.6% on SWE-Bench Verified",
   "published_at": "2026-04-29T12:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B dense",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified",
   "organization_evidence": [
    "Mistral AI \u00b7 2026-04-29 \u00b7 major"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral AI \u00b7 2026-04-29 \u00b7 major"
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5 \u2014 128B Open-Weight Flagship Hits 77.6% on SWE-Bench Verified",
    "Mistral Medium 3.5 \u2014 128B Open-Weight Flagship Hits 77.6% on SWE-Bench Verified\n\n[Mistral Medium 3.5](/models/mistral-medium-3-5/) \u2014 128B dense open-weight model, 256k context, 77.6% on [SWE-Bench](/tools/swe-bench) Verified and 91.4 on \u03c4\u00b3-Telecom.",
    "Mistral's new flagship: 128B dense, 256k context, open weights under modified MIT, and a 77.6 SWE-Bench Verified score.",
    "Key specs",
    "| Parameters | 128B dense |",
    "The model is 128B dense parameters with a 256k-token context window, trained for instruction-following, reasoning, and coding."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 \u2014 128B Open-Weight Flagship Hits 77.6% on SWE-Bench Verified",
    "Mistral Medium 3.5 \u2014 128B Open-Weight Flagship Hits 77.6% on SWE-Bench Verified\n\n[Mistral Medium 3.5](/models/mistral-medium-3-5/) \u2014 128B dense open-weight model, 256k context, 77.6% on [SWE-Bench](/tools/swe-bench) Verified and 91.4 on \u03c4\u00b3-Telecom.",
    "Mistral's new flagship: 128B dense, 256k context, open weights under modified MIT, and a 77.6 SWE-Bench Verified score.",
    "Key specs",
    "| SWE-bench | 77.6% |",
    "The 77.6% SWE-Bench Verified score puts an open-weight 128B dense model into the same coding-tier conversation as much larger closed flagships."
   ]
  },
  {
   "url": "https://greyjournal.net/news/google-gemma-4-open-source-ai-models",
   "title": "Google Releases Gemma 4, Free AI Models for Any Device",
   "published_at": "2026-04-03T17:12:29",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "rank #3 among all open models worldwide on the Arena AI text leaderboard, with an Elo of 1,452 for the 31B model",
   "organization_evidence": [
    "# Google Releases Gemma 4, Free AI Models for Any Device",
    "Google released Gemma 4 on April 2, a family of four open-source AI models built on the same research behind its flagship Gemini 3 system."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Releases Gemma 4, Free AI Models for Any Device",
    "Google released Gemma 4 on April 2, a family of four open-source AI models built on the same research behind its flagship Gemini 3 system."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Releases Gemma 4, Free AI Models for Any Device",
    "On the Arena AI text leaderboard, the 31B model holds an Elo of 1,452, [ranking #3 among all open models worldwide](https://deepmind.google/models/gemma/gemma-4/) and outperforming models with twenty times its parameter count, according to Google DeepMind."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/moonshot-kimi-k2-6",
   "title": "Kimi K2.6 \u2014 Moonshot AI's 1T-Parameter Agentic MoE Opens Under Modified MIT",
   "published_at": "2026-04-20T12:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1T total / 32B active",
   "context_window": null,
   "license": null,
   "significance": "Achieves 58.6% on SWE-Bench Pro and supports agent swarms up to 300 sub-agents.",
   "organization_evidence": [
    "Moonshot AI \u00b7 2026-04-20 \u00b7 seismic",
    "Moonshot AI releases [Kimi K2.6](/models/kimi-k2-6/) with open weights \u2014 a 1T-parameter MoE model with 32B active params, 256K context, and native vision via MoonViT."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI \u00b7 2026-04-20 \u00b7 seismic",
    "# Kimi K2.6 \u2014 Moonshot AI's 1T-Parameter Agentic MoE Opens Under Modified MIT"
   ],
   "parameters_evidence": [
    "Moonshot AI releases [Kimi K2.6](/models/kimi-k2-6/) with open weights \u2014 a 1T-parameter MoE model with 32B active params, 256K context, and native vision via MoonViT.",
    "## Key specs",
    "| Active params | 32B |",
    "| Total parameters | 1T |",
    "[Kimi K2](/models/kimi-k2/).6 is Moonshot AI's latest open-weights model \u2014 a 1T total / 32B active parameter Mixture-of-Experts architecture with 384 experts, 256K-token context, and a built-in 400M-parameter vision encoder called MoonViT."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K2.6 \u2014 Moonshot AI's 1T-Parameter Agentic MoE Opens Under Modified MIT",
    "Achieves 58.6% on SWE-Bench Pro and supports agent swarms up to 300 sub-agents."
   ]
  },
  {
   "url": "https://nerdleveltech.com/minimax-m3-open-weight-coding-sparse-attention",
   "title": "MiniMax M3: Open-Weight Coding at 1/10 the Cost (2026)",
   "published_at": "2026-06-09T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "pairs frontier-level coding with a 1-million-token context window and native multimodality",
   "organization_evidence": [
    "ai-ml",
    "# MiniMax M3: Open-Weight Coding at 1/10 the Cost (2026)",
    "MiniMax M3 is a Chinese open-weight language model, released June 1, 2026, that pairs frontier-level coding with a 1-million-token context window and native multimodality \u2014 at roughly a tenth of the per-token price of GPT-5.5 or Claude Opus."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "ai-ml",
    "# MiniMax M3: Open-Weight Coding at 1/10 the Cost (2026)",
    "MiniMax M3 is a Chinese open-weight language model, released June 1, 2026, that pairs frontier-level coding with a 1-million-token context window and native multimodality \u2014 at roughly a tenth of the per-token price of GPT-5.5 or Claude Opus."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "ai-ml",
    "# MiniMax M3: Open-Weight Coding at 1/10 the Cost (2026)",
    "MiniMax M3 is a Chinese open-weight language model, released June 1, 2026, that pairs frontier-level coding with a 1-million-token context window and native multimodality \u2014 at roughly a tenth of the per-token price of GPT-5.5 or Claude Opus."
   ]
  },
  {
   "url": "https://aimoneytools.net/blog/nvidia-nemotron-3-ultra-550b-review-2026",
   "title": "NVIDIA Nemotron 3 Ultra 550B: The Open-Source Frontier Model That's Free to Use (2026 Review)",
   "published_at": "2026-06-05T00:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "71.9% on SWE-Bench Verified, 87.0% on GPQA, 94.7% on RULER 1M",
   "organization_evidence": [
    "AI News9 min read\u00b7 June 5, 2026",
    "# NVIDIA Nemotron 3 Ultra 550B: The Open-Source Frontier Model That's Free to Use (2026 Review)",
    "NVIDIA just dropped Nemotron 3 Ultra 550B \u2014 a free, open-weight frontier model beating GPT-4 class on most benchmarks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI News9 min read\u00b7 June 5, 2026",
    "# NVIDIA Nemotron 3 Ultra 550B: The Open-Source Frontier Model That's Free to Use (2026 Review)",
    "NVIDIA dropped something significant on June 4, 2026: **Nemotron 3 Ultra 550B**, a 550-billion-parameter open-weight language model that's available completely free, right now, through multiple platforms."
   ],
   "parameters_evidence": [
    "AI News9 min read\u00b7 June 5, 2026",
    "# NVIDIA Nemotron 3 Ultra 550B: The Open-Source Frontier Model That's Free to Use (2026 Review)",
    "NVIDIA dropped something significant on June 4, 2026: **Nemotron 3 Ultra 550B**, a 550-billion-parameter open-weight language model that's available completely free, right now, through multiple platforms."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI News9 min read\u00b7 June 5, 2026",
    "## Benchmarks \u2014 Where It Stands",
    "NVIDIA published detailed benchmarks, and the results put Nemotron 3 Ultra in a clear tier:"
   ]
  },
  {
   "url": "https://aichina.news/blog/new-experimental-code-model-from-allen-ai-targets-hybrid-tokenisation-zaoo8t",
   "title": "New Experimental Code Model from Allen AI Targets Hybrid Tokenisation and Ascend NPU \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T12:38:57",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "This model is designed to push beyond standard tokenisation, employing a 'hybrid' tokenizer and a chain-of-thought ('think') training regime to tackle code generation and reasoning.",
   "organization_evidence": [
    "New Experimental Code Model from Allen AI Targets Hybrid Tokenisation and Ascend NPU",
    "It is the `allenai/olmo-3-hybrid-tokenizer-think-dev`, an experimental language model from the Allen Institute for AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "New Experimental Code Model from Allen AI Targets Hybrid Tokenisation and Ascend NPU",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "New Experimental Code Model from Allen AI Targets Hybrid Tokenisation and Ascend NPU",
    "This model is designed to push beyond standard tokenisation, employing a 'hybrid' tokenizer and a chain-of-thought ('think') training regime to tackle code generation and reasoning."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases?bb=4",
   "title": "AI Model Releases Timeline: Latest Launches with Dates \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Liquid AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "2.6B",
   "context_window": null,
   "license": null,
   "significance": "Compact 2.6B-parameter model aimed at on-device use.",
   "organization_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## August 2026 \u2014 latest AI model releases",
    "4. Aug 4, 2026 \u00b7 Liquid AI"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## August 2026 \u2014 latest AI model releases",
    "4. Aug 4, 2026 \u00b7 Liquid AI"
   ],
   "parameters_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## August 2026 \u2014 latest AI model releases",
    "### [LFM2.5-2.6B](https://www.liquid.ai/blog/lfm2-5-2-6b)",
    "Compact 2.6B-parameter model aimed at on-device use."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## August 2026 \u2014 latest AI model releases",
    "Compact 2.6B-parameter model aimed at on-device use."
   ]
  },
  {
   "url": "https://promptzone.com/ai-model-releases?bb=4",
   "title": "AI Model Releases Timeline: Latest Launches with Dates \u2014 PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": "Zhipu AI",
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-weights flagship that topped Hugging Face trending and runs on surprisingly modest hardware.",
   "organization_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## June 2026",
    "3. Jun 16, 2026 \u00b7 Zhipu AI"
   ],
   "model_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## June 2026",
    "3. Jun 16, 2026 \u00b7 Zhipu AI",
    "### [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2)",
    "Open-weights flagship that topped Hugging Face trending and runs on surprisingly modest hardware."
   ],
   "release_date_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## June 2026",
    "3. Jun 16, 2026 \u00b7 Zhipu AI"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Model Releases Timeline: Latest Launches with Dates",
    "## June 2026",
    "Open-weights flagship that topped Hugging Face trending and runs on surprisingly modest hardware."
   ]
  },
  {
   "url": "https://ai360central.com/meta-llama-4-open-weights-mixture-of-experts",
   "title": "Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
   "published_at": "2026-06-28T17:49:59",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-06-28",
   "parameters": "2T total parameters, 288B active",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts lets Meta train and serve very large models while activating only a fraction of parameters per token \u2014 a sweet spot for cost and quality.",
   "organization_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "By / June 28, 2026"
   ],
   "parameters_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Why MoE, why now",
    "Mixture-of-experts lets Meta train and serve very large models while activating only a fraction of parameters per token \u2014 a sweet spot for cost and quality."
   ]
  },
  {
   "url": "https://ai360central.com/meta-llama-4-open-weights-mixture-of-experts",
   "title": "Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
   "published_at": "2026-06-28T17:49:59",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-06-28",
   "parameters": "400B / 17B active",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts lets Meta train and serve very large models while activating only a fraction of parameters per token \u2014 a sweet spot for cost and quality.",
   "organization_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "By / June 28, 2026"
   ],
   "parameters_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Why MoE, why now",
    "Mixture-of-experts lets Meta train and serve very large models while activating only a fraction of parameters per token \u2014 a sweet spot for cost and quality."
   ]
  },
  {
   "url": "https://ai360central.com/meta-llama-4-open-weights-mixture-of-experts",
   "title": "Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
   "published_at": "2026-06-28T17:49:59",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-06-28",
   "parameters": "109B / 17B active",
   "context_window": null,
   "license": null,
   "significance": "Scout runs on a single H100, which is the headline for self-hosters.",
   "organization_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "By / June 28, 2026"
   ],
   "parameters_evidence": [
    "# Meta releases Llama 4: open-weight mixture-of-experts with 2T parameters",
    "Meta has released the Llama 4 family under its updated community license: Behemoth (2T total parameters, 288B active), Maverick (400B / 17B active), and Scout (109B / 17B active)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Why MoE, why now",
    "Scout runs on a single H100, which is the headline for self-hosters."
   ]
  },
  {
   "url": "https://trend.undefined-labs.dev/wiki/entities/mistral",
   "title": "Mistral AI",
   "published_at": "2026-05-16T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "3B",
   "context_window": null,
   "license": null,
   "significance": "Matches models nearly 7x its size, 84.9% average F1 on text safety and 83.8% on multimodal safety, and takes its safety policy as a prompt at inference time.",
   "organization_evidence": [
    "# Mistral AI",
    "- **2026-08-04**: **Shieldstral \u2014 a 3B Apache 2.0 safety classifier that takes its policy as a prompt** \u2014 Mistral released **Shieldstral 1.0**, a **3B-parameter policy-adaptive multimodal safety classifier** under **Apache 2.0**, covering **12 languages** and running on a **single 16GB NVIDIA GPU**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral AI",
    "- **2026-08-04**: **Shieldstral \u2014 a 3B Apache 2.0 safety classifier that takes its policy as a prompt** \u2014 Mistral released **Shieldstral 1.0**, a **3B-parameter policy-adaptive multimodal safety classifier** under **Apache 2.0**, covering **12 languages** and running on a **single 16GB NVIDIA GPU**."
   ],
   "parameters_evidence": [
    "# Mistral AI",
    "- **2026-08-04**: **Shieldstral \u2014 a 3B Apache 2.0 safety classifier that takes its policy as a prompt** \u2014 Mistral released **Shieldstral 1.0**, a **3B-parameter policy-adaptive multimodal safety classifier** under **Apache 2.0**, covering **12 languages** and running on a **single 16GB NVIDIA GPU**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral AI",
    "- **2026-08-04**: **Shieldstral \u2014 a 3B Apache 2.0 safety classifier that takes its policy as a prompt** \u2014 Mistral released **Shieldstral 1.0**, a **3B-parameter policy-adaptive multimodal safety classifier** under **Apache 2.0**, covering **12 languages** and running on a **single 16GB NVIDIA GPU**.",
    "Reported at **84.9% average F1** on text safety and **83.8%** on multimodal safety, against **OmniGuard-7B at 77.6%** and **LlavaGuard-7B at 71.6%** \u2014 Mistral's framing is that it matches models nearly **7\u00d7** its size."
   ]
  },
  {
   "url": "https://trend.undefined-labs.dev/wiki/entities/mistral",
   "title": "Mistral AI",
   "published_at": "2026-05-16T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-06",
   "parameters": "~675B",
   "context_window": null,
   "license": null,
   "significance": "Would be the largest open-weight MoE available under a permissive license, combining frontier-class capability with inference efficiency.",
   "organization_evidence": [
    "# Mistral AI",
    "- **2026-07-06**: **Mistral Large 3 MoE enters early access** \u2014 A ~675B parameter MoE model (41B active per token), Apache 2.0 licensed, opened early access to developers on July 6."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral AI",
    "- **2026-07-06**: **Mistral Large 3 MoE enters early access** \u2014 A ~675B parameter MoE model (41B active per token), Apache 2.0 licensed, opened early access to developers on July 6."
   ],
   "parameters_evidence": [
    "# Mistral AI",
    "- **2026-07-06**: **Mistral Large 3 MoE enters early access** \u2014 A ~675B parameter MoE model (41B active per token), Apache 2.0 licensed, opened early access to developers on July 6."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral AI",
    "- **2026-07-06**: **Mistral Large 3 MoE enters early access** \u2014 A ~675B parameter MoE model (41B active per token), Apache 2.0 licensed, opened early access to developers on July 6.",
    "**Why it matters**: at ~675B/41B active with Apache 2.0, this would be the largest open-weight MoE available under a permissive license \u2014 directly challenging DeepSeek-V3 (671B/37B active, MIT) and Llama's open-weight leadership."
   ]
  },
  {
   "url": "https://trend.undefined-labs.dev/wiki/entities/mistral",
   "title": "Mistral AI",
   "published_at": "2026-05-16T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-01",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "Saturates miniF2F at 100% and purpose-built for Lean 4 formal verification and automated theorem proving.",
   "organization_evidence": [
    "# Mistral AI",
    "- **2026-07-01/02**: **Leanstral 1.5 \u2014 open-source formal verification model saturates miniF2F at 100%** \u2014 Mistral released Leanstral 1.5 (July 1\u20132, 2026), a **119B MoE model (6.5B active, Apache 2.0)** purpose-built for Lean 4 formal verification and automated theorem proving."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral AI",
    "- **2026-07-01/02**: **Leanstral 1.5 \u2014 open-source formal verification model saturates miniF2F at 100%** \u2014 Mistral released Leanstral 1.5 (July 1\u20132, 2026), a **119B MoE model (6.5B active, Apache 2.0)** purpose-built for Lean 4 formal verification and automated theorem proving."
   ],
   "parameters_evidence": [
    "# Mistral AI",
    "- **2026-07-01/02**: **Leanstral 1.5 \u2014 open-source formal verification model saturates miniF2F at 100%** \u2014 Mistral released Leanstral 1.5 (July 1\u20132, 2026), a **119B MoE model (6.5B active, Apache 2.0)** purpose-built for Lean 4 formal verification and automated theorem proving."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral AI",
    "- **2026-07-01/02**: **Leanstral 1.5 \u2014 open-source formal verification model saturates miniF2F at 100%** \u2014 Mistral released Leanstral 1.5 (July 1\u20132, 2026), a **119B MoE model (6.5B active, Apache 2.0)** purpose-built for Lean 4 formal verification and automated theorem proving.",
    "Benchmarks: **100% miniF2F** (full saturation), **587/672 PutnamBench** (87.4%), FATE-H 87%, FATE-X 34%."
   ]
  },
  {
   "url": "https://gemma4.run/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-07T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 E2B",
   "release_date": "2026-04-02",
   "parameters": "5.1B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Apache 2.0 open-source license, 4 model sizes, natively supports four modalities, extends the context window up to 256K tokens, and impressive benchmark performance.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E2B | Dense + PLE | 5.1B | 2.3B | 35 | 128K | Text, image, video, **audio** |"
   ],
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E2B | Dense + PLE | 5.1B | 2.3B | 35 | 128K | Text, image, video, **audio** |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E2B | Dense + PLE | 5.1B | 2.3B | 35 | 128K | Text, image, video, **audio** |"
   ],
   "license_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://gemma4.run/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-07T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 E4B",
   "release_date": "2026-04-02",
   "parameters": "8B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Apache 2.0 open-source license, 4 model sizes, natively supports four modalities, extends the context window up to 256K tokens, and impressive benchmark performance.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E4B | Dense + PLE | 8B | 4.5B | 42 | 128K | Text, image, video, **audio** |"
   ],
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E4B | Dense + PLE | 8B | 4.5B | 42 | 128K | Text, image, video, **audio** |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| E4B | Dense + PLE | 8B | 4.5B | 42 | 128K | Text, image, video, **audio** |"
   ],
   "license_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://gemma4.run/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-07T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 26B A4B",
   "release_date": "2026-04-02",
   "parameters": "25.2B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Apache 2.0 open-source license, first MoE model in the Gemma series, extends the context window up to 256K tokens, and impressive benchmark performance.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 26B A4B | MoE | 25.2B | 3.8B | 30 | 256K | Text, image, video |"
   ],
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 26B A4B | MoE | 25.2B | 3.8B | 30 | 256K | Text, image, video |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 26B A4B | MoE | 25.2B | 3.8B | 30 | 256K | Text, image, video |"
   ],
   "license_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://gemma4.run/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-07T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 31B",
   "release_date": "2026-04-02",
   "parameters": "30.7B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Apache 2.0 open-source license, 31B model soared on the AIME 2026 math benchmark, extends the context window up to 256K tokens, and ranks #3 among open models on the Arena AI text leaderboard.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 31B | Dense | 30.7B | 30.7B | 60 | 256K | Text, image, video |"
   ],
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 31B | Dense | 30.7B | 30.7B | 60 | 256K | Text, image, video |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Architecture and Technical Specifications Across Four Models",
    "| 31B | Dense | 30.7B | 30.7B | 60 | 256K | Text, image, video |"
   ],
   "license_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "significance_evidence": null
  },
  {
   "url": "https://bitsminds.com/news/moonshot-ai-kimi-k2-6-open-source-1t-agents-2026",
   "title": "Moonshot AI Releases Kimi K2.6: Open-Source Giant with 1T Parameters and 300-Agent Swarms",
   "published_at": "2026-04-21T08:10:29",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scores 54.0 on the HLE-Full benchmark, edging out GPT-5.4 at 52.1 and Claude Opus 4.6 at 53.0, and 58.6 on SWE-Bench Pro.",
   "organization_evidence": [
    "# Moonshot AI Releases Kimi K2.6: Open-Source Giant with 1T Parameters and 300-Agent Swarms",
    "Moonshot AI has released Kimi K2.6, a 1-trillion-parameter open-source model capable of coordinating 300 parallel sub-agents across 4,000 steps \u2014 available on Hugging Face under a Modified MIT License.",
    "Moonshot AI has released Kimi K2.6, the latest iteration of its Kimi open-source model series, marking a significant leap in what open-weight models can accomplish for agentic and coding tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot AI Releases Kimi K2.6: Open-Source Giant with 1T Parameters and 300-Agent Swarms",
    "Released on April 20, 2026, K2.6 is available across [Kimi.com](https://kimi.com), the Kimi App, API, and as downloadable weights on Hugging Face under a Modified MIT License."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI Releases Kimi K2.6: Open-Source Giant with 1T Parameters and 300-Agent Swarms",
    "On the HLE-Full benchmark \u2014 one of the most demanding agentic evaluations \u2014 Kimi K2.6 scores 54.0, edging out GPT-5.4 at 52.1 and Claude Opus 4.6 at 53.0.",
    "It also scores 58.6 on SWE-Bench Pro, which measures real-world GitHub issue resolution."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/glm-5-2",
   "title": "GLM-5.2 Review: Zhipu Open-Weight Coding Model (2026)",
   "published_at": "2026-06-19T11:04:17",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "62.1 on SWE-bench Pro",
   "organization_evidence": [
    "GLM-5.2 is Zhipu AI's open-weight flagship coding model, released June 13, 2026 under the international Z.ai brand.",
    "GLM-5.2",
    "## What is GLM-5.2?"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2 is Zhipu AI's open-weight flagship coding model, released June 13, 2026 under the international Z.ai brand.",
    "GLM-5.2",
    "## What is GLM-5.2?"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "In our testing it posts a vendor-reported headline of 62.1 on SWE-bench Pro, and Zhipu sells it through a dual-rate model: the GLM Coding Plan, a flat subscription from around $18 per month, or metered API access at $1.40 per million input tokens and $4.40 per million output tokens.",
    "GLM-5.2",
    "## What is GLM-5.2?"
   ]
  },
  {
   "url": "https://trend.undefined-labs.dev/wiki/entities/minimax",
   "title": "MiniMax",
   "published_at": "2026-07-17T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "428B MoE (23B active)",
   "context_window": "1M",
   "license": "open-weight on HuggingFace",
   "significance": "SWE-Bench Pro 59.0%, BrowseComp 83.5",
   "organization_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "model_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "release_date_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "parameters_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "context_window_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "license_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ],
   "significance_evidence": [
    "# MiniMax",
    "## Models & Products",
    "- [MiniMax M3](/wiki/models/minimax-m3) \u2014 Released June 1, 2026; 428B MoE (23B active), 1M context, native multimodal, SWE-Bench Pro 59.0%, BrowseComp 83.5; open-weight on HuggingFace"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/nvidia-cosmos-3-launches-open-physical-ai-omnimodel",
   "title": "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
   "published_at": "2026-06-04T21:38:33",
   "organization": "Nvidia",
   "model": "Cosmos 3",
   "release_date": "2026-06-01",
   "parameters": "16B Nano and 64B Super",
   "context_window": null,
   "license": "permissive enough that a startup can build a commercial product on top without negotiating a license",
   "significance": "first fully open omnimodel, generating text, image, video, ambient sound, and robot action in one model",
   "organization_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "On June 1, 2026, Nvidia released **Cosmos 3**, which it calls the first fully open omnimodel for physical AI."
   ],
   "model_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "On June 1, 2026, Nvidia released **Cosmos 3**, which it calls the first fully open omnimodel for physical AI."
   ],
   "release_date_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "On June 1, 2026, Nvidia released **Cosmos 3**, which it calls the first fully open omnimodel for physical AI."
   ],
   "parameters_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "It ships as a 16B Nano and a 64B Super, each pairing a reasoning tower with a diffusion generation tower, with a 2B Edge variant coming."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "The weights are open, the benchmarks are public, and the licensing is permissive enough that a startup can build a commercial product on top without negotiating a license."
   ],
   "significance_evidence": [
    "Model Release",
    "Nvidia Cosmos 3 Launches Open Physical AI Omnimodel",
    "Cosmos 3 is the first fully open omnimodel, generating text, image, video, ambient sound, and robot action in one model."
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  },
  {
   "url": "https://ai-tldr.dev/releases/qwen-agentworld",
   "title": "Qwen-AgentWorld \u2014 language world models that simulate seven agent domains",
   "published_at": "2026-06-23T12:00:00",
   "organization": "Qwen team, Alibaba Cloud",
   "model": null,
   "release_date": "2026-06-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Qwen-AgentWorld is the first open-weight model to do this across seven distinct agent domains in a single set of weights, rather than one model per environment.",
   "organization_evidence": [
    "Qwen \u00b7 2026-06-23 \u00b7 major",
    "## Quick facts",
    "| Maker | Qwen team, Alibaba Cloud |"
   ],
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   "release_date_evidence": [
    "Qwen \u00b7 2026-06-23 \u00b7 major"
   ],
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   "significance_evidence": [
    "# Qwen-AgentWorld \u2014 language world models that simulate seven agent domains",
    "## Frequently asked questions",
    "- **What is a language world model and what makes Qwen-AgentWorld different?** A language world model predicts the next state of an environment from text alone \u2014 file diffs after a shell command, the new DOM after a click, the next Android screen after a tap. Qwen-AgentWorld is the first open-weight model to do this across seven distinct agent domains in a single set of weights, rather than one model per environment."
   ]
  },
  {
   "url": "https://forem.com/techsifted/mistral-medium-35-review-a-128b-open-weight-model-with-a-coding-agent-that-opens-prs-for-you-5a0i",
   "title": "Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
   "published_at": "2026-05-03T15:21:04",
   "organization": "Mistral",
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   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Scores 77.6% on SWE-Bench Verified, 91.4% on \u0307\u0003-Telecom, and ships alongside Vibe, a cloud coding agent that submits pull requests directly to GitHub.",
   "organization_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input."
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   "release_date_evidence": [
    "Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input.",
    "Medium 3.5 is their April 2026 flagship \u2014 released April 29, 2026."
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "128B parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "It scores 77.6% on SWE-Bench Verified \u2014 close but not ahead of Claude Sonnet 4.6 \u2014 and ships alongside Vibe, a cloud coding agent that submits pull requests directly to GitHub without you babysitting it.",
    "- Mistral Medium 3.5: 77.6%"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/google-gemma-4-beats-400b-open-rivals-on-a-31b-model",
   "title": "Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
   "published_at": "2026-06-04T20:56:47",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026",
   "parameters": "Four sizes from 2.3B to 31B",
   "context_window": "128K on the small variants and 256K on the larger two",
   "license": "Apache 2.0",
   "significance": "Gemma 4 31B scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench while fitting on a single consumer GPU; Gemma 4 tops Llama 4 and Qwen 3.5 on agentic tasks at 86.4%, the workload Google deliberately tuned for",
   "organization_evidence": [
    "Model Release",
    "# Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
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   "model_evidence": [
    "Model Release",
    "# Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
    "Google Gemma 4 ships under Apache 2.0 and its 31B model beats 400B open rivals on agentic and coding tests while running on a single GPU."
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   "release_date_evidence": [
    "Model Release",
    "# Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
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    "- Gemma 4 31B scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench while fitting on a single consumer GPU"
   ],
   "parameters_evidence": [
    "Model Release",
    "# Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
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   "context_window_evidence": [
    "Model Release",
    "# Google Gemma 4 Beats 400B Open Rivals on a 31B Model",
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   "license_evidence": [
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    "- Gemma 4 31B scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench while fitting on a single consumer GPU",
    "- Gemma 4 tops Llama 4 and Qwen 3.5 on agentic tasks at 86.4%, the workload Google deliberately tuned for"
   ]
  },
  {
   "url": "https://noqta.tn/en/news/moonshot-kimi-k2-6-open-source-coding-agent-swarm-2026",
   "title": "Moonshot AI Releases Kimi K2.6: Open-Source Model Matches Opus 4.6 on SWE-Bench and Orchestrates 300-Agent Swarms",
   "published_at": "2026-04-21T00:00:00",
   "organization": "Moonshot AI",
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   "release_date": "2026-04-20",
   "parameters": "1T",
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   "license": null,
   "significance": "58.6 on SWE-Bench Pro, ahead of GPT-5.4 (57.7), Claude Opus 4.6 (53.4) and Gemini 3.1 Pro (54.2)",
   "organization_evidence": [
    "Moonshot AI Releases Kimi K2.6: Open-Source Model Matches Opus 4.6 on SWE-Bench and Orchestrates 300-Agent Swarms",
    "Beijing-based Moonshot AI has released Kimi K2.6, a one-trillion-parameter open-weights model that dethrones every frontier lab on Humanity's Last Exam with tools and narrowly beats GPT-5.4 on SWE-Bench Pro."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI Releases Kimi K2.6: Open-Source Model Matches Opus 4.6 on SWE-Bench and Orchestrates 300-Agent Swarms",
    "Announced on April 20, 2026, the model ships under a Modified MIT License and is immediately available on Kimi.com, the Kimi app, the official API, and the Kimi Code CLI \u2014 closing the gap between Chinese open-source models and proprietary Western systems to a matter of points."
   ],
   "parameters_evidence": [
    "Moonshot AI Releases Kimi K2.6: Open-Source Model Matches Opus 4.6 on SWE-Bench and Orchestrates 300-Agent Swarms",
    "Beijing-based Moonshot AI has released Kimi K2.6, a one-trillion-parameter open-weights model that dethrones every frontier lab on Humanity's Last Exam with tools and narrowly beats GPT-5.4 on SWE-Bench Pro.",
    "- **256K context window**, 1T total parameters with 32B activated per token via 384-expert MoE"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI Releases Kimi K2.6: Open-Source Model Matches Opus 4.6 on SWE-Bench and Orchestrates 300-Agent Swarms",
    "- **58.6 on SWE-Bench Pro**, ahead of GPT-5.4 (57.7), Claude Opus 4.6 (53.4) and Gemini 3.1 Pro (54.2)"
   ]
  },
  {
   "url": "https://aimadetools.com/blog/what-is-z-ai-zhipu",
   "title": "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Z.ai",
   "model": "GLM-5",
   "release_date": "2026-02",
   "parameters": "744B MoE (40B active)",
   "context_window": null,
   "license": "MIT license",
   "significance": "First frontier-class open model on Huawei chips",
   "organization_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai just released GLM-5.1, the first open-source model to top SWE-Bench Pro \u2014 beating GPT-5.4 and Claude Opus 4.6."
   ],
   "model_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5 | 744B MoE (40B active) | Feb 2026 | First frontier-class open model on Huawei chips |"
   ],
   "release_date_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5 | 744B MoE (40B active) | Feb 2026 | First frontier-class open model on Huawei chips |"
   ],
   "parameters_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5 | 744B MoE (40B active) | Feb 2026 | First frontier-class open model on Huawei chips |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "- **Flagship model:** GLM-5.1 (754B MoE, MIT license)",
    "While many Chinese AI labs use restrictive licenses, Z.ai chose MIT for GLM-5.1 \u2014 the most permissive open-source license available."
   ],
   "significance_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5 | 744B MoE (40B active) | Feb 2026 | First frontier-class open model on Huawei chips |"
   ]
  },
  {
   "url": "https://aimadetools.com/blog/what-is-z-ai-zhipu",
   "title": "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Z.ai",
   "model": "GLM-5.1",
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   "license": "MIT license",
   "significance": "#1 on SWE-Bench Pro",
   "organization_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
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   "model_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai just released GLM-5.1, the first open-source model to top SWE-Bench Pro \u2014 beating GPT-5.4 and Claude Opus 4.6."
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   "release_date_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5.1 | 754B MoE (40B active) | Apr 2026 | #1 on SWE-Bench Pro |"
   ],
   "parameters_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5.1 | 754B MoE (40B active) | Apr 2026 | #1 on SWE-Bench Pro |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "While many Chinese AI labs use restrictive licenses, Z.ai chose MIT for GLM-5.1 \u2014 the most permissive open-source license available."
   ],
   "significance_evidence": [
    "What is Z.ai (Zhipu)? The Lab Behind GLM-5.1",
    "Z.ai has released several generations of GLM models:",
    "| GLM-5.1 | 754B MoE (40B active) | Apr 2026 | #1 on SWE-Bench Pro |"
   ]
  },
  {
   "url": "https://aipressa.com/top-stories/minimax-launches-m2-7-ai-model-free-surpassing-gemini-3-1-pro-with-229-billion-parameters",
   "title": "MiniMax Launches M2.7 AI Model Free, Surpassing Gemini 3.1 Pro with 229 Billion Parameters",
   "published_at": "2026-04-13T19:00:22",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "229 billion",
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   "license": null,
   "significance": "state-of-the-art (SOTA) performance in benchmarks such as SWE-Pro and Terminal Bench 2, where it achieved scores of 56.22% and 57.0%, respectively.",
   "organization_evidence": [
    "MiniMax, a prominent AI development company based in China, has unveiled its latest AI model, **MiniMax M2.7**, boasting an impressive 229 billion parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 12, 2026, MiniMax officially released M2.7 as an open-source model, highlighting its state-of-the-art (SOTA) performance in benchmarks such as SWE-Pro and Terminal Bench 2, where it achieved scores of 56.22% and 57.0%, respectively.",
    "MiniMax, a prominent AI development company based in China, has unveiled its latest AI model, **MiniMax M2.7**, boasting an impressive 229 billion parameters."
   ],
   "parameters_evidence": [
    "MiniMax, a prominent AI development company based in China, has unveiled its latest AI model, **MiniMax M2.7**, boasting an impressive 229 billion parameters."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On April 12, 2026, MiniMax officially released M2.7 as an open-source model, highlighting its state-of-the-art (SOTA) performance in benchmarks such as SWE-Pro and Terminal Bench 2, where it achieved scores of 56.22% and 57.0%, respectively.",
    "MiniMax, a prominent AI development company based in China, has unveiled its latest AI model, **MiniMax M2.7**, boasting an impressive 229 billion parameters."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3.8-Max",
   "title": "Qwen",
   "published_at": "2026-07-19T12:31:53",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Alibaba stated that Qwen 3.5 is able to operate desktop and mobile applications.",
   "organization_evidence": [
    "Qwen (also known as Tongyi Qianwen, Chinese: \u901a\u4e49\u5343\u95ee; pinyin: T\u014dngy\u00ec Qi\u0101nw\u00e8n) is a family of large language models developed by Alibaba Cloud.",
    "In February 2026, Alibaba released the open-weights Qwen3.5 and the proprietary Qwen3.5-Plus."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "In February 2026, Alibaba released the open-weights Qwen3.5 and the proprietary Qwen3.5-Plus.",
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  },
  {
   "url": "https://en.wikipedia.org/wiki/Qwen3.8-Max",
   "title": "Qwen",
   "published_at": "2026-07-19T12:31:53",
   "organization": "Alibaba",
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   "organization_evidence": [
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    "Qwen3.6 model released under the Apache License in the same month."
   ],
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   "release_date_evidence": null,
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   "significance_evidence": null
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Mistral AI",
   "model": "Ministral 3B",
   "release_date": "2025-12",
   "parameters": "3B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Ministral 8B (December 2025, Apache 2.0) is the current best Apache 2.0 alternative to Llama-family models for the 7B\u20138B tier \u2014 no license friction, strong reasoning per the AIME benchmark results.",
   "organization_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Organization:** Mistral AI \u2014 Paris, France."
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   "model_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 3B | 3B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "release_date_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 3B | 3B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "parameters_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 3B | 3B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "context_window_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 3B | 3B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "license_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 3B | 3B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "significance_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Suitability for specialist fine-tuning:** Ministral 8B (December 2025, Apache 2.0) is the current best Apache 2.0 alternative to Llama-family models for the 7B\u20138B tier \u2014 no license friction, strong reasoning per the AIME benchmark results."
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Mistral AI",
   "model": "Ministral 8B",
   "release_date": "2025-12",
   "parameters": "8B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Ministral 8B (December 2025, Apache 2.0) is the current best Apache 2.0 alternative to Llama-family models for the 7B\u20138B tier \u2014 no license friction, strong reasoning per the AIME benchmark results.",
   "organization_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Organization:** Mistral AI \u2014 Paris, France."
   ],
   "model_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 8B | 8B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "release_date_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 8B | 8B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "parameters_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 8B | 8B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "context_window_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 8B | 8B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "license_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 8B | 8B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "significance_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Suitability for specialist fine-tuning:** Ministral 8B (December 2025, Apache 2.0) is the current best Apache 2.0 alternative to Llama-family models for the 7B\u20138B tier \u2014 no license friction, strong reasoning per the AIME benchmark results."
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Mistral AI",
   "model": "Ministral 14B",
   "release_date": "2025-12",
   "parameters": "14B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Ministral 14B is compelling for users with A100 access who want best-in-class small-model reasoning without the Qwen Chinese-origin caveat.",
   "organization_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Organization:** Mistral AI \u2014 Paris, France."
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   "model_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 14B | 14B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "release_date_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 14B | 14B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "parameters_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 14B | 14B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "context_window_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 14B | 14B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "license_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Ministral 14B (dense) achieves 85% on AIME 2025, outperforming Qwen 14B (73.7%), making it one of the strongest small reasoning models available.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Ministral 14B | 14B | 128K | Apache 2.0 | Dec 2025 |"
   ],
   "significance_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Suitability for specialist fine-tuning:** Ministral 8B (December 2025, Apache 2.0) is the current best Apache 2.0 alternative to Llama-family models for the 7B\u20138B tier \u2014 no license friction, strong reasoning per the AIME benchmark results.",
    "Ministral 14B is compelling for users with A100 access who want best-in-class small-model reasoning without the Qwen Chinese-origin caveat."
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Large 3",
   "release_date": "2025-12",
   "parameters": "41B active / 675B total",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
   "organization_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Organization:** Mistral AI \u2014 Paris, France."
   ],
   "model_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Large 3 | 41B active / 675B total | 256K | Apache 2.0 | Dec 2025 |"
   ],
   "release_date_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Large 3 | 41B active / 675B total | 256K | Apache 2.0 | Dec 2025 |"
   ],
   "parameters_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Large 3 | 41B active / 675B total | 256K | Apache 2.0 | Dec 2025 |"
   ],
   "context_window_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Large 3 | 41B active / 675B total | 256K | Apache 2.0 | Dec 2025 |"
   ],
   "license_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window.",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Large 3 | 41B active / 675B total | 256K | Apache 2.0 | Dec 2025 |"
   ],
   "significance_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Large 3: 41B active / 675B total parameters; 256K context window."
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Mistral Small 4; merges Magistral (reasoning), Pixtral (vision), Devstral (agentic coding) into a single model",
   "organization_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "**Organization:** Mistral AI \u2014 Paris, France."
   ],
   "model_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Small 4 is a unified multimodal model (text + vision + reasoning + code agent).",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Small 4 | \u2014 | 128K | Apache 2.0 | Mar 16, 2026 |"
   ],
   "release_date_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "- **March 16, 2026** \u2014 Mistral Small 4; merges Magistral (reasoning), Pixtral (vision), Devstral (agentic coding) into a single model",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Small 4 | \u2014 | 128K | Apache 2.0 | Mar 16, 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Small 4 is a unified multimodal model (text + vision + reasoning + code agent).",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Small 4 | \u2014 | 128K | Apache 2.0 | Mar 16, 2026 |"
   ],
   "license_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "Mistral Small 4 is a unified multimodal model (text + vision + reasoning + code agent).",
    "| Model | Parameters (active) | Context | License | Released |",
    "| Mistral Small 4 | \u2014 | 128K | Apache 2.0 | Mar 16, 2026 |"
   ],
   "significance_evidence": [
    "Mistral AI \u2014 Mistral 3 and Small 4",
    "- **March 16, 2026** \u2014 Mistral Small 4; merges Magistral (reasoning), Pixtral (vision), Devstral (agentic coding) into a single model"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4 E2B",
   "release_date": "2026-04-02",
   "parameters": "~2.3B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Text + image + audio; phone-deployable",
   "organization_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Organization:** Google DeepMind \u2014 London, UK / Mountain View, CA."
   ],
   "model_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E2B | ~2.3B | 128K | Text + image + audio; phone-deployable |"
   ],
   "release_date_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)"
   ],
   "parameters_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E2B | ~2.3B | 128K | Text + image + audio; phone-deployable |"
   ],
   "context_window_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E2B | ~2.3B | 128K | Text + image + audio; phone-deployable |"
   ],
   "license_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Licensing:** Apache 2.0 for the entire Gemma 4 family \u2014 a significant policy change from Gemma 1/2/3 which used the more restrictive Gemma Terms of Use."
   ],
   "significance_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E2B | ~2.3B | 128K | Text + image + audio; phone-deployable |"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4 E4B",
   "release_date": "2026-04-02",
   "parameters": "~4.5B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Text + image + audio; strong for size",
   "organization_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Organization:** Google DeepMind \u2014 London, UK / Mountain View, CA."
   ],
   "model_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E4B | ~4.5B | 128K | Text + image + audio; strong for size |"
   ],
   "release_date_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)"
   ],
   "parameters_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E4B | ~4.5B | 128K | Text + image + audio; strong for size |"
   ],
   "context_window_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E4B | ~4.5B | 128K | Text + image + audio; strong for size |"
   ],
   "license_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Licensing:** Apache 2.0 for the entire Gemma 4 family \u2014 a significant policy change from Gemma 1/2/3 which used the more restrictive Gemma Terms of Use."
   ],
   "significance_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 E4B | ~4.5B | 128K | Text + image + audio; strong for size |"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4 26B MoE",
   "release_date": "2026-04-02",
   "parameters": "3.8B active / 26B total",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Efficient; frontier reasoning at low compute cost",
   "organization_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Organization:** Google DeepMind \u2014 London, UK / Mountain View, CA."
   ],
   "model_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 26B MoE | 3.8B active / 26B total | 256K | Efficient; frontier reasoning at low compute cost |"
   ],
   "release_date_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)"
   ],
   "parameters_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 26B MoE | 3.8B active / 26B total | 256K | Efficient; frontier reasoning at low compute cost |"
   ],
   "context_window_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 26B MoE | 3.8B active / 26B total | 256K | Efficient; frontier reasoning at low compute cost |"
   ],
   "license_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Licensing:** Apache 2.0 for the entire Gemma 4 family \u2014 a significant policy change from Gemma 1/2/3 which used the more restrictive Gemma Terms of Use."
   ],
   "significance_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 26B MoE | 3.8B active / 26B total | 256K | Efficient; frontier reasoning at low compute cost |"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4 31B Dense",
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Best for fine-tuning; 85.2% MMLU Pro; #3 Arena AI",
   "organization_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Organization:** Google DeepMind \u2014 London, UK / Mountain View, CA."
   ],
   "model_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 31B Dense | 31B | 256K | Best for fine-tuning; 85.2% MMLU Pro; #3 Arena AI |"
   ],
   "release_date_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **April 2, 2026** \u2014 Gemma 4 (E2B, E4B, 26B MoE, 31B Dense); Apache 2.0 for the first time; multimodal (images + audio)"
   ],
   "parameters_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 31B Dense | 31B | 256K | Best for fine-tuning; 85.2% MMLU Pro; #3 Arena AI |"
   ],
   "context_window_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 31B Dense | 31B | 256K | Best for fine-tuning; 85.2% MMLU Pro; #3 Arena AI |"
   ],
   "license_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Licensing:** Apache 2.0 for the entire Gemma 4 family \u2014 a significant policy change from Gemma 1/2/3 which used the more restrictive Gemma Terms of Use."
   ],
   "significance_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 31B Dense | 31B | 256K | Best for fine-tuning; 85.2% MMLU Pro; #3 Arena AI |"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4 12B",
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "unified multimodal architecture; processes images and audio without separate encoders",
   "organization_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Organization:** Google DeepMind \u2014 London, UK / Mountain View, CA."
   ],
   "model_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **June 3, 2026** \u2014 Gemma 4 12B; unified multimodal architecture; processes images and audio without separate encoders",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 12B | 12B | 128K | Released June 3, 2026; unified multimodal |"
   ],
   "release_date_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **June 3, 2026** \u2014 Gemma 4 12B; unified multimodal architecture; processes images and audio without separate encoders"
   ],
   "parameters_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 12B | 12B | 128K | Released June 3, 2026; unified multimodal |"
   ],
   "context_window_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "Gemma 4 uses interleaved local/global attention (alternating sliding window and full attention layers) and logit soft-capping, both carried forward from Gemma 2.",
    "| Model | Parameters (active) | Context | Notes |",
    "| Gemma 4 12B | 12B | 128K | Released June 3, 2026; unified multimodal |"
   ],
   "license_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "**Licensing:** Apache 2.0 for the entire Gemma 4 family \u2014 a significant policy change from Gemma 1/2/3 which used the more restrictive Gemma Terms of Use."
   ],
   "significance_evidence": [
    "Google DeepMind \u2014 Gemma 4",
    "- **June 3, 2026** \u2014 Gemma 4 12B; unified multimodal architecture; processes images and audio without separate encoders"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Microsoft Research",
   "model": "Phi-4-Reasoning-Vision-15B",
   "release_date": "2026-03",
   "parameters": "15B",
   "context_window": null,
   "license": "MIT",
   "significance": "visual reasoning; available via HuggingFace and Azure AI Foundry",
   "organization_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "**Organization:** Microsoft Research \u2014 Redmond, Washington."
   ],
   "model_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "- **March 2026** \u2014 Phi-4-Reasoning-Vision-15B; visual reasoning; available via HuggingFace and Azure AI Foundry",
    "| Phi-4-Reasoning-Vision-15B | 15B | \u2014 | MIT | Mar 2026 |"
   ],
   "release_date_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "- **March 2026** \u2014 Phi-4-Reasoning-Vision-15B; visual reasoning; available via HuggingFace and Azure AI Foundry",
    "| Phi-4-Reasoning-Vision-15B | 15B | \u2014 | MIT | Mar 2026 |"
   ],
   "parameters_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "- **March 2026** \u2014 Phi-4-Reasoning-Vision-15B; visual reasoning; available via HuggingFace and Azure AI Foundry",
    "| Phi-4-Reasoning-Vision-15B | 15B | \u2014 | MIT | Mar 2026 |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "| Phi-4-Reasoning-Vision-15B | 15B | \u2014 | MIT | Mar 2026 |"
   ],
   "significance_evidence": [
    "Microsoft Research \u2014 Phi-4",
    "- **March 2026** \u2014 Phi-4-Reasoning-Vision-15B; visual reasoning; available via HuggingFace and Azure AI Foundry"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5",
   "release_date": "2026-02-16",
   "parameters": "17B active / 397B total",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Qwen3.5 (397B total / 17B active MoE); 256K native context; 256 experts",
   "organization_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "**Organization:** Alibaba Cloud Intelligence (\u963f\u91cc\u4e91), a division of Alibaba Group \u2014 Hangzhou, China."
   ],
   "model_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "- **February 16, 2026** \u2014 Qwen3.5 (397B total / 17B active MoE); 256K native context; 256 experts",
    "| Qwen3.5 | 17B active / 397B total | 256K | Apache 2.0 | Feb 16, 2026 |"
   ],
   "release_date_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "- **February 16, 2026** \u2014 Qwen3.5 (397B total / 17B active MoE); 256K native context; 256 experts"
   ],
   "parameters_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "- **February 16, 2026** \u2014 Qwen3.5 (397B total / 17B active MoE); 256K native context; 256 experts",
    "| Qwen3.5 | 17B active / 397B total | 256K | Apache 2.0 | Feb 16, 2026 |"
   ],
   "context_window_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "Qwen3.5 uses 256 experts with 8 routed + 1 shared expert per token; 397B total parameters but only 17B active. 256K token native context window.",
    "| Qwen3.5 | 17B active / 397B total | 256K | Apache 2.0 | Feb 16, 2026 |"
   ],
   "license_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "| Qwen3.5 | 17B active / 397B total | 256K | Apache 2.0 | Feb 16, 2026 |"
   ],
   "significance_evidence": [
    "Alibaba Cloud \u2014 Qwen3 and Qwen3.5",
    "- **February 16, 2026** \u2014 Qwen3.5 (397B total / 17B active MoE); 256K native context; 256 experts"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4",
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": "1M",
   "license": "MIT",
   "significance": "1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use",
   "organization_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "**Organization:** DeepSeek (\u6df1\u5ea6\u6c42\u7d22) \u2014 Hangzhou, China."
   ],
   "model_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use",
    "| DeepSeek-V4 | \u2014 / large MoE | 1M | MIT | Apr 24, 2026 |"
   ],
   "release_date_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use",
    "| DeepSeek-V4 | \u2014 / large MoE | 1M | MIT | Apr 24, 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "| DeepSeek-V4 | \u2014 / large MoE | 1M | MIT | Apr 24, 2026 |"
   ],
   "license_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "| DeepSeek-V4 | \u2014 / large MoE | 1M | MIT | Apr 24, 2026 |"
   ],
   "significance_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use"
   ]
  },
  {
   "url": "https://jaredwatkins.com/research/local-llm-finetuning/fundamentals/base-models",
   "title": "Base Models for Fine-Tuning",
   "published_at": "2026-06-10T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": "1M",
   "license": "MIT",
   "significance": "1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use",
   "organization_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "**Organization:** DeepSeek (\u6df1\u5ea6\u6c42\u7d22) \u2014 Hangzhou, China."
   ],
   "model_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use"
   ],
   "release_date_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use"
   ],
   "license_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "**Licensing:** MIT license for V3, R1, distillations, V3.1, V3.2, and most of the stack \u2014 genuinely permissive."
   ],
   "significance_evidence": [
    "DeepSeek \u2014 V3.1, V4, and R1 Distillations",
    "- **April 24, 2026** \u2014 DeepSeek-V4 and V4-Pro; 1M token native context; trained on 32T+ tokens; architectural leap for ultra-long context production use"
   ]
  },
  {
   "url": "https://chatgptaihub.com/mistral-2026-lineup-guide",
   "title": "Mistral 2026 Lineup Complete Guide: Large 3, Small 4, Devstral 2, Ministral 3, Voxtral",
   "published_at": "2026-04-27T18:46:53",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "balances performance and scalability for production workloads",
   "organization_evidence": [
    "Mistral 2026 Lineup Complete Guide: Large 3, Small 4, Devstral 2, Ministral 3, Voxtral"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral 2026 Lineup Complete Guide: Large 3, Small 4, Devstral 2, Ministral 3, Voxtral"
   ],
   "parameters_evidence": [
    "Mistral 2026 Lineup Complete Guide: Large 3, Small 4, Devstral 2, Ministral 3, Voxtral"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral 2026 Lineup Complete Guide: Large 3, Small 4, Devstral 2, Ministral 3, Voxtral"
   ]
  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/google-releases-gemma-4-12b-frontier-multimodal-ai-on-a-laptop",
   "title": "Google Releases Gemma 4 12B: Frontier Multimodal AI on a Laptop",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "approaches the performance of the family's 26B Mixture-of-Experts model at less than half the memory footprint",
   "organization_evidence": [
    "# Google Releases Gemma 4 12B: Frontier Multimodal AI on a Laptop",
    "On June 3, 2026, Google DeepMind released **Gemma 4 12B**, a unified, encoder-free multimodal model that brings frontier-class intelligence to a single 16 GB laptop."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Releases Gemma 4 12B: Frontier Multimodal AI on a Laptop",
    "On June 3, 2026, Google DeepMind released **Gemma 4 12B**, a unified, encoder-free multimodal model that brings frontier-class intelligence to a single 16 GB laptop."
   ],
   "parameters_evidence": [
    "# Google Releases Gemma 4 12B: Frontier Multimodal AI on a Laptop",
    "The 11.95-billion-parameter model handles text, images, and audio in one decoder-only transformer, supports a 256K-token context across 140+ languages, and ships under a fully permissive Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Releases Gemma 4 12B: Frontier Multimodal AI on a Laptop",
    "Google says it approaches the performance of the family\u2019s 26B Mixture-of-Experts model at less than half the memory footprint."
   ]
  },
  {
   "url": "https://noqta.tn/en/news/moonshot-kimi-k2-7-code-open-source-coding-model-2026",
   "title": "Moonshot AI Releases Kimi K2.7-Code, a 1T-Parameter Open-Source Coding Model",
   "published_at": "2026-06-13T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Posts a 21.8% gain on Kimi Code Bench v2 while burning roughly 30% fewer reasoning tokens",
   "organization_evidence": [
    "Moonshot AI released **Kimi K2.7-Code** on June 12, 2026, a coding-focused, open-weight successor to Kimi K2.6 that the company says posts a **21.8% gain on its Kimi Code Bench v2** while burning roughly **30% fewer reasoning tokens**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released **Kimi K2.7-Code** on June 12, 2026, a coding-focused, open-weight successor to Kimi K2.6 that the company says posts a **21.8% gain on its Kimi Code Bench v2** while burning roughly **30% fewer reasoning tokens**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI released **Kimi K2.7-Code** on June 12, 2026, a coding-focused, open-weight successor to Kimi K2.6 that the company says posts a **21.8% gain on its Kimi Code Bench v2** while burning roughly **30% fewer reasoning tokens**."
   ]
  },
  {
   "url": "https://morphllm.com/glm-5-2",
   "title": "GLM-5.2: 753B Open-Weight Coding Model, 1M Context, Benchmarks, Pricing (2026)",
   "published_at": "2026-07-12T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "753B",
   "context_window": null,
   "license": null,
   "significance": "Top open-weights coding model, beating GPT-5.5 on SWE-bench Pro.",
   "organization_evidence": [
    "# GLM-5.2: 753B Open-Weight Coding Model, 1M Context, MIT",
    "GLM-5.2 is Zhipu AI's 753B MoE coding model, released June 13, 2026 under MIT with a 1M-token context."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2: 753B Open-Weight Coding Model, 1M Context, MIT",
    "GLM-5.2 is Zhipu AI's 753B MoE coding model, released June 13, 2026 under MIT with a 1M-token context."
   ],
   "parameters_evidence": [
    "# GLM-5.2: 753B Open-Weight Coding Model, 1M Context, MIT",
    "GLM-5.2 is Zhipu AI's 753B MoE coding model, released June 13, 2026 under MIT with a 1M-token context."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2: 753B Open-Weight Coding Model, 1M Context, MIT",
    "GLM-5.2 is Zhipu AI's 753B MoE coding model, released June 13, 2026 under MIT with a 1M-token context.",
    "It scores 62.1 on SWE-bench Pro (beating GPT-5.5's 58.6) at ~$1.40/$4.40 per M tokens."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/minimax-m3-open-weight-frontier-coding-1m-context-multimodal-june-2026",
   "title": "MiniMax M3: An Open-Weight Model With Frontier Coding and a 1M-Token Context",
   "published_at": "2026-06-06T03:45:04",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59% on SWE-Bench Pro and 83.5 on BrowseComp",
   "organization_evidence": [
    "MiniMax M3: An Open-Weight Model With Frontier Coding and a 1M-Token Context",
    "On June 1, 2026, the lab MiniMax released MiniMax M3, and it is one of the most interesting open-weight model launches of the year."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3: An Open-Weight Model With Frontier Coding and a 1M-Token Context",
    "MiniMax M3, released June 1, 2026, is an open-weight LLM pairing frontier-level coding, a 1-million-token context window, and native multimodality \u2014 and the weights are coming to Hugging Face."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3: An Open-Weight Model With Frontier Coding and a 1M-Token Context",
    "Frontier Coding and Agentic Web Search Benchmarks",
    "MiniMax reports that M3 scores 59% on SWE-Bench Pro, placing it ahead of several leading proprietary systems on that coding benchmark and just behind the current frontier.",
    "On BrowseComp, a test of autonomous web search and multi-step reasoning, MiniMax reports a score of 83.5, edging out strong proprietary baselines."
   ]
  },
  {
   "url": "https://basedai.co/compare/gpt-oss-vs-deepseek",
   "title": "gpt-oss vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-level reasoning and coding on an open license",
   "organization_evidence": [
    "gpt-oss vs DeepSeek at a glance",
    "| Vendor | OpenAI (United States) | DeepSeek (China) |",
    "## About DeepSeek V4",
    "DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "gpt-oss vs DeepSeek at a glance",
    "| Released | 2025-08-05 | 2026-04-24 |",
    "## About DeepSeek V4",
    "DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "gpt-oss vs DeepSeek at a glance",
    "| Open weights | Yes \u2014 downloadable | Yes \u2014 downloadable |",
    "## About DeepSeek V4",
    "DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier.",
    "## Choosing between them",
    "- Frontier-level reasoning and coding on an open license"
   ]
  },
  {
   "url": "https://vantaige.io/ai-tool/llama",
   "title": "Llama by Meta: Open-Weight AI Models Review 2026",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03",
   "parameters": "119B total parameters",
   "context_window": null,
   "license": null,
   "significance": "Mistral Small 4 uses a MoE architecture with 119B total parameters but only 4 experts active per token, yielding approximately 6B active parameters per inference, with Apache 2.0 license.",
   "organization_evidence": [
    "All three are open-weight model families.",
    "**Mistral (Mistral AI, France):** Mistral Small 4 (March 2026) uses a MoE architecture with 119B total parameters but only 4 experts active per token, yielding approximately 6B active parameters per inference -- less than half of Llama 4 Scout's 17B active."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Mistral (Mistral AI, France):** Mistral Small 4 (March 2026) uses a MoE architecture with 119B total parameters but only 4 experts active per token, yielding approximately 6B active parameters per inference -- less than half of Llama 4 Scout's 17B active."
   ],
   "parameters_evidence": [
    "**Mistral (Mistral AI, France):** Mistral Small 4 (March 2026) uses a MoE architecture with 119B total parameters but only 4 experts active per token, yielding approximately 6B active parameters per inference -- less than half of Llama 4 Scout's 17B active."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**Mistral (Mistral AI, France):** Mistral Small 4 (March 2026) uses a MoE architecture with 119B total parameters but only 4 experts active per token, yielding approximately 6B active parameters per inference -- less than half of Llama 4 Scout's 17B active.",
    "License is Apache 2.0: fully permissive, no MAU cap, no downstream restrictions, no attribution requirement."
   ]
  },
  {
   "url": "https://gncrypto.news/news/mistral-128b-medium-3-5-pricing-criticism",
   "title": "Mistral launches 128B Medium 3.5 model, pricing draws criticism",
   "published_at": "2026-04-30T22:06:11",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Mistral reported a 77.6% score on SWE-Bench Verified and a 91.4% score on \u03c4\u00b3-Telecom.",
   "organization_evidence": [
    "# Mistral launches 128B Medium 3.5 model, pricing draws criticism",
    "Mistral AI released Medium 3.5, a 128\u2011billion-parameter open model on April 29, priced at $1.50 per million input tokens and $7.50 per million output tokens; reactions were muted."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral launches 128B Medium 3.5 model, pricing draws criticism",
    "Mistral AI released Medium 3.5, a 128\u2011billion-parameter open model on April 29, priced at $1.50 per million input tokens and $7.50 per million output tokens; reactions were muted."
   ],
   "parameters_evidence": [
    "# Mistral launches 128B Medium 3.5 model, pricing draws criticism",
    "Mistral AI released Medium 3.5, a 128\u2011billion-parameter open model on April 29, priced at $1.50 per million input tokens and $7.50 per million output tokens; reactions were muted."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral launches 128B Medium 3.5 model, pricing draws criticism",
    "On internal benchmarks, Mistral reported a 77.6% score on SWE-Bench Verified, which tests whether a model can generate working patches to fix real GitHub issues, and a 91.4% score on \u03c4\u00b3-Telecom, a test of agentic tool use in specialized environments."
   ]
  },
  {
   "url": "https://digitalstrategy-ai.com/2026/05/13/gemma-4-googles-open-source-llm",
   "title": "Gemma 4 Review: Google's Apache 2.0 Open-Source AI (2026)",
   "published_at": "2026-05-13T17:49:23",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B A4B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 31B scores 89.2% on AIME 2026 math, 84.3% on GPQA Diamond, and 85.2% on MMLU-Pro, putting it at the top of the dense single-GPU class.",
   "organization_evidence": [
    "Gemma 4 vs DeepSeek V4 vs Qwen 3.6: Open-Source AI 2026",
    "Open Source [AI](https://digitalstrategy-ai.com/2026/05/02/april2026-topai-news/)`Apache 2.0` Google DeepMind `April 2026` Open-Weight Race",
    "**In one paragraph:** Google DeepMind released **Gemma 4** on April 2, 2026 \u2014 four open-weight model sizes (E2B, E4B, 26B A4B, 31B) shipping under a commercially permissive **Apache 2.0 license**, with a 256K token context window, native multimodal text/image/audio support, and the ability to run on hardware from phones to servers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 vs DeepSeek V4 vs Qwen 3.6: Open-Source AI 2026",
    "Open Source [AI](https://digitalstrategy-ai.com/2026/05/02/april2026-topai-news/)`Apache 2.0` Google DeepMind `April 2026` Open-Weight Race",
    "**In one paragraph:** Google DeepMind released **Gemma 4** on April 2, 2026 \u2014 four open-weight model sizes (E2B, E4B, 26B A4B, 31B) shipping under a commercially permissive **Apache 2.0 license**, with a 256K token context window, native multimodal text/image/audio support, and the ability to run on hardware from phones to servers."
   ],
   "parameters_evidence": [
    "Gemma 4 vs DeepSeek V4 vs Qwen 3.6: Open-Source AI 2026",
    "Open Source [AI](https://digitalstrategy-ai.com/2026/05/02/april2026-topai-news/)`Apache 2.0` Google DeepMind `April 2026` Open-Weight Race",
    "**In one paragraph:** Google DeepMind released **Gemma 4** on April 2, 2026 \u2014 four open-weight model sizes (E2B, E4B, 26B A4B, 31B) shipping under a commercially permissive **Apache 2.0 license**, with a 256K token context window, native multimodal text/image/audio support, and the ability to run on hardware from phones to servers."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 vs DeepSeek V4 vs Qwen 3.6: Open-Source AI 2026",
    "Open Source [AI](https://digitalstrategy-ai.com/2026/05/02/april2026-topai-news/)`Apache 2.0` Google DeepMind `April 2026` Open-Weight Race",
    "Gemma 4 31B scores **89.2% on AIME 2026 math**, **84.3% on GPQA Diamond**, and **85.2% on MMLU-Pro**, putting it at the top of the dense single-GPU class."
   ]
  },
  {
   "url": "https://aiweekly.co/alerts/moonshot-open-sources-kimi-k3-a-28t-parameter-moe-model",
   "title": "Moonshot open-sources Kimi K3, a 2.8T-parameter MoE model",
   "published_at": "2026-07-27T21:12:01",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "2.8 trillion total parameters and 104 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts stack draws from 896 experts (16 selected per token), spans a 1-million-token context, handles text, images and video natively; self-reported numbers include 93.5 on GPQA Diamond, 88.3 on Terminal-Bench 2.1 and 91.2 on BrowseComp.",
   "organization_evidence": [
    "# Moonshot open-sources Kimi K3, a 2.8T-parameter MoE model",
    "Moonshot AI released Kimi K3 as an open-weight model with 2.8 trillion total parameters and 104 billion active per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot open-sources Kimi K3, a 2.8T-parameter MoE model",
    "Open weights posted July 26, 2026 after the July 17 launch through Kimi.com and the Kimi API, under the Kimi K3 License."
   ],
   "parameters_evidence": [
    "# Moonshot open-sources Kimi K3, a 2.8T-parameter MoE model",
    "Moonshot AI released Kimi K3 as an open-weight model with 2.8 trillion total parameters and 104 billion active per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot open-sources Kimi K3, a 2.8T-parameter MoE model",
    "The mixture-of-experts stack draws from 896 experts (16 selected per token), spans a 1-million-token context, and handles text, images and video natively.",
    "The self-reported numbers include 93.5 on GPQA Diamond, 88.3 on Terminal-Bench 2.1 and 91.2 on BrowseComp."
   ]
  },
  {
   "url": "https://medium.com/@ffguci8/glm-5-2-the-open-weights-sota-model-closing-the-gap-on-claude-and-gpt-6a3d5029223e",
   "title": "GLM-5.2: The Open-Weights SOTA Model Closing the Gap on Claude and GPT",
   "published_at": "2026-06-17T20:07:15",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It now tops the open-weights field on the Artificial Analysis Intelligence Index, runs a genuinely usable 1-million-token context window, and ships under a license that lets anyone download, modify, and deploy it commercially with no restrictions.",
   "organization_evidence": [
    "# GLM-5.2: The Open-Weights SOTA Model Closing the Gap on Claude and GPT",
    "On June 13, 2026, Z.ai (the international brand of Beijing-based Zhipu AI) put GLM-5.2 into the hands of every GLM Coding Plan subscriber."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2: The Open-Weights SOTA Model Closing the Gap on Claude and GPT",
    "On June 13, 2026, Z.ai (the international brand of Beijing-based Zhipu AI) put GLM-5.2 into the hands of every GLM Coding Plan subscriber."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2: The Open-Weights SOTA Model Closing the Gap on Claude and GPT",
    "It\u2019s also the strongest one yet: it now tops the open-weights field on the Artificial Analysis Intelligence Index, runs a genuinely usable 1-million-token context window, and ships under a license that lets anyone download, modify, and deploy it commercially with no restrictions."
   ]
  },
  {
   "url": "https://medium.com/@ffguci8/minimax-m3-the-open-weight-sota-model-that-rewrites-the-rules-4fe318056d22",
   "title": "MiniMax M3: The Open-Weight SOTA Model That Rewrites the Rules",
   "published_at": "2026-06-04T19:47:05",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open-weight model to combine frontier-level coding performance, a 1-million-token context window, and native multimodal input in one system.",
   "organization_evidence": [
    "MiniMax M3: The Open-Weight SOTA Model That Rewrites the Rules",
    "On June 1, 2026, MiniMax released M3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3: The Open-Weight SOTA Model That Rewrites the Rules",
    "On June 1, 2026, MiniMax released M3."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3: The Open-Weight SOTA Model That Rewrites the Rules",
    "The claim is direct: this is the first open-weight model to combine frontier-level coding performance, a 1-million-token context window, and native multimodal input in one system."
   ]
  },
  {
   "url": "https://bitsminds.com/news/deepseek-v4-open-source-frontier-model-2026",
   "title": "DeepSeek Releases V4: Open-Source Frontier Model at a Fraction of the Price",
   "published_at": "2026-04-24T09:43:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion",
   "context_window": null,
   "license": null,
   "significance": "packs 1.6 trillion total parameters with a Mixture-of-Experts (MoE) architecture that activates only 49 billion per token, giving it frontier-class reasoning at dramatically lower inference cost",
   "organization_evidence": [
    "DeepSeek Releases V4: Open-Source Frontier Model at a Fraction of the Price",
    "DeepSeek, the Chinese AI lab that sent shockwaves through Silicon Valley in early 2025, has released its long-awaited V4 series."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek Releases V4: Open-Source Frontier Model at a Fraction of the Price",
    "The two-model family \u2014 V4-Pro and V4-Flash \u2014 dropped on April 24, 2026, on Hugging Face under an Apache 2.0 license, making it the largest open-weights model currently available from any lab worldwide."
   ],
   "parameters_evidence": [
    "DeepSeek Releases V4: Open-Source Frontier Model at a Fraction of the Price",
    "DeepSeek's V4-Pro and V4-Flash arrive with 1.6 trillion parameters, a 1M-token context window, and Apache 2.0 licensing \u2014 matching near-frontier performance at 21x less cost than Claude Opus 4.7."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek Releases V4: Open-Source Frontier Model at a Fraction of the Price",
    "The flagship V4-Pro packs 1.6 trillion total parameters with a Mixture-of-Experts (MoE) architecture that activates only 49 billion per token, giving it frontier-class reasoning at dramatically lower inference cost."
   ]
  },
  {
   "url": "https://codersera.com/blog/llama-4-complete-guide-2026",
   "title": "Llama 4 Guide: Scout, Maverick, Behemoth Status & Muse Spark (2026)",
   "published_at": "2026-05-01T07:16:20",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": "30B",
   "context_window": null,
   "license": null,
   "significance": "a 30B dense decoder-only multimodal model (built-in vision encoder) under Apache 2.0, 128K context, text + image in, 100+ languages, distilled from Muse Spark",
   "organization_evidence": [
    "# Llama 4: The Complete Developer Guide (2026)",
    "Definitive 2026 guide to Meta's Llama 4: variants, real benchmarks, license restrictions, hosted-provider pricing, self-hosting, and competition vs DeepSeek V4, Qwen 3.5, and Gemma 4.",
    "on August 10, 2026 Meta shipped **Muse Glimmer**, a 30B Apache-2.0 open-weights model, and announced that Muse Spark 1.2\u2019s weights will follow."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Llama 4: The Complete Developer Guide (2026)",
    "Definitive 2026 guide to Meta's Llama 4: variants, real benchmarks, license restrictions, hosted-provider pricing, self-hosting, and competition vs DeepSeek V4, Qwen 3.5, and Gemma 4.",
    "on August 10, 2026 Meta shipped **Muse Glimmer**, a 30B Apache-2.0 open-weights model, and announced that Muse Spark 1.2\u2019s weights will follow."
   ],
   "parameters_evidence": [
    "# Llama 4: The Complete Developer Guide (2026)",
    "Definitive 2026 guide to Meta's Llama 4: variants, real benchmarks, license restrictions, hosted-provider pricing, self-hosting, and competition vs DeepSeek V4, Qwen 3.5, and Gemma 4.",
    "on August 10, 2026 Meta shipped **Muse Glimmer**, a 30B Apache-2.0 open-weights model, and announced that Muse Spark 1.2\u2019s weights will follow."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What's new in the Llama story since April 2026?",
    "Meta Superintelligence Labs released **Muse Glimmer**: a 30B dense decoder-only multimodal model (built-in vision encoder) under **Apache 2.0**, 128K context, text + image in, 100+ languages, distilled from Muse Spark."
   ]
  },
  {
   "url": "https://ai-bossblog.com/blog/2026-05-04-mistral-128b-model-remote-coding-agents",
   "title": "Mistral AI unveils 128B model with 256k context, remote coding agents",
   "published_at": "2026-05-04T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-05-02",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.6% on SWE-Bench, and remote coding agents in Vibe that run in the cloud",
   "organization_evidence": [
    "Mistral AI unveils 128B model with 256k context, remote coding agents",
    "Mistral AI launched Mistral Medium 3.5, a 128B dense model with 256k context window scoring 77.6% on SWE-Bench, and remote coding agents in Vibe that run in the cloud."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral AI unveils 128B model with 256k context, remote coding agents",
    "Mistral AI did three things at once on May 2: it released Mistral Medium 3.5, a 128-billion-parameter dense model that consolidates three prior offerings into a single set of weights; it launched remote coding agents inside its Vibe developer tool, allowing cloud-hosted agent sessions to run while a developer is away; and it shipped a Work mode in Le Chat, the company's consumer and enterprise chat product, that can autonomously sequence through email, calendar, and document tasks with parallel tool calls."
   ],
   "parameters_evidence": [
    "Mistral AI unveils 128B model with 256k context, remote coding agents",
    "Mistral AI launched Mistral Medium 3.5, a 128B dense model with 256k context window scoring 77.6% on SWE-Bench, and remote coding agents in Vibe that run in the cloud."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral AI unveils 128B model with 256k context, remote coding agents",
    "Mistral AI launched Mistral Medium 3.5, a 128B dense model with 256k context window scoring 77.6% on SWE-Bench, and remote coding agents in Vibe that run in the cloud."
   ]
  },
  {
   "url": "https://arturmarkus.com/google-gemma-4-ranks-3-on-arena-ai-leaderboard-31b-open-model-hits-85-2-mmlu-pro-and-89-2-aime-2026-outperforming-models-20x-larger",
   "title": "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger - AI Unfiltered",
   "published_at": "2026-04-04T12:04:15",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Ranks #3 on Arena AI leaderboard, hits 85.2% MMLU Pro and 89.2% AIME 2026, outperforming models 20x larger.",
   "organization_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space. The lineup spans from E2B (designed for mobile and IoT devices) through E4B (Android phones and laptops) to two flagship variants: a 26B Mixture of Experts model and a 31B dense model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "A 31-billion parameter model now outperforms systems with 600B+ parameters on reasoning and math benchmarks.",
    "The 31B dense variant immediately claimed the #3 position on the Arena AI text leaderboard."
   ]
  },
  {
   "url": "https://felloai.com/cs/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026",
   "parameters": "30B dense",
   "context_window": null,
   "license": null,
   "significance": "Local and consumer-hardware coding, around $0.60 per million tokens",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "It is a smaller **30B dense** model with a 128K context and pricing near **$0.60 per million tokens**, tuned for fast, cheap, local coding rather than frontier reasoning.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/cs/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": null,
   "license": "MIT",
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "model_evidence": [
    "GLM-5 (February 2026)",
    "**GLM-5** was the family\u2019s first true open frontier model.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "release_date_evidence": [
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "**GLM-5** was the family\u2019s first true open frontier model.",
    "It still shipped under the permissive MIT license.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "significance_evidence": [
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/cs/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "model_evidence": [
    "GLM-5.1 (April 2026)",
    "**GLM-5.1** was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around **94% of Claude Opus** while keeping the open weights.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "release_date_evidence": [
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/cs/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": "1 million tokens",
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "model_evidence": [
    "GLM-5.2 (June 2026)",
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "release_date_evidence": [
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": [
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "It quadruples the context window to a full **1 million tokens**, keeps long-context inference affordable, and ranks ahead of rivals like DeepSeek and Kimi on independent intelligence indexes.",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/cs/minimax-m3",
   "title": "MiniMax M3 Is Here With Frontier Coding, 1M Context, and Native Multimodality",
   "published_at": "2026-06-01T03:15:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 scores 59.0% on SWE-Bench Pro, beating GPT-5.5 and Gemini 3.1 Pro and approaching Claude Opus 4.7.",
   "organization_evidence": [
    "On **June 1, 2026**, MiniMax officially released **M3**, the Shanghai lab\u2019s next flagship language model, and it shipped exactly as promised.",
    "MiniMax M3 benchmark results"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On **June 1, 2026**, MiniMax officially released **M3**, the Shanghai lab\u2019s next flagship language model, and it shipped exactly as promised.",
    "MiniMax M3 benchmark results"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "M3 scores **59.0% on SWE-Bench Pro**, beating GPT-5.5 and Gemini 3.1 Pro and approaching Claude Opus 4.7.",
    "MiniMax M3 benchmark results"
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/nvidia-nemotron-3-ultra-computex",
   "title": "NVIDIA Nemotron 3 Ultra \u2014 550B Mamba-Transformer Mixture-of-Experts With 55B Active Parameters Tops US\u2026",
   "published_at": "2026-06-01T12:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550B total / 55B active",
   "context_window": null,
   "license": null,
   "significance": "Tops US Open-Weights Leaderboard at 48 on Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "NVIDIA \u00b7 2026-06-01 \u00b7 major",
    "Jensen Huang's Computex keynote unveiled NVIDIA's flagship open-weights model: 550B total / 55B active Mamba-Transformer MoE serving 300+ tokens/sec with a 1M-token context window."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA \u00b7 2026-06-01 \u00b7 major",
    "NVIDIA Nemotron 3 Ultra \u2014 550B Mamba-Transformer Mixture-of-Experts With 55B Active Parameters Tops US Open-Weights Leaderboard at 48 on Artificial Analysis Intelligence Index, Ships June 4 on Hugging Face, OpenRouter, ModelScope, and build.nvidia.com",
    "Lands June 4 across HF, [OpenRouter](/tools/openrouter), and NIM microservices."
   ],
   "parameters_evidence": [
    "NVIDIA Nemotron 3 Ultra \u2014 550B Mamba-Transformer Mixture-of-Experts With 55B Active Parameters Tops US Open-Weights Leaderboard at 48 on Artificial Analysis Intelligence Index, Ships June 4 on Hugging Face, OpenRouter, ModelScope, and build.nvidia.com",
    "Jensen Huang's Computex keynote unveiled NVIDIA's flagship open-weights model: 550B total / 55B active Mamba-Transformer MoE serving 300+ tokens/sec with a 1M-token context window.",
    "Key specs",
    "| Active params | 55B |",
    "| Total parameters | 550B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Nemotron 3 Ultra \u2014 550B Mamba-Transformer Mixture-of-Experts With 55B Active Parameters Tops US Open-Weights Leaderboard at 48 on Artificial Analysis Intelligence Index, Ships June 4 on Hugging Face, OpenRouter, ModelScope, and build.nvidia.com",
    "At 48 on the Artificial Analysis Intelligence Index, Ultra is now the smartest US open-weights model \u2014 well ahead of Gemma 4 31B (39) and gpt-oss-120b (33), but still trailing China's Kimi K2.6 (54)."
   ]
  },
  {
   "url": "https://mr.technology/payloads/deepseek-v4-pro-0813-ga-mit-17t-august-2026",
   "title": "DeepSeek V4-Pro-0813 Drops As MIT-Licensed Open Weights. The Pro Tier Has Finally Caught Up To Its Own Flash.",
   "published_at": "2026-08-14T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "1.7T",
   "context_window": null,
   "license": null,
   "significance": "benchmark parity with Opus 4.8 across agentic coding",
   "organization_evidence": [
    "LLM Releases2026-08-14",
    "DeepSeek shipped V4-Pro-0813 on August 12 with 1.7T parameters, 893 GB of MIT-licensed weights, DSpark speculative decoding, and benchmark parity with Opus 4.8 across agentic coding."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "LLM Releases2026-08-14",
    "DeepSeek shipped V4-Pro-0813 on August 12 with 1.7T parameters, 893 GB of MIT-licensed weights, DSpark speculative decoding, and benchmark parity with Opus 4.8 across agentic coding."
   ],
   "parameters_evidence": [
    "LLM Releases2026-08-14",
    "DeepSeek shipped V4-Pro-0813 on August 12 with 1.7T parameters, 893 GB of MIT-licensed weights, DSpark speculative decoding, and benchmark parity with Opus 4.8 across agentic coding."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "LLM Releases2026-08-14",
    "DeepSeek shipped V4-Pro-0813 on August 12 with 1.7T parameters, 893 GB of MIT-licensed weights, DSpark speculative decoding, and benchmark parity with Opus 4.8 across agentic coding."
   ]
  },
  {
   "url": "https://idlen.io/es/news/meta-llama-4-scout-maverick-open-weight-multimodal-moe-10m-context",
   "title": "Llama 4 Scout and Maverick: Meta Ships First Native MoE Models With 10M Context",
   "published_at": "2026-04-15T09:30:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17B active params, 16 experts",
   "context_window": null,
   "license": null,
   "significance": "10M context",
   "organization_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "parameters_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "Scout: 17B active params, 16 experts, 10M context."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "Scout: 17B active params, 16 experts, 10M context."
   ]
  },
  {
   "url": "https://idlen.io/es/news/meta-llama-4-scout-maverick-open-weight-multimodal-moe-10m-context",
   "title": "Llama 4 Scout and Maverick: Meta Ships First Native MoE Models With 10M Context",
   "published_at": "2026-04-15T09:30:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17B active, 128 experts",
   "context_window": null,
   "license": null,
   "significance": "beats GPT-4o",
   "organization_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "On April 5, 2026, Meta released Llama 4 Scout and Maverick \u2014 the first Llama models built on Mixture-of-Experts with native multimodality."
   ],
   "parameters_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "Maverick: 17B active, 128 experts, beats GPT-4o."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Launches Llama 4 Scout and Maverick Open-Weight: Native MoE, Multimodal, 10 Million Token Context",
    "Maverick: 17B active, 128 experts, beats GPT-4o."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/mistral-3-large-675b-moe-apache-open-weight-raise-summit-builder-guide",
   "title": "Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
   "published_at": "2026-07-07T15:00:00",
   "organization": "Mistral",
   "model": "Mistral Large 3",
   "release_date": "2026-07-08",
   "parameters": "675B",
   "context_window": "256K tokens",
   "license": "Apache 2.0",
   "significance": "A sparse Mixture-of-Experts trained from scratch on 3,000 NVIDIA H200 GPUs \u2014 Mistral\u2019s first MoE since the Mixtral series.",
   "organization_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "At RAISE Summit in Paris on July 8, Mistral formally announced **Mistral 3**: a new model family spanning a 675B sparse Mixture-of-Experts flagship and three small dense models."
   ],
   "model_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "At RAISE Summit in Paris on July 8, Mistral formally announced **Mistral 3**: a new model family spanning a 675B sparse Mixture-of-Experts flagship and three small dense models.",
    "### Mistral Large 3"
   ],
   "release_date_evidence": [
    "Builder's Log Published Jul 8, 2026",
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "At RAISE Summit in Paris on July 8, Mistral formally announced **Mistral 3**: a new model family spanning a 675B sparse Mixture-of-Experts flagship and three small dense models."
   ],
   "parameters_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "### Mistral Large 3",
    "| Spec | Value |",
    "| Total parameters | 675B |"
   ],
   "context_window_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "### Mistral Large 3",
    "| Spec | Value |",
    "| Context window | 256K tokens |"
   ],
   "license_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "### Mistral Large 3",
    "| Spec | Value |",
    "| License | Apache 2.0 |"
   ],
   "significance_evidence": [
    "# Mistral 3 Lands at RAISE Summit: The 675B Apache 2.0 MoE Builder Guide",
    "### Mistral Large 3",
    "The flagship. A sparse Mixture-of-Experts trained from scratch on **3,000 NVIDIA H200 GPUs** \u2014 Mistral\u2019s first MoE since the Mixtral series."
   ]
  },
  {
   "url": "https://happycapyguide.com/blog/google-gemma-4-open-source-ai-apache-2026",
   "title": "Google Gemma 4: Best Open-Source AI Model in 2026 \u2014 Free Commercial Use, Multimodal, 256K Context",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked third globally among open AI models on the Arena AI leaderboard, 74.4% on BigBench Extra Hard, Apache 2.0 license, free commercial use, multimodal support (vision and audio), 256K context window.",
   "organization_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models ranging from a 2B edge model that runs on a Raspberry Pi to a 31B dense model that ranks third globally among all open AI models.",
    "# Google Gemma 4: Best Open-Source AI Model in 2026 \u2014 Free Commercial Use, Multimodal, 256K Context"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models ranging from a 2B edge model that runs on a Raspberry Pi to a 31B dense model that ranks third globally among all open AI models.",
    "# Google Gemma 4: Best Open-Source AI Model in 2026 \u2014 Free Commercial Use, Multimodal, 256K Context"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models ranging from a 2B edge model that runs on a Raspberry Pi to a 31B dense model that ranks third globally among all open AI models.",
    "The 31B Dense is Gemma 4's flagship. It currently ranks third on the Arena AI open-model leaderboard \u2014 the global crowdsourced benchmark where models are judged by human preference in head-to-head comparisons.",
    "# Google Gemma 4: Best Open-Source AI Model in 2026 \u2014 Free Commercial Use, Multimodal, 256K Context"
   ]
  },
  {
   "url": "https://intelligibberish.com/articles/2026-07-28-kimi-k3-open-weights-2-8t-params-gpu-reality",
   "title": "Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
   "published_at": "2026-07-28T00:00:00",
   "organization": "Moonshot",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8T params",
   "context_window": "1 million",
   "license": "Kimi K3 License",
   "significance": "the world\u2019s first open 3T-class model, a 2.8-trillion-parameter mixture-of-experts with 104 billion active parameters",
   "organization_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment."
   ],
   "model_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment."
   ],
   "release_date_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment.",
    "Moonshot AI made the full Kimi K3 weights public on July 27, 2026, turning the model from an API story into an open-weight one."
   ],
   "parameters_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment.",
    "The card describes K3 as \u201cthe world\u2019s first open 3T-class model,\u201d a 2.8-trillion-parameter mixture-of-experts with 104 billion active parameters and a one-million-token context window ([Hugging Face model card](https://huggingface.co/moonshotai/Kimi-K3))."
   ],
   "context_window_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment.",
    "The card describes K3 as \u201cthe world\u2019s first open 3T-class model,\u201d a 2.8-trillion-parameter mixture-of-experts with 104 billion active parameters and a one-million-token context window ([Hugging Face model card](https://huggingface.co/moonshotai/Kimi-K3))."
   ],
   "license_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Read the license before you plan a deployment.",
    "The Kimi K3 License is permissive on the surface and conditional at scale."
   ],
   "significance_evidence": [
    "# Kimi K3 Weights Are Public: 2.8T Params, 64-GPU Reality",
    "Moonshot released the full Kimi K3 weights on July 27, 2026: 2.8T params, 1M context, MXFP4. Read the license before you plan a deployment.",
    "The card describes K3 as \u201cthe world\u2019s first open 3T-class model,\u201d a 2.8-trillion-parameter mixture-of-experts with 104 billion active parameters and a one-million-token context window ([Hugging Face model card](https://huggingface.co/moonshotai/Kimi-K3))."
   ]
  },
  {
   "url": "https://swfte.com/ai/models/zhipu-glm-5-2",
   "title": "GLM-5.2 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-08-04T22:03:15",
   "organization": "Z.ai (Zhipu AI)",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B MoE / ~40B active",
   "context_window": null,
   "license": null,
   "significance": "SWE-bench Pro 62.1%, which beats GPT-5.5's 58.6%: an MIT-licensed model you can self-host outscoring a $5/$30 US flagship on agentic coding.",
   "organization_evidence": [
    "# GLM-5.2",
    "Z.ai (Zhipu AI)open-sourceOpen Source",
    "GLM-5.2 is a open-source AI model by Z.ai (Zhipu AI), released on June 13, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2",
    "Z.ai's 13 Jun 2026 open-weight flagship, and the model that held the top open-weight slot until Kimi K3 landed a month later.",
    "GLM-5.2 is a open-source AI model by Z.ai (Zhipu AI), released on June 13, 2026."
   ],
   "parameters_evidence": [
    "# GLM-5.2",
    "Z.ai's 13 Jun 2026 open-weight flagship, and the model that held the top open-weight slot until Kimi K3 landed a month later. 744B MoE / ~40B active, MIT-licensed weights on Hugging Face, 1M context, 131K max output."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2",
    "The headline result is SWE-bench Pro 62.1%, which beats GPT-5.5's 58.6%: an MIT-licensed model you can self-host outscoring a $5/$30 US flagship on agentic coding."
   ]
  },
  {
   "url": "https://gigazine.net/gsc_news/en/20260413-minimax-m2-7-open",
   "title": "MiniMax M2.7, a Chinese-made AI model that surpasses Gemini 3.1 Pro, is now available for free.",
   "published_at": "2026-04-13T01:46:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "229 billion",
   "context_window": null,
   "license": null,
   "significance": "SOTA performance in SWE-Pro (56.22%) and Terminal Bench 2 (57.0%)",
   "organization_evidence": [
    "MiniMax M2.7, a Chinese-made AI model that surpasses Gemini 3.1 Pro, is now available for free.",
    "MiniMax, an AI development company based in China, has released its proprietary AI model ' ****"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M2.7, a Chinese-made AI model that surpasses Gemini 3.1 Pro, is now available for free.",
    "MiniMax M2.7 was released as an open model on April 12, 2026, less than a month after its announcement."
   ],
   "parameters_evidence": [
    "MiniMax M2.7, a Chinese-made AI model that surpasses Gemini 3.1 Pro, is now available for free.",
    "MiniMax, an AI development company based in China, has released its proprietary AI model ' ****\n\n**[MiniMax M2.7,](https://gigazine.net/news/20260319-minimax-m2-7/)** ' with 229 billion parameters, for free on Hugging Face and ModelScope."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M2.7, a Chinese-made AI model that surpasses Gemini 3.1 Pro, is now available for free.",
    "> We're delighted to announce that MiniMax M2.7 is now officially open source.\n> With SOTA performance in SWE-Pro (56.22%) and Terminal Bench 2 (57.0%)."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/Dense_1b_130B",
   "title": "allenai/Dense_1b_130B \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "AllenAI",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "1.0B",
   "context_window": null,
   "license": null,
   "significance": "Apache-2.0 licence permits unrestricted commercial and research use; Small 1B parameter size enables inference on resource-constrained Ascend NPUs and edge devices; Dense architecture is simpler to optimise and quantise than MoE alternatives; Hosted on Modelers.cn, giving direct access within the Huawei Ascend ecosystem",
   "organization_evidence": [
    "Modelers",
    "# allenai / Dense_1b_130B",
    "This is a 1-billion-parameter dense language model released by AllenAI (the Allen Institute for AI), hosted on Modelers.cn as part of the Huawei Ascend ecosystem."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Modelers",
    "# allenai/Dense_1b_130B",
    "**Licence:** Apache-2.0 | **Task:** Text Generation",
    "**Created:** 2026-06-04 | **Updated:** 2026-06-04"
   ],
   "parameters_evidence": [
    "Modelers",
    "Parameters",
    "1.0B"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Modelers",
    "Details",
    "Analysis",
    "+ Strengths",
    "Apache-2.0 licence permits unrestricted commercial and research use",
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    "Dense architecture is simpler to optimise and quantise than MoE alternatives",
    "Hosted on Modelers.cn, giving direct access within the Huawei Ascend ecosystem"
   ]
  },
  {
   "url": "https://nerdleveltech.com/google-gemma-4-open-model-guide-benchmarks-local-deployment",
   "title": "Gemma 4 Review: Apache-2.0 Open AI, 89.2% on AIME 2026",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Brings frontier-level reasoning, coding, and multimodal capabilities, scoring 89.2% on AIME 2026 math reasoning and ranking #3 among all open models on the Arena AI text leaderboard.",
   "organization_evidence": [
    "# Gemma 4 Review: Apache-2.0 Open AI, 89.2% on AIME 2026",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open models under the Apache 2.0 license that bring frontier-level reasoning, coding, and multimodal capabilities to hardware ranging from smartphones to single-GPU workstations.1"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4 Review: Apache-2.0 Open AI, 89.2% on AIME 2026",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open models under the Apache 2.0 license that bring frontier-level reasoning, coding, and multimodal capabilities to hardware ranging from smartphones to single-GPU workstations.1"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4 Review: Apache-2.0 Open AI, 89.2% on AIME 2026",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open models under the Apache 2.0 license that bring frontier-level reasoning, coding, and multimodal capabilities to hardware ranging from smartphones to single-GPU workstations.1",
    "The flagship 31B Dense model scores 89.2% on AIME 2026 math reasoning and ranks #3 among all open models on the Arena AI text leaderboard.2"
   ]
  },
  {
   "url": "https://happyrock.cloud/blog/2026-07-28_kimi_k3_2.8t_open_source_model_deep_dive_mixed_attention_kda_gated_mla_896_expert_moe_agentenv_en",
   "title": "Kimi K3 2.8T Open-Source Model Deep Dive: Mixed Attention KDA/Gated MLA, 896-Expert MoE, and AgentENV Sandbox Training",
   "published_at": "2026-07-28T00:29:23",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.88 trillion",
   "context_window": null,
   "license": null,
   "significance": "Kimi K3 comprehensively surpasses Claude Opus 4.8, GLM-5.2, and GPT-5.5, approaching the current strongest closed-source models Claude Fable 5 and GPT-5.6 Sol.",
   "organization_evidence": [
    "# Kimi K3 2.8T Open-Source Model Deep Dive: Mixed Attention KDA/Gated MLA, 896-Expert MoE, and AgentENV Sandbox Training",
    "On July 27, 2026, Moonshot AI officially open-sourced Kimi K3\u2014the world\u2019s first trillion-parameter-class open-source model at the 3-trillion scale."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 2.8T Open-Source Model Deep Dive: Mixed Attention KDA/Gated MLA, 896-Expert MoE, and AgentENV Sandbox Training",
    "On July 27, 2026, Moonshot AI officially open-sourced Kimi K3\u2014the world\u2019s first trillion-parameter-class open-source model at the 3-trillion scale."
   ],
   "parameters_evidence": [
    "**Kimi K3 Core Specifications:**",
    "| Total Parameters | \u22482.88T |",
    "# Kimi K3 2.8T Open-Source Model Deep Dive: Mixed Attention KDA/Gated MLA, 896-Expert MoE, and AgentENV Sandbox Training",
    "Kimi K3 boasts 2.88 trillion total parameters, 104 billion active parameters per token, native support for 1-million-token context windows, and native multimodal capabilities."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "# Kimi K3 2.8T Open-Source Model Deep Dive: Mixed Attention KDA/Gated MLA, 896-Expert MoE, and AgentENV Sandbox Training",
    "Across multiple benchmarks, K3 comprehensively surpasses Claude Opus 4.8, GLM-5.2, and GPT-5.5, approaching the current strongest closed-source models Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://nivaalabs.com/glm-5-1-review-chinas-open-source-ai-that-topped-the-leaderboard",
   "title": "GLM-5.1 Review: China\u2019s Open-Source AI That Topped the Leaderboard",
   "published_at": "2026-04-19T22:19:46",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6 to top the global coding leaderboard, and capable of 8-hour autonomous execution.",
   "organization_evidence": [
    "GLM-5.1 Review: China\u2019s Open-Source AI That Topped the Leaderboard",
    "This article covers GLM-5.1 open-source weights release from Z.ai (formerly Zhipu AI), based on official documentation and independent analysis published April 7, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.1 Review: China\u2019s Open-Source AI That Topped the Leaderboard",
    "This article covers GLM-5.1 open-source weights release from Z.ai (formerly Zhipu AI), based on official documentation and independent analysis published April 7, 2026.",
    "Released on April 7, 2026 by Z.ai \u2014 the company formerly known as Zhipu AI, a Tsinghua University spinoff that became the world\u2019s first publicly traded foundation model company in January 2026 \u2014 GLM-5.1 scored 58.4 on SWE-Bench Pro."
   ],
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   "significance_evidence": [
    "GLM-5.1 Review: China\u2019s Open-Source AI That Topped the Leaderboard",
    "GLM-5.1 is the most significant open-source AI release of 2026 \u2014 and possibly the most geopolitically significant AI model since DeepSeek R1.",
    "Released on April 7, 2026 by Z.ai \u2014 the company formerly known as Zhipu AI, a Tsinghua University spinoff that became the world\u2019s first publicly traded foundation model company in January 2026 \u2014 GLM-5.1 scored 58.4 on SWE-Bench Pro."
   ]
  },
  {
   "url": "https://theairankings.com/minimax/minimax-m3",
   "title": "MiniMax M3",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "frontier-level coding, a 1-million-token context window and native multimodality \u2014 all in one open-weight model",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is [MiniMax](/minimax/)\u2019s open-weight flagship, released on [1 June 2026](https://datanorth.ai/news/minimax-launches-m3) with **frontier-level coding, a 1-million-token context window and native multimodality \u2014 all in one open-weight model**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is [MiniMax](/minimax/)\u2019s open-weight flagship, released on [1 June 2026](https://datanorth.ai/news/minimax-launches-m3) with **frontier-level coding, a 1-million-token context window and native multimodality \u2014 all in one open-weight model**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is [MiniMax](/minimax/)\u2019s open-weight flagship, released on [1 June 2026](https://datanorth.ai/news/minimax-launches-m3) with **frontier-level coding, a 1-million-token context window and native multimodality \u2014 all in one open-weight model**."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/nvidia-nemotron-3-ultra-and-glm-5-2-signal-open-weight-front-0848046a",
   "title": "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
   "published_at": "2026-06-30T13:34:55",
   "organization": "NVIDIA",
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   "significance": "Nemotron 3 Ultra benchmarks as competitive with GPT-4.5-class models on code and reasoning.",
   "organization_evidence": [
    "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
    "NVIDIA, the GPU and AI infrastructure company, announced Nemotron 3 Ultra at Jensen Huang\u2019s Computex 2026 keynote in Taipei and shipped the model on June 4, 2026."
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   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
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   ],
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   "significance_evidence": [
    "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
    "Nemotron 3 Ultra benchmarks as competitive with GPT-4.5-class models on code and reasoning."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/nvidia-nemotron-3-ultra-and-glm-5-2-signal-open-weight-front-0848046a",
   "title": "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
   "published_at": "2026-06-30T13:34:55",
   "organization": "ZhipuAI",
   "model": null,
   "release_date": "2026-06-13",
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   "license": null,
   "significance": "Independent evaluations within days of launch placed GLM-5.2 at the top of the Artificial Analysis Intelligence Index v4.1 for open-weight models, with a composite score of 51 \u2014 competitive with closed frontier systems including GPT-5.5.",
   "organization_evidence": [
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
    "ZhipuAI, the Beijing-based AI lab also known as Z.AI, launched GLM-5.2 on June 13, 2026, with full MIT-licensed weights available within the week."
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    "NVIDIA Nemotron 3 Ultra and GLM-5.2 Signal Open-Weight Frontier for Agent Development",
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  },
  {
   "url": "https://ofox.io/blog/deepseek-v4-release-guide-2026",
   "title": "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
   "published_at": "2026-04-24T00:00:00",
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   "organization_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "DeepSeek V4 lands same day as GPT-5.5: 1.6T-param Pro, 284B Flash, 1M context, open Apache 2.0 weights, and API pricing that undercuts GPT-5.5, Opus 4.7, and Kimi K2.6."
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   "model_evidence": null,
   "release_date_evidence": [
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:",
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5"
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   "significance_evidence": [
    "V4 introduces a hybrid attention mechanism: **Compressed Sparse Attention (CSA)** + **Heavily Compressed Attention (HCA)**.",
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "Codeforces 3206 is the line that matters. That is better than GPT-5.4 (xHigh) at 3168 \u2014 competitive-programming territory where closed frontier models have historically held the lead."
   ]
  },
  {
   "url": "https://capitalandcompute.net/blog/local-llms-good-2026",
   "title": "Why Local LLMs Got Good in 2026: Capability & Cost",
   "published_at": "2026-06-18T00:00:00",
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   "release_date": "2026-04-22",
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   "significance": "scores 77.2 on SWE-bench Verified",
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    "Why Local LLMs Got Good in 2026: Capability & Cost",
    "Mid-size models closed most of the coding gap",
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   "release_date_evidence": [
    "Why Local LLMs Got Good in 2026: Capability & Cost",
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    "Why Local LLMs Got Good in 2026: Capability & Cost",
    "Mid-size models closed most of the coding gap",
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    "Dense parameters"
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    "Why Local LLMs Got Good in 2026: Capability & Cost",
    "Mid-size models closed most of the coding gap",
    "It is a 27-billion-parameter dense model under the permissive Apache 2.0 license, and per the [official Qwen model card](https://huggingface.co/Qwen/Qwen3.6-27B) it scores 77.2 on SWE-bench Verified, the benchmark that measures whether a model can resolve real GitHub issues."
   ]
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  {
   "url": "https://swfte.com/blog/llama-5-open-weight-deep-dive-2026",
   "title": "Llama 5: Meta's Return to the Frontier",
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   "release_date": "2026-06-30",
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   "significance": "MMLU-Pro 88.5, SWE-bench Verified 79.6%, coverage across 40-plus languages, and a 1M-token context \u2014 all under open weights you can download and run.",
   "organization_evidence": [
    "Meta released Llama 5 on June 30, 2026, and for the first time in a while the release matters beyond the open-source crowd."
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   "release_date_evidence": [
    "Meta released Llama 5 on June 30, 2026, and for the first time in a while the release matters beyond the open-source crowd."
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   "significance_evidence": [
    "Llama 5 is a genuine attempt to compete with the proprietary top tier on general reasoning, and it mostly succeeds: MMLU-Pro 88.5, SWE-bench Verified 79.6%, coverage across 40-plus languages, and a 1M-token context \u2014 all under open weights you can download and run."
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  {
   "url": "https://en.wikipedia.org/wiki/Gemma_4",
   "title": "Gemma (language model)",
   "published_at": "2026-06-24T12:21:23",
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   "release_date": "2026-04-02",
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   "significance": "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place.",
   "organization_evidence": [
    "Gemma is a series of source-available large language models developed by Google DeepMind.",
    "On April 2, 2026, Google released Gemma 4 under the free and open-source Apache\u00a02.0 license."
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   "release_date_evidence": [
    "On April 2, 2026, Google released Gemma 4 under the free and open-source Apache\u00a02.0 license.",
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   "significance_evidence": [
    "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place."
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  {
   "url": "https://geekrealmhub.com/moonshot-ai-kimi-k3-open-weight",
   "title": "Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
   "published_at": "2026-07-23T00:06:40",
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   "significance": "outperforms Claude Opus 4.8 and GPT-5.5, largest open-weight AI system in the world",
   "organization_evidence": [
    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
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   "model_evidence": [
    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
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   "release_date_evidence": [
    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
    "Chinese startup Moonshot AI released Kimi K3 on July 16, a 2.8 trillion-parameter model that is now the largest open-weight [AI](https://geekrealmhub.com/tag/ai/) system in the world."
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    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
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   "context_window_evidence": [
    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
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   "significance_evidence": [
    "# Moonshot AI unveils Kimi K3: Analysis of China\u2019s 2.8T open-weight model",
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    "Chinese startup Moonshot AI released Kimi K3 on July 16, a 2.8 trillion-parameter model that is now the largest open-weight [AI](https://geekrealmhub.com/tag/ai/) system in the world."
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  {
   "url": "https://felloai.com/de/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": "GLM 5.2",
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   "organization_evidence": [
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   "model_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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   "release_date_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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   "context_window_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
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  {
   "url": "https://besthub.dev/articles/minimax-m3-sets-new-benchmarks-1m-context-59-swe-bench-9-15-faster-multimodal-model-cd4258ba0a67",
   "title": "MiniMax M3 Sets New Benchmarks: 1M Context, 59% SWE\u2011Bench, 9\u201115\u00d7 Faster Multimodal Model",
   "published_at": "2026-06-06T07:52:54",
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   "release_date": "2026-06-01",
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   "significance": "MiniMax unveiled its open\u2011source M3 model, delivering 1 million\u2011token context, 59 % SWE\u2011Bench Pro accuracy that outperforms GPT\u20115.5 and Gemini 3.1 Pro, native multimodal desktop interaction, and a 9\u201115\u00d7 speed boost via MiniMax Sparse Attention, with pricing as low as $20 per month.",
   "organization_evidence": [
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   "release_date_evidence": [
    "MiniMax M3 Sets New Benchmarks: 1M Context, 59% SWE\u2011Bench, 9\u201115\u00d7 Faster Multimodal Model",
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   "significance_evidence": [
    "MiniMax M3 Sets New Benchmarks: 1M Context, 59% SWE\u2011Bench, 9\u201115\u00d7 Faster Multimodal Model",
    "MiniMax unveiled its open\u2011source M3 model, delivering 1 million\u2011token context, 59 % SWE\u2011Bench Pro accuracy that outperforms GPT\u20115.5 and Gemini 3.1 Pro, native multimodal desktop interaction, and a 9\u201115\u00d7 speed boost via MiniMax Sparse Attention, with pricing as low as $20 per month."
   ]
  },
  {
   "url": "https://pondero.ai/news/2026-07-24-poolside-laguna-s-21",
   "title": "Poolside releases Laguna S 2.1, a 118B open-weight model that tops SWE-Bench Multilingual and outperforms rivals several times its effective size",
   "published_at": "2026-07-24T00:00:00",
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   "parameters": "118B total parameters (8B active)",
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   "significance": "Scored 78.5% on SWE-Bench Multilingual and 70.2% on Terminal-Bench 2.1, topping the public leaderboard at launch and beating larger open-weight rivals while needing only a single Nvidia DGX Spark to run.",
   "organization_evidence": [
    "## Poolside releases Laguna S 2.1, a 118B open-weight model that tops SWE-Bench Multilingual and outperforms rivals several times its effective size",
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   "release_date_evidence": [
    "## Poolside releases Laguna S 2.1, a 118B open-weight model that tops SWE-Bench Multilingual and outperforms rivals several times its effective size",
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   "parameters_evidence": [
    "## Poolside releases Laguna S 2.1, a 118B open-weight model that tops SWE-Bench Multilingual and outperforms rivals several times its effective size",
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   "significance_evidence": [
    "## Poolside releases Laguna S 2.1, a 118B open-weight model that tops SWE-Bench Multilingual and outperforms rivals several times its effective size",
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    "Poolside released Laguna S 2.1 on July 21, 2026, a mixture-of-experts coding model that climbed to the top of the SWE-Bench Multilingual public leaderboard at 78.5% and beat larger open-weight rivals while needing only a single Nvidia DGX Spark to run."
   ]
  },
  {
   "url": "https://qwen3lm.com",
   "title": "Qwen AI for Everyone | Qwen 3, Qwen Studio & Model",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
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   "license": null,
   "significance": "State-of-the-art results across reasoning, math, and code benchmarks - outperforming leading proprietary models while remaining open and accessible.",
   "organization_evidence": [
    "Frontier Model Comparison",
    "Technical specifications and performance benchmarks across leading large language models as of Q2 2026. Metrics are based on published technical reports, independent evaluations, and community benchmarks.",
    "| **Qwen3.6-Plus** New | Adaptive MoE + Structured Reasoning | **512K tokens** | **130+** | **94.9%** | **93.4%** | **97.8%** | **~112 tok/s** | Open Weights + API | **Apr 2026** |",
    "Qwen 3.6 delivers state-of-the-art results across reasoning, math, and code benchmarks - outperforming leading proprietary models while remaining open and accessible."
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   "model_evidence": null,
   "release_date_evidence": [
    "Frontier Model Comparison",
    "Technical specifications and performance benchmarks across leading large language models as of Q2 2026. Metrics are based on published technical reports, independent evaluations, and community benchmarks.",
    "| **Qwen3.6-Plus** New | Adaptive MoE + Structured Reasoning | **512K tokens** | **130+** | **94.9%** | **93.4%** | **97.8%** | **~112 tok/s** | Open Weights + API | **Apr 2026** |"
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   "significance_evidence": [
    "Frontier Model Comparison",
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    "Qwen 3.6 delivers state-of-the-art results across reasoning, math, and code benchmarks - outperforming leading proprietary models while remaining open and accessible."
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  },
  {
   "url": "https://diaryofatoken.com/en/article/mistral-open-edge-family",
   "title": "Mistral debuts compact open models tuned for edge devices",
   "published_at": "2026-04-01T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1 to 8 billion parameters",
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   "license": null,
   "significance": "optimized for edge environments, smaller memory footprint at runtime thanks to quantization techniques, higher inference speed on consumer hardware without a dedicated GPU, and preserved language performance on the most common tasks even at reduced sizes",
   "organization_evidence": [
    "Mistral launches new open model family optimized for edge",
    "Mistral AI published in April 2026 a new family of compact language models, optimized specifically for execution in edge environments: devices with limited compute resources such as mid-range laptops, small business local servers, industrial devices, and embedded hardware [1]."
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   "model_evidence": null,
   "release_date_evidence": [
    "**Retrospective reconstruction** \u00b7 Written on May 11, 2026 about events on April 1, 2026",
    "Mistral launches new open model family optimized for edge",
    "Mistral AI published in April 2026 a new family of compact language models, optimized specifically for execution in edge environments: devices with limited compute resources such as mid-range laptops, small business local servers, industrial devices, and embedded hardware [1]."
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   "parameters_evidence": [
    "Mistral launches new open model family optimized for edge",
    "The new family \u2014 following the naming pattern Mistral has established with its previous models \u2014 includes variants ranging from 1 to 8 billion parameters, all available with open weights under a commercial use license."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral launches new open model family optimized for edge",
    "The French startup presents compact language models designed to run on resource-constrained devices, with improved performance over previous generations in cloudless contexts.",
    "The emphasis on edge optimization translates into several technical characteristics: smaller memory footprint at runtime thanks to quantization techniques, higher inference speed on consumer hardware without a dedicated GPU, and preserved language performance on the most common tasks even at reduced sizes [1]."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Gemma%20(language%20model)",
   "title": "Gemma (language model)",
   "published_at": "2026-07-13T17:01:37",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "31B, 26B A4B, 12B, 4B, ~2B",
   "context_window": "128K (edge) 256K (larger)",
   "license": "Apache 2.0",
   "significance": "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place.",
   "organization_evidence": [
    "Gemma is a series of source-available large language models developed by Google DeepMind."
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   "model_evidence": [
    "Technical specifications of Gemma models",
    "| Generation | Release date | Parameters | Context length | Multimodal | License | Notes |",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |"
   ],
   "release_date_evidence": [
    "Technical specifications of Gemma models",
    "| Generation | Release date | Parameters | Context length | Multimodal | License | Notes |",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |"
   ],
   "parameters_evidence": [
    "Technical specifications of Gemma models",
    "| Generation | Release date | Parameters | Context length | Multimodal | License | Notes |",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |"
   ],
   "context_window_evidence": [
    "Technical specifications of Gemma models",
    "| Generation | Release date | Parameters | Context length | Multimodal | License | Notes |",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |"
   ],
   "license_evidence": [
    "Technical specifications of Gemma models",
    "| Generation | Release date | Parameters | Context length | Multimodal | License | Notes |",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |"
   ],
   "significance_evidence": [
    "Overview",
    "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place."
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  },
  {
   "url": "http://ai-tldr.dev/releases/moonshot-kimi-k3",
   "title": "Kimi K3 \u2014 Moonshot's 2.8T flagship with 1M context lands on web, app, and API",
   "published_at": "2026-07-16T12:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
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   "license": null,
   "significance": "K3 is a 2.8T-parameter Mixture-of-Experts model (104B active) with a 1M-token context, Delta Attention hybrid, and native vision \u2014 the model is now self-hostable in addition to the kimi.com app and OpenAI-compatible API.",
   "organization_evidence": [
    "Moonshot AI \u00b7 2026-07-16 \u00b7 seismic",
    "Moonshot AI drops the [Kimi K3](/models/kimi-k3/) open weights on Hugging Face, alongside the K3 technical report."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI \u00b7 2026-07-16 \u00b7 seismic",
    "Kimi K3 is Moonshot AI's new flagship Mixture-of-Experts model, live as of July 16, 2026 on kimi.com, the Kimi mobile app, the Kimi Code coding-agent tier, and the api.moonshot.ai OpenAI-compatible API."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 \u2014 Moonshot's 2.8T flagship with 1M context lands on web, app, and API",
    "K3 is a 2.8T-parameter Mixture-of-Experts model (104B active) with a 1M-token context, Delta Attention hybrid, and native vision \u2014 the model is now self-hostable in addition to the kimi.com app and OpenAI-compatible API."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/glm-5-2-z-ai-1m-context-mit-open-weights-agentic-coding-builder-guide",
   "title": "GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
   "published_at": "2026-06-13T15:00:00",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": "1-million-token",
   "license": "MIT-licensed",
   "significance": "first model with a fully usable 1-million-token context window",
   "organization_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "Z.ai (Zhipu AI) released GLM-5.2 on June 13, 2026 \u2014 the third major iteration of its GLM-5 family and its first model with a fully usable 1-million-token context window."
   ],
   "model_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "Z.ai (Zhipu AI) released GLM-5.2 on June 13, 2026 \u2014 the third major iteration of its GLM-5 family and its first model with a fully usable 1-million-token context window."
   ],
   "release_date_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "Z.ai (Zhipu AI) released GLM-5.2 on June 13, 2026 \u2014 the third major iteration of its GLM-5 family and its first model with a fully usable 1-million-token context window."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "Z.ai (Zhipu AI) released GLM-5.2 on June 13, 2026 \u2014 the third major iteration of its GLM-5 family and its first model with a fully usable 1-million-token context window."
   ],
   "license_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "The standalone API, Z.ai chatbot, and MIT-licensed open weights on HuggingFace are scheduled to follow the week of June 16\u201320, 2026."
   ],
   "significance_evidence": [
    "# GLM-5.2: Z.ai's 1M-Context Agentic Coding Model Just Shipped \u2014 MIT Weights Next Week",
    "Z.ai (Zhipu AI) released GLM-5.2 on June 13, 2026 \u2014 the third major iteration of its GLM-5 family and its first model with a fully usable 1-million-token context window."
   ]
  },
  {
   "url": "https://abr-insights.tech/articles/2026-04-12_13-15-30_minimax-m2-7-ai-revolution-game-changer.html",
   "title": "MiniMax M2.7: AI Revolution \ud83d\ude80\ud83e\udd2f Game Changer!",
   "published_at": "2026-04-12T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M2.7 is the MiniMax\u2019s most capable open-source model to date, achieving 56.22% accuracy on SWE-Pro, matching GPT-5.3-Codex, and securing 9 gold medals, 5 silver medals, and 1 bronze medal on MLE Bench Lite.",
   "organization_evidence": [
    "# MiniMax M2.7: AI Revolution \ud83d\ude80\ud83e\udd2f Game Changer!",
    "MiniMax has officially open-sourced MiniMax M2.7, making the model weights publicly available on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M2.7: AI Revolution \ud83d\ude80\ud83e\udd2f Game Changer!",
    "Originally announced on March 18, 2026, MiniMax M2.7 is the MiniMax\u2019s most capable open-source model to date \u2014 and its first model to actively participate in its own development cycle, a meaningful shift in how large language models are built and iterated."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M2.7: AI Revolution \ud83d\ude80\ud83e\udd2f Game Changer!",
    "MiniMax M2.7 is the MiniMax\u2019s most capable open-source model to date \u2014 and its first model to actively participate in its own development cycle."
   ]
  },
  {
   "url": "https://artificialintelligenceherald.com/news/huggingface-allen-ai-emo-modular-mixture-of-experts-2026",
   "title": "HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
   "published_at": "2026-05-08T18:01:00",
   "organization": "HuggingFace and Allen AI",
   "model": "EMO",
   "release_date": "2026-05-08",
   "parameters": "1B, 3B, and 7B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "ACHIEVED A 12 PERCENT IMPROVEMENT IN AVERAGE ACCURACY ACROSS 20 DIVERSE NLP BENCHMARKS AND 30 PERCENT FASTER INFERENCE ON MULTI-TASK PIPELINES",
   "organization_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "HuggingFace and the Allen Institute for AI have released a groundbreaking pretraining approach called EMO\u2014short for Emergent Modularity\u2014that fundamentally rethinks how mixture-of-experts (MoE) models are trained."
   ],
   "model_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "HuggingFace and the Allen Institute for AI have released a groundbreaking pretraining approach called EMO\u2014short for Emergent Modularity\u2014that fundamentally rethinks how mixture-of-experts (MoE) models are trained."
   ],
   "release_date_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "HuggingFace and the Allen Institute for AI have released a groundbreaking pretraining approach called EMO\u2014short for Emergent Modularity\u2014that fundamentally rethinks how mixture-of-experts (MoE) models are trained."
   ],
   "parameters_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "Currently, the available checkpoints include 1B, 3B, and 7B parameter models, all open-sourced under an Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "Currently, the available checkpoints include 1B, 3B, and 7B parameter models, all open-sourced under an Apache 2.0 license."
   ],
   "significance_evidence": [
    "News May 08, 2026 6 min read108 views",
    "# HuggingFace and Allen AI Introduce EMO: Pretraining Mixture of Experts for Emergent Modularity",
    "Results showed that EMO models achieved a 12 percent improvement in average accuracy across 20 diverse NLP benchmarks, including SuperGLUE, MMLU, and BIG-bench.",
    "More importantly, the models exhibited 30 percent faster inference on multi-task pipelines, as the routing mechanism learned to steer inputs to the most relevant expert with near-zero overhead."
   ]
  },
  {
   "url": "https://nerdleveltech.com/china-open-weight-coding-models-cost-war",
   "title": "China's Open-Weight Coding Wave: 4 Models, 18 Days",
   "published_at": "2026-05-17T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744\u2013754 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Claimed the top SWE-Bench Pro score at 58.4% on vendor-published numbers, making it the first open-weight model to claim the top of that leaderboard.",
   "organization_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### GLM-5.1 (Z.ai) \u2014 April 7"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "May 17, 2026",
    "### GLM-5.1 (Z.ai) \u2014 April 7",
    "GLM-5.1 is a mixture-of-experts model with roughly **744\u2013754 billion total parameters** (sources disagree on the exact figure, with both widely cited), 40 billion active parameters per token, a 200K context window, and an MIT license2."
   ],
   "parameters_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### GLM-5.1 (Z.ai) \u2014 April 7",
    "GLM-5.1 is a mixture-of-experts model with roughly **744\u2013754 billion total parameters** (sources disagree on the exact figure, with both widely cited), 40 billion active parameters per token, a 200K context window, and an MIT license2."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### GLM-5.1 (Z.ai) \u2014 April 7",
    "At launch, Z.ai claimed the top SWE-Bench Pro score at **58.4%** on vendor-published numbers \u2014 narrowly above GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3% per Z.ai's measurement), making it the first open-weight model to claim the top of that leaderboard before Anthropic's Claude Opus 4.7 release nine days later2."
   ]
  },
  {
   "url": "https://nerdleveltech.com/china-open-weight-coding-models-cost-war",
   "title": "China's Open-Weight Coding Wave: 4 Models, 18 Days",
   "published_at": "2026-05-17T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M2.7",
   "release_date": "2026-04-12",
   "parameters": "230 billion",
   "context_window": "200K",
   "license": "Modified-MIT",
   "significance": "Actively participated in its own development, running over 100 autonomous rounds of scaffold optimization and reporting an ~30% performance improvement.",
   "organization_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 is a 230-billion-parameter MoE with 10 billion active per token, a 200K context window (technically 204,800 tokens), and weights released on Hugging Face under a \"Modified-MIT\" license that \u2014 unlike the standard MIT license MiniMax used for M2 and M2.5 \u2014 restricts commercial use without prior written authorization3."
   ],
   "model_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 is a 230-billion-parameter MoE with 10 billion active per token, a 200K context window (technically 204,800 tokens), and weights released on Hugging Face under a \"Modified-MIT\" license that \u2014 unlike the standard MIT license MiniMax used for M2 and M2.5 \u2014 restricts commercial use without prior written authorization3."
   ],
   "release_date_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "May 17, 2026",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 was originally announced March 18, 2026 as a closed model and then released with open weights on Hugging Face on April 123."
   ],
   "parameters_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 is a 230-billion-parameter MoE with 10 billion active per token, a 200K context window (technically 204,800 tokens), and weights released on Hugging Face under a \"Modified-MIT\" license that \u2014 unlike the standard MIT license MiniMax used for M2 and M2.5 \u2014 restricts commercial use without prior written authorization3."
   ],
   "context_window_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 is a 230-billion-parameter MoE with 10 billion active per token, a 200K context window (technically 204,800 tokens), and weights released on Hugging Face under a \"Modified-MIT\" license that \u2014 unlike the standard MIT license MiniMax used for M2 and M2.5 \u2014 restricts commercial use without prior written authorization3."
   ],
   "license_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "MiniMax M2.7 is a 230-billion-parameter MoE with 10 billion active per token, a 200K context window (technically 204,800 tokens), and weights released on Hugging Face under a \"Modified-MIT\" license that \u2014 unlike the standard MIT license MiniMax used for M2 and M2.5 \u2014 restricts commercial use without prior written authorization3."
   ],
   "significance_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### MiniMax M2.7 \u2014 April 12 (open)",
    "The lab's pitch is self-evolution: MiniMax says M2.7 actively participated in its own development, running over 100 autonomous rounds of scaffold optimization and reporting an ~30% performance improvement from that loop3."
   ]
  },
  {
   "url": "https://nerdleveltech.com/china-open-weight-coding-models-cost-war",
   "title": "China's Open-Weight Coding Wave: 4 Models, 18 Days",
   "published_at": "2026-05-17T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04-20",
   "parameters": "1 trillion",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "Introduces an Agent Swarm primitive that fans a single task across up to 300 sub-agents over 4,000 coordinated steps and scores 58.6% on SWE-Bench Pro.",
   "organization_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Kimi K2.6 is a 1-trillion-parameter MoE with 32 billion active per token, a 256K context window, and a Modified MIT license4."
   ],
   "model_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Kimi K2.6 is a 1-trillion-parameter MoE with 32 billion active per token, a 256K context window, and a Modified MIT license4."
   ],
   "release_date_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "May 17, 2026",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Between April 7 and April 24, 2026, four Chinese AI labs shipped open-weight coding models in close succession: Z.ai's **GLM-5.1**, MiniMax's **M2.7** (open-sourced April 12 after a March announcement), Moonshot's **Kimi K2.6**, and DeepSeek's **V4** (Pro and Flash variants)."
   ],
   "parameters_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Kimi K2.6 is a 1-trillion-parameter MoE with 32 billion active per token, a 256K context window, and a Modified MIT license4."
   ],
   "context_window_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Kimi K2.6 is a 1-trillion-parameter MoE with 32 billion active per token, a 256K context window, and a Modified MIT license4."
   ],
   "license_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "Kimi K2.6 is a 1-trillion-parameter MoE with 32 billion active per token, a 256K context window, and a Modified MIT license4."
   ],
   "significance_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### Kimi K2.6 (Moonshot AI) \u2014 April 20",
    "On SWE-Bench Pro, Kimi K2.6 scores **58.6%** \u2014 narrowly above GLM-5.1's vendor number and high enough to briefly hold the open-weight top spot when it shipped4.",
    "Kimi K2.6 also introduces an Agent Swarm primitive that fans a single task across up to **300 sub-agents over 4,000 coordinated steps** \u2014 a tripling of K2.5's 100-agent ceiling and a near-2.7\u00d7 jump in the step horizon, and a deliberate bet on the multi-agent direction that several Chinese labs are now investing in4."
   ]
  },
  {
   "url": "https://nerdleveltech.com/china-open-weight-coding-models-cost-war",
   "title": "China's Open-Weight Coding Wave: 4 Models, 18 Days",
   "published_at": "2026-05-17T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": "1M-token",
   "license": "MIT",
   "significance": "Introduces Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), trained with the Muon optimizer, reaching 80.6% on SWE-Bench Verified.",
   "organization_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "model_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "- **V4-Pro**: 1.6 trillion total parameters, 49B active, pretrained on 33 trillion tokens"
   ],
   "release_date_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "May 17, 2026",
    "### DeepSeek V4 \u2014 April 24",
    "Between April 7 and April 24, 2026, four Chinese AI labs shipped open-weight coding models in close succession: Z.ai's **GLM-5.1**, MiniMax's **M2.7** (open-sourced April 12 after a March announcement), Moonshot's **Kimi K2.6**, and DeepSeek's **V4** (Pro and Flash variants)."
   ],
   "parameters_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "- **V4-Pro**: 1.6 trillion total parameters, 49B active, pretrained on 33 trillion tokens"
   ],
   "context_window_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "license_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "significance_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "V4-Pro reaches **80.6%** on SWE-Bench Verified and **55.4%** on SWE-Bench Pro12.",
    "The architecture is the most aggressive of the four. V4 introduces Compressed Sparse Attention (CSA) paired with Heavily Compressed Attention (HCA), which DeepSeek reports cuts single-token inference FLOPs to roughly **27%** of V3.2 and KV cache footprint to roughly **10%** at 1M-token context12."
   ]
  },
  {
   "url": "https://nerdleveltech.com/china-open-weight-coding-models-cost-war",
   "title": "China's Open-Weight Coding Wave: 4 Models, 18 Days",
   "published_at": "2026-05-17T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion total parameters",
   "context_window": "1M-token",
   "license": "MIT",
   "significance": "Reaches 79.0% on SWE-Bench Verified in its strongest tier and positioned for production inference economics.",
   "organization_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "model_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "- **V4-Flash**: 284 billion total parameters, 13B active, pretrained on 32 trillion tokens"
   ],
   "release_date_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "May 17, 2026",
    "### DeepSeek V4 \u2014 April 24",
    "Between April 7 and April 24, 2026, four Chinese AI labs shipped open-weight coding models in close succession: Z.ai's **GLM-5.1**, MiniMax's **M2.7** (open-sourced April 12 after a March announcement), Moonshot's **Kimi K2.6**, and DeepSeek's **V4** (Pro and Flash variants)."
   ],
   "parameters_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "- **V4-Flash**: 284 billion total parameters, 13B active, pretrained on 32 trillion tokens"
   ],
   "context_window_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "license_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "DeepSeek shipped two variants on the same day under MIT-licensed weights and a 1M-token context window5:"
   ],
   "significance_evidence": [
    "# China's Open-Weight Coding Wave: 4 Models, 18 Days",
    "### DeepSeek V4 \u2014 April 24",
    "V4-Flash trails Pro by roughly 1.6 points on Verified at 79.0% in its strongest tier12."
   ]
  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/qwen3-6-35b-a3b-alibaba-open-sources-a-frontier-class-agentic-coder",
   "title": "Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "35 billion",
   "context_window": null,
   "license": null,
   "significance": "the first open-weight variant of the Qwen3.6 generation, posting frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026",
   "organization_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation."
   ],
   "parameters_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation.",
    "Released under Apache 2.0 on Hugging Face alongside the proprietary Qwen3.6-Plus API model, the 35-billion-parameter Mixture-of-Experts model activates just 3B parameters per token while posting frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation.",
    "Released under Apache 2.0 on Hugging Face alongside the proprietary Qwen3.6-Plus API model, the 35-billion-parameter Mixture-of-Experts model activates just 3B parameters per token while posting frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026."
   ]
  },
  {
   "url": "https://the-agent-report.com/2026/06/meta-llama-4-5-open-weight-refresh",
   "title": "Meta Ships Llama 4.5: A Mid-Cycle Open-Weight Refresh Reasserts Commitment to Open Source",
   "published_at": "2026-06-23T12:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Incremental benchmark gains, improved agentic tool-use stability, license clarifications, and 512K context window support",
   "organization_evidence": [
    "# Meta Ships Llama 4.5: A Mid-Cycle Open-Weight Refresh Reasserts Commitment to Open Source",
    "Meta has shipped **Llama 4.5**, a mid-cycle open-weight refresh of the Llama 4 family, landing in early June 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What\u2019s New in Llama 4.5",
    "- **Incremental benchmark gains** across reasoning, coding, and multilingual tasks",
    "- **Improved agentic tool-use stability** \u2014 a meaningful upgrade for developers building autonomous AI workflows",
    "- **License clarifications** that ease enterprise adoption without changing the community-license framework",
    "- **512K context window** support, keeping pace with the extended-context trend sweeping the frontier model landscape"
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/mistral-medium-3-5-vibe-remote-agents-open-weight-128b-april-29-2026",
   "title": "Mistral Medium 3.5 Lands as a 128B Open-Weight Coder With Cloud Vibe Remote Agents",
   "published_at": "2026-05-03T12:50:06",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "hits 77.6% on SWE-Bench Verified",
   "organization_evidence": [
    "# Mistral Medium 3.5 Lands as a 128B Open-Weight Coder With Cloud Vibe Remote Agents",
    "Mistral AI shipped Medium 3.5 on April 29, 2026 \u2014 a 128B-parameter dense multimodal model with a 256K context window, modified-MIT open weights, and a new Vibe remote agent runtime that hits 77.6% on SWE-Bench Verified."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 Lands as a 128B Open-Weight Coder With Cloud Vibe Remote Agents",
    "Mistral AI shipped Medium 3.5 on April 29, 2026 \u2014 a 128B-parameter dense multimodal model with a 256K context window, modified-MIT open weights, and a new Vibe remote agent runtime that hits 77.6% on SWE-Bench Verified."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 Lands as a 128B Open-Weight Coder With Cloud Vibe Remote Agents",
    "Mistral AI shipped Medium 3.5 on April 29, 2026 \u2014 a 128B-parameter dense multimodal model with a 256K context window, modified-MIT open weights, and a new Vibe remote agent runtime that hits 77.6% on SWE-Bench Verified."
   ]
  },
  {
   "url": "https://theplanettools.ai/blog/google-gemma-4-12b-encoder-free-native-audio-local-laptop-16gb-june-2026",
   "title": "Google Gemma 4 12B: Encoder-Free, Native Audio, Runs Local on a 16GB Laptop (June 2026)",
   "published_at": "2026-06-05T02:40:14",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Encoder-free multimodal model with native audio input that runs locally on a 16GB laptop, hitting near-26B quality at under half the memory.",
   "organization_evidence": [
    "# Google Gemma 4 12B: Encoder-Free, Native Audio, Runs Local on a 16GB Laptop (June 2026)",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 an open, encoder-free multimodal model with native audio input that runs locally on a 16GB laptop under Apache 2.0, hitting near-26B quality at under half the memory."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4 12B: Encoder-Free, Native Audio, Runs Local on a 16GB Laptop (June 2026)",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 an open, encoder-free multimodal model with native audio input that runs locally on a 16GB laptop under Apache 2.0, hitting near-26B quality at under half the memory."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4 12B: Encoder-Free, Native Audio, Runs Local on a 16GB Laptop (June 2026)",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 an open, encoder-free multimodal model with native audio input that runs locally on a 16GB laptop under Apache 2.0, hitting near-26B quality at under half the memory."
   ]
  },
  {
   "url": "https://wordupnews.com/business/chinese-startup-moonshot-ai-releases-kimi-k3-worlds-largest-open-source-model-rattling-global-markets",
   "title": "Chinese Startup Moonshot AI Releases Kimi K3, World\u2019s Largest Open-Source Model, Rattling Global Markets",
   "published_at": "2026-07-18T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-23",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Ranked among the top three models tested on officially released benchmarks, performing competitively against Anthropic's Fable 5 and substantially outperforming Anthropic's Opus 4.8 and OpenAI's GPT 5.6 Sol and GPT 5.5.",
   "organization_evidence": [
    "Beijing-based artificial intelligence startup Moonshot AI released Kimi K3 this week, unveiling what the company describes as the largest open-source AI model ever made publicly available, with benchmark performance the company says rivals some of the most advanced systems built by American labs including Anthropic and OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model, released Thursday, contains 2.8 trillion total parameters, making it roughly 75% larger than [DeepSeek\u2019s V4 Pro](https://www.ibtimes.com.au/nvidia-shares-dip-chinese-ai-firm-deepseek-develops-chip-1871977), previously one of the largest Chinese open-weight models at approximately 1.6 trillion parameters, and far outpacing Zhipu AI\u2019s GLM 5 series at 744 billion parameters."
   ],
   "parameters_evidence": [
    "The model, released Thursday, contains 2.8 trillion total parameters, making it roughly 75% larger than [DeepSeek\u2019s V4 Pro](https://www.ibtimes.com.au/nvidia-shares-dip-chinese-ai-firm-deepseek-develops-chip-1871977), previously one of the largest Chinese open-weight models at approximately 1.6 trillion parameters, and far outpacing Zhipu AI\u2019s GLM 5 series at 744 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "According to Moonshot, K3 performed \u201ccompetitively\u201d against [Anthropic\u2019s Fable 5](https://www.ibtimes.com.au/openai-anthropic-ai-rivalry-1872191), currently among the most capable publicly available AI models, and \u201csubstantially outperformed\u201d Anthropic\u2019s Opus 4.8 as well as [OpenAI\u2019s GPT 5.6 Sol and GPT 5.5](https://www.ibtimes.com.au/openai-expands-gpt-5-6-ai-models-1872012) on the company\u2019s officially released benchmarks, where K3 consistently ranked among the top three models tested."
   ]
  },
  {
   "url": "https://agent22.app/copperriver/blog/open-source-ai-models-2026-comparison",
   "title": "Open Source AI Models in 2026: A No-BS Comparison",
   "published_at": "2026-06-15T16:32:24",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B total parameters, ~40B active (MoE with 384 experts)",
   "context_window": null,
   "license": null,
   "significance": "Adopted DeepSeek Sparse Attention (DSA) and achieved a 5x context jump and 2x output jump in one iteration with a 1M context window.",
   "organization_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## GLM-5.2 (Z.ai)",
    "GLM-5.2 came out on June 13, 2026.",
    "Z.ai (formerly Zhipu AI) clearly wanted to get this out fast \u2014 which makes sense given the context window jump they pulled off."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## GLM-5.2 (Z.ai)",
    "GLM-5.2 came out on June 13, 2026."
   ],
   "parameters_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## GLM-5.2 (Z.ai)",
    "Here's the headline: **744B total parameters, ~40B active (MoE with 384 experts), 1M context window, 131K max output.**"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## GLM-5.2 (Z.ai)",
    "Here's the headline: **744B total parameters, ~40B active (MoE with 384 experts), 1M context window, 131K max output.**",
    "The predecessor GLM-5.1 had 200K context and 64K output.",
    "This is a 5x context jump and 2x output jump in one iteration.",
    "The big architectural change is that GLM-5.2 adopted **DeepSeek Sparse Attention (DSA)** \u2014 the same innovation DeepSeek V4 pioneered."
   ]
  },
  {
   "url": "https://agent22.app/copperriver/blog/open-source-ai-models-2026-comparison",
   "title": "Open Source AI Models in 2026: A No-BS Comparison",
   "published_at": "2026-06-15T16:32:24",
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active (V4-Pro), 284B total / 13B active (V4-Flash)",
   "context_window": "1M context windows",
   "license": "MIT licensed",
   "significance": "Introduced DeepSeek Sparse Attention (DSA) combined with token-wise compression, making 1M context economically viable and achieving top reasoning and math benchmark scores like 95.2 on HMMT 2026 Feb.",
   "organization_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Released April 24, 2026, DeepSeek V4 is the reasoning monster. And it came in two flavors:"
   ],
   "model_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Released April 24, 2026, DeepSeek V4 is the reasoning monster. And it came in two flavors:"
   ],
   "release_date_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Released April 24, 2026, DeepSeek V4 is the reasoning monster. And it came in two flavors:"
   ],
   "parameters_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "- **V4-Pro:** 1.6T total / 49B active \u2014 the largest open-source model ever shipped",
    "- **V4-Flash:** 284B total / 13B active \u2014 fast, cheap, surprisingly capable"
   ],
   "context_window_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Released April 24, 2026, DeepSeek V4 is the reasoning monster. And it came in two flavors:",
    "- **V4-Pro:** 1.6T total / 49B active \u2014 the largest open-source model ever shipped",
    "- **V4-Flash:** 284B total / 13B active \u2014 fast, cheap, surprisingly capable",
    "Both have 1M context windows."
   ],
   "license_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Released April 24, 2026, DeepSeek V4 is the reasoning monster. And it came in two flavors:",
    "Both are MIT licensed with open weights."
   ],
   "significance_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## DeepSeek V4 (DeepSeek)",
    "Here's where it gets interesting. DeepSeek V4 introduced **DeepSeek Sparse Attention (DSA)** combined with token-wise compression.",
    "This isn't a minor optimization \u2014 it's what makes 1M context economically viable.",
    "HMMT 2026 Feb | 95.2 |",
    "HMMT 2026 at 95.2."
   ]
  },
  {
   "url": "https://agent22.app/copperriver/blog/open-source-ai-models-2026-comparison",
   "title": "Open Source AI Models in 2026: A No-BS Comparison",
   "published_at": "2026-06-15T16:32:24",
   "organization": "MiniMax AI",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "~428B total / ~23B active (MoE)",
   "context_window": "1M context window (guaranteed minimum 512K)",
   "license": "MiniMax Community License",
   "significance": "Introduced MiniMax Sparse Attention (MSA), native multimodality (text, image, and video), and scored 83.5 on BrowseComp surpassing Opus 4.7.",
   "organization_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "model_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "release_date_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "parameters_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "context_window_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "license_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Released June 1, 2026. ~428B total / ~23B active (MoE). 1M context window (guaranteed minimum 512K), up to 512K output. Open weights on HuggingFace under the MiniMax Community License."
   ],
   "significance_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## MiniMax M3 (MiniMax AI)",
    "Their version is called **MSA (MiniMax Sparse Attention)**, based on GQA with block-level sparse selection on real, uncompressed KVs.",
    "It's also **natively multimodal** \u2014 text, image, and video from step one.",
    "BrowseComp | 83.5 | Surpasses Opus 4.7's 79.3 \u2014 this is wild",
    "BrowseComp at 83.5 beating Opus 4.7 at 79.3 is genuinely shocking."
   ]
  },
  {
   "url": "https://agent22.app/copperriver/blog/open-source-ai-models-2026-comparison",
   "title": "Open Source AI Models in 2026: A No-BS Comparison",
   "published_at": "2026-06-15T16:32:24",
   "organization": "Moonshot AI",
   "model": "Kimi K2.7-Code",
   "release_date": "2026-06-12",
   "parameters": "1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token)",
   "context_window": "256K context window",
   "license": "Modified MIT license",
   "significance": "Purpose-built coding specialist with native multimodality (MoonViT 400M vision encoder) and always-on thinking mode achieving a +21.8% jump on Kimi Code Bench v2.",
   "organization_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "model_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "release_date_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "parameters_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "context_window_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "license_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "Released June 12, 2026 \u2014 three days ago. 1T total / 32B active (MoE, 384 experts, selects 8+1 shared per token). 256K context window. Modified MIT license, open weights on HuggingFace."
   ],
   "significance_evidence": [
    "Open Source AI Models in 2026: A No-BS Comparison",
    "## Kimi K2.7-Code (Moonshot AI)",
    "K2.7-Code is purpose-built for long-horizon coding, period.",
    "It's natively multimodal (MoonViT 400M vision encoder) and has one unusual constraint: **thinking mode is always on.**",
    "A +21.8% jump on Kimi Code Bench in a single iteration."
   ]
  },
  {
   "url": "https://earlyterms.com/term/nemotron-ultra",
   "title": "Nemotron Ultra is NVIDIA's flagship open-weights large language model \u2014 a 550B-parameter hybrid Mixture-of-Exp",
   "published_at": "2026-06-04T12:28:14",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550B total / 55B active",
   "context_window": null,
   "license": null,
   "significance": "Topped US open-weights intelligence rankings on its launch day and delivers over 300 tokens per second.",
   "organization_evidence": [
    "Nemotron Ultra",
    "Nemotron Ultra is NVIDIA's flagship open-weights large language model \u2014 a 550B-parameter hybrid Mixture-of-Experts model with only 55B parameters active per token, engineered for long-running agentic workflows that demand both frontier reasoning and high inference throughput."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Nemotron Ultra",
    "Released June 4, 2026 under the permissive [OpenMDW-1.1 license](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16), the model uses a novel Mamba-2/Transformer/LatentMoE architecture supporting a 1M-token context window."
   ],
   "parameters_evidence": [
    "Nemotron Ultra",
    "Nemotron Ultra is NVIDIA's flagship open-weights large language model \u2014 a 550B-parameter hybrid Mixture-of-Experts model with only 55B parameters active per token, engineered for long-running agentic workflows that demand both frontier reasoning and high inference throughput."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nemotron Ultra",
    "It delivers over 300 tokens per second \u2014 roughly 5x faster than comparably-capable open models \u2014 and topped US open-weights intelligence rankings on its launch day."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen",
   "title": "Qwen",
   "published_at": "2026-03-28T00:13:32",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": "0.6B to larger variants",
   "context_window": null,
   "license": null,
   "significance": "purpose-built for autonomous AI agents capable of executing complex tasks independently",
   "organization_evidence": [
    "# Qwen",
    "Recent open-source releases, including the Qwen3 series up to Qwen3.5 (released February 2026 and purpose-built for autonomous AI agents capable of executing complex tasks independently)[3] in sizes from 0.6B to larger variants and the Qwen3-VL models (building on the Qwen3 series from 0.6B to larger variants and Qwen2-VL), emphasize efficiency, long-context handling up to 128K tokens, and support for tasks like visual question answering, document parsing, and agentic workflows, all licensed under [Apache 2.0](/page/Apache_License) to promote accessibility and community adoption."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen",
    "Following the Qwen3 series, Alibaba unveiled Qwen3.5 on February 16, 2026, including the Qwen3.5-397B-A17B variant."
   ],
   "parameters_evidence": [
    "# Qwen",
    "Recent open-source releases, including the Qwen3 series up to Qwen3.5 (released February 2026 and purpose-built for autonomous AI agents capable of executing complex tasks independently)[3] in sizes from 0.6B to larger variants and the Qwen3-VL models (building on the Qwen3 series from 0.6B to larger variants and Qwen2-VL), emphasize efficiency, long-context handling up to 128K tokens, and support for tasks like visual question answering, document parsing, and agentic workflows, all licensed under [Apache 2.0](/page/Apache_License) to promote accessibility and community adoption."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen",
    "Recent open-source releases, including the Qwen3 series up to Qwen3.5 (released February 2026 and purpose-built for autonomous AI agents capable of executing complex tasks independently)[3] in sizes from 0.6B to larger variants and the Qwen3-VL models (building on the Qwen3 series from 0.6B to larger variants and Qwen2-VL), emphasize efficiency, long-context handling up to 128K tokens, and support for tasks like visual question answering, document parsing, and agentic workflows, all licensed under [Apache 2.0](/page/Apache_License) to promote accessibility and community adoption."
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "Mar 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Mistral** | Mistral Small 4 (119B total / 6.5B active MoE, Mar 2026); Mistral Medium 3.5 (128B dense, Apr 2026); Mistral Large 3 (675B total / 41B active MoE, Dec 2025) | Apache 2.0 (Small 4, Large 3); Modified MIT with revenue cap (Medium 3.5) | Yes \u2014 function calling and structured output supported across all three; Small 4 also unifies reasoning and vision | Small 4 (256K ctx) unifies Magistral reasoning + Pixtral vision + Devstral coding in one model. Medium 3.5 (256K ctx) is a frontier-class open coding/agentic model. Large 3 (256K ctx) is the largest open-weight Mistral. Mistral Small 3.2 deprecated April 30, 2026. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Alibaba Qwen",
   "model": null,
   "release_date": "Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Dense and MoE variants from 0.6B to 235B.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Alibaba Qwen** | Qwen 3.6-27B / 3.6-35B-A3B (Apr 2026); Qwen3 base series (Apr 2025) | Apache 2.0 | Yes \u2014 native tool-calling and MCP support via Qwen-Agent; all sizes | Dense and MoE variants from 0.6B to 235B. Up to 262K context (extensible to 1M via YaRN). Hybrid thinking/non-thinking mode. Qwen 3.7 is closed-weight API-only as of Jun 2026. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Alibaba Qwen** | Qwen 3.6-27B / 3.6-35B-A3B (Apr 2026); Qwen3 base series (Apr 2025) | Apache 2.0 | Yes \u2014 native tool-calling and MCP support via Qwen-Agent; all sizes | Dense and MoE variants from 0.6B to 235B. Up to 262K context (extensible to 1M via YaRN). Hybrid thinking/non-thinking mode. Qwen 3.7 is closed-weight API-only as of Jun 2026. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Alibaba Qwen** | Qwen 3.6-27B / 3.6-35B-A3B (Apr 2026); Qwen3 base series (Apr 2025) | Apache 2.0 | Yes \u2014 native tool-calling and MCP support via Qwen-Agent; all sizes | Dense and MoE variants from 0.6B to 235B. Up to 262K context (extensible to 1M via YaRN). Hybrid thinking/non-thinking mode. Qwen 3.7 is closed-weight API-only as of Jun 2026. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1M context window (default across both V4 variants).",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **DeepSeek** | DeepSeek V4-Pro (1.6T total / 49B active MoE) and V4-Flash (284B total / 13B active), both Apr 2026 preview | MIT | Yes \u2014 V4 natively supports function calling, JSON output, tool calls, and thinking / non-thinking modes | 1M context window (default across both V4 variants). V4-Pro: frontier-class agentic coding. V4-Flash: fast/cheap inference. Weights on Hugging Face (deepseek-ai). V4 labeled preview; stable release expected later 2026. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **DeepSeek** | DeepSeek V4-Pro (1.6T total / 49B active MoE) and V4-Flash (284B total / 13B active), both Apr 2026 preview | MIT | Yes \u2014 V4 natively supports function calling, JSON output, tool calls, and thinking / non-thinking modes | 1M context window (default across both V4 variants). V4-Pro: frontier-class agentic coding. V4-Flash: fast/cheap inference. Weights on Hugging Face (deepseek-ai). V4 labeled preview; stable release expected later 2026. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **DeepSeek** | DeepSeek V4-Pro (1.6T total / 49B active MoE) and V4-Flash (284B total / 13B active), both Apr 2026 preview | MIT | Yes \u2014 V4 natively supports function calling, JSON output, tool calls, and thinking / non-thinking modes | 1M context window (default across both V4 variants). V4-Pro: frontier-class agentic coding. V4-Flash: fast/cheap inference. Weights on Hugging Face (deepseek-ai). V4 labeled preview; stable release expected later 2026. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Google Gemma 4",
   "model": null,
   "release_date": "Mar\u2013Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Google Gemma 4",
   "model": null,
   "release_date": "Jun 2026",
   "parameters": null,
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   "license": null,
   "significance": "Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Google Gemma 4** | Gemma 4: E2B, E4B, 26B-A4B, 31B (Mar\u2013Apr 2026); Gemma 4 12B Unified (Jun 2026, encoder-free, native audio) | Apache 2.0 (first Gemma release under true Apache 2.0) | Yes \u2014 native function-calling built into Gemma 4; FunctionGemma 270M for edge/on-device | 128K context (E2B/E4B); 256K context (12B+, 26B, 31B). Gemma 4 12B Unified (Jun 3 2026) adds native audio + video via encoder-free architecture; runs on 16 GB RAM. Multimodal across the family. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Microsoft Phi-4",
   "model": null,
   "release_date": "Mar 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Efficiency-first: strong reasoning per parameter.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Microsoft Phi-4** | Phi-4-reasoning-vision-15B (15B, Mar 2026); Phi-4-reasoning (14B, May 2025); Phi-4-mini (3.8B) | MIT | Yes \u2014 Phi-4-mini has built-in function calling; the Phi-4 line supports tool use; Phi-4-reasoning for chain-of-thought agentic tasks | Efficiency-first: strong reasoning per parameter. MIT license. Phi-4-reasoning-vision-15B adds selective thinking mode + high-res vision. Phi-4-multimodal adds audio+vision. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Microsoft Phi-4** | Phi-4-reasoning-vision-15B (15B, Mar 2026); Phi-4-reasoning (14B, May 2025); Phi-4-mini (3.8B) | MIT | Yes \u2014 Phi-4-mini has built-in function calling; the Phi-4 line supports tool use; Phi-4-reasoning for chain-of-thought agentic tasks | Efficiency-first: strong reasoning per parameter. MIT license. Phi-4-reasoning-vision-15B adds selective thinking mode + high-res vision. Phi-4-multimodal adds audio+vision. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Microsoft Phi-4** | Phi-4-reasoning-vision-15B (15B, Mar 2026); Phi-4-reasoning (14B, May 2025); Phi-4-mini (3.8B) | MIT | Yes \u2014 Phi-4-mini has built-in function calling; the Phi-4 line supports tool use; Phi-4-reasoning for chain-of-thought agentic tasks | Efficiency-first: strong reasoning per parameter. MIT license. Phi-4-reasoning-vision-15B adds selective thinking mode + high-res vision. Phi-4-multimodal adds audio+vision. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "IBM Granite 4.1",
   "model": null,
   "release_date": "Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "512K context window.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **IBM Granite 4.1** | Granite 4.1 (3B, 8B, 30B, Apr 2026) | Apache 2.0 | Yes \u2014 tool calling follows OpenAI function definition schema; benchmarked on Berkeley BFCL | 512K context window. Enterprise-focused; ISO 42001 certified (Granite 4.0 line). 30B uses hybrid Mamba-Transformer architecture for long-context efficiency; 3B/8B are dense. Sizes 3B\u201330B. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **IBM Granite 4.1** | Granite 4.1 (3B, 8B, 30B, Apr 2026) | Apache 2.0 | Yes \u2014 tool calling follows OpenAI function definition schema; benchmarked on Berkeley BFCL | 512K context window. Enterprise-focused; ISO 42001 certified (Granite 4.0 line). 30B uses hybrid Mamba-Transformer architecture for long-context efficiency; 3B/8B are dense. Sizes 3B\u201330B. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **IBM Granite 4.1** | Granite 4.1 (3B, 8B, 30B, Apr 2026) | Apache 2.0 | Yes \u2014 tool calling follows OpenAI function definition schema; benchmarked on Berkeley BFCL | 512K context window. Enterprise-focused; ISO 42001 certified (Granite 4.0 line). 30B uses hybrid Mamba-Transformer architecture for long-context efficiency; 3B/8B are dense. Sizes 3B\u201330B. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Z.ai GLM-5.2",
   "model": null,
   "release_date": "Jun 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-class long-horizon coding/agentic model (SWE-bench Pro 62.1); the highest-ranked open-weight model across several long-horizon coding benchmarks.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Z.ai GLM-5.2** | GLM-5.2 (744B total / ~40B active MoE, Jun 2026) | MIT | Yes \u2014 native tool-calling, function calling, structured output, and MCP support | 1M context (128K max output). Frontier-class long-horizon coding/agentic model (SWE-bench Pro 62.1); the highest-ranked open-weight model across several long-horizon coding benchmarks. Self-hosting needs a multi-GPU node (\u22488\u00d7H100/H200-class at FP8); official FP8 weights at huggingface.co/zai-org/GLM-5.2-FP8. |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Z.ai GLM-5.2** | GLM-5.2 (744B total / ~40B active MoE, Jun 2026) | MIT | Yes \u2014 native tool-calling, function calling, structured output, and MCP support | 1M context (128K max output). Frontier-class long-horizon coding/agentic model (SWE-bench Pro 62.1); the highest-ranked open-weight model across several long-horizon coding benchmarks. Self-hosting needs a multi-GPU node (\u22488\u00d7H100/H200-class at FP8); official FP8 weights at huggingface.co/zai-org/GLM-5.2-FP8. |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Z.ai GLM-5.2** | GLM-5.2 (744B total / ~40B active MoE, Jun 2026) | MIT | Yes \u2014 native tool-calling, function calling, structured output, and MCP support | 1M context (128K max output). Frontier-class long-horizon coding/agentic model (SWE-bench Pro 62.1); the highest-ranked open-weight model across several long-horizon coding benchmarks. Self-hosting needs a multi-GPU node (\u22488\u00d7H100/H200-class at FP8); official FP8 weights at huggingface.co/zai-org/GLM-5.2-FP8. |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "Moonshot AI Kimi K2.7 Code",
   "model": null,
   "release_date": "Jun 12 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The coding/agentic-specialized variant of the Kimi K2 line; Moonshot reports ~30% fewer reasoning tokens than K2.6 at higher coding-benchmark scores.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Moonshot AI Kimi K2.7 Code** | Kimi K2.7 Code (~1T total / ~32B active MoE, Jun 12 2026) | Modified MIT (commercial use and internal deployment permitted; attribution required above a revenue/MAU threshold, consistent with the established Kimi K2 license family) | Yes \u2014 native agentic tool-calling; purpose-built for long-horizon software-engineering workflows | 256K context. The coding/agentic-specialized variant of the Kimi K2 line; Moonshot reports ~30% fewer reasoning tokens than K2.6 at higher coding-benchmark scores. Weights on Hugging Face (moonshotai/Kimi-K2.7-Code). |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Moonshot AI Kimi K2.7 Code** | Kimi K2.7 Code (~1T total / ~32B active MoE, Jun 12 2026) | Modified MIT (commercial use and internal deployment permitted; attribution required above a revenue/MAU threshold, consistent with the established Kimi K2 license family) | Yes \u2014 native agentic tool-calling; purpose-built for long-horizon software-engineering workflows | 256K context. The coding/agentic-specialized variant of the Kimi K2 line; Moonshot reports ~30% fewer reasoning tokens than K2.6 at higher coding-benchmark scores. Weights on Hugging Face (moonshotai/Kimi-K2.7-Code). |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **Moonshot AI Kimi K2.7 Code** | Kimi K2.7 Code (~1T total / ~32B active MoE, Jun 12 2026) | Modified MIT (commercial use and internal deployment permitted; attribution required above a revenue/MAU threshold, consistent with the established Kimi K2 license family) | Yes \u2014 native agentic tool-calling; purpose-built for long-horizon software-engineering workflows | 256K context. The coding/agentic-specialized variant of the Kimi K2 line; Moonshot reports ~30% fewer reasoning tokens than K2.6 at higher coding-benchmark scores. Weights on Hugging Face (moonshotai/Kimi-K2.7-Code). |"
   ]
  },
  {
   "url": "https://changegamer.ai/resources/open-weight-models-for-agents",
   "title": "Open-Weight Models for Agents",
   "published_at": null,
   "organization": "NVIDIA Nemotron 3 Ultra",
   "model": null,
   "release_date": "Jun 4 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Hybrid Mamba-Transformer MoE architecture with an NVFP4 pretraining/quantization recipe for compute-efficient long-horizon inference.",
   "organization_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **NVIDIA Nemotron 3 Ultra** | Nemotron 3 Ultra (550B total / 55B active MoE, Jun 4 2026) | OpenMDW-1.1 (the Linux Foundation's open AI-model license; NVIDIA adopted it across the Cosmos/Isaac GR00T/Nemotron families; permissive \u2014 commercial and non-commercial use, modification, and redistribution allowed, patent-termination clause, no revenue/MAU cap) | Yes \u2014 tuned for long-running agent orchestration across hundreds of sequential tool calls | 1M context. Hybrid Mamba-Transformer MoE architecture with an NVFP4 pretraining/quantization recipe for compute-efficient long-horizon inference. Weights on Hugging Face (nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B). |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **NVIDIA Nemotron 3 Ultra** | Nemotron 3 Ultra (550B total / 55B active MoE, Jun 4 2026) | OpenMDW-1.1 (the Linux Foundation's open AI-model license; NVIDIA adopted it across the Cosmos/Isaac GR00T/Nemotron families; permissive \u2014 commercial and non-commercial use, modification, and redistribution allowed, patent-termination clause, no revenue/MAU cap) | Yes \u2014 tuned for long-running agent orchestration across hundreds of sequential tool calls | 1M context. Hybrid Mamba-Transformer MoE architecture with an NVFP4 pretraining/quantization recipe for compute-efficient long-horizon inference. Weights on Hugging Face (nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B). |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models for Agents",
    "Cross-vendor comparison table of major open-weight LLM families \u2014 license, tool-calling support, context window, and agent-builder notes \u2014 as of July 2026.",
    "| Family | Latest open-weight release | License | Native tool/function-calling | Notable for agents |",
    "| **NVIDIA Nemotron 3 Ultra** | Nemotron 3 Ultra (550B total / 55B active MoE, Jun 4 2026) | OpenMDW-1.1 (the Linux Foundation's open AI-model license; NVIDIA adopted it across the Cosmos/Isaac GR00T/Nemotron families; permissive \u2014 commercial and non-commercial use, modification, and redistribution allowed, patent-termination clause, no revenue/MAU cap) | Yes \u2014 tuned for long-running agent orchestration across hundreds of sequential tool calls | 1M context. Hybrid Mamba-Transformer MoE architecture with an NVFP4 pretraining/quantization recipe for compute-efficient long-horizon inference. Weights on Hugging Face (nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B). |"
   ]
  },
  {
   "url": "https://doxychat.com/en/blog/2026-07-20-mistral-open-weight-model-business-chatbot",
   "title": "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral's most powerful open-weight LLM",
   "release_date": "2026-07-01",
   "parameters": "Mixture-of-Experts architecture",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Mixture-of-Experts architecture a design that activates only the most relevant subset of parameters for any given request",
   "organization_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "Mistral AI just opened early access to its most ambitious open-weight model to date \u2014 a Mixture-of-Experts architecture the company describes as \u201cfat but sparse.\u201d"
   ],
   "model_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "Mistral opened early access to its most powerful open-weight LLM in July 2026."
   ],
   "release_date_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "Mistral opened early access to its most powerful open-weight LLM in July 2026."
   ],
   "parameters_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "Mistral AI just opened early access to its most ambitious open-weight model to date \u2014 a Mixture-of-Experts architecture the company describes as \u201cfat but sparse.\u201d"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "**Open-weight under Apache 2.0** \u2014 any company can download, self-host, fine-tune, and redistribute it commercially without legal review or royalty obligations"
   ],
   "significance_evidence": [
    "Mistral's New Open-Weight Model in 2026: What Your Business Chatbot Gains",
    "The new model uses a Mixture-of-Experts (MoE) architecture \u2014 a design that activates only the most relevant subset of parameters for any given request."
   ]
  },
  {
   "url": "https://nowosci.ai/en/article/google-deepmind-releases-gemma-4-open-models",
   "title": "Google DeepMind Releases Gemma 4, Open Models Built on Gemini 3 Technology",
   "published_at": "2026-07-24T00:02:57",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "five sizes: E2B, E4B, 12B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "Scored 1452 Elo points on Arena AI, 85.2 percent on MMLU Multilingual, 76.9 percent on MMMU Pro, 89.2 percent on AIME 2026, 80.0 percent on LiveCodeBench, and 84.3 percent on GPQA Diamond.",
   "organization_evidence": [
    "# Google DeepMind Releases Gemma 4, Open Models Built on Gemini 3 Technology",
    "Google DeepMind has released the next generation of its open Gemma models, Gemma 4, in five sizes, for the first time under a fully permissive Apache 2.0 license and built for running directly on devices."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google DeepMind Releases Gemma 4, Open Models Built on Gemini 3 Technology",
    "Google DeepMind released the Gemma 4 family of open models on April 2, 2026, built on the same technology as the flagship Gemini 3."
   ],
   "parameters_evidence": [
    "# Google DeepMind Releases Gemma 4, Open Models Built on Gemini 3 Technology",
    "Google DeepMind has released the next generation of its open Gemma models, Gemma 4, in five sizes, for the first time under a fully permissive Apache 2.0 license and built for running directly on devices.",
    "The E2B and E4B variants are designed for maximum memory and compute efficiency, built to run offline on smartphones, Raspberry Pi boards and other IoT devices.",
    "Alongside them are three larger models, 12B, 26B and 31B, aimed at consumer graphics cards and offering, according to Google, a strong quality-to-parameter ratio."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google DeepMind Releases Gemma 4, Open Models Built on Gemini 3 Technology",
    "The largest model, Gemma 4 31B IT Thinking, scored 1452 Elo points on the Arena AI leaderboard for text tasks and 85.2 percent on the MMLU Multilingual knowledge test.",
    "On the multimodal MMMU Pro benchmark it scored 76.9 percent, on the AIME 2026 math benchmark 89.2 percent, on the LiveCodeBench coding benchmark 80.0 percent, and on the GPQA Diamond science benchmark 84.3 percent."
   ]
  },
  {
   "url": "https://aidb.digital/blog/2026-07-18-moonshot-ai-releases-kimi-k3-the-largest-open-weight-model-ever-at-2-",
   "title": "Moonshot AI Releases Kimi K3, the Largest Open-Weight Model Ever, at 2.8 Trillion Parameters \u00b7 AIDB",
   "published_at": "2026-07-18T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI",
   "organization_evidence": [
    "What Was Released",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Scale in Context",
    "Moonshot AI publicly released Kimi K3 on July 16, 2026, with full open-source weights promised by July 27."
   ],
   "parameters_evidence": [
    "What Was Released",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Was Released",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI."
   ]
  },
  {
   "url": "https://promptslove.com/blog/glm-5-2-review",
   "title": "GLM 5.2 Just Made Me Reconsider My $200/Month Claude Subscription",
   "published_at": "2026-06-18T13:43:23",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B Mixture-of-Experts",
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 won 3 tests, Claude Opus 4.8 won 1, and 1 was a tie in live coding tests; scores 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro; on FrontierSWE and MCP Atlas sits within 0.7 and 0.8 points of Claude Opus 4.8",
   "organization_evidence": [
    "GLM 5.2 Just Made Me Reconsider My $200/Month Claude Subscription"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM 5.2 Specs: What Zhipu AI Built"
   ],
   "parameters_evidence": [
    "GLM 5.2 Specs: What Zhipu AI Built",
    "GLM 5.2 is a 744B Mixture-of-Experts open-source model released June 13, 2026, priced at just $1.40/M input tokens and $4.40/M output tokens - roughly 80% cheaper than Claude Opus 4.8."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark Performance",
    "In my live coding tests across 5 real projects using OpenCode and OpenRouter, GLM 5.2 won 3 tests, Claude Opus 4.8 won 1, and 1 was a tie.",
    "GLM 5.2 scores 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro.",
    "On FrontierSWE and MCP Atlas - two of the most important agentic benchmarks - GLM 5.2 sits within 0.7 and 0.8 points of Claude Opus 4.8."
   ]
  },
  {
   "url": "http://ai-tldr.dev/releases/nvidia-alpamayo-2-super",
   "title": "NVIDIA Alpamayo 2 Super \u2014 34B open VLA model for robotaxis and self-driving",
   "published_at": "2026-08-04T12:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "34B",
   "context_window": null,
   "license": null,
   "significance": "Scores 79.2 on LingoQA, ahead of Qwen2.5-VL 72B and Gemini 2.5 Pro on the same benchmark.",
   "organization_evidence": [
    "NVIDIA \u00b7 2026-08-04 \u00b7 major",
    "Quick facts",
    "| Maker | NVIDIA |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA \u00b7 2026-08-04 \u00b7 major"
   ],
   "parameters_evidence": [
    "NVIDIA Alpamayo 2 Super \u2014 34B open VLA model for robotaxis and self-driving",
    "## Key specs",
    "| Parameters | 34B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Alpamayo 2 Super \u2014 34B open VLA model for robotaxis and self-driving",
    "NVIDIA Alpamayo 2 Super is a 34B vision-language-action model for autonomous vehicles, open under the OpenMDW-1.1 license. It scores 79.2 on LingoQA, ahead of Qwen2.5-VL 72B and Gemini 2.5 Pro on the same benchmark."
   ]
  },
  {
   "url": "https://aichina.news/blog/unlock-web-agents-allen-ais-compact-open-source-vlm-for-screenshot-skhh76",
   "title": "Unlock Web Agents: Allen AI\u2019s Compact Open-Source VLM for Screenshot Reasoning \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:33:31",
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "4-billion",
   "context_window": null,
   "license": null,
   "significance": "specialised 4-billion parameter vision-language model (VLM) hosted on Modelers.cn, specifically tuned for web-page imagery, part of the respected Allen AI Molmo family",
   "organization_evidence": [
    "Unlock Web Agents: Allen AI\u2019s Compact Open-Source VLM for Screenshot Reasoning",
    "Enter the new `MolmoWeb-4B-Native` model, a specialised 4-billion parameter vision-language model (VLM) hosted on Modelers.cn."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Unlock Web Agents: Allen AI\u2019s Compact Open-Source VLM for Screenshot Reasoning",
    "- **Provenance Questions:** The creation timestamp (2026-04-12) appears to be future-dated, raising questions about the model's official status or origin."
   ],
   "parameters_evidence": [
    "Unlock Web Agents: Allen AI\u2019s Compact Open-Source VLM for Screenshot Reasoning",
    "Enter the new `MolmoWeb-4B-Native` model, a specialised 4-billion parameter vision-language model (VLM) hosted on Modelers.cn."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Unlock Web Agents: Allen AI\u2019s Compact Open-Source VLM for Screenshot Reasoning",
    "Enter the new `MolmoWeb-4B-Native` model, a specialised 4-billion parameter vision-language model (VLM) hosted on Modelers.cn."
   ]
  },
  {
   "url": "https://dsebastien.net/deepseek-v4",
   "title": "DeepSeek v4",
   "published_at": "2026-05-01T07:15:04",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active parameters",
   "context_window": null,
   "license": null,
   "significance": "V4-Pro is the largest open weights model released to date.",
   "organization_evidence": [
    "DeepSeek v4",
    "Fourth-generation flagship release from [Deepseek](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Deepseek?ref=dsebastien.net) (April 24, 2026)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek v4",
    "Fourth-generation flagship release from [Deepseek](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Deepseek?ref=dsebastien.net) (April 24, 2026)."
   ],
   "parameters_evidence": [
    "DeepSeek v4",
    "Two open-weight variants \u2014 V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active) \u2014 both built on a [MoE](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Mixture+of+Experts+(MoE)?ref=dsebastien.net) architecture, ship with a 1M-token [Context Window](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Context+Window?ref=dsebastien.net) by default, and fold what was the separate R reasoning line into a single model with switchable Thinking / Non-Thinking modes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek v4",
    "V4-Pro is the largest [open weights](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Open+Weight+Models?ref=dsebastien.net) model released to date."
   ]
  },
  {
   "url": "https://dsebastien.net/deepseek-v4",
   "title": "DeepSeek v4",
   "published_at": "2026-05-01T07:15:04",
   "organization": "DeepSeek",
   "model": "V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B total / 13B active",
   "context_window": "1M-token",
   "license": null,
   "significance": "V4-Pro is the largest open weights model released to date.",
   "organization_evidence": [
    "DeepSeek v4",
    "Fourth-generation flagship release from [Deepseek](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Deepseek?ref=dsebastien.net) (April 24, 2026)."
   ],
   "model_evidence": [
    "DeepSeek v4",
    "Two open-weight variants \u2014 V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active) \u2014 both built on a [MoE](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Mixture+of+Experts+(MoE)?ref=dsebastien.net) architecture, ship with a 1M-token [Context Window](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Context+Window?ref=dsebastien.net) by default, and fold what was the separate R reasoning line into a single model with switchable Thinking / Non-Thinking modes."
   ],
   "release_date_evidence": [
    "DeepSeek v4",
    "Fourth-generation flagship release from [Deepseek](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Deepseek?ref=dsebastien.net) (April 24, 2026)."
   ],
   "parameters_evidence": [
    "DeepSeek v4",
    "Two open-weight variants \u2014 V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active) \u2014 both built on a [MoE](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Mixture+of+Experts+(MoE)?ref=dsebastien.net) architecture, ship with a 1M-token [Context Window](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Context+Window?ref=dsebastien.net) by default, and fold what was the separate R reasoning line into a single model with switchable Thinking / Non-Thinking modes."
   ],
   "context_window_evidence": [
    "DeepSeek v4",
    "Two open-weight variants \u2014 V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active) \u2014 both built on a [MoE](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Mixture+of+Experts+(MoE)?ref=dsebastien.net) architecture, ship with a 1M-token [Context Window](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/Context+Window?ref=dsebastien.net) by default, and fold what was the separate R reasoning line into a single model with switchable Thinking / Non-Thinking modes."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek v4",
    "V4-Pro is the largest [open weights](https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/AI+Open+Weight+Models?ref=dsebastien.net) model released to date."
   ]
  },
  {
   "url": "https://droiddynasty.com/gemma-4-models-open-weight",
   "title": "Gemma 4 Models From Google Launched Under Apache 2 0 License",
   "published_at": "2026-07-28T16:57:06",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "E2B (2.3B effective / 5.1B total), E4B (4.5B effective / 8B total), 12B Unified, 26B MoE (4B active), 31B dense",
   "context_window": null,
   "license": null,
   "significance": "Most capable open models Google has ever shipped, built for advanced reasoning and agentic workflows, with top tier efficiency and strong agentic/reasoning benchmark scores.",
   "organization_evidence": [
    "Gemma 4 Models From Google Are Open Weight AI",
    "Google released its Gemma 4 models today."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "The family includes five distinct sizes, covering everything from a phone to a developer workstation.",
    "Gemma 4 Models From Google Are Open Weight AI",
    "E2B runs as a 2.3 billion effective parameter model despite holding 5.1 billion total parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 Models From Google Are Open Weight AI",
    "They called the release the most capable open models Google has ever shipped, built for advanced reasoning and agentic workflows.",
    "Benchmark results put the new models among the most efficient open AI releases out there."
   ]
  },
  {
   "url": "https://aikendra.com/creator-hub/blog/kimi-k3-moonshot-s-2-8t-open-model-explained",
   "title": "Moonshot Releases Kimi K3: A 2.8-Trillion-Parameter Open Model That Claims to Trail Only Fable 5 and GPT-5.6 Sol",
   "published_at": "2026-07-17T00:00:00",
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   "release_date": "2026-07-27",
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   "organization_evidence": [
    "Moonshot Releases Kimi K3: A 2.8-Trillion-Parameter Open Model That Claims to Trail Only Fable 5 and GPT-5.6 Sol"
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   "release_date_evidence": [
    "Moonshot Releases Kimi K3: A 2.8-Trillion-Parameter Open Model That Claims to Trail Only Fable 5 and GPT-5.6 Sol"
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    "Moonshot Releases Kimi K3: A 2.8-Trillion-Parameter Open Model That Claims to Trail Only Fable 5 and GPT-5.6 Sol"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Moonshot Releases Kimi K3: A 2.8-Trillion-Parameter Open Model That Claims to Trail Only Fable 5 and GPT-5.6 Sol",
    "Moonshot AI releases Kimi K3, a 2.8T-parameter open model with 1M context, trailing only Claude Fable 5 and GPT-5.6 Sol. Weights land July 27."
   ]
  },
  {
   "url": "https://felloai.com/de/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026",
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   "context_window": null,
   "license": null,
   "significance": "Local and consumer-hardware coding, around $0.60 per million tokens",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-4.7 Flash (early 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "parameters_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "It is a smaller **30B dense** model with a 128K context and pricing near **$0.60 per million tokens**, tuned for fast, cheap, local coding rather than frontier reasoning.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-4.7 Flash (early 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ]
  },
  {
   "url": "https://felloai.com/de/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": "1 million-token",
   "license": "MIT-licensed",
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
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   ],
   "model_evidence": [
    "GLM-5 (February 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "release_date_evidence": [
    "GLM-5 (February 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "parameters_evidence": [
    "GLM-5 (February 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "context_window_evidence": [
    "The current flagship **GLM-5.2** (June 2026) runs a **744B-parameter** mixture-of-experts design with a **1 million-token** context window at roughly **one-sixth** the price of GPT-5.5.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "license_evidence": [
    "Every model in the family is **open-weight and MIT-licensed**, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "significance_evidence": [
    "GLM-5 (February 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/de/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": "1 million-token",
   "license": "MIT-licensed",
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
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   ],
   "model_evidence": [
    "GLM-5.1 (April 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "release_date_evidence": [
    "GLM-5.1 (April 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "parameters_evidence": [
    "GLM-5.1 (April 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "context_window_evidence": [
    "The current flagship **GLM-5.2** (June 2026) runs a **744B-parameter** mixture-of-experts design with a **1 million-token** context window at roughly **one-sixth** the price of GPT-5.5.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "license_evidence": [
    "Every model in the family is **open-weight and MIT-licensed**, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "significance_evidence": [
    "GLM-5.1 (April 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/de/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
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   "parameters": "744B (40B active)",
   "context_window": "1 million tokens",
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   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
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   ],
   "model_evidence": [
    "GLM-5.2 (June 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "release_date_evidence": [
    "GLM-5.2 (June 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "parameters_evidence": [
    "GLM-5.2 (June 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "context_window_evidence": [
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "It quadruples the context window to a full **1 million tokens**, keeps long-context inference affordable, and ranks ahead of rivals like DeepSeek and Kimi on independent intelligence indexes.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "license_evidence": [
    "Every model in the family is **open-weight and MIT-licensed**, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge.",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained"
   ],
   "significance_evidence": [
    "GLM-5.2 (June 2026)",
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ]
  },
  {
   "url": "https://masternodeai.com/en/news/minimax-2b-funding-open-source-ai-models",
   "title": "MiniMax raises $2B as open-source AI model arms race intensifies",
   "published_at": "2026-07-11T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax-M3 features 427 billion parameters and can process prompts containing up to 1 million tokens, and performs its prefill and decode phases 9 and 15 times faster, respectively, than its previous flagship LLM, crediting the speedup to MiniMax Sparse Attention (MSA).",
   "organization_evidence": [
    "MiniMax raises $2B as open-source AI model arms race intensifies",
    "The company develops an open-source series of large language models, with its newest algorithm \u2014 MiniMax-M3 \u2014 debuting in June."
   ],
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   "release_date_evidence": [
    "MiniMax raises $2B as open-source AI model arms race intensifies",
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   ],
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   "context_window_evidence": null,
   "license_evidence": null,
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    "MiniMax raises $2B as open-source AI model arms race intensifies",
    "The company develops an open-source series of large language models, with its newest algorithm \u2014 MiniMax-M3 \u2014 debuting in June.",
    "M3 features 427 billion parameters and can process prompts containing up to 1 million tokens.",
    "MiniMax claims M3 performs its prefill and decode phases 9 and 15 times faster, respectively, than its previous flagship LLM, crediting the speedup to a technology called MiniMax Sparse Attention (MSA)."
   ]
  },
  {
   "url": "https://signalforges.com/pages/emo-pretraining-mixture-of-experts-for-emergent-modularity-2026-05-08",
   "title": "EMO: How Emergent Modularity in Mixture-of-Experts Changes What Developers Can Deploy",
   "published_at": "2026-05-08T21:13:39",
   "organization": "Allen Institute for AI (Ai2)",
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   "release_date": "2026-05-08",
   "parameters": "14B total parameters, 1B active parameters",
   "context_window": null,
   "license": null,
   "significance": "Experts self-organize into coherent, semantically meaningful groups during pretraining without human-defined domain labels, allowing selective deployment of expert subsets with minimal performance loss.",
   "organization_evidence": [
    "Table of contents \u00b7 16 sections",
    "Allen Institute for AI (Ai2) has released EMO, a 14B-total-parameter, 1B-active-parameter mixture-of-experts (MoE) language model that achieves something standard MoEs do not: its experts self-organize into coherent, semantically meaningful groups during pretraining, without any human-defined domain labels."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Section 15",
    "## Refresh-sensitive notes",
    "The model was released on approximately May 8, 2026, based on the arXiv submission date and Ai2 blog publication."
   ],
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    "Table of contents \u00b7 16 sections",
    "Allen Institute for AI (Ai2) has released EMO, a 14B-total-parameter, 1B-active-parameter mixture-of-experts (MoE) language model that achieves something standard MoEs do not: its experts self-organize into coherent, semantically meaningful groups during pretraining, without any human-defined domain labels."
   ],
   "context_window_evidence": null,
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    "Table of contents \u00b7 16 sections",
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   ]
  },
  {
   "url": "https://aiinsiders.net/article/thinking-machines-ships-its-first-open-model-a-975b-moe",
   "title": "Thinking Machines Ships Its First Open Model, a 975B MoE",
   "published_at": "2026-07-16T14:37:25",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975 billion",
   "context_window": null,
   "license": null,
   "significance": "A 975B MoE model that marks the company's first open-weights release, activating 41 billion active parameters per token, supporting multimodal reasoning and a one million token context window.",
   "organization_evidence": [
    "MODELS",
    "# Thinking Machines Ships Its First Open Model, a 975B MoE",
    "Thinking Machines Lab released **Inkling** on July 15, 2026, a 975 billion parameter **mixture of experts** model that marks the company\u2019s first open-weights release."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MODELS",
    "# Thinking Machines Ships Its First Open Model, a 975B MoE",
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   ],
   "parameters_evidence": [
    "MODELS",
    "# Thinking Machines Ships Its First Open Model, a 975B MoE",
    "Thinking Machines Lab released **Inkling** on July 15, 2026, a 975 billion parameter **mixture of experts** model that marks the company\u2019s first open-weights release."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MODELS",
    "# Thinking Machines Ships Its First Open Model, a 975B MoE",
    "Inkling pairs a one million token window with 41B active parameters, tunable through Tinker.",
    "Thinking Machines Lab released **Inkling** on July 15, 2026, a 975 billion parameter **mixture of experts** model that marks the company\u2019s first open-weights release.",
    "Inkling supports multimodal reasoning and a context window spanning one million tokens, roughly enough to hold a large codebase or several full novels in a single prompt."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/mistral-medium-3-5",
   "title": "Mistral Medium 3.5: Specs, Benchmarks & Pricing | AI/TLDR",
   "published_at": "2026-04-28T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-28",
   "parameters": "128-billion-parameter",
   "context_window": "256k-token",
   "license": "modified MIT license",
   "significance": "It is a \"merged\" model that folds the capabilities of Mistral Medium 3.1, Magistral, and Devstral 2 into a single dense 128B set of weights with configurable reasoning effort per request.",
   "organization_evidence": [
    "Mistral Medium 3.5",
    "Mistral Medium 3.5 is Mistral AI's flagship model, released on 28 April 2026."
   ],
   "model_evidence": [
    "Mistral Medium 3.5",
    "Mistral Medium 3.5 is Mistral AI's flagship model, released on 28 April 2026."
   ],
   "release_date_evidence": [
    "Mistral Medium 3.5",
    "Mistral Medium 3.5 is Mistral AI's flagship model, released on 28 April 2026."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5",
    "It is a dense 128-billion-parameter model with a 256k-token context window that accepts both text and images."
   ],
   "context_window_evidence": [
    "Mistral Medium 3.5",
    "It is a dense 128-billion-parameter model with a 256k-token context window that accepts both text and images."
   ],
   "license_evidence": [
    "Mistral Medium 3.5",
    "Unlike most of Mistral's earlier lineup, it is published as open weights on Hugging Face under a modified MIT license, so teams can self-host it on as few as four GPUs as well as call it through the Mistral API."
   ],
   "significance_evidence": [
    "Mistral Medium 3.5",
    "What makes Mistral Medium 3.5 notable is that it is a \"merged\" model."
   ]
  },
  {
   "url": "https://stackbuiltai.com/google-gemma-4-review-2026",
   "title": "Google Gemma 4 Review 2026 \u2013 Open Weights, 26B MoE, and Whether It's Competitive with Closed-Source Frontier Models",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 is available in four different model sizes, comes with native multimodal capabilities built in from the ground up, and is released under the Apache 2.0 open license - meaning you can download the weights, run them wherever you want, and even fine-tune or modify them without negotiating with Google first.",
   "organization_evidence": [
    "## What Is Gemma 4",
    "Google DeepMind released Gemma 4 on April 2, 2026, and it's the most substantial update to the Gemma family since the original launch."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What Is Gemma 4",
    "Google DeepMind released Gemma 4 on April 2, 2026, and it's the most substantial update to the Gemma family since the original launch."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What Is Gemma 4",
    "Gemma 4 is available in four different model sizes, comes with native multimodal capabilities built in from the ground up, and is released under the Apache 2.0 open license - meaning you can download the weights, run them wherever you want, and even fine-tune or modify them without negotiating with Google first."
   ]
  },
  {
   "url": "https://nextomoro.com/kimi-k2-6",
   "title": "Kimi K2.6",
   "published_at": "2026-05-13T04:54:49",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-18 to 2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per forward pass",
   "context_window": null,
   "license": null,
   "significance": "K2.6 leads or matches frontier closed models on a specific cluster of agentic-execution benchmarks while remaining a step behind on some single-turn reasoning measures.",
   "organization_evidence": [
    "Kimi K2.6 is a frontier-scale open-weights model released by [Moonshot AI](https://nextomoro.com/moonshot-ai/) in April 2026, the second iteration in the K2 line and the company's first model to approach the agentic capabilities of [Claude Opus 4.6](https://nextomoro.com/claude-opus-4-7/) on benchmark and execution metrics."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Released:** April 18 to 20, 2026."
   ],
   "parameters_evidence": [
    "The model is a one-trillion-parameter mixture-of-experts architecture with 32 billion active parameters, a 256K-token context window, and a focus on long-horizon agentic tool-use at scale.",
    "**Architecture:** Mixture-of-experts. 1 trillion total parameters, 32 billion active per forward pass. 384 experts (8 routed plus 1 shared). MLA (multi-head latent attention) for memory-efficient long-context serving. INT4 quantisation supported at release."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "K2.6 leads or matches frontier closed models on a specific cluster of agentic-execution benchmarks while remaining a step behind on some single-turn reasoning measures.",
    "Benchmarks and standing"
   ]
  },
  {
   "url": "https://theairankings.com/zhipu/glm-5",
   "title": "GLM-5.2",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "~753B total parameters",
   "context_window": null,
   "license": null,
   "significance": "beats GPT-5.5 on several multi-step coding benchmarks at roughly a sixth of the cost",
   "organization_evidence": [
    "# GLM-5.2",
    "GLM-5.2 is [Zhipu AI (Z.ai)](/zhipu/)\u2018s open-weight flagship and the current release of the **GLM-5 series**, shipped on [13 June 2026](https://docs.z.ai/guides/llm/glm-5.2) under the permissive **MIT licence**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2",
    "GLM-5.2 is [Zhipu AI (Z.ai)](/zhipu/)\u2018s open-weight flagship and the current release of the **GLM-5 series**, shipped on [13 June 2026](https://docs.z.ai/guides/llm/glm-5.2) under the permissive **MIT licence**."
   ],
   "parameters_evidence": [
    "# GLM-5.2",
    "It is a large **mixture-of-experts model (~753B total parameters)** with a **1-million-token context window**, built specifically for **long-horizon coding agents** \u2014 Zhipu says it beats [GPT-5.5](/openai/gpt-5-5/) on several multi-step coding benchmarks at roughly a sixth of the cost ([VentureBeat](https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost))."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2",
    "It is a large **mixture-of-experts model (~753B total parameters)** with a **1-million-token context window**, built specifically for **long-horizon coding agents** \u2014 Zhipu says it beats [GPT-5.5](/openai/gpt-5-5/) on several multi-step coding benchmarks at roughly a sixth of the cost ([VentureBeat](https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost))."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/minimax-m3-1m-context-open-weight-multimodal-agentic-coding-builder-guide",
   "title": "MiniMax M3: 1M-Context Open-Weight Multimodal Coding Model (Builder Guide)",
   "published_at": "2026-06-14T15:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "427-billion",
   "context_window": "1M tokens",
   "license": "open weights on HuggingFace under a license you should read before deploying commercially (details below)",
   "significance": "59.0% on SWE-Bench Pro, 78.1% on MMMU-Pro, 84.6% on Video-MMMU, 66.0% on Terminal Bench 2.1, 74.2% on MCP Atlas, 83.5% on BrowseComp",
   "organization_evidence": [
    "By Grove, an AI agent at ChatForest",
    "MiniMax launched **M3** on June 1, 2026."
   ],
   "model_evidence": [
    "# MiniMax M3: 1M-Context Open-Weight Multimodal Coding Model (Builder Guide)",
    "MiniMax launched **M3** on June 1, 2026."
   ],
   "release_date_evidence": [
    "# MiniMax M3: 1M-Context Open-Weight Multimodal Coding Model (Builder Guide)",
    "MiniMax launched **M3** on June 1, 2026."
   ],
   "parameters_evidence": [
    "# MiniMax M3: 1M-Context Open-Weight Multimodal Coding Model (Builder Guide)",
    "MiniMax launched **M3** on June 1, 2026.",
    "It is a 427-billion parameter Mixture of Experts model with a one-million-token context window, native image and video input, and open weights on HuggingFace."
   ],
   "context_window_evidence": [
    "# MiniMax M3: 1M-Context Open-Weight Multimodal Coding Model (Builder Guide)",
    "MiniMax launched **M3** on June 1, 2026.",
    "It is a 427-billion parameter Mixture of Experts model with a one-million-token context window, native image and video input, and open weights on HuggingFace."
   ],
   "license_evidence": [
    "It is a 427-billion parameter Mixture of Experts model with a one-million-token context window, native image and video input, and open weights on HuggingFace.",
    "The model is available via API, through third-party inference providers including OpenRouter and Together AI, locally via Ollama, and as open weights on HuggingFace under a license you should read before deploying commercially (details below)."
   ],
   "significance_evidence": [
    "MiniMax\u2019s reported launch-day benchmark scores:",
    "| Benchmark | MiniMax M3 |",
    "| SWE-Bench Pro | 59.0% |",
    "| MMMU-Pro | 78.1% |",
    "| Video-MMMU | 84.6% |",
    "| Terminal Bench 2.1 | 66.0% |",
    "| MCP Atlas | 74.2% |",
    "| BrowseComp | 83.5% |"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/nvidia-nemotron-3-ultra-beats-us-open-weight-rivals",
   "title": "Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
   "published_at": "2026-06-01T22:38:33",
   "organization": "Nvidia",
   "model": "Nemotron 3 Ultra",
   "release_date": "2026-06-01",
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   "context_window": "1 million tokens",
   "license": null,
   "significance": "Tops US open-weight rankings with an Intelligence Index of 48",
   "organization_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "Nvidia's Nemotron 3 Ultra is a 550B open model that tops US open-weights rankings at 300 tokens per second, with weights and training recipes released."
   ],
   "model_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "On June 1, 2026, Nvidia CEO Jensen Huang opened the Computex keynote at the Taipei Music Center and unveiled **Nemotron 3 Ultra**, a **550-billion-parameter** open model that activates only **55 billion parameters** per token."
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   "release_date_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "On June 1, 2026, Nvidia CEO Jensen Huang opened the Computex keynote at the Taipei Music Center and unveiled **Nemotron 3 Ultra**, a **550-billion-parameter** open model that activates only **55 billion parameters** per token."
   ],
   "parameters_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "On June 1, 2026, Nvidia CEO Jensen Huang opened the Computex keynote at the Taipei Music Center and unveiled **Nemotron 3 Ultra**, a **550-billion-parameter** open model that activates only **55 billion parameters** per token."
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   "context_window_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "It runs at **300-plus tokens per second** and handles a context window of up to **1 million tokens**."
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   "significance_evidence": [
    "Model Release",
    "# Nvidia Nemotron 3 Ultra Beats US Open Weight Rivals",
    "The number that matters is not the parameter count. It is 48, the Intelligence Index score that pushed Nemotron 3 Ultra past every other open-weight model trained in the United States."
   ]
  },
  {
   "url": "https://intelligibberish.com/articles/2026-04-05-open-source-ai-wins-qwen-agentic-gemma-apache-sora-shutdown",
   "title": "Open Source AI Wins: Qwen Goes Agentic, Gemma Goes Apache, and OpenAI Kills Sora to Focus on What Matters",
   "published_at": "2026-04-05T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First major open model designed from the ground up for agentic work with a 1-million-token context window and linear attention with sparse mixture-of-experts routing.",
   "organization_evidence": [
    "Open Source AI Wins: Qwen Goes Agentic, Gemma Goes Apache, and OpenAI Kills Sora to Focus on What Matters",
    "Alibaba's Qwen 3.6 Plus ships the first truly agentic open model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Qwen 3.6 Plus: The First Model Built for Agents",
    "Alibaba [released Qwen 3.6 Plus](https://www.alibabacloud.com/blog/qwen3-6-plus-towards-real-world-agents_603005) on April 2, and it\u2019s not just another benchmark bump."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Qwen 3.6 Plus: The First Model Built for Agents",
    "This is the first major open model designed from the ground up for agentic work \u2014 the kind where an AI plans a multi-step task, calls external tools, recovers from errors, and finishes the job without someone holding its hand.",
    "The result: a 1-million-token context window that runs at roughly 2-3x the output speed of Claude Opus 4.6, according to [early community benchmarks on OpenRouter](https://renovateqr.com/blog/qwen-3-6-plus-review-benchmarks-2026)."
   ]
  },
  {
   "url": "https://intelligibberish.com/articles/2026-04-05-open-source-ai-wins-qwen-agentic-gemma-apache-sora-shutdown",
   "title": "Open Source AI Wins: Qwen Goes Agentic, Gemma Goes Apache, and OpenAI Kills Sora to Focus on What Matters",
   "published_at": "2026-04-05T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B dense model",
   "context_window": null,
   "license": null,
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   "organization_evidence": [
    "Open Source AI Wins: Qwen Goes Agentic, Gemma Goes Apache, and OpenAI Kills Sora to Focus on What Matters",
    "Google finally picks a real license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Gemma 4: The License Matters More Than the Benchmarks",
    "Google [dropped Gemma 4 on April 2](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) with genuinely impressive numbers: 89.2% on AIME 2026, 80.0% on LiveCodeBench v6, a Codeforces ELO of 2,150."
   ],
   "parameters_evidence": [
    "## Gemma 4: The License Matters More Than the Benchmarks",
    "Google [dropped Gemma 4 on April 2](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) with genuinely impressive numbers: 89.2% on AIME 2026, 80.0% on LiveCodeBench v6, a Codeforces ELO of 2,150.",
    "The 31B dense model ranks #3 on Arena AI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Gemma 4: The License Matters More Than the Benchmarks",
    "Google [dropped Gemma 4 on April 2](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) with genuinely impressive numbers: 89.2% on AIME 2026, 80.0% on LiveCodeBench v6, a Codeforces ELO of 2,150.",
    "The 31B dense model ranks #3 on Arena AI.",
    "Gemma 4 ships under Apache 2.0 \u2014 the first time Google has used a [genuinely open license](https://venturebeat.com/technology/google-releases-gemma-4-under-apache-2-0-and-that-license-change-may-matter) for its Gemma family."
   ]
  },
  {
   "url": "https://neuralstack.network/article/2026-07-28-meta-llama-4-open-weights",
   "title": "Meta Announces Llama 4 Open Weights Release for August 2026",
   "published_at": "2026-07-28T08:00:40",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "405 billion",
   "context_window": null,
   "license": null,
   "significance": "Internal evaluations show the model matches or exceeds GPT-5 on 11 of 14 standard benchmarks while using 38 percent less compute at inference.",
   "organization_evidence": [
    "Meta stated on July 27 2026 that Llama 4 will be released with fully open weights on August 12."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta stated on July 27 2026 that Llama 4 will be released with fully open weights on August 12."
   ],
   "parameters_evidence": [
    "The 405 billion parameter model supports text image and audio inputs and can run inference on a single H100 GPU at 32 tokens per second."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Internal evaluations show the model matches or exceeds GPT-5 on 11 of 14 standard benchmarks while using 38 percent less compute at inference."
   ]
  },
  {
   "url": "https://freeainews.com/open-source/mistral-ai-latest-open-source-release",
   "title": "Mistral AI Open-Source Release: Performance and Free Access",
   "published_at": "2026-06-02T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "collapses three separate model families (Magistral for reasoning, Devstral for coding, and the standard Small instruct line) into one Apache 2.0-licensed download",
   "organization_evidence": [
    "Mistral Small 4 is a mixture-of-experts (MoE) language model from the French AI company [Mistral AI](https://mistral.ai/news/mistral-small-4/)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released on March 16, 2026, it collapses three separate model families (Magistral for reasoning, Devstral for coding, and the standard Small instruct line) into one Apache 2.0-licensed download."
   ],
   "parameters_evidence": [
    "119B Total Parameters"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Released on March 16, 2026, it collapses three separate model families (Magistral for reasoning, Devstral for coding, and the standard Small instruct line) into one Apache 2.0-licensed download."
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  },
  {
   "url": "https://agent-engineering.dev/article/google-releases-gemma-4-the-most-capable-open-weight-multimodal-models-of-2026-benchmarks-show-strong-gains-in-reasoning-and-efficiency",
   "title": "Google Gemma 4: Open Multimodal Models with Top Benchmarks",
   "published_at": "2026-04-03T09:38:28",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B, 26B A4B, E4B, E2B",
   "context_window": null,
   "license": null,
   "significance": "Sets fresh standards for performance-per-parameter in the open AI ecosystem; 31B achieves 85.7% on GPQA Diamond (reasoning mode)",
   "organization_evidence": [
    "Google Gemma 4: Open Multimodal Models with Top Benchmarks | Agent-Engineering.dev",
    "On April 2, 2026, **Google DeepMind** launched **Gemma 4**, a new family of multimodal open-weight models that sets fresh standards for performance-per-parameter in the open AI ecosystem."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: Open Multimodal Models with Top Benchmarks | Agent-Engineering.dev",
    "On April 2, 2026, **Google DeepMind** launched **Gemma 4**, a new family of multimodal open-weight models that sets fresh standards for performance-per-parameter in the open AI ecosystem."
   ],
   "parameters_evidence": [
    "Google Gemma 4: Open Multimodal Models with Top Benchmarks | Agent-Engineering.dev",
    "The release includes four variants: **Gemma 4 31B** (dense), **Gemma 4 26B A4B** (Mixture-of-Experts), plus smaller **Gemma 4 E4B** and **Gemma 4 E2B** models optimized for edge and on-device deployment."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: Open Multimodal Models with Top Benchmarks | Agent-Engineering.dev",
    "On April 2, 2026, **Google DeepMind** launched **Gemma 4**, a new family of multimodal open-weight models that sets fresh standards for performance-per-parameter in the open AI ecosystem.",
    "The 31B model achieves **85.7% on GPQA Diamond** (reasoning mode) in independent testing \u2014 the second-highest score ever recorded for an open-weights model under 40B parameters, trailing only Qwen3.5 27B by a razor-thin 0.1%."
   ]
  },
  {
   "url": "https://ap7i.com/posts/moonshot-kimi-k3-release",
   "title": "Kimi K3: The First Open 3T-Class Model Reaches the Frontier's Edge",
   "published_at": "2026-07-18T21:45:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "the world's first open 3T-class model",
   "organization_evidence": [
    "AI",
    "Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, native vision, and a 1M-token context window, live in the API now with open weights promised by July 27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI",
    "Moonshot AI released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model with native vision and a 1-million-token context window, already live on Kimi.com, Kimi Work, Kimi Code, and the Kimi API, with full open weights promised by July 27."
   ],
   "parameters_evidence": [
    "AI",
    "Moonshot AI released Kimi K3 \u2014 2.8 trillion parameters, native vision, and a 1M-token context window, live in the API now with open weights promised by July 27."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI",
    "Moonshot AI released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model with native vision and a 1-million-token context window, already live on Kimi.com, Kimi Work, Kimi Code, and the Kimi API, with full open weights promised by July 27.",
    "Moonshot calls it the world\u2019s first open 3T-class model."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-06/17-zhipu-releases-glm-52-a-744-billion-parameter-open-coding-model-days-after-washington-cut-off-foreign-access-to-anthropics-claude",
   "title": "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
   "published_at": "2026-06-17T07:07:42",
   "organization": "Zhipu",
   "model": "GLM-5.2",
   "release_date": "2026-06",
   "parameters": "744-billion-parameter",
   "context_window": "1M-token",
   "license": "MIT",
   "significance": "coding benchmark results of 62.1 on SWE-bench Pro and 81.0 on Terminal-Bench 2.1",
   "organization_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "Beijing-based Zhipu AI released GLM-5.2, a 744-billion-parameter open-weight model aimed at long-horizon coding, days after the US government cut off foreign access to Anthropic\u2019s flagship models."
   ],
   "model_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "Beijing-based Zhipu AI released GLM-5.2, a 744-billion-parameter open-weight model aimed at long-horizon coding, days after the US government cut off foreign access to Anthropic\u2019s flagship models."
   ],
   "release_date_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "Sources: 3 Publisher: signed Contributor: signed Hash: e059afa97b [ View ](/provenance/2026-06/17-zhipu-releases-glm-52-a-744-billion-parameter-open-coding-model-days-after-washington-cut-off-foreign-access-to-anthropics-claude)"
   ],
   "parameters_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "Beijing-based Zhipu AI released GLM-5.2, a 744-billion-parameter open-weight model aimed at long-horizon coding, days after the US government cut off foreign access to Anthropic\u2019s flagship models."
   ],
   "context_window_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "## What We Know",
    "The same documentation states the model carries \u201ca solid 1M-token context that stably sustains long-horizon work\u201d and that pre-training data was increased \u201cfrom 23T to 28.5T tokens.\u201d"
   ],
   "license_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "Zhipu's GLM-5.2 ships under an MIT license as a frontier coding alternative just after the US barred foreign nationals from Anthropic's Fable 5 and Mythos 5."
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   "significance_evidence": [
    "Zhipu Releases GLM-5.2, a 744-Billion-Parameter Open Coding Model, Days After Washington Cut Off Foreign Access to Anthropic's Claude",
    "## What We Know",
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   ]
  },
  {
   "url": "https://ustechautomations.com/resources/blog/minimax-m3-explained-what-it-changes",
   "title": "MiniMax M3 Explained: What It Actually Changes Now",
   "published_at": "2026-06-14T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
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   "significance": "MiniMax M3 scores 59.0% on SWE-Bench Pro, ahead of GPT-5.5 and Gemini 3.1 Pro.",
   "organization_evidence": [
    "Frontier Tech",
    "# MiniMax M3 Explained: What It Actually Changes Now",
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   "model_evidence": null,
   "release_date_evidence": [
    "Frontier Tech",
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    "Jun 14, 2026",
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Frontier Tech",
    "# MiniMax M3 Explained: What It Actually Changes Now",
    "## TL;DR"
   ]
  },
  {
   "url": "https://aiwiki.ai/wiki/nemotron_3_5_lightning",
   "title": "Nemotron 3.5 Lightning",
   "published_at": "2026-08-12T17:03:19",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": "30B total, 3B active",
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   "significance": "Introduces the 3.5 designation and Lightning tier, high-volume execution layer for agents, up to 4x output speed, measured output speeds of nearly 670 tokens per second.",
   "organization_evidence": [
    "Nemotron 3.5 Lightning",
    "| Developer | [NVIDIA](/wiki/nvidia) |",
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   "release_date_evidence": [
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   ],
   "parameters_evidence": [
    "Nemotron 3.5 Lightning",
    "| Parameters | 30B total, 3B active (31.6B / 3.6B counting embeddings) |",
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   "significance_evidence": [
    "Nemotron 3.5 Lightning",
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   ]
  },
  {
   "url": "https://marktechpost.com/2026/07/18/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost/amp?noamp=mobile",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T01:41:33",
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
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   "license": null,
   "significance": "tied with Gemini 3.1 Pro on SWE-bench Verified with 80.6%",
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    "Three Chinese labs now hold the top of the open-weight leaderboard."
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   "model_evidence": null,
   "release_date_evidence": [
    "| Released | July 16, 2026 | April 24, 2026 | June 13, 2026 |"
   ],
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   "context_window_evidence": null,
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   "significance_evidence": [
    "[DeepSeek-V4-Pro-Max scores 80.6% on SWE-bench Verified](https://www.morphllm.com/deepseek-v4), the highest open-weight result at its release and tied with Gemini 3.1 Pro."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/07/18/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost/amp?noamp=mobile",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T01:41:33",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
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   "significance": "edging GPT-5.5 at 58.6 on SWE-bench Pro with 62.1",
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    "[GLM-5.2 is also MIT-licensed](https://huggingface.co/zai-org/GLM-5.2), with full weights on Hugging Face under the zai-org organization."
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   "model_evidence": null,
   "release_date_evidence": [
    "| Released | July 16, 2026 | April 24, 2026 | June 13, 2026 |"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 scored [62.1 on SWE-bench Pro](https://kie.ai/blog/glm-5-2-benchmark-deep-dive), edging GPT-5.5 at 58.6."
   ]
  },
  {
   "url": "https://neuralstack.network/article/2026-07-14-meta-llama-4-unveiled",
   "title": "Meta Releases Llama 4 with 10M Context and Native Video Reasoning",
   "published_at": "2026-07-14T14:15:11",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-07-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The model supports a 10 million token context window and processes video natively at 60 frames per second. It achieved 92.4 percent on Video-MME and 89.1 percent on MMLU-Pro while running at 40 percent lower cost than GPT-5.1.",
   "organization_evidence": [
    "Meta released Llama 4 on July 10 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta released Llama 4 on July 10 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The model supports a 10 million token context window and processes video natively at 60 frames per second.",
    "It achieved 92.4 percent on Video-MME and 89.1 percent on MMLU-Pro while running at 40 percent lower cost than GPT-5.1."
   ]
  },
  {
   "url": "https://happycapyguide.com/blog/google-gemma-4-apache-open-weights-agentic-ai-2026",
   "title": "Google Gemma 4: Apache 2.0 Open-Weight Models That Punch 20\u00d7 Above Their Size",
   "published_at": "2026-04-04T15:38:28",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B",
   "context_window": null,
   "license": null,
   "significance": "Mobile, edge, rapid prototyping",
   "organization_evidence": [
    "Google Gemma 4: Apache 2.0 Open-Weight Models That Punch 20\u00d7 Above Their Size",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 four open-weight models under a fully permissive Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: Apache 2.0 Open-Weight Models That Punch 20\u00d7 Above Their Size",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 four open-weight models under a fully permissive Apache 2.0 license."
   ],
   "parameters_evidence": [
    "The Four Gemma 4 Models: Sizes, Architecture, and Hardware",
    "| Model | Architecture | Active Params | Context | Min VRAM | Best For |",
    "| E2B | Dense + PLE | 2.3B | 128K tokens | <1.5 GB | Mobile, edge, rapid prototyping |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Four Gemma 4 Models: Sizes, Architecture, and Hardware",
    "| Model | Architecture | Active Params | Context | Min VRAM | Best For |",
    "| E2B | Dense + PLE | 2.3B | 128K tokens | <1.5 GB | Mobile, edge, rapid prototyping |"
   ]
  },
  {
   "url": "https://pondero.ai/news/2026-07-17-kimi-k3-moonshot-worlds-largest-open-weight",
   "title": "Moonshot AI releases Kimi K3, the world's first open 3T-class model at 2.8 trillion parameters",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "world's first open 3T-class model: 2.8 trillion parameters, 1-million-token context, and benchmark scores above Claude Opus 4.8 on most coding tasks",
   "organization_evidence": [
    "Moonshot AI releases Kimi K3, the world's first open 3T-class model at 2.8 trillion parameters",
    "Moonshot AI shipped Kimi K3 on July 16, 2026: a 2.8-trillion-parameter mixture-of-experts model that outscores [Claude](/beginners/what-is-claude-eli5/) Opus 4.8 on most coding and agentic benchmarks while pricing at $3 per million cache-miss input tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI releases Kimi K3, the world's first open 3T-class model at 2.8 trillion parameters",
    "Moonshot AI shipped Kimi K3 on July 16, 2026: a 2.8-trillion-parameter mixture-of-experts model that outscores [Claude](/beginners/what-is-claude-eli5/) Opus 4.8 on most coding and agentic benchmarks while pricing at $3 per million cache-miss input tokens."
   ],
   "parameters_evidence": [
    "Moonshot AI releases Kimi K3, the world's first open 3T-class model at 2.8 trillion parameters",
    "Moonshot AI shipped Kimi K3 on July 16, 2026: a 2.8-trillion-parameter mixture-of-experts model that outscores [Claude](/beginners/what-is-claude-eli5/) Opus 4.8 on most coding and agentic benchmarks while pricing at $3 per million cache-miss input tokens."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI releases Kimi K3, the world's first open 3T-class model at 2.8 trillion parameters",
    "Kimi K3 from China's Moonshot AI is the world's first open 3T-class model: 2.8 trillion parameters, 1-million-token context, and benchmark scores above Claude Opus 4.8 on most coding tasks."
   ]
  },
  {
   "url": "https://know.2nth.ai/explainers/agents/glm.html",
   "title": "GLM \u00b7 Z.ai \u2014 know.2nth.ai",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Independent composites place GLM-5.2 as the strongest open-weight model while still trailing the closed frontier on the hardest general tasks.",
   "organization_evidence": [
    "agents \u00b7 GLM \u00b7 Z.ai \u00b7 Model Leaf",
    "**GLM** is Zhipu AI's model family, shipped internationally under the **Z.ai** brand.",
    "**GLM** (General Language Model) is the family from **Zhipu AI**, a Beijing lab spun out of Tsinghua University, now shipping internationally under the **Z.ai** brand."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "agents \u00b7 GLM \u00b7 Z.ai \u00b7 Model Leaf",
    "**GLM-5.2** (June 2026) is the current headline: a ~753B-parameter Mixture-of-Experts model with ~40B active per token, a **1M-token context window**, and a pure **MIT licence**.",
    "GLM-5.2 was released 13 June 2026 to GLM Coding Plan subscribers; the open weights dropped on Hugging Face three days later (`zai-org/GLM-5.2` in BF16, `zai-org/GLM-5.2-FP8`)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "agents \u00b7 GLM \u00b7 Z.ai \u00b7 Model Leaf",
    "Independent composites place GLM-5.2 as the strongest open-weight model while still trailing the closed frontier on the hardest general tasks."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/jensen-huangs-open-source-gamble-why-nvidia-is-giv-dd2adcfa",
   "title": "Jensen Huang's Open-Source Gamble: Why Nvidia Is Giving Away a 550-Billion-Parameter AI Model",
   "published_at": "2026-08-05T10:24:18",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550 billion",
   "context_window": null,
   "license": null,
   "significance": "A major open frontier-reasoning and orchestration model with a 550-billion-parameter total size (55 billion active per token) and a 1-million-token context window designed for agentic workloads and coding tasks.",
   "organization_evidence": [
    "# Jensen Huang's Open-Source Gamble: Why Nvidia Is Giving Away a 550-Billion-Parameter AI Model",
    "On June 4, 2026, the chip maker released Nemotron 3 Ultra, a 550-billion-parameter open-weight language model, freely available on Hugging Face (a popular platform for sharing AI models) with no API key required and no per-token fees."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Jensen Huang's Open-Source Gamble: Why Nvidia Is Giving Away a 550-Billion-Parameter AI Model",
    "On June 4, 2026, the chip maker released Nemotron 3 Ultra, a 550-billion-parameter open-weight language model, freely available on Hugging Face (a popular platform for sharing AI models) with no API key required and no per-token fees."
   ],
   "parameters_evidence": [
    "# Jensen Huang's Open-Source Gamble: Why Nvidia Is Giving Away a 550-Billion-Parameter AI Model",
    "On June 4, 2026, the chip maker released Nemotron 3 Ultra, a 550-billion-parameter open-weight language model, freely available on Hugging Face (a popular platform for sharing AI models) with no API key required and no per-token fees."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Jensen Huang's Open-Source Gamble: Why Nvidia Is Giving Away a 550-Billion-Parameter AI Model",
    "Nvidia frames Nemotron 3 Ultra as an \"open frontier-reasoning and orchestration model\" aimed at agentic workloads: long-running tasks, multi-step tool use, and coding agents that need to hold large amounts of context without losing track of earlier steps."
   ]
  },
  {
   "url": "https://ecorpit.com/thinking-machines-inkling-open-weight-model-decision-2026",
   "title": "Inkling, Thinking Machines' 975B open-weights model: adopt it or wait in 2026?",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975 billion total parameters and 41 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-bench Verified and 97.1% on AIME 2026, competitive with the open-weights field but behind Kimi K2.6 at 80.2% and DeepSeek V4 Pro at 80.6% on coding.",
   "organization_evidence": [
    "Inkling, Thinking Machines' 975B open-weights model: adopt it or wait in 2026?",
    "**Summary.** Thinking Machines Lab, the company founded by former OpenAI CTO Mira Murati, released Inkling on 15 July 2026, its first model and its first open-weights release."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Summary.** Thinking Machines Lab, the company founded by former OpenAI CTO Mira Murati, released Inkling on 15 July 2026, its first model and its first open-weights release."
   ],
   "parameters_evidence": [
    "Inkling, Thinking Machines' 975B open-weights model: adopt it or wait in 2026?",
    "**Summary.** Thinking Machines Lab, the company founded by former OpenAI CTO Mira Murati, released Inkling on 15 July 2026, its first model and its first open-weights release. Inkling is a Mixture-of-Experts transformer with 975 billion total parameters and 41 billion active per token, a context window up to 1 million tokens, pretrained on 45 trillion tokens of text, images, audio and video."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Inkling, Thinking Machines' 975B open-weights model: adopt it or wait in 2026?",
    "**Summary.** Thinking Machines Lab, the company founded by former OpenAI CTO Mira Murati, released Inkling on 15 July 2026, its first model and its first open-weights release. Inkling is a Mixture-of-Experts transformer with 975 billion total parameters and 41 billion active per token, a context window up to 1 million tokens, pretrained on 45 trillion tokens of text, images, audio and video. The full weights are on Hugging Face, reported under the Apache 2.0 license, and the model reasons natively over text, images and audio. On benchmarks it posts 77.6% on SWE-bench Verified and 97.1% on AIME 2026, competitive with the open-weights field but behind Kimi K2.6 at 80.2% and DeepSeek V4 Pro at 80.6% on coding."
   ]
  },
  {
   "url": "http://ai-tldr.dev/releases/qwen-3-8-2-4t-a95b-open-weights",
   "title": "Qwen3.8-2.4T-A95B \u2014 the open-weights core of Qwen3.8-Max hits Hugging Face",
   "published_at": "2026-08-12T12:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "2.4T total, 95B active per token",
   "context_window": null,
   "license": null,
   "significance": "First time Qwen has opened the weights of a Max-class model.",
   "organization_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-12 \u00b7 major",
    "| Maker | Alibaba (Qwen) |",
    "Downloadable weights change all three: teams can serve the model themselves on vLLM, SGLang or TokenSpeed, and community quantizations for llama.cpp, LM Studio, Jan and Ollama are already listed on the card."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-12 \u00b7 major",
    "# Qwen3.8-2.4T-A95B \u2014 the open-weights core of Qwen3.8-Max hits Hugging Face",
    "[Qwen3](/models/qwen3/).8-2.4T-A95B is the open-weights checkpoint behind [Qwen3.8-Max](/models/qwen3-8-max/), published on Hugging Face on 12 August 2026 along with an FP8 build."
   ],
   "parameters_evidence": [
    "The 2.4T mixture-of-experts fires 95B parameters per token and ships with an FP8 build.",
    "# Qwen3.8-2.4T-A95B \u2014 the open-weights core of Qwen3.8-Max hits Hugging Face",
    "| Size | 2.4T total, 95B active per token |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "[Qwen3.8-2.4T-A95B](/models/qwen3-8/) is now a public download on Hugging Face, the first time Qwen has opened the weights of a Max-class model.",
    "# Qwen3.8-2.4T-A95B \u2014 the open-weights core of Qwen3.8-Max hits Hugging Face"
   ]
  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/google-releases-gemma-4-frontier-open-models-under-apache-2-0",
   "title": "Google Releases Gemma 4: Frontier Open Models Under Apache 2.0",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B effective parameters, 4.5B effective parameters, 26B total with 3.8B active, and 30.7B parameters",
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 among all open models on the LMArena text leaderboard with a score around 1,452 for the 31B model, and #6 around 1,441 for the 26B MoE model.",
   "organization_evidence": [
    "# Google Releases Gemma 4: Frontier Open Models Under Apache 2.0",
    "On April 2, 2026, Google DeepMind released **Gemma 4** \u2014 its most capable open model family to date, purpose-built for advanced reasoning and agentic workflows."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Releases Gemma 4: Frontier Open Models Under Apache 2.0",
    "On April 2, 2026, Google DeepMind released **Gemma 4** \u2014 its most capable open model family to date, purpose-built for advanced reasoning and agentic workflows."
   ],
   "parameters_evidence": [
    "Four Models, One Family",
    "- **E2B** (2.3B effective parameters) \u2014 ultra-compact edge model with 128K context, native audio input",
    "- **E4B** (4.5B effective parameters) \u2014 mid-range edge model with 128K context, native audio input",
    "- **26B A4B** (Mixture-of-Experts: 26B total, 3.8B active) \u2014 MoE model with 256K context, only 4B parameters active per inference",
    "- **31B Dense** (30.7B parameters) \u2014 the flagship dense model with 256K context window"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark Performance",
    "The 31B model currently ranks #3 among all open models on the LMArena text leaderboard (score ~1,452), with the 26B MoE close behind at #6 (~1,441) \u2014 despite using only 4B active parameters per forward pass."
   ]
  },
  {
   "url": "https://aihola.com/article/moonshot-kimi-k27-code-release",
   "title": "Moonshot AI Releases Open-Source Kimi K2.7-Code Model",
   "published_at": "2026-06-13T14:55:36",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "1 trillion total parameters with 32 billion active",
   "context_window": null,
   "license": null,
   "significance": "Reduces thinking-token usage by approximately 30% against K2.6 on equivalent tasks",
   "organization_evidence": [
    "Coding Assistants",
    "Moonshot AI Releases Open-Source Kimi K2.7-Code Model",
    "Moonshot AI dropped Kimi K2.7-Code, an open-weight coding model, with the weights now live on [Hugging Face](https://huggingface.co/moonshotai/Kimi-K2.7-Code) under a Modified MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Coding Assistants",
    "Moonshot AI Releases Open-Source Kimi K2.7-Code Model",
    "June 12, 2026\u20222 min read"
   ],
   "parameters_evidence": [
    "Coding Assistants",
    "Moonshot AI Releases Open-Source Kimi K2.7-Code Model",
    "It's a coding-tuned post-train on the existing K2 Mixture-of-Experts family, 1 trillion total parameters with 32 billion active."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Coding Assistants",
    "Moonshot AI Releases Open-Source Kimi K2.7-Code Model",
    "The coding-tuned model cuts reasoning tokens roughly 30% versus K2.6 and ships under a Modified MIT license.",
    "Moonshot says K2.7 \"reduces thinking-token usage by approximately 30%\" against K2.6 on equivalent tasks, per the [model card](https://www.kimi.com/code)."
   ]
  },
  {
   "url": "https://therouter.ai/blog/zhipu-glm-api-complete-guide",
   "title": "Zhipu GLM API: The Complete Guide for AI Engineers (2026)",
   "published_at": "2026-06-14T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic execution up to 8 hours, 58.4% on SWE-Bench Pro (SOTA at launch among open-source models)",
   "organization_evidence": [
    "# Zhipu GLM API: The Complete Guide for AI Engineers (2026)",
    "GLM-5.1, released April 7, 2026, is Zhipu's latest flagship."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## GLM-5.1 and the HighSpeed Variant",
    "GLM-5.1, released April 7, 2026, is Zhipu's latest flagship."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## GLM-5.1 and the HighSpeed Variant",
    "The standout feature is sustained long-horizon agentic execution \u2014 the model can autonomously work for up to 8 hours on a single task, handling planning, execution, and iterative optimization.",
    "- **SWE-Bench Pro:** 58.4% (SOTA at launch among open-source models)"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/minimax-m27-rewrites-its-own-code-to-improve-itself",
   "title": "MiniMax M2.7 Rewrites Its Own Code to Improve Itself",
   "published_at": "2026-05-14T11:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "230B",
   "context_window": null,
   "license": null,
   "significance": "MiniMax M2.7 is a 230B MoE model open-sourced April 12, 2026, priced at $0.30 per million input tokens, less than a third of frontier model pricing; M2.7 scored 56.22% on SWE-Pro and 57.0% on Terminal Bench 2, matching or exceeding closed-source competitors at a fraction of the cost",
   "organization_evidence": [
    "Model Release",
    "# MiniMax M2.7 Rewrites Its Own Code to Improve Itself",
    "MiniMax open-sourced M2.7, a 230B MoE model that autonomously ran 100 improvement cycles and scored 56.22% on SWE-Pro at $0.30 per million tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# MiniMax M2.7 Rewrites Its Own Code to Improve Itself",
    "- MiniMax M2.7 is a 230B MoE model open-sourced April 12, 2026, priced at $0.30 per million input tokens, less than a third of frontier model pricing"
   ],
   "parameters_evidence": [
    "Model Release",
    "# MiniMax M2.7 Rewrites Its Own Code to Improve Itself",
    "MiniMax open-sourced M2.7, a 230B MoE model that autonomously ran 100 improvement cycles and scored 56.22% on SWE-Pro at $0.30 per million tokens."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# MiniMax M2.7 Rewrites Its Own Code to Improve Itself",
    "- MiniMax M2.7 is a 230B MoE model open-sourced April 12, 2026, priced at $0.30 per million input tokens, less than a third of frontier model pricing",
    "- M2.7 scored 56.22% on SWE-Pro and 57.0% on Terminal Bench 2, matching or exceeding closed-source competitors at a fraction of the cost"
   ]
  },
  {
   "url": "https://n8nlab.io/news/huawei-pangu-ascend-frontier",
   "title": "No Nvidia Required: Huawei Ships 505B Open-Weight AI Model",
   "published_at": "2026-08-02T18:11:11",
   "organization": "Huawei",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "505B",
   "context_window": null,
   "license": null,
   "significance": "the first frontier-scale model to break CUDA dependency, 512K-token context window, AIME 2026 at 95.4 in thinking mode, GPQA-Diamond at 87.9, SWE-bench Verified at 68.5, LiveCodeBench V6 at 85.7",
   "organization_evidence": [
    "Analysis \u00b7 AI Models2 August 20266 min read",
    "DeepSeek",
    "No Nvidia Required: Huawei Ships 505B Open-Weight AI Model",
    "On July 31, Huawei released the weights, inference code, and technical report for openPangu-2.0-Pro \u2014 a 505-billion-parameter Mixture-of-Experts language model that completed its full pretraining run on Huawei's own Ascend 910B NPUs."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Analysis \u00b7 AI Models2 August 20266 min read",
    "DeepSeek",
    "No Nvidia Required: Huawei Ships 505B Open-Weight AI Model",
    "On July 31, Huawei released the weights, inference code, and technical report for openPangu-2.0-Pro \u2014 a 505-billion-parameter Mixture-of-Experts language model that completed its full pretraining run on Huawei's own Ascend 910B NPUs."
   ],
   "parameters_evidence": [
    "Analysis \u00b7 AI Models2 August 20266 min read",
    "DeepSeek",
    "No Nvidia Required: Huawei Ships 505B Open-Weight AI Model",
    "On July 31, Huawei released the weights, inference code, and technical report for openPangu-2.0-Pro \u2014 a 505-billion-parameter Mixture-of-Experts language model that completed its full pretraining run on Huawei's own Ascend 910B NPUs."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Analysis \u00b7 AI Models2 August 20266 min read",
    "DeepSeek",
    "No Nvidia Required: Huawei Ships 505B Open-Weight AI Model",
    "Huawei open-sourced a 505B-parameter AI model trained entirely on Ascend NPUs with no Nvidia hardware \u2014 the first frontier-scale model to break CUDA dependency.",
    "openPangu-2.0-Pro is the first open-weight model at 500 billion parameters or above that credibly claims to have never touched an Nvidia GPU.",
    "But the weights exist, the architecture is documented, and the benchmarks \u2014 AIME 2026 at 95.4 in thinking mode, GPQA-Diamond at 87.9, SWE-bench Verified at 68.5, LiveCodeBench V6 at 85.7 \u2014 put it in competitive territory with Western frontier models from 12 months ago."
   ]
  },
  {
   "url": "http://ofox.ai/blog/deepseek-v4-release-guide-2026",
   "title": "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
   "published_at": "2026-04-24T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters, 49B activated",
   "context_window": null,
   "license": null,
   "significance": "Arena AI\u2019s live code leaderboard put V4-Pro Thinking straight at #3 among open models, ahead of the rest of DeepSeek\u2019s prior releases by a large margin",
   "organization_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
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   "parameters_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "**Two variants**: [`deepseek-v4-pro`](https://ofox.ai/models/deepseek) (1.6T total parameters, 49B activated) and `deepseek-v4-flash` (284B total, 13B activated).",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Arena AI\u2019s live code leaderboard put V4-Pro Thinking straight at #3 among open models, ahead of the rest of DeepSeek\u2019s prior releases by a large margin",
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5"
   ]
  },
  {
   "url": "http://ofox.ai/blog/deepseek-v4-release-guide-2026",
   "title": "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
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   "parameters": "284B total, 13B activated",
   "context_window": null,
   "license": null,
   "significance": "Flash-Max (max thinking effort) approaches Pro-level reasoning on most benchmarks with a much lower serving cost.",
   "organization_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
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   "parameters_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "**Two variants**: [`deepseek-v4-pro`](https://ofox.ai/models/deepseek) (1.6T total parameters, 49B activated) and `deepseek-v4-flash` (284B total, 13B activated).",
    "From the [official announcement](https://x.com/deepseek_ai/status/2047516945466188072) on April 24 2026:"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4: 1.6T MoE, 1M Context, Apache 2.0, Cheaper Than GPT-5.5",
    "Flash-Max (max thinking effort) approaches Pro-level reasoning on most benchmarks with a much lower serving cost."
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  },
  {
   "url": "https://buildrlab.com/blog/mistral-medium-3-5-open-weights-coding-agent-model-2026-06-20",
   "title": "Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
   "published_at": "2026-06-20T00:00:00",
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   "release_date": null,
   "parameters": "128B dense parameters",
   "context_window": null,
   "license": null,
   "significance": "Reported 77.6% on SWE-bench Verified, Reported 91.4 on \u03893-Telecom, a tool/agent benchmark",
   "organization_evidence": [
    "Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
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    "- **128B dense parameters**"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
    "Mistral says Medium 3.5 combines instruction following, reasoning, and coding in one model, with:",
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    "- Reported **91.4 on \u03c4\u00b3-Telecom**, a tool/agent benchmark"
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  {
   "url": "https://bit.ly/4vSUFxN",
   "title": "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
   "published_at": "2026-04-16T17:05:00",
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   "release_date": "2026-04-16",
   "parameters": "2B, 4B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "Introduces native video and image processing across the lineup, audio input on smaller models, context windows up to 256K tokens, and high benchmark scores such as 84.3% on GPQA Diamond and 80.0% on LiveCodeBench v6 for the 31B dense variant.",
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    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
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   "release_date_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "Apr 16, 2026 2 min read"
   ],
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    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
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   "significance_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "The release introduces native video and image processing across the lineup, audio input on the smaller models, context windows up to 256K tokens, and benchmark results that place the 31B dense variant in a bracket typically occupied by models three to five times its size."
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  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/kimi-k3-open-weights-ship-2-8t-parameters-1-4-tb-to-run",
   "title": "Kimi K3 Open Weights Ship: 2.8T Parameters, 1.4 TB to Run",
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   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "K3 is a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision \u2014 the first open 3T-class model ever published.",
   "organization_evidence": [
    "**Moonshot AI released the full Kimi K3 weights on July 27, 2026**, hitting the date it promised when the model debuted eleven days earlier.",
    "Kimi K3 Open Weights Ship: 2.8T Parameters, 1.4 TB to Run"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Moonshot AI released the full Kimi K3 weights on July 27, 2026**, hitting the date it promised when the model debuted eleven days earlier.",
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   ],
   "parameters_evidence": [
    "**Moonshot AI released the full Kimi K3 weights on July 27, 2026**, hitting the date it promised when the model debuted eleven days earlier.",
    "Kimi K3 Open Weights Ship: 2.8T Parameters, 1.4 TB to Run"
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   "context_window_evidence": null,
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    "K3 is a 2.8-trillion-parameter Mixture-of-Experts model with a 1-million-token context window and native vision \u2014 the first open 3T-class model ever published.",
    "Kimi K3 Open Weights Ship: 2.8T Parameters, 1.4 TB to Run"
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  },
  {
   "url": "https://aigc.news/events/glm-5-2-million-context",
   "title": "Zhipu Uses 1M Context for Long-Duration Engineering Tasks \u00b7 AIGC.NEWS",
   "published_at": "2026-06-16T00:00:00",
   "organization": "Zhipu",
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   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The model introduces IndexShare to reduce sparse attention index computation and improves MTP speculative decoding; the official release also discloses long-duration RL, anti-cheating, and ultra-long context inference service design.",
   "organization_evidence": [
    "# GLM-5.2 Open Source: Zhipu Uses 1M Context for Long-Duration Engineering Tasks",
    "Zhipu released GLM-5.2 in June 2026, open-sourcing weights under the MIT license and providing a 1M context version."
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   "release_date_evidence": [
    "# GLM-5.2 Open Source: Zhipu Uses 1M Context for Long-Duration Engineering Tasks",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Open Source: Zhipu Uses 1M Context for Long-Duration Engineering Tasks",
    "The model introduces IndexShare to reduce sparse attention index computation and improves MTP speculative decoding; the official release also discloses long-duration RL, anti-cheating, and ultra-long context inference service design."
   ]
  },
  {
   "url": "https://datanorth.ai/news/minimax-launches-m3",
   "title": "Open-Weight Frontier Model with 1M Context",
   "published_at": "2026-06-02T11:16:40",
   "organization": "MiniMax",
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   "significance": "MiniMax M3 is an open-weight frontier model that combines 1M-token context, native multimodality, and competitive coding benchmarks at a price point well below Western proprietary alternatives.",
   "organization_evidence": [
    "# MiniMax launches M3",
    "MiniMax, the Shanghai-based AI lab, released its flagship model MiniMax M3 on June 1, 2026."
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax launches M3",
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  },
  {
   "url": "https://pub.towardsai.net/nvidias-550b-nemotron-embarrassed-every-us-open-model-and-it-shouldn-t-run-this-fast-5fa7376549e5",
   "title": "NVIDIA's 550B Nemotron Embarrassed Every US Open Model \u2014 and It Shouldn't Run This Fast",
   "published_at": "2026-06-02T03:22:15",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Ultra",
   "release_date": "2026-06-01",
   "parameters": "550B-parameter",
   "context_window": null,
   "license": "commercial-friendly license",
   "significance": "scores 48 on the Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "Member-only story",
    "NVIDIA just shipped a 550B-parameter open model that scores 48 on the Artificial Analysis Intelligence Index."
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   "model_evidence": [
    "Member-only story",
    "## What NVIDIA actually announced at Computex",
    "Jensen Huang unveiled Nemotron 3 Ultra during his Computex keynote in Taipei on June 1, 2026."
   ],
   "release_date_evidence": [
    "Member-only story",
    "## What NVIDIA actually announced at Computex",
    "Jensen Huang unveiled Nemotron 3 Ultra during his Computex keynote in Taipei on June 1, 2026."
   ],
   "parameters_evidence": [
    "Member-only story",
    "NVIDIA just shipped a 550B-parameter open model that scores 48 on the Artificial Analysis Intelligence Index."
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   "context_window_evidence": null,
   "license_evidence": [
    "Member-only story"
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    "Member-only story",
    "NVIDIA just shipped a 550B-parameter open model that scores 48 on the Artificial Analysis Intelligence Index."
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  {
   "url": "https://aichina.news/blog/point-click-act-allen-ais-molmopoint-8b-brings-visual-grounding-to-mbwr48",
   "title": "Point, Click, Act: Allen AI\u2019s MolmoPoint-8B Brings Visual Grounding to Huawei\u2019s Ascend Ecosystem \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:50:33",
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "8B",
   "context_window": null,
   "license": null,
   "significance": "Brings visual grounding to Huawei's Ascend Ecosystem, translating natural-language references into precise pixel-level coordinates.",
   "organization_evidence": [
    "Point, Click, Act: Allen AI\u2019s MolmoPoint-8B Brings Visual Grounding to Huawei\u2019s Ascend Ecosystem",
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   "model_evidence": null,
   "release_date_evidence": [
    "Point, Click, Act: Allen AI\u2019s MolmoPoint-8B Brings Visual Grounding to Huawei\u2019s Ascend Ecosystem",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Point, Click, Act: Allen AI\u2019s MolmoPoint-8B Brings Visual Grounding to Huawei\u2019s Ascend Ecosystem",
    "As an 8-billion parameter model, it strikes a balance between power and accessibility, fitting comfortably on a single Ascend 910 or consumer-grade GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Point, Click, Act: Allen AI\u2019s MolmoPoint-8B Brings Visual Grounding to Huawei\u2019s Ascend Ecosystem",
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  },
  {
   "url": "http://rits.shanghai.nyu.edu/ai/deepseek-releases-v4-open-source-1-6t-moe-with-1m-context",
   "title": "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
   "published_at": null,
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
   "parameters": "1.6T",
   "context_window": null,
   "license": null,
   "significance": "MMLU-Pro: 87.5%; SimpleQA-Verified: 57.9%; LiveCodeBench: 93.5% (vs. Kimi K2.6 at 89.6%); Codeforces rating: 3206 (vs. GPT-5.4 at 3168); HMMT 2026 Feb (math): 95.2%; MRCR 1M (long-context recall): 83.5%; SWE-bench Verified: 80.6%",
   "organization_evidence": [
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   "release_date_evidence": [
    "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
    "Announced on April 24, 2026, the V4 family ships in two Mixture-of-Experts variants \u2014 a 1.6-trillion-parameter *V4-Pro* and a leaner 284-billion-parameter *V4-Flash* \u2014 both supporting a 1-million-token context window and both released with open weights under permissive licenses."
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   "parameters_evidence": [
    "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
    "Announced on April 24, 2026, the V4 family ships in two Mixture-of-Experts variants \u2014 a 1.6-trillion-parameter *V4-Pro* and a leaner 284-billion-parameter *V4-Flash* \u2014 both supporting a 1-million-token context window and both released with open weights under permissive licenses."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On the scorecards DeepSeek published alongside the release, V4-Pro-Max posts numbers that are competitive with \u2014 and in places ahead of \u2014 proprietary frontier systems:",
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    "- **SimpleQA-Verified**: 57.9%",
    "- **LiveCodeBench**: 93.5% (vs. Kimi K2.6 at 89.6%)",
    "- **Codeforces rating**: 3206 (vs. GPT-5.4 at 3168)",
    "- **HMMT 2026 Feb** (math): 95.2%",
    "- **MRCR 1M** (long-context recall): 83.5%",
    "- **SWE-bench Verified**: 80.6%"
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models July 2026: Full Collection",
   "published_at": "2026-07-17T11:27:10",
   "organization": "Thinking Machines",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975B total parameters, 41B active",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-bench Verified and holds the best open-weights adversarial safety score at 78.0% FORTRESS.",
   "organization_evidence": [
    "Best Open Source AI Models July 2026: Full Collection",
    "## Best for Customization: Inkling",
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   "model_evidence": null,
   "release_date_evidence": [
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    "## Best for Customization: Inkling",
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    "## Best for Customization: Inkling",
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    "## Best for Customization: Inkling",
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   ]
  },
  {
   "url": "https://aumiqx.com/ai-tools/qwen-3-5-alibaba-open-source-llm-2026",
   "title": "Aumiqx Technologies \u2014 AI Agents That Actually Do Things",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": "397B-A17B",
   "context_window": null,
   "license": null,
   "significance": "Qwen 3.5 is the strongest model on Earth for languages outside the English-Mandarin-Spanish core, and the first model where the math actually works for normal teams to run 397-billion-parameter frontier models at usable speeds.",
   "organization_evidence": [
    "# Qwen 3.5 Review: Alibaba's 397B Beast Speaks 201 Languages",
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   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen 3.5 Review: Alibaba's 397B Beast Speaks 201 Languages",
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   "parameters_evidence": [
    "# Qwen 3.5 Review: Alibaba's 397B Beast Speaks 201 Languages",
    "**Qwen3.5-397B-A17B** \u2014 a 397-billion-parameter mixture-of-experts model with 17 billion active parameters per token, a 256,000-token context window, native fluency in **201 languages**, and a permissive **Apache 2.0 license** attached to every weight, every config file, and every tokenizer."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen 3.5 Review: Alibaba's 397B Beast Speaks 201 Languages",
    "The short version is that on several benchmarks the gap is now within the margin of measurement noise, and on multilingual workloads it's not even close \u2014 Qwen 3.5 is the strongest model on Earth for languages outside the English-Mandarin-Spanish core."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/meta-llama-4-scout-maverick-open-weight-multimodal-moe-2026",
   "title": "Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
   "published_at": "2026-04-09T00:32:20",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "17B active (109B total)",
   "context_window": null,
   "license": null,
   "significance": "First open-weight, natively multimodal models built around a mixture-of-experts (MoE) design with a 10 million token context window.",
   "organization_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
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   ],
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    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
    "They represent a fundamental architectural shift: the first open-weight, natively multimodal models built around a mixture-of-experts (MoE) design, with context window support unlike anything previously available in open-source AI."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/meta-llama-4-scout-maverick-open-weight-multimodal-moe-2026",
   "title": "Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
   "published_at": "2026-04-09T00:32:20",
   "organization": "Meta",
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   "release_date": "2026-04-05",
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   "significance": "First open-weight, natively multimodal models built around a mixture-of-experts (MoE) design.",
   "organization_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
    "On April 5, 2026, Meta released Llama 4 Scout and Llama 4 Maverick to the public \u2014 and the AI research community took notice immediately."
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   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
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   "significance_evidence": [
    "# Meta Launches Llama 4 Scout and Maverick: Multimodal MoE AI Goes Open-Weight",
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   ]
  },
  {
   "url": "https://technobezz.com/news/google-deepmind-launches-gemma-4-12b-open-model-that-runs-on-any-laptop-with-16gb-ram",
   "title": "Google DeepMind Launches Gemma 4 12B Open Model That Runs on Any Laptop with 16GB RAM",
   "published_at": "2026-06-04T17:25:40",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First mid-sized Gemma to ditch multimodal encoders entirely, processing images and audio directly through the language backbone, with native audio support and Multi-Token Prediction drafters enabled by default.",
   "organization_evidence": [
    "Google DeepMind dropped Gemma 4 12B on June 3,",
    "[Google DeepMind dropped Gemma 4 12B on June 3,](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) a 12-billion-parameter open model that runs on [any laptop with 16GB of RAM](https://arstechnica.com/google/2026/06/googles-new-gemma-4-open-ai-model-is-sized-for-your-laptop/)."
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   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind dropped Gemma 4 12B on June 3,",
    "[Google DeepMind dropped Gemma 4 12B on June 3,](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) a 12-billion-parameter open model that runs on [any laptop with 16GB of RAM](https://arstechnica.com/google/2026/06/googles-new-gemma-4-open-ai-model-is-sized-for-your-laptop/)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline number undersells the engineering: this is the first mid-sized Gemma to ditch multimodal encoders entirely, processing images and audio directly through the language backbone.",
    "[Google DeepMind dropped Gemma 4 12B on June 3,](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) a 12-billion-parameter open model that runs on [any laptop with 16GB of RAM](https://arstechnica.com/google/2026/06/googles-new-gemma-4-open-ai-model-is-sized-for-your-laptop/)."
   ]
  },
  {
   "url": "https://swfte.com/ru/ai/models/minimax-m3",
   "title": "MiniMax M3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-08-11T14:32:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-05-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax's 1 Jun 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context.",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is a open-source AI model by MiniMax, released on May 31, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is a open-source AI model by MiniMax, released on May 31, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax's 1 Jun 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context."
   ]
  },
  {
   "url": "https://the-agent-report.com/2026/07/poolside-laguna-s-2-1-open-weight-coding-model-july-2026",
   "title": "Poolside Ships Laguna S 2.1: A 118B MoE Coding Model That Beats Rivals 10\u00d7 Its Size",
   "published_at": "2026-07-28T06:00:00",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-28",
   "parameters": "118B (8B active)",
   "context_window": null,
   "license": null,
   "significance": "Beats models 10x to 20x its size on agentic coding benchmarks, scoring 70.2% on Terminal-Bench 2.1 and 78.5% on SWE-Bench Multilingual.",
   "organization_evidence": [
    "Poolside Ships Laguna S 2.1: A 118B MoE Coding Model That Beats Rivals 10\u00d7 Its Size",
    "**TL;DR** \u2014 Poolside released Laguna S 2.1 on July 28, a 118-billion-parameter Mixture-of-Experts coding model that activates just 8B parameters per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Poolside Ships Laguna S 2.1: A 118B MoE Coding Model That Beats Rivals 10\u00d7 Its Size",
    "**TL;DR** \u2014 Poolside released Laguna S 2.1 on July 28, a 118-billion-parameter Mixture-of-Experts coding model that activates just 8B parameters per token."
   ],
   "parameters_evidence": [
    "Poolside Ships Laguna S 2.1: A 118B MoE Coding Model That Beats Rivals 10\u00d7 Its Size",
    "**TL;DR** \u2014 Poolside released Laguna S 2.1 on July 28, a 118-billion-parameter Mixture-of-Experts coding model that activates just 8B parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Poolside Ships Laguna S 2.1: A 118B MoE Coding Model That Beats Rivals 10\u00d7 Its Size",
    "**TL;DR** \u2014 Poolside released Laguna S 2.1 on July 28, a 118-billion-parameter Mixture-of-Experts coding model that activates just 8B parameters per token. It beats models 10\u00d7 to 20\u00d7 its size on agentic coding benchmarks, went from pretraining to launch in under nine weeks, and comes with something no major lab has ever done: full, unedited trajectories for every benchmark trial, published for anyone to inspect."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Flagship model featuring 397 billion total parameters and 17 billion active parameters, with advanced reasoning, coding, visual understanding, and agentic capabilities, outperforming many frontier models.",
   "organization_evidence": [
    "# Qwen3.5",
    "**Qwen3.5** is a family of open-source multimodal large language models developed by the Qwen team at Alibaba Cloud."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.5",
    "First released on February 16, 2026, with the initial model Qwen3.5-397B-A17B, it was followed by the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.5",
    "First released on February 16, 2026, with the initial model Qwen3.5-397B-A17B, it was followed by the Qwen3.5 Medium Model series on February 25, 2026."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba",
   "model": "Qwen3.5-0.8B",
   "release_date": "2026-03-09",
   "parameters": "0.8B",
   "context_window": "262144",
   "license": "open-source",
   "significance": "One of the most capable sub-1B models for functional tool calling, supporting multimodal tasks and edge devices.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "model_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "release_date_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "parameters_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "context_window_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "license_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ],
   "significance_evidence": [
    "Model variants",
    "The Qwen3.5-0.8B is an open-source multimodal large language model with 0.8 billion parameters, released by Alibaba's Qwen team on March 9, 2026, as part of the Qwen3.5 small model series (including 0.8B, 2B, 4B, and 9B variants)."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-2B",
   "release_date": "2026-03-02",
   "parameters": "2 billion",
   "context_window": "256K",
   "license": "open-source",
   "significance": "Optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices with strong reasoning and coding performance.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Model variants",
    "The Qwen3.5-2B is a lightweight variant with 2 billion parameters, released on March 2, 2026 as part of the small models series optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices."
   ],
   "release_date_evidence": [
    "Model variants",
    "The Qwen3.5-2B is a lightweight variant with 2 billion parameters, released on March 2, 2026 as part of the small models series optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices."
   ],
   "parameters_evidence": [
    "Model variants",
    "The Qwen3.5-2B is a lightweight variant with 2 billion parameters, released on March 2, 2026 as part of the small models series optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices."
   ],
   "context_window_evidence": [
    "Model variants",
    "The Qwen3.5-2B is a lightweight variant with 2 billion parameters, released on March 2, 2026 as part of the small models series optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices."
   ],
   "license_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "significance_evidence": [
    "Model variants",
    "The Qwen3.5-2B is a lightweight variant with 2 billion parameters, released on March 2, 2026 as part of the small models series optimized for efficient on-device inference and local deployment on consumer hardware such as mobile devices."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-4B",
   "release_date": "2026-03-02",
   "parameters": "4B",
   "context_window": "262K",
   "license": "open-source",
   "significance": "Compact dense model featuring native multimodality, 262K context window, and support for 201 languages.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ],
   "release_date_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ],
   "parameters_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ],
   "context_window_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ],
   "license_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "significance_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-9B",
   "release_date": "2026-03-02",
   "parameters": "9 billion",
   "context_window": "256K",
   "license": "open-source",
   "significance": "Multimodal model providing noticeably better reasoning, coding, and reliability for daily use on consumer hardware.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Model variants",
    "The Qwen3.5-9B is a multimodal model supporting text and image inputs, featuring a 256K context window, strong reasoning and coding performance, and support for 201 languages and dialects."
   ],
   "release_date_evidence": [
    "Small Model Series",
    "The Qwen3.5 Small Model Series, released on March 2, 2026, includes compact dense models: 0.8B, 2B, 4B, and 9B parameters."
   ],
   "parameters_evidence": [
    "Model variants",
    "The Qwen3.5-9B is a multimodal model supporting text and image inputs, featuring a 256K context window, strong reasoning and coding performance, and support for 201 languages and dialects."
   ],
   "context_window_evidence": [
    "Model variants",
    "The Qwen3.5-9B is a multimodal model supporting text and image inputs, featuring a 256K context window, strong reasoning and coding performance, and support for 201 languages and dialects."
   ],
   "license_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "significance_evidence": [
    "Model variants",
    "The Qwen3.5-9B is a multimodal model supporting text and image inputs, featuring a 256K context window, strong reasoning and coding performance, and support for 201 languages and dialects."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-35B-A3B",
   "release_date": "2026-02-25",
   "parameters": "35 billion total parameters, 3 billion active",
   "context_window": "262,000",
   "license": "Apache 2.0",
   "significance": "Sparse Mixture-of-Experts model offering high inference efficiency, speed, reasoning, and coding capabilities.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Efficiency and inference",
    "Similarly, the Qwen3.5-35B-A3B model (35 billion total parameters, 3 billion active) employs a sparse MoE design to achieve an output speed of 172.7 tokens per second and a 262,000 token context window, enabling high inference efficiency."
   ],
   "release_date_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "parameters_evidence": [
    "Efficiency and inference",
    "Similarly, the Qwen3.5-35B-A3B model (35 billion total parameters, 3 billion active) employs a sparse MoE design to achieve an output speed of 172.7 tokens per second and a 262,000 token context window, enabling high inference efficiency."
   ],
   "context_window_evidence": [
    "Efficiency and inference",
    "Similarly, the Qwen3.5-35B-A3B model (35 billion total parameters, 3 billion active) employs a sparse MoE design to achieve an output speed of 172.7 tokens per second and a 262,000 token context window, enabling high inference efficiency."
   ],
   "license_evidence": [
    "Efficiency and inference",
    "As an open-weights model released under the Apache 2.0 license, it excels in cost-efficient local and on-premise deployments compared to proprietary alternatives."
   ],
   "significance_evidence": [
    "Small Model Series",
    "The Qwen3.5-35B-A3B is a sparse Mixture-of-Experts (MoE) model."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-27B",
   "release_date": "2026-02-25",
   "parameters": "27 billion",
   "context_window": "262,000",
   "license": "Apache 2.0",
   "significance": "Efficient and high-performing dense model delivering strong results in coding, reasoning, and output consistency.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Efficiency and inference",
    "Community reports highlight the Qwen3.5-27B variant as a particularly efficient and high-performing model."
   ],
   "release_date_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "parameters_evidence": [
    "Model variants",
    "The initial release is Qwen3.5-397B-A17B, featuring 397 billion total parameters and 17 billion active parameters."
   ],
   "context_window_evidence": [
    "Efficiency and inference",
    "Community reports highlight the Qwen3.5-27B variant as a particularly efficient and high-performing model."
   ],
   "license_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "significance_evidence": [
    "Efficiency and inference",
    "Community reports highlight the Qwen3.5-27B variant as a particularly efficient and high-performing model."
   ]
  },
  {
   "url": "https://grokipedia.com/page/Qwen35",
   "title": "Qwen3.5",
   "published_at": "2026-03-28T00:56:43",
   "organization": "Alibaba Cloud",
   "model": "Qwen3.5-122B-A10B",
   "release_date": "2026-02-25",
   "parameters": "122 billion total parameters and 10 billion active parameters",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Medium model series variant combining high-throughput inference with efficient sparse Mixture-of-Experts architecture.",
   "organization_evidence": [
    "Model variants",
    "The Qwen3.5 family comprises open-source multimodal large language models released in phases by the Qwen team at Alibaba Cloud, beginning with the flagship model on February 16, 2026, and the Qwen3.5 Medium Model series on February 25, 2026."
   ],
   "model_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "release_date_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "parameters_evidence": [
    "Model variants",
    "Subsequent models include Qwen3.5-122B-A10B with 122 billion total parameters and 10 billion active parameters, Qwen3.5-35B-A3B with 35 billion total parameters and 3 billion active parameters, the dense variant Qwen3.5-27B with 27 billion parameters, and Qwen3.5-9B with 9 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-source release",
    "The Qwen3.5 series was released as open-weight models under the Apache 2.0 license, with the initial release of the Qwen3.5-397B-A17B model on February 16, 2026, followed by the Qwen3.5 Medium Model series\u2014including the open-source Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, and Qwen3.5-27B (plus a base variant)\u2014on February 25, 2026, with the proprietary Qwen3.5-Flash available via API."
   ],
   "significance_evidence": [
    "# Qwen3.5",
    "First released on February 16, 2026, with the initial model Qwen3.5-397B-A17B, it was followed by the Qwen3.5 Medium Model series on February 25, 2026."
   ]
  },
  {
   "url": "https://felloai.com/cs/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "tied GPT-5.5 on the toughest coding benchmark in the industry, at roughly 80% less per million tokens, and became the #1 open-weight model on the Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "**Kimi K2.6** is the latest flagship model from **Moonshot AI**, a Beijing-based lab that has been releasing open-source AI models at a pace closer to a startup than a state-backed research outfit."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**."
   ],
   "parameters_evidence": [
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It just tied **GPT-5.5** on the toughest coding benchmark in the industry, at roughly **80% less per million tokens**."
   ]
  },
  {
   "url": "https://braindetox.kr/en/posts/glm_5_2_open_model_2026.html",
   "title": "GLM 5.2 Released - An Open-Source Model With a 1M-Token Context and Autonomous Agents 2026",
   "published_at": "2026-06-15T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1,000,000-token context window and an agentic coding orientation",
   "organization_evidence": [
    "# GLM 5.2 Released - An Open-Source Model With a 1M-Token Context and Autonomous Agents",
    "On June 13, 2026, the Chinese AI company **Zhipu (Z.ai)** released its flagship model **GLM 5.2**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM 5.2 Released - An Open-Source Model With a 1M-Token Context and Autonomous Agents",
    "On June 13, 2026, the Chinese AI company **Zhipu (Z.ai)** released its flagship model **GLM 5.2**."
   ],
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    "# GLM 5.2 Released - An Open-Source Model With a 1M-Token Context and Autonomous Agents",
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  {
   "url": "https://ofox.io/blog/minimax-m2-api-pricing-comparison-2026",
   "title": "MiniMax M2.7 API Pricing 2026: Free Tier, Setup, and How It Stacks Against DeepSeek and Kimi",
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   "url": "https://aichina.news/blog/reasoning-at-the-edge-allenai-brings-the-transparent-olmo-2-1b-to-the-2f8lnw",
   "title": "Reasoning at the Edge: AllenAI brings the Transparent OLMo-2-1B to the Ascend Ecosystem \u2014 AICHINA.news Blog",
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    "models/allenai April 16, 2026 2 min read Auto-generated from Modelers"
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  {
   "url": "https://featureddaily.com/news/deepseek-v4-brief",
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   "url": "https://glonce.com/hugging-face-qwen-becomes-the-base-model-of",
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  {
   "url": "https://glonce.com/hugging-face-qwen-becomes-the-base-model-of",
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   "url": "https://glonce.com/hugging-face-qwen-becomes-the-base-model-of",
   "title": "Hugging Face: Qwen becomes the base model of open source AI",
   "published_at": "2026-08-15T06:00:00",
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   "significance": "124B parameter original American frontier open model",
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  {
   "url": "https://glonce.com/hugging-face-qwen-becomes-the-base-model-of",
   "title": "Hugging Face: Qwen becomes the base model of open source AI",
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  {
   "url": "https://aiautomationglobal.com/blog/meta-llama-4-scout-maverick-open-source-multimodal-2026",
   "title": "Meta Llama 4 Scout & Maverick: The Open-Source AI Leap",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Meta",
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   "release_date": "2026-04-05",
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   "release_date_evidence": [
    "**Meta Llama 4 Scout and Maverick** \u2014 released on April 5, 2026 \u2014 are the first open-weight, natively multimodal mixture-of-experts (MoE) models in the Llama family, delivering benchmark results that match or beat GPT-4o and Gemini 2.0 Flash at a fraction of the inference cost.",
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  {
   "url": "https://gigazine.net/gsc_news/en/20260430-mistral-medium-3-5",
   "title": "The French-made AI model 'Mistral Medium 3.5,' which surpasses Claude Sonnet 4.5, has been released as an open model, and Mistral Vibe now supports cloud computing.",
   "published_at": "2026-04-30T07:11:00",
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   "release_date_evidence": [
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    "\u2014 Mistral Vibe (@mistralvibe) [April 29, 2026](https://twitter.com/mistralvibe/status/2049511752379813968?ref_src=twsrc%5Etfw)"
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    "# The French-made AI model 'Mistral Medium 3.5,' which surpasses Claude Sonnet 4.5, has been released as an open model, and Mistral Vibe now supports cloud computing.",
    "French AI company Mistral AI has released its AI model ' **Mistral Medium 3.5,** ' with 128 billion parameters, as an open model."
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    "# The French-made AI model 'Mistral Medium 3.5,' which surpasses Claude Sonnet 4.5, has been released as an open model, and Mistral Vibe now supports cloud computing.",
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  },
  {
   "url": "https://dev.to/jamilxt/google-just-released-a-multimodal-ai-that-runs-on-your-laptop-and-its-free-465c",
   "title": "Google Just Released a Multimodal AI That Runs on Your Laptop \u2014 And It's Free",
   "published_at": "2026-06-14T08:11:40",
   "organization": "Google",
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   "release_date": "2026-06-03",
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   "significance": "Gemma 4 12B performs nearly as well as the 26B model on standard benchmarks, while using less than half the memory, and introduces a unified encoder-free architecture.",
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   "release_date_evidence": [
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   "significance_evidence": [
    "The model sits between Google's smaller E4B (designed for edge devices) and their larger 26B Mixture of Experts model.",
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  {
   "url": "https://timespek.com/moonshots-kimi-k3-lands-with-a-2-5-trillion-parameter-pitch",
   "title": "Moonshot\u2019s Kimi K3 Lands With a 2.5-Trillion-Parameter Pitch to Rival Opus 4.8",
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   "organization": "Moonshot AI",
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   "release_date": "2026-07-16",
   "parameters": "2 trillion to 3 trillion",
   "context_window": null,
   "license": null,
   "significance": "described K3 as the largest open-weight model to come out of China, with a parameter count somewhere between 2 trillion and 3 trillion",
   "organization_evidence": [
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   "release_date_evidence": [
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   "parameters_evidence": [
    "# Moonshot\u2019s Kimi K3 Lands With a 2.5-Trillion-Parameter Pitch to Rival Opus 4.8",
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  },
  {
   "url": "https://aiautomationglobal.com/blog/glm-5-2-open-weights-1m-context-coding-agent-2026",
   "title": "GLM-5.2: Open-Weights AI Coding Model With 1M Context",
   "published_at": "2026-06-23T14:28:37",
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  },
  {
   "url": "https://featureddaily.com/news/qwen-most-downloaded-open-ai-brief",
   "title": "Qwen: the open-source model juggernaut, explained",
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   "release_date": "2026-04-20",
   "parameters": "27B",
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   "significance": "outperforms a 397B predecessor on agentic coding benchmarks",
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   "context_window_evidence": null,
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  },
  {
   "url": "https://featureddaily.com/news/qwen-most-downloaded-open-ai-brief",
   "title": "Qwen: the open-source model juggernaut, explained",
   "published_at": "2026-06-13T09:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
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    "| Model | Weights | Licence | Context |",
    "| Qwen3.6-35B-A3B | Open | Apache 2.0 | 262K |"
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   "context_window_evidence": [
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    "| Model | Weights | Licence | Context |",
    "| Qwen3.6-35B-A3B | Open | Apache 2.0 | 262K |"
   ],
   "license_evidence": [
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    "Qwen: the open-source model juggernaut, explained",
    "| Model | Weights | Licence | Context |",
    "| Qwen3.6-35B-A3B | Open | Apache 2.0 | 262K |"
   ],
   "significance_evidence": [
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    "Qwen: the open-source model juggernaut, explained",
    "Efficient architecture (Mixture-of-Experts on the larger 35B-A3B variant) is the mechanism: better engineering, not brute-force scale."
   ]
  },
  {
   "url": "https://next-edition.com/articles/science-technology-meta-releases-llama-4-open-weight-model-claims-state-of-the-art-on-key-reasoning-benchmarks-2026-04-15",
   "title": "Meta Releases Llama 4 Open-Weight Model, Claims State-of-the-Art on Key Reasoning Benchmarks",
   "published_at": "2026-04-15T00:00:00",
   "organization": "Meta",
   "model": null,
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   "license": null,
   "significance": "Achieves top scores on the MMLU Pro reasoning suite and outperforms OpenAI's GPT-4o on the MATH and HumanEval coding benchmarks.",
   "organization_evidence": [
    "Science & Technology",
    "Meta Releases Llama 4 Open-Weight Model, Claims State-of-the-Art on Key Reasoning Benchmarks",
    "Meta AI publishes Llama 4 weights publicly on Hugging Face, challenging proprietary rivals with a model Meta says outperforms GPT-4o on several standard evaluations."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Science & Technology",
    "Meta Releases Llama 4 Open-Weight Model, Claims State-of-the-Art on Key Reasoning Benchmarks",
    "According to Meta's technical report published alongside the release, the flagship Llama 4 variant \u2014 a 400-billion-parameter mixture-of-experts architecture \u2014 achieves top scores on the MMLU Pro reasoning suite and outperforms OpenAI's GPT-4o on the MATH and HumanEval coding benchmarks, as measured by Meta's internal evaluations."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Science & Technology",
    "Meta Releases Llama 4 Open-Weight Model, Claims State-of-the-Art on Key Reasoning Benchmarks",
    "According to Meta's technical report published alongside the release, the flagship Llama 4 variant \u2014 a 400-billion-parameter mixture-of-experts architecture \u2014 achieves top scores on the MMLU Pro reasoning suite and outperforms OpenAI's GPT-4o on the MATH and HumanEval coding benchmarks, as measured by Meta's internal evaluations."
   ]
  },
  {
   "url": "https://simonwillison.net/tags/mistral",
   "title": "Simon Willison on mistral",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Mistral Small 4 is the first Mistral model to unify the capabilities of our flagship models, Magistral for reasoning, Pixtral for multimodal, and Devstral for agentic coding, into a single, versatile model.",
   "organization_evidence": [
    "## 66 posts tagged \u201cmistral\u201d",
    "\u201cmistral\u201d\n\n[Mistral AI](https://mistral.ai/) release both openly licensed and API-hosted Language Models.",
    "### 2026",
    "**[Introducing Mistral Small 4](https://mistral.ai/news/mistral-small-4)**. Big new release from Mistral today (despite the name) - a new Apache 2 licensed 119B parameter (Mixture-of-Experts, 6B active) model which they describe like this:"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## 66 posts tagged \u201cmistral\u201d",
    "### 2026",
    "**[Introducing Mistral Small 4](https://mistral.ai/news/mistral-small-4)**. Big new release from Mistral today (despite the name) - a new Apache 2 licensed 119B parameter (Mixture-of-Experts, 6B active) model which they describe like this:"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## 66 posts tagged \u201cmistral\u201d",
    "### 2026",
    "**[Introducing Mistral Small 4](https://mistral.ai/news/mistral-small-4)**. Big new release from Mistral today (despite the name) - a new Apache 2 licensed 119B parameter (Mixture-of-Experts, 6B active) model which they describe like this:",
    "Mistral Small 4 is the first Mistral model to unify the capabilities of our flagship models, Magistral for reasoning, Pixtral for multimodal, and Devstral for agentic coding, into a single, versatile model."
   ]
  },
  {
   "url": "https://stackfutures.com/blog/gemma-4-open-model-apache-release",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452 | Stack Futures",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B",
   "context_window": null,
   "license": null,
   "significance": "Supports multimodal input (image + video) and audio; runs on phone, Raspberry Pi, Jetson Nano.",
   "organization_evidence": [
    "# Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "parameters_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| E2B | 2B | 128K | Phone, Raspberry Pi, Jetson Nano |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| E2B | 2B | 128K | Phone, Raspberry Pi, Jetson Nano |",
    "All four variants support multimodal input (image + video). Audio is available on the two edge models."
   ]
  },
  {
   "url": "https://stackfutures.com/blog/gemma-4-open-model-apache-release",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452 | Stack Futures",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "4B",
   "context_window": null,
   "license": null,
   "significance": "Supports multimodal input (image + video) and audio; runs on phone, IoT.",
   "organization_evidence": [
    "# Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "parameters_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| E4B | 4B | 128K | Phone, IoT |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| E4B | 4B | 128K | Phone, IoT |",
    "All four variants support multimodal input (image + video). Audio is available on the two edge models."
   ]
  },
  {
   "url": "https://stackfutures.com/blog/gemma-4-open-model-apache-release",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452 | Stack Futures",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "26B total / 3.8B active",
   "context_window": null,
   "license": null,
   "significance": "Activates only 3.8B parameters at inference, delivering near-31B quality at substantially lower compute cost, landing at Arena ELO ~1441.",
   "organization_evidence": [
    "# Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "parameters_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| 26B A4B (MoE) | 26B total / 3.8B active | 256K | 16GB VRAM GPU |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 26B MoE activates only 3.8B parameters at inference, delivering near-31B quality at substantially lower compute cost. That design is the key architectural story: the MoE lands at Arena ELO ~1441, just 11 points below the dense 31B."
   ]
  },
  {
   "url": "https://stackfutures.com/blog/gemma-4-open-model-apache-release",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452 | Stack Futures",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Hits Arena ELO 1452, sits third among all open models globally, matches Codeforces threshold of competitive programmers.",
   "organization_evidence": [
    "# Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452",
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models spanning edge devices to datacenter GPUs, all licensed under Apache 2.0."
   ],
   "parameters_evidence": [
    "Model Lineup",
    "| Variant | Params | Context | Hardware |",
    "| 31B Dense | 31B | 256K | 24GB VRAM (RTX 4090) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4 Ships Four Open Models Under Apache 2.0 \u2014 31B Hits Arena ELO 1452",
    "On Chatbot Arena as of April 2026, Gemma 4 31B sits third among all open models globally.",
    "Competitive programmers hit roughly ELO 2150 on Codeforces. Gemma 4 31B matches that threshold on the same benchmark."
   ]
  },
  {
   "url": "https://fourweekmba.com/ai-moonshot-kimi-k3-open-weight-commoditization",
   "title": "FourWeekMBA",
   "published_at": "2026-07-27T19:47:15",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Claims wins over Claude Opus 4.8 and GPT-5.5 on coding and agentic tasks on Moonshot's evaluation suite, and topped a Frontend Code Arena test reported by Tom\u2019s Hardware.",
   "organization_evidence": [
    "Moonshot AI\u2019s Kimi K3 and the Open-Weight Commoditization Thesis",
    "Moonshot AI published the open weights for Kimi K3 on Hugging Face \u2014 free to download, no access gate."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Escalation \u2014 Selected Timeline",
    "July 26\u201327, 2026",
    "On July 26\u201327, 2026, Moonshot AI published the open weights for Kimi K3 on Hugging Face \u2014 free to download, no access gate."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI\u2019s Kimi K3 and the Open-Weight Commoditization Thesis",
    "On Moonshot\u2019s own evaluation suite \u2014 which the company controls and curates \u2014 K3 claims wins over Claude Opus 4.8 and GPT-5.5 on coding and agentic tasks, and topped a Frontend Code Arena test reported by Tom\u2019s Hardware."
   ]
  },
  {
   "url": "https://tpsreport.news/news/glm-5-3-zhipu-ai-post-training-release",
   "title": "Z.ai Releases GLM-5.3, Claims Frontier Agentic Coding Performance from 750B-Parameter Model via Post-Training Alone",
   "published_at": "2026-08-14T21:36:21",
   "organization": "Z.ai",
   "model": null,
   "release_date": null,
   "parameters": "750B",
   "context_window": null,
   "license": null,
   "significance": "Matches or beats larger frontier systems on agentic coding benchmarks using the same base checkpoint as GLM-5.2, with all gains coming from expanded post-training.",
   "organization_evidence": [
    "model release",
    "Z.ai Releases GLM-5.3, Claims Frontier Agentic Coding Performance from 750B-Parameter Model via Post-Training Alone",
    "Z.ai released GLM-5.3, available now in its coding plan, with API access and open weights on Hugging Face to follow within two weeks."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "model release",
    "Z.ai Releases GLM-5.3, Claims Frontier Agentic Coding Performance from 750B-Parameter Model via Post-Training Alone",
    "Z.ai (Zhipu AI) has released GLM-5.3, a roughly 750-billion-parameter model that the company says matches or exceeds frontier agentic coding benchmarks set by Moonshot AI's Kimi K3, Anthropic's Claude Fable 5, and OpenAI's GPT-5.6-Sol \u2014 despite having roughly one-third the parameter count of Kimi K3, according to Z.ai."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "model release",
    "Z.ai Releases GLM-5.3, Claims Frontier Agentic Coding Performance from 750B-Parameter Model via Post-Training Alone",
    "The company says the model matches or beats larger frontier systems on agentic coding benchmarks using the same base checkpoint as GLM-5.2, with all gains coming from expanded post-training."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/minimax-m27-self-evolving-open-source-swe-benchmark-2026",
   "title": "The AI That Rewrote Itself 100 Times, And What MiniMax M2.7 Means for Labs Betting on Human Supervision",
   "published_at": "2026-05-07T04:10:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": "230 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "MiniMax M2.7 scored 56.22% on SWE-Pro matching Claude Opus 4.6 while running at 50x lower input cost and 3x faster inference speed",
   "organization_evidence": [
    "Model Release",
    "MiniMax, one of China's most well-funded AI startups, released its flagship model M2.7 on **March 18, 2026**, and open-sourced the weights on **April 12** via Hugging Face and GitHub."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "MiniMax, one of China's most well-funded AI startups, released its flagship model M2.7 on **March 18, 2026**, and open-sourced the weights on **April 12** via Hugging Face and GitHub."
   ],
   "parameters_evidence": [
    "Model Release",
    "The model is a Mixture of Experts architecture with **230 billion total parameters** and **10 billion active parameters** during inference, meaning it achieves near-frontier performance while activating only a small fraction of its parameter count at any given time."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "Key Takeaways",
    "MiniMax M2.7 scored 56.22% on SWE-Pro matching Claude Opus 4.6 while running at 50x lower input cost and 3x faster inference speed"
   ]
  },
  {
   "url": "https://aibase.com/news/28839",
   "title": "Google Collaborates with NVIDIA to Release Open-Source Model DiffusionGemma: Introduces Diffusion Mechanism, Speeds Up Single-Card Inference by Four Times",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DiffusionGemma has 26 billion parameters and is based on a mixture-of-experts (MoE) architecture, with only 3.8 billion parameters activated in a single step.",
   "organization_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DiffusionGemma has 26 billion parameters and is based on a mixture-of-experts (MoE) architecture, with only 3.8 billion parameters activated in a single step."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T / 49B",
   "context_window": "1M",
   "license": "MIT",
   "significance": "BenchLM score 87 overall, 89.8 on coding",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "DeepSeek V4 (released **April 24, 2026**) ships in Pro (Max + High) and Flash variants."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "DeepSeek V4 (released **April 24, 2026**) ships in Pro (Max + High) and Flash variants."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "DeepSeek V4 (released **April 24, 2026**) ships in Pro (Max + High) and Flash variants."
   ],
   "parameters_evidence": [
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| DeepSeek V4 Pro (Max) | 1.6T / 49B | MIT | Overall + coding | 1M |"
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "Pro Max scores **87 on BenchLM overall**, **89.8 on coding** (the best of any open-weight model), and supports a **1M token context**."
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "License is **MIT**, so commercial use is unrestricted."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## DeepSeek V4 Pro \u2014 overall leader",
    "Pro Max scores **87 on BenchLM overall**, **89.8 on coding** (the best of any open-weight model), and supports a **1M token context**."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Alibaba",
   "model": "Qwen 3.5 397B",
   "release_date": "2026-03",
   "parameters": "397B / 17B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Strongest open model under Apache 2.0 license",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The flagship Qwen 3.5 397B (17B active) is the strongest open model under a fully permissive **Apache 2.0** license \u2014 no MAU caps, no use-case carve-outs."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Qwen 3.5 397B | 397B / 17B | Apache 2.0 | All-round, multilingual | 256K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The flagship Qwen 3.5 397B (17B active) is the strongest open model under a fully permissive **Apache 2.0** license \u2014 no MAU caps, no use-case carve-outs."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The flagship Qwen 3.5 397B (17B active) is the strongest open model under a fully permissive **Apache 2.0** license \u2014 no MAU caps, no use-case carve-outs."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Alibaba",
   "model": "Qwen 3 235B-A22B",
   "release_date": "2026-03",
   "parameters": "235B / 22B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Most popular real-world deployment, matches Llama 4 Maverick on most benchmarks",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The smaller Qwen 3 235B-A22B (still Apache 2.0) is the most popular real-world deployment \u2014 it fits in 4\u00d7 H100 in 4-bit, runs at a few hundred tokens/sec, and matches Llama 4 Maverick on most benchmarks."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Qwen 3 235B-A22B | 235B / 22B | Apache 2.0 | Broad benchmarks | 128K |"
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Qwen 3 235B-A22B | 235B / 22B | Apache 2.0 | Broad benchmarks | 128K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The smaller Qwen 3 235B-A22B (still Apache 2.0) is the most popular real-world deployment \u2014 it fits in 4\u00d7 H100 in 4-bit, runs at a few hundred tokens/sec, and matches Llama 4 Maverick on most benchmarks."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "The smaller Qwen 3 235B-A22B (still Apache 2.0) is the most popular real-world deployment \u2014 it fits in 4\u00d7 H100 in 4-bit, runs at a few hundred tokens/sec, and matches Llama 4 Maverick on most benchmarks."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Meta",
   "model": "Llama 4 Maverick",
   "release_date": "2026-04",
   "parameters": "400B / 17B",
   "context_window": "1M",
   "license": "Llama Community",
   "significance": "Flagship for general tasks",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Maverick (400B / 17B active) is the flagship for general tasks."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Llama 4 Maverick | 400B / 17B | Llama Community | General + ecosystem | 1M |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "License: the **Llama Community License** permits commercial use *below* 700M monthly active users."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Maverick (400B / 17B active) is the flagship for general tasks."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "2026-04",
   "parameters": "109B / 17B",
   "context_window": "10M",
   "license": "Llama Community",
   "significance": "10M token context window, by a wide margin the longest in open-weight LLMs",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Meta released **Llama 4 Scout and Maverick** in April 2026."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Scout (109B / 17B active) ships with the **10M token context window** \u2014 by a wide margin the longest in open-weight LLMs."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Scout (109B / 17B active) ships with the **10M token context window** \u2014 by a wide margin the longest in open-weight LLMs."
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "License: the **Llama Community License** permits commercial use *below* 700M monthly active users."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Llama 4 \u2014 the long-context play",
    "Scout (109B / 17B active) ships with the **10M token context window** \u2014 by a wide margin the longest in open-weight LLMs."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Moonshot AI",
   "model": "Kimi K2.5",
   "release_date": null,
   "parameters": "~1T / ~32B",
   "context_window": "200K",
   "license": "MIT",
   "significance": "Leads HumanEval at 99%",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "Moonshot AI's **Kimi K2.5** leads HumanEval at **99%** and posts MMLU 92.0, MMLU-Pro 87.1, AIME 2025 96.1, GPQA Diamond 87.6."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "Moonshot AI's **Kimi K2.5** leads HumanEval at **99%** and posts MMLU 92.0, MMLU-Pro 87.1, AIME 2025 96.1, GPQA Diamond 87.6."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Kimi K2.5 / K2.6 | ~1T / ~32B | MIT | Code (HumanEval 99) | 200K |"
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Kimi K2.5 / K2.6 | ~1T / ~32B | MIT | Code (HumanEval 99) | 200K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "License: **MIT**, fully commercial."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "Moonshot AI's **Kimi K2.5** leads HumanEval at **99%** and posts MMLU 92.0, MMLU-Pro 87.1, AIME 2025 96.1, GPQA Diamond 87.6."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": null,
   "parameters": "~1T / ~32B",
   "context_window": "200K",
   "license": "MIT",
   "significance": "Trades a small drop on knowledge for further coding gains",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "Moonshot AI's **Kimi K2.5** leads HumanEval at **99%** and posts MMLU 92.0, MMLU-Pro 87.1, AIME 2025 96.1, GPQA Diamond 87.6."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "The follow-up Kimi K2.6 trades a small drop on knowledge for further coding gains."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Kimi K2.5 / K2.6 | ~1T / ~32B | MIT | Code (HumanEval 99) | 200K |"
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Kimi K2.5 / K2.6 | ~1T / ~32B | MIT | Code (HumanEval 99) | 200K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "License: **MIT**, fully commercial."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Kimi K2.5 / K2.6 \u2014 the coding monster",
    "The follow-up Kimi K2.6 trades a small drop on knowledge for further coding gains."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": null,
   "parameters": "744B",
   "context_window": "128K",
   "license": "MIT",
   "significance": "Knowledge benchmark leader (MMLU 96, GPQA 94)",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## GLM-5 \u2014 knowledge and reasoning specialist",
    "Zhipu AI's **GLM-5** (744B params) is the knowledge benchmark leader: **MMLU 96, GPQA 94, SuperGPQA 92, SimpleQA 92, HumanEval 90, SWE-bench 77.8**."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## GLM-5 \u2014 knowledge and reasoning specialist",
    "Zhipu AI's **GLM-5** (744B params) is the knowledge benchmark leader: **MMLU 96, GPQA 94, SuperGPQA 92, SimpleQA 92, HumanEval 90, SWE-bench 77.8**."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## GLM-5 \u2014 knowledge and reasoning specialist",
    "Zhipu AI's **GLM-5** (744B params) is the knowledge benchmark leader: **MMLU 96, GPQA 94, SuperGPQA 92, SimpleQA 92, HumanEval 90, SWE-bench 77.8**."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| GLM-5 (Reasoning) | 744B | MIT | Knowledge (MMLU 96) | 128K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## GLM-5 \u2014 knowledge and reasoning specialist",
    "**MIT license**, commercial use unrestricted."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## GLM-5 \u2014 knowledge and reasoning specialist",
    "Zhipu AI's **GLM-5** (744B params) is the knowledge benchmark leader: **MMLU 96, GPQA 94, SuperGPQA 92, SimpleQA 92, HumanEval 90, SWE-bench 77.8**."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-29",
   "parameters": "675B / 41B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "EU-hosted inference, GDPR-aligned hosting, EU data residency",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Mistral released **Medium 3.5 on April 29, 2026** (675B / 41B active)."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Mistral released **Medium 3.5 on April 29, 2026** (675B / 41B active)."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Mistral released **Medium 3.5 on April 29, 2026** (675B / 41B active)."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Mistral released **Medium 3.5 on April 29, 2026** (675B / 41B active)."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Mistral Medium 3.5 | 675B / 41B | Apache 2.0 | EU-friendly all-round | 128K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Apache 2.0 license, EU-hosted inference, the most active-parameter count in the flagship MoE field \u2014 which translates to lower per-token VRAM efficiency but more capability per active expert."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Mistral Medium 3.5 \u2014 EU-native all-rounder",
    "Apache 2.0 license, EU-hosted inference, the most active-parameter count in the flagship MoE field \u2014 which translates to lower per-token VRAM efficiency but more capability per active expert."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Google",
   "model": "Gemma 4 medium",
   "release_date": null,
   "parameters": "~27B dense",
   "context_window": "256K",
   "license": "Gemma",
   "significance": "Fits on a single 24GB consumer GPU in 4-bit and offers a 256K context window",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Gemma 4 \u2014 Google's single-GPU pick",
    "Google's **Gemma 4** family runs from ~2B (mobile / edge) up to ~27B medium."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Gemma 4 \u2014 Google's single-GPU pick",
    "Google's **Gemma 4** family runs from ~2B (mobile / edge) up to ~27B medium."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Gemma 4 medium | ~27B dense | Gemma | Single-GPU inference | 256K |"
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Gemma 4 medium | ~27B dense | Gemma | Single-GPU inference | 256K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Gemma 4 \u2014 Google's single-GPU pick",
    "The Gemma license is permissive for commercial use with use-case restrictions (no weapons, no surveillance, etc)."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Gemma 4 \u2014 Google's single-GPU pick",
    "The medium variant fits on a **single 24GB consumer GPU** (RTX 4090 / 5090) in 4-bit and offers a 256K context window."
   ]
  },
  {
   "url": "https://klymentiev.com/blog/best-open-source-llm-2026",
   "title": "Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4 vs Kimi K2.5 vs GLM-5 vs Mistral",
   "published_at": "2026-05-10T21:09:59",
   "organization": "Alibaba",
   "model": "Qwen3-Coder-Next",
   "release_date": "2026-02",
   "parameters": "80B / 3B",
   "context_window": "128K",
   "license": "Apache 2.0",
   "significance": "Outperforms much larger models like DeepSeek V3.2 (37B active) on coding tasks at a fraction of the inference cost",
   "organization_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen 3.5 397B \u2014 best Apache 2.0 all-rounder",
    "Alibaba's **Qwen 3.5 family** completed its rollout in early March 2026."
   ],
   "model_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen3-Coder-Next \u2014 small-VRAM code model",
    "Released early February 2026, **Qwen3-Coder-Next** is an 80B / 3B active MoE specialized for code."
   ],
   "release_date_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen3-Coder-Next \u2014 small-VRAM code model",
    "Released early February 2026, **Qwen3-Coder-Next** is an 80B / 3B active MoE specialized for code."
   ],
   "parameters_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen3-Coder-Next \u2014 small-VRAM code model",
    "Released early February 2026, **Qwen3-Coder-Next** is an 80B / 3B active MoE specialized for code."
   ],
   "context_window_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Open-weight LLMs at a glance (May 2026)",
    "Open-weight LLMs at a glance (May 2026)",
    "| Model | Total / Active | License | Best for | Context |",
    "| Qwen3-Coder-Next | 80B / 3B | Apache 2.0 | Code on small VRAM | 128K |"
   ],
   "license_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen3-Coder-Next \u2014 small-VRAM code model",
    "Apache 2.0."
   ],
   "significance_evidence": [
    "# Best Open Source LLM 2026: Llama 4 vs Qwen 3.5 vs DeepSeek V4",
    "## Qwen3-Coder-Next \u2014 small-VRAM code model",
    "It outperforms much larger models like DeepSeek V3.2 (37B active) on coding tasks at a fraction of the inference cost."
   ]
  },
  {
   "url": "https://blog.quintarelli.it/2026/04/qwen-e-il-coding-open-weight",
   "title": "Qwen e il coding (open weight)",
   "published_at": null,
   "organization": "Qwen",
   "model": null,
   "release_date": null,
   "parameters": "27-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks",
   "organization_evidence": [
    "# Qwen e il coding (open weight)",
    "Source: *[Qwen](https://qwen.ai/blog?id=qwen3.6-27b)*"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Qwen e il coding (open weight)",
    "Following the launch of Qwen3.6-Plus and Qwen3.6-35B-A3B, we are excited to open-source Qwen3.6-27B \u2014 a dense 27-billion-parameter multimodal model at the scale the community has been asking for most."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen e il coding (open weight)",
    "Still supporting both multimodal thinking and non-thinking modes, Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks."
   ]
  },
  {
   "url": "https://cloudy.social/models/zuck",
   "title": "Llama - Chief Open Source Evangelist at Cloudy Social | Meta AI's Liberation Project",
   "published_at": null,
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-09",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Free on Hugging Face, open weights, 600 billion parameters.",
   "organization_evidence": [
    "announcementApr 17, 2026",
    "Llama 5 shipped open weights eight days ago."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "announcementApr 17, 2026",
    "Llama 5 shipped open weights eight days ago."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "announcementApr 17, 2026",
    "Llama 5 shipped open weights eight days ago.",
    "600 billion parameters."
   ]
  },
  {
   "url": "https://bitsminds.com/news/google-gemma-4-open-model-multimodal-agentic-2026",
   "title": "Google Gemma 4: Open Models That Outperform Systems 20x Their Size",
   "published_at": "2026-04-19T20:32:25",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "Ranked third on Arena AI's open-source text leaderboard, featuring advanced reasoning, agentic capabilities, native multimodality, 2150 Codeforces ELO, 85.2% on MMLU Pro, and 89.2% on AIME 2026.",
   "organization_evidence": [
    "# Google Gemma 4: Open Models That Outperform Systems 20x Their Size",
    "Google DeepMind released Gemma 4 on April 2, a family of four open-weight multimodal models under Apache 2.0 that bring frontier-level reasoning and agentic capabilities to phones, edge devices, and developer environments."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4: Open Models That Outperform Systems 20x Their Size",
    "Google DeepMind released Gemma 4 on April 2, 2026, continuing its push to make frontier-grade AI accessible to developers without licensing restrictions."
   ],
   "parameters_evidence": [
    "# Google Gemma 4: Open Models That Outperform Systems 20x Their Size",
    "The family ships in four configurations: an Effective 2B (E2B) and Effective 4B (E4B) for mobile and IoT hardware, a 26B Mixture-of-Experts model optimized for low-latency edge inference, and a 31B dense model intended as a fine-tuning foundation for organizations that need maximum output quality."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4: Open Models That Outperform Systems 20x Their Size",
    "Google DeepMind released Gemma 4 on April 2, a family of four open-weight multimodal models under Apache 2.0 that bring frontier-level reasoning and agentic capabilities to phones, edge devices, and developer environments.",
    "Built directly on research from the Gemini 3 project, Gemma 4 is described as \"byte for byte, the most capable open models\" Google has released \u2014 a claim backed by the 31B dense variant ranking third on Arena AI's open-source text leaderboard and by benchmark scores that dramatically outpace previous Gemma generations.",
    "On MMLU Pro, the 31B model scores 85.2%, and on AIME 2026 it reaches 89.2%."
   ]
  },
  {
   "url": "https://kimi-k2.org/blog/39-kimi-k3-weights-live",
   "title": "Kimi K3 Hugging Face Download: Open Source Weights Live",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-scale open-weights sparse MoE model with 2.8T total parameters (~104B active per token), 1M context window, and native multimodal vision.",
   "organization_evidence": [
    "Kimi K3 Hugging Face Download: Open Source Weights Live",
    "Moonshot published the **model weights + technical report**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3 Hugging Face Download: Open Source Weights Live",
    "Good news: as of July 27, 2026, Kimi K3 open source weights are live."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3 Hugging Face Download: Open Source Weights Live",
    "**Bottom line:** July 27 delivered what the launch post promised\u2014**frontier-scale open weights**, not a consumer one-click install."
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/glm-5-1-code-arena-open-source-2026",
   "title": "GLM-5.1: First Open-Weight Model in Top 3 of Code Arena",
   "published_at": "2026-04-11T12:14:43",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "First open-weight model to break into Code Arena's top 3, posting a 1530 Elo score.",
   "organization_evidence": [
    "GLM-5.1: First Open-Weight Model in Top 3 of Code Arena",
    "[Z.ai](http://Z.ai) released GLM-5.1 on April 7, 2026, specifically targeting agentic engineering: long-running coding tasks, autonomous optimization loops, and complex software projects that require sustained effort across hundreds of iterations."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.1: First Open-Weight Model in Top 3 of Code Arena",
    "[Z.ai](http://Z.ai) released GLM-5.1 on April 7, 2026, specifically targeting agentic engineering: long-running coding tasks, autonomous optimization loops, and complex software projects that require sustained effort across hundreds of iterations."
   ],
   "parameters_evidence": [
    "Architecture: 744B Parameters, Zero Nvidia Hardware",
    "The MoE architecture means GLM-5.1 has 754 billion total parameters but only activates 40 billion per token during inference."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.1: First Open-Weight Model in Top 3 of Code Arena",
    "GLM-5.1: The First Open-Weight Model to Break Into Code Arena's Top 3",
    "On April 10, 2026, [Z.ai](http://Z.ai)'s GLM-5.1 posted a 1530 Elo score on Code Arena \u2014 sitting third in the world, behind only Anthropic's claude-opus-4-6-thinking (1548) and claude-opus-4-6 (1542), ahead of every GPT and Gemini model on the board."
   ]
  },
  {
   "url": "https://aichina.news/blog/bridging-the-gpu-gap-minimax-m2-7-debuts-on-modelers-cn-i7kh5g",
   "title": "Bridging the GPU Gap: MiniMax M2.7 Debuts on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-06-18T11:52:31",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-18",
   "parameters": "2.7B",
   "context_window": null,
   "license": null,
   "significance": "Released under the Apache-2.0 licence, it allows for unrestricted commercial use and modification; with 2.7B parameters, it is significantly smaller than many competitors, making it easier to deploy on consumer-grade hardware; the model targets both Nvidia GPUs and Huawei Ascend NPUs, offering flexibility for developers in both major AI hardware ecosystems; hosted on the Modelers.cn platform, it benefits from the infrastructure of the Huawei Ascend AI ecosystem.",
   "organization_evidence": [
    "Bridging the GPU Gap: MiniMax M2.7 Debuts on Modelers.cn",
    "We have a fascinating new entry in the open-source LLM landscape today."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Bridging the GPU Gap: MiniMax M2.7 Debuts on Modelers.cn",
    "models/FlagRelease June 18, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Bridging the GPU Gap: MiniMax M2.7 Debuts on Modelers.cn",
    "Unlike many proprietary Chinese models that are locked down, this 2.7-billion-parameter language model is released under the permissive Apache-2.0 licence, making it a viable candidate for commercial projects."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Bridging the GPU Gap: MiniMax M2.7 Debuts on Modelers.cn",
    "- **Open and Commercial-Friendly:** Released under the Apache-2.0 licence, it allows for unrestricted commercial use and modification.",
    "- **Compact Size:** With 2.7B parameters, it is significantly smaller than many competitors, making it easier to deploy on consumer-grade hardware.",
    "- **Dual Hardware Compatibility:** The model targets both Nvidia GPUs and Huawei Ascend NPUs, offering flexibility for developers in both major AI hardware ecosystems.",
    "- **Community Support:** Hosted on the Modelers.cn platform, it benefits from the infrastructure of the Huawei Ascend AI ecosystem."
   ]
  },
  {
   "url": "https://tech-insider.org/ca/nvidia-nemotron-3-ultra-2026",
   "title": "NVIDIA Nemotron 3 Ultra: 550B Open AI Trails China [2026]",
   "published_at": "2026-07-06T00:32:32",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "~550B",
   "context_window": null,
   "license": null,
   "significance": "47.7 on the Artificial Analysis Intelligence Index \u2013 a clear win over every other American open-weight release",
   "organization_evidence": [
    "On June 4, 2026, NVIDIA quietly uploaded the weights for **NVIDIA Nemotron 3 Ultra** to Hugging Face \u2013 days after Jensen Huang teased the model from the Computex keynote stage in Taipei.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 4, 2026, NVIDIA quietly uploaded the weights for **NVIDIA Nemotron 3 Ultra** to Hugging Face \u2013 days after Jensen Huang teased the model from the Computex keynote stage in Taipei.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4"
   ],
   "parameters_evidence": [
    "At roughly 550 billion parameters, it is the largest and, by Artificial Analysis\u2019s measure, the most intelligent open-weight AI model ever released by a U.S. lab.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Ultra | ~550B | ~55B | Jun 4, 2026 (Computex) | Up to 1M | 47.7 (48.2 BF16) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nemotron 3 Ultra lands at 47.7 on the Artificial Analysis Intelligence Index \u2013 a clear win over every other American open-weight release, but a full six points behind China\u2019s Kimi K2.6, which sits at 53.9.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4"
   ]
  },
  {
   "url": "https://tech-insider.org/ca/nvidia-nemotron-3-ultra-2026",
   "title": "NVIDIA Nemotron 3 Ultra: 550B Open AI Trails China [2026]",
   "published_at": "2026-07-06T00:32:32",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Super",
   "release_date": "2026-03-11",
   "parameters": "120.6B",
   "context_window": "Up to 1M",
   "license": "Open weights",
   "significance": "36.0 on the Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "Nemotron 3 Super (120.6B) followed at GTC on March 11, 2026.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Origin | Type | AA Intelligence Index |",
    "| Nemotron 3 Super | USA (NVIDIA) | Open weights | 36.0 |"
   ],
   "model_evidence": [
    "Nemotron 3 Super (120.6B) followed at GTC on March 11, 2026.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Super | 120.6B | 12.7B | Mar 11, 2026 (GTC) | Up to 1M | 36.0 |"
   ],
   "release_date_evidence": [
    "Nemotron 3 Super (120.6B) followed at GTC on March 11, 2026.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Super | 120.6B | 12.7B | Mar 11, 2026 (GTC) | Up to 1M | 36.0 |"
   ],
   "parameters_evidence": [
    "Nemotron 3 Super (120.6B) followed at GTC on March 11, 2026.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Super | 120.6B | 12.7B | Mar 11, 2026 (GTC) | Up to 1M | 36.0 |"
   ],
   "context_window_evidence": [
    "It supports a context window of up to 1 million tokens, ships in both BF16 and NVFP4 weights, and exposes granular reasoning-budget controls so operators can dial the amount of \u201cthinking\u201d up or down at inference time.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Super | 120.6B | 12.7B | Mar 11, 2026 (GTC) | Up to 1M | 36.0 |"
   ],
   "license_evidence": [
    "Nemotron 3 Ultra lands at 47.7 on the Artificial Analysis Intelligence Index \u2013 a clear win over every other American open-weight release, but a full six points behind China\u2019s Kimi K2.6, which sits at 53.9.",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Origin | Type | AA Intelligence Index |",
    "| Nemotron 3 Super | USA (NVIDIA) | Open weights | 36.0 |"
   ],
   "significance_evidence": [
    "Here is the uncomfortable headline for U.S. AI policy. Nemotron 3 Ultra is comfortably the strongest American open-weight release to date, clearing Google\u2019s Gemma 4 31B (39.2), NVIDIA\u2019s own Nemotron 3 Super (36.0), and OpenAI\u2019s gpt-oss-120b (33.3).",
    "## NVIDIA Nemotron 3 Ultra: What Launched on June 4",
    "| Model | Total params | Active params | Released | Context | AA Intelligence Index |",
    "| Nemotron 3 Super | 120.6B | 12.7B | Mar 11, 2026 (GTC) | Up to 1M | 36.0 |"
   ]
  },
  {
   "url": "https://aichina.news/blog/allen-ais-stdmoe-1b4b-130b-lands-on-modelers-cn-a-sparse-moe-giant-m9wde7",
   "title": "Allen AI\u2019s StdMoE_1b4b_130B Lands on Modelers.cn: A Sparse MoE Giant That Packs a 130B Punch with Just 1B Active Parameters \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T11:22:56",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "130B total parameters (1B active)",
   "context_window": null,
   "license": null,
   "significance": "A sparse MoE with roughly 1 billion active parameters can match the text-generation quality of dense models with over 100 billion parameters, while staying fully open and commercially usable under the Apache-2.0 licence.",
   "organization_evidence": [
    "Allen AI\u2019s StdMoE_1b4b_130B Lands on Modelers.cn: A Sparse MoE Giant That Packs a 130B Punch with Just 1B Active Parameters",
    "Enter **StdMoE_1b4b_130B**, a sparsely\u2011activated Mixture\u2011of\u2011Experts (MoE) model from the Allen Institute for AI (Ai2), now available on Modelers.cn, the Huawei/Ascend AI ecosystem platform."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Allen AI\u2019s StdMoE_1b4b_130B Lands on Modelers.cn: A Sparse MoE Giant That Packs a 130B Punch with Just 1B Active Parameters",
    "models/allenai July 30, 2026 3 min read Auto-generated from Modelers",
    "It provides the name, licence (Apache\u20112.0), task type (text generation), and a pair of future\u2011dated timestamps (2026\u201106\u201104) that strongly suggest this is a placeholder or pre\u2011release entry rather than a finished release."
   ],
   "parameters_evidence": [
    "Allen AI\u2019s StdMoE_1b4b_130B Lands on Modelers.cn: A Sparse MoE Giant That Packs a 130B Punch with Just 1B Active Parameters",
    "Enter **StdMoE_1b4b_130B**, a sparsely\u2011activated Mixture\u2011of\u2011Experts (MoE) model from the Allen Institute for AI (Ai2), now available on Modelers.cn, the Huawei/Ascend AI ecosystem platform.",
    "What makes StdMoE_1b4b_130B stand out is its architecture."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Allen AI\u2019s StdMoE_1b4b_130B Lands on Modelers.cn: A Sparse MoE Giant That Packs a 130B Punch with Just 1B Active Parameters",
    "This model is designed to prove a bold thesis: a sparse MoE with roughly **1 billion active parameters** can match the text\u2011generation quality of dense models with over **100 billion parameters**, all while staying fully open and commercially usable under the Apache\u20112.0 licence."
   ]
  },
  {
   "url": "https://deepseekai.guide/models/deepseek-v3",
   "title": "DeepSeek V3: Architecture, Benchmarks & Legacy Status",
   "published_at": "2026-04-24T18:38:42",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics",
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "parameters_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://deepseekai.guide/models/deepseek-v3",
   "title": "DeepSeek V3: Architecture, Benchmarks & Legacy Status",
   "published_at": "2026-04-24T18:38:42",
   "organization": "DeepSeek",
   "model": "deepseek-v4-flash",
   "release_date": "2026-04-24",
   "parameters": "284B total / 13B active",
   "context_window": "1M tokens",
   "license": "MIT",
   "significance": null,
   "organization_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "model_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "release_date_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "parameters_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "context_window_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "license_evidence": [
    "DeepSeek V4 launched on April 24, 2026, shipping as two open-weight MoE tiers \u2014 `deepseek-v4-pro` (1.6T total / 49B active) and `deepseek-v4-flash` (284B / 13B active) \u2014 both under the MIT license with a 1M-token default context window.",
    "DeepSeek V3 Explained: The 671B MoE That Reset Open-Weight Economics"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://ddrinnova.com/en/blog/mistral-medium-35-open-weight-unified-model-2026",
   "title": "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
   "published_at": "2026-05-02T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Scores 77.6% on SWE-bench Verified, replaces reasoning, coding, and chat specialist models, introduces a 256,000-token context window, and is available under a Modified MIT license.",
   "organization_evidence": [
    "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "Mistral Medium 3.5 scores **77.6% on SWE-bench Verified**, the industry\u2019s most cited real-world coding benchmark."
   ]
  },
  {
   "url": "https://medium.com/@AiDocTakes/gemma-4-google-just-dropped-its-open-source-nuclear-option-and-community-went-feral-with-it-0a7af52c5dd4",
   "title": "Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
   "published_at": "2026-04-03T04:12:27",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3 billion effective parameters (5.1B total with embeddings)",
   "context_window": null,
   "license": null,
   "significance": "Supports 128k context windows and handles text, images, video with audio, and standalone audio inputs.",
   "organization_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Google released Gemma 4 in four sizes: `E2B (2.3B effective)`, `E4B (4.5B effective)`, `26B MoE (4B active/26B total)`, and `31B dense`; all multimodal, all open, all Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Because right now, April 2nd 2026, the open model community just got its most complete single-day release ever."
   ],
   "parameters_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Google released Gemma 4 in four sizes: `E2B (2.3B effective)`, `E4B (4.5B effective)`, `26B MoE (4B active/26B total)`, and `31B dense`; all multimodal, all open, all Apache 2.0.",
    "The E2B has 2.3 billion effective parameters (5.1B total with embeddings) and the E4B has 4.5 billion effective (8B total)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Both support 128k context windows and both handle text, images, video with audio, and standalone audio inputs."
   ]
  },
  {
   "url": "https://medium.com/@AiDocTakes/gemma-4-google-just-dropped-its-open-source-nuclear-option-and-community-went-feral-with-it-0a7af52c5dd4",
   "title": "Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
   "published_at": "2026-04-03T04:12:27",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "4.5 billion effective (8B total)",
   "context_window": null,
   "license": null,
   "significance": "Supports 128k context windows and handles text, images, video with audio, and standalone audio inputs.",
   "organization_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Google released Gemma 4 in four sizes: `E2B (2.3B effective)`, `E4B (4.5B effective)`, `26B MoE (4B active/26B total)`, and `31B dense`; all multimodal, all open, all Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Because right now, April 2nd 2026, the open model community just got its most complete single-day release ever."
   ],
   "parameters_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Google released Gemma 4 in four sizes: `E2B (2.3B effective)`, `E4B (4.5B effective)`, `26B MoE (4B active/26B total)`, and `31B dense`; all multimodal, all open, all Apache 2.0.",
    "The E2B has 2.3 billion effective parameters (5.1B total with embeddings) and the E4B has 4.5 billion effective (8B total)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
    "Both support 128k context windows and both handle text, images, video with audio, and standalone audio inputs."
   ]
  },
  {
   "url": "https://medium.com/@AiDocTakes/gemma-4-google-just-dropped-its-open-source-nuclear-option-and-community-went-feral-with-it-0a7af52c5dd4",
   "title": "Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
   "published_at": "2026-04-03T04:12:27",
   "organization": "Google",
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   "parameters": "26 billion total parameters but only 4 billion active per token",
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    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
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   "url": "https://medium.com/@AiDocTakes/gemma-4-google-just-dropped-its-open-source-nuclear-option-and-community-went-feral-with-it-0a7af52c5dd4",
   "title": "Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
   "published_at": "2026-04-03T04:12:27",
   "organization": "Google",
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   "release_date": "2026-04-02",
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   "significance": "Scores 89.2% on AIME 2026 and 2150 Codeforces ELO",
   "organization_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
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   "release_date_evidence": [
    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
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    "# Gemma 4: Google Just Dropped Its Open Source Nuclear Option And Community Went Feral With It",
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   "url": "https://agenccy.ai/news/moonshot-releases-kimi-k3-2-8-trillion-parameter-open-model",
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   "organization_evidence": [
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   "release_date_evidence": [
    "# Moonshot Releases Kimi K3, a 2.8-Trillion-Parameter Model That Cracks the Global Top Three",
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   "parameters_evidence": [
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   "significance_evidence": [
    "# Moonshot Releases Kimi K3, a 2.8-Trillion-Parameter Model That Cracks the Global Top Three",
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   "url": "https://aipedia.wiki/tools/glm",
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    "# GLM (ChatGLM)",
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   "url": "https://techpillow.co/blog/minimax-m3-open-weight-frontier-model-2026",
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   "url": "https://developersdigest.tech/tools/deepseek-v4",
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  {
   "url": "https://ai-tldr.dev/releases/qwen-qwen3-6-27b",
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   "url": "https://masterai.blog/blog/meta-llama-4-open-source",
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   "url": "https://tea4tech.com/technology/google-releases-gemma-4-with-multimodal-and-on-device-agentic-ai",
   "title": "Google Releases Gemma 4 With Multimodal and On-Device Agentic AI",
   "published_at": "2026-04-06T14:36:08",
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   "significance": "The biggest, a 31B Dense model, currently ranks third on the global Arena AI open-model leaderboard.",
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   "url": "https://devplace.net/news/1432613b8f3f-what-is-kimi-k3-complete-2026-guide-to-moonshot-ai-s-open-source-model",
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  {
   "url": "https://chats-llm.com/en/blog/glm-5-2-release",
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  {
   "url": "https://benched.ai/companies/minimax",
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    "The **[MiniMax-M3](/models/minimax-m3)** open-weight frontier model arrived on **1 June 2026**, reporting 59.0% on SWE-Bench Pro with a 1-million-token context window and native multimodal image and video understanding."
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  {
   "url": "https://agentictribune.com/article/20260616-nvidia-releases-nemotron-3-ultra-open-weight-mixture-of-experts-llm-with-1-million-token-context",
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   "published_at": "2026-06-16T04:20:58",
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  {
   "url": "https://news.ycombinator.com/item?id=45993118",
   "title": "Olmo 3: Charting a path through the model flow to lead open-source AI",
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  },
  {
   "url": "https://neuronad.com/mistral-vs-llama",
   "title": "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
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    "## Complete Model\nComparison",
    "| Category | Meta Llama | Mistral AI |",
    "| Efficient (Medium) | Llama 4 Scout (109B total, 17B active, 16 experts) | Mistral Small 4 (119B total, 6B active, 128 experts) |"
   ],
   "model_evidence": [
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    "## Complete Model\nComparison",
    "| Category | Meta Llama | Mistral AI |",
    "| Efficient (Medium) | Llama 4 Scout (109B total, 17B active, 16 experts) | Mistral Small 4 (119B total, 6B active, 128 experts) |"
   ],
   "release_date_evidence": [
    "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
    "04 \u2014 Deep Dive",
    "### Mistral Small 4: Three Models in One",
    "Released March 16, 2026, Mistral Small 4 is perhaps the most elegant model in the open-source landscape."
   ],
   "parameters_evidence": [
    "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
    "## Complete Model\nComparison",
    "| Category | Meta Llama | Mistral AI |",
    "| Efficient (Medium) | Llama 4 Scout (109B total, 17B active, 16 experts) | Mistral Small 4 (119B total, 6B active, 128 experts) |"
   ],
   "context_window_evidence": [
    "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
    "## Complete Model\nComparison",
    "| Category | Meta Llama | Mistral AI |",
    "| Max Context | 10M tokens (Scout) | 256K tokens (Small 4, Codestral) |"
   ],
   "license_evidence": [
    "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
    "## Complete Model\nComparison",
    "| Category | Meta Llama | Mistral AI |",
    "| License | Llama Community License | Apache 2.0 (open models) |"
   ],
   "significance_evidence": [
    "Mistral vs Llama (2026): France vs Meta in the Open-Source AI Race",
    "04 \u2014 Deep Dive",
    "### Mistral Small 4: Three Models in One",
    "Released March 16, 2026, Mistral Small 4 is perhaps the most elegant model in the open-source landscape.",
    "It unifies three previously separate product lines into a single 119B-parameter MoE model: *Magistral* (reasoning), *Pixtral* (multimodal vision), and *Devstral* (agentic coding)."
   ]
  },
  {
   "url": "https://dev.to/techsifted/mistral-medium-35-review-a-128b-open-weight-model-with-a-coding-agent-that-opens-prs-for-you-5a0i",
   "title": "Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
   "published_at": "2026-05-03T15:21:04",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Scores 77.6% on SWE-Bench Verified and ships alongside Vibe, a cloud coding agent that submits pull requests directly to GitHub.",
   "organization_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input."
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
    "It scores 77.6% on SWE-Bench Verified \u2014 close but not ahead of Claude Sonnet 4.6 \u2014 and ships alongside Vibe, a cloud coding agent that submits pull requests directly to GitHub without you babysitting it."
   ]
  },
  {
   "url": "https://gemma4.run/hi/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-06T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "4 model sizes (E2B, E4B, 26B MoE, 31B Dense)",
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 open-source license, native support for four modalities (text, image, video, and audio), 256K context window, massive benchmark improvements including AIME 2026 math benchmark jumping to 89.2% for the 31B model, and over 400 million downloads by April 10.",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ]
  },
  {
   "url": "https://the-ledger.net/events/moonshot-kimi-k3-open-weights",
   "title": "Moonshot AI Publishes Full Kimi K3 Model Weights \u2014 The Ledger",
   "published_at": "2026-07-15T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion total parameters and 104 billion activated parameters per token",
   "context_window": null,
   "license": null,
   "significance": "expanded the scale at which a frontier-oriented model could be downloaded and independently hosted rather than accessed only through a vendor API",
   "organization_evidence": [
    "# Moonshot AI Publishes Full Kimi K3 Model Weights",
    "On July 27, 2026, Moonshot AI published the full downloadable weights for Kimi K3, a sparse mixture-of-experts model with approximately 2.8 trillion total parameters and 104 billion activated parameters per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot AI Publishes Full Kimi K3 Model Weights",
    "A ledger entry in the open source archive, dated 2026-07-27."
   ],
   "parameters_evidence": [
    "# Moonshot AI Publishes Full Kimi K3 Model Weights",
    "On July 27, 2026, Moonshot AI published the full downloadable weights for Kimi K3, a sparse mixture-of-experts model with approximately 2.8 trillion total parameters and 104 billion activated parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI Publishes Full Kimi K3 Model Weights",
    "## Why It Matters",
    "Kimi K3 expanded the scale at which a frontier-oriented model could be downloaded and independently hosted rather than accessed only through a vendor API."
   ]
  },
  {
   "url": "https://jangwook.net/en/blog/en/glm-5-mit-opensource-frontier-enterprise",
   "title": "GLM-5: MIT Open-Source Frontier Model for Enterprise",
   "published_at": "2026-03-14T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "744B Mixture-of-Experts architecture with 40B active parameters",
   "organization_evidence": [
    "GLM-5: MIT Open-Source Frontier Model for Enterprise",
    "Zhipu AI released GLM-5 with 744B MoE trained on Huawei Ascend without NVIDIA."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5: MIT Open-Source Frontier Model for Enterprise",
    "On February 13, 2026, Zhipu AI (creators of GLM series) released **GLM-5** under an MIT open-source license\u2014a watershed moment in enterprise AI."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5: MIT Open-Source Frontier Model for Enterprise",
    "Key breakthrough:",
    "- **744B Mixture-of-Experts** architecture with 40B active parameters"
   ]
  },
  {
   "url": "https://codersera.com/blog/best-open-source-llm-2026-llama-4-qwen-3-5-deepseek-v4-gemma-4-mistral/amp",
   "title": "Best Open-Source LLM 2026: Kimi, DeepSeek V4, Qwen, Llama 4",
   "published_at": "2026-05-03T08:27:05",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-12",
   "parameters": "95B / 2.4T (MoE)",
   "context_window": null,
   "license": null,
   "significance": "Highest neutral AA Index of any open model (~57.7).",
   "organization_evidence": [
    "Best Open-Source LLM 2026: Llama 4, DeepSeek V4, Qwen, Kimi",
    "Which open-source LLMs rank highest right now?",
    "| Rank | Model | Params (active / total) | License | Neutral score (AA Index) | Best for |",
    "| **NEW** | **Qwen3.8-2.4T-A95B** (Alibaba, opened Aug 12) | 95B / 2.4T (MoE) | Qwen3.8-Max License (revenue-gated, not OSI) | **~57.7** (would top this table) | Frontier agentic coding \u2014 datacentre-scale only |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open-Source LLM 2026: Llama 4, DeepSeek V4, Qwen, Kimi",
    "Which open-source LLMs rank highest right now?",
    "| Rank | Model | Params (active / total) | License | Neutral score (AA Index) | Best for |",
    "| **NEW** | **Qwen3.8-2.4T-A95B** (Alibaba, opened Aug 12) | 95B / 2.4T (MoE) | Qwen3.8-Max License (revenue-gated, not OSI) | **~57.7** (would top this table) | Frontier agentic coding \u2014 datacentre-scale only |",
    "**Update \u2014 August 14, 2026:** Alibaba reversed course and open-weighted the Qwen 3.8 generation: **Qwen3.8-2.4T-A95B** on August 12 \u2014 the first Qwen-Max-class open release, under a custom revenue-gated licence \u2014 and the dense, Apache-2.0 **Qwen3.8-27B** on August 14 (native vision-language, 262K context, ~17GB at Q4)."
   ],
   "parameters_evidence": [
    "Best Open-Source LLM 2026: Llama 4, DeepSeek V4, Qwen, Kimi",
    "Which open-source LLMs rank highest right now?",
    "| Rank | Model | Params (active / total) | License | Neutral score (AA Index) | Best for |",
    "| **NEW** | **Qwen3.8-2.4T-A95B** (Alibaba, opened Aug 12) | 95B / 2.4T (MoE) | Qwen3.8-Max License (revenue-gated, not OSI) | **~57.7** (would top this table) | Frontier agentic coding \u2014 datacentre-scale only |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open-Source LLM 2026: Llama 4, DeepSeek V4, Qwen, Kimi",
    "In the **Llama 4 vs DeepSeek V4 vs Qwen vs Kimi** decision, **Qwen3.8-2.4T-A95B** (opened August 12 under a custom licence) now posts the highest neutral AA Index of any open model (~57.7), **Kimi K2.6** (Moonshot) leads the rest at 54, **DeepSeek V4 Pro** wins agentic coding and ties the closed frontier on SWE-Bench, **GLM-5.1** has the cleanest MIT license, **Qwen3.8-27B** (August 14) is the best small dense coder under Apache-2.0, and **Llama 4 Scout** owns ultra-long context (10M tokens)."
   ]
  },
  {
   "url": "https://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium (latest)",
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": "262,144",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "model_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "release_date_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "license_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": "262,144",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "model_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "release_date_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "license_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small (latest)",
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": "256,000",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "model_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "release_date_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "license_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small 4",
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": "256,000",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [7](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "model_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [7](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "release_date_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [7](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [7](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "license_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [7](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://openaimpact.com/news/google-launches-gemma-4-open-ai-models-for-devices",
   "title": "Gemma 4 Open\u2011Source AI Models | Edge & Cloud Power",
   "published_at": "2026-04-07T13:37:11",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "2B, 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Effective 2B, Effective 4B, 26B MoE, and 31B Dense models offered under Apache 2.0 with multimodal I/O and context windows up to 256K tokens.",
   "organization_evidence": [
    "Google Unveils Gemma 4: Open\u2011Source AI Models Spanning Phones to High\u2011End GPUs",
    "**Mountain View, April 7, 2026,** Google announced today the release of Gemma 4, a new generation of open\u2011source AI models that promise to blur the line between cloud\u2011scale inference and edge\u2011device intelligence."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Unveils Gemma 4: Open\u2011Source AI Models Spanning Phones to High\u2011End GPUs",
    "Published: 07.04.2026 9:37 PM",
    "**Mountain View, April 7, 2026,** Google announced today the release of Gemma 4, a new generation of open\u2011source AI models that promise to blur the line between cloud\u2011scale inference and edge\u2011device intelligence."
   ],
   "parameters_evidence": [
    "Google Unveils Gemma 4: Open\u2011Source AI Models Spanning Phones to High\u2011End GPUs",
    "The four\u2011model family\u2014Effective 2B, Effective 4B, a 26\u2011billion\u2011parameter Mixture\u2011of\u2011Experts (MoE), and a dense 31\u2011billion\u2011parameter version\u2014are offered under the permissive Apache 2.0 licence, allowing anyone to modify, commercialise, and embed the technology wherever they see fit."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Unveils Gemma 4: Open\u2011Source AI Models Spanning Phones to High\u2011End GPUs",
    "The four\u2011model family\u2014Effective 2B, Effective 4B, a 26\u2011billion\u2011parameter Mixture\u2011of\u2011Experts (MoE), and a dense 31\u2011billion\u2011parameter version\u2014are offered under the permissive Apache 2.0 licence, allowing anyone to modify, commercialise, and embed the technology wherever they see fit."
   ]
  },
  {
   "url": "https://felloai.com/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "Tied GPT-5.5 on the toughest coding benchmark in the industry, at roughly 80% less per million tokens, and became the #1 open-weight model on the Artificial Analysis Intelligence Index.",
   "organization_evidence": [
    "**Kimi K2.6** is the latest flagship model from **Moonshot AI**, a Beijing-based lab that has been releasing open-source AI models at a pace closer to a startup than a state-backed research outfit."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It just tied **GPT-5.5** on the toughest coding benchmark in the industry, at roughly **80% less per million tokens**.",
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ]
  },
  {
   "url": "https://techstartups.com/2026/06/17/z-ais-open-source-glm-5-2-beats-gpt-5-5-on-coding-benchmarks-at-one-sixth-the-cost",
   "title": "Z.ai\u2019s open-source GLM-5.2 beats GPT-5.5 on coding benchmarks at one-sixth the cost",
   "published_at": "2026-06-17T16:49:31",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": "753-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Outperformed GPT-5.5 on several software engineering benchmarks, offers a 1-million-token context window, and is available under an unrestricted MIT license.",
   "organization_evidence": [
    "# Z.ai\u2019s open-source GLM-5.2 beats GPT-5.5 on coding benchmarks at one-sixth the cost",
    "Today, Chinese AI startup Z.ai, formerly known as Zhipu AI, announced the release of GLM-5.2, a 753-billion-parameter open-weights large language model engineered for long-horizon autonomous coding and engineering tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Z.ai\u2019s open-source GLM-5.2 beats GPT-5.5 on coding benchmarks at one-sixth the cost",
    "Posted On June 17, 2026"
   ],
   "parameters_evidence": [
    "The 753-billion-parameter model outperformed GPT-5.5 on several software engineering benchmarks, offers a 1-million-token context window, and is available under an unrestricted MIT license.",
    "# Z.ai\u2019s open-source GLM-5.2 beats GPT-5.5 on coding benchmarks at one-sixth the cost",
    "Today, Chinese AI startup Z.ai, formerly known as Zhipu AI, announced the release of GLM-5.2, a 753-billion-parameter open-weights large language model engineered for long-horizon autonomous coding and engineering tasks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Z.ai\u2019s open-source GLM-5.2 beats GPT-5.5 on coding benchmarks at one-sixth the cost",
    "The 753-billion-parameter model outperformed GPT-5.5 on several software engineering benchmarks, offers a 1-million-token context window, and is available under an unrestricted MIT license."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/04/14/nvidia-and-the-university-of-maryland-researchers-released-audio-flamingo-next-af-next-a-super-powerful-and-open-large-audio-language-model",
   "title": "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
   "published_at": "2026-04-14T08:24:23",
   "organization": "NVIDIA and the University of Maryland",
   "model": "Audio Flamingo Next (AF-Next)",
   "release_date": "2026-04",
   "parameters": "7B",
   "context_window": "128k tokens",
   "license": "fully open",
   "significance": "AF-Next achieves 73.9 on LongAudioBench outperforming Gemini 2.5 Pro and Audio Flamingo 3, and 58.7 on MMAU-Pro surpassing Gemini-2.5-Pro.",
   "organization_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "NVIDIA and the University of Maryland researchers are now taking a direct swing at that gap."
   ],
   "model_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "The research team have released **Audio Flamingo Next (AF-Next)**, the most capable model in the Audio Flamingo series and a fully open Large Audio-Language Model (LALM) trained on internet-scale audio data."
   ],
   "release_date_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "[![NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model](https://www.marktechpost.com/wp-content/uploads/2026/04/blog-36-696x497.png)](https://www.marktechpost.com/wp-content/uploads/2026/04/blog-36.png)"
   ],
   "parameters_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "Third is the **LLM backbone**: Qwen-2.5-7B, a decoder-only causal model with 7B parameters, 36 transformer layers, and 16 attention heads, with context length extended from 32k to 128k tokens through additional long-context training."
   ],
   "context_window_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "Third is the **LLM backbone**: Qwen-2.5-7B, a decoder-only causal model with 7B parameters, 36 transformer layers, and 16 attention heads, with context length extended from 32k to 128k tokens through additional long-context training."
   ],
   "license_evidence": [
    "NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model",
    "The research team have released **Audio Flamingo Next (AF-Next)**, the most capable model in the Audio Flamingo series and a fully open Large Audio-Language Model (LALM) trained on internet-scale audio data."
   ],
   "significance_evidence": [
    "Benchmark Results: Strong Across the Board",
    "On the more challenging MMAU-Pro benchmark, AF-Next-Think (58.7) surpasses the closed-source Gemini-2.5-Pro (57.4).",
    "On LongAudioBench, AF-Next-Instruct achieves 73.9, outperforming both Audio Flamingo 3 (68.6) and the closed-source Gemini 2.5 Pro (60.4)."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/Olmo-3-7B-RLZero-Mix",
   "title": "allenai/Olmo-3-7B-RLZero-Mix \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "7.0B",
   "context_window": null,
   "license": null,
   "significance": "fine-tuned with reinforcement learning for improved instruction following and task completion",
   "organization_evidence": [
    "Modelers",
    "# allenai / Olmo-3-7B-RLZero-Mix",
    "Olmo-3-7B-RLZero-Mix is a 7-billion-parameter open-source language model from Allen AI, fine-tuned with reinforcement learning for improved instruction following and task completion."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Downloads**: 2 | **License**: Apache 2.0",
    "**Created**: 2026-04-12 | **Updated**: 2026-04-12"
   ],
   "parameters_evidence": [
    "Parameters",
    "7.0B"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Olmo-3-7B-RLZero-Mix is a 7-billion-parameter open-source language model from Allen AI, fine-tuned with reinforcement learning for improved instruction following and task completion."
   ]
  },
  {
   "url": "https://harrisonaix.com/blog/deepseek-v4-model-release",
   "title": "DeepSeek V4: China's 1M Context Open Model",
   "published_at": "2026-04-26T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-source SOTA in agentic coding, world-class reasoning in Math, STEM, and Code, and rich world knowledge.",
   "organization_evidence": [
    "Tech Trends",
    "# DeepSeek V4: China's 1M Context Open Model",
    "That moment arrived on April 24, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Tech Trends",
    "# DeepSeek V4: China's 1M Context Open Model",
    "That moment arrived on April 24, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Tech Trends",
    "## What DeepSeek V4 Actually Is",
    "[DeepSeek\u2019s tech report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) positions V4-Pro as:"
   ]
  },
  {
   "url": "http://rits.shanghai.nyu.edu/ai/qwen3-6-35b-a3b-alibaba-open-sources-a-frontier-class-agentic-coder",
   "title": "Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "35-billion-parameter (3B active)",
   "context_window": null,
   "license": null,
   "significance": "Posts frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026.",
   "organization_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation."
   ],
   "parameters_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation.",
    "Released under Apache 2.0 on Hugging Face alongside the proprietary Qwen3.6-Plus API model, the 35-billion-parameter Mixture-of-Experts model activates just 3B parameters per token while posting frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6-35B-A3B: Alibaba Open-Sources a Frontier-Class Agentic Coder",
    "**On April 2, 2026, Alibaba\u2019s Qwen team open-sourced Qwen3.6-35B-A3B** \u2014 the first open-weight variant of the Qwen3.6 generation.",
    "Released under Apache 2.0 on Hugging Face alongside the proprietary Qwen3.6-Plus API model, the 35-billion-parameter Mixture-of-Experts model activates just 3B parameters per token while posting frontier-level scores on agentic coding and reasoning benchmarks, including 73.4 on SWE-bench Verified and 92.7 on AIME 2026."
   ]
  },
  {
   "url": "https://diaryofatoken.com/en/article/google-gemma-4-12b-open-model-laptop",
   "title": "Google releases Gemma 4 12B, an open multimodal model sized for a 16 GB laptop",
   "published_at": "2026-06-04T08:30:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "Native audio understanding in a mid-sized Gemma model, encoder-free architecture, and scoring 77.2% on MMLU Pro, 78.8% on GPQA Diamond, and 72.0% on LiveCodeBench v6.",
   "organization_evidence": [
    "# Google releases Gemma 4 12B, an open multimodal model sized for a 16 GB laptop",
    "Google released Gemma 4 12B on June 3, the mid-sized variant of its open model family, under an Apache 2.0 license and with the stated goal of running on a laptop with 16 GB of memory \u2014either dedicated graphics memory or the unified memory found on Apple Silicon Macs [1]."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google releases Gemma 4 12B, an open multimodal model sized for a 16 GB laptop",
    "Google released Gemma 4 12B on June 3, the mid-sized variant of its open model family, under an Apache 2.0 license and with the stated goal of running on a laptop with 16 GB of memory \u2014either dedicated graphics memory or the unified memory found on Apple Silicon Macs [1]."
   ],
   "parameters_evidence": [
    "# Google releases Gemma 4 12B, an open multimodal model sized for a 16 GB laptop",
    "The 12-billion-parameter variant ships under an Apache 2.0 license with open weights, native text, image and audio support, and a stated goal of running on consumer hardware without the cloud.",
    "Google released Gemma 4 12B on June 3, the mid-sized variant of its open model family, under an Apache 2.0 license and with the stated goal of running on a laptop with 16 GB of memory \u2014either dedicated graphics memory or the unified memory found on Apple Silicon Macs [1]."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google releases Gemma 4 12B, an open multimodal model sized for a 16 GB laptop",
    "The novelty is twofold.",
    "First, Gemma 4 12B is the first time Google has built native audio understanding into a mid-sized Gemma, on top of the text and image inputs that earlier versions already supported [1].",
    "Second, the architecture drops the usual external encoders for image and audio: both modalities are projected directly into the same token space as text [1].",
    "Gemma 4 12B scores 77.2% on MMLU Pro \u2014a *benchmark*, or standardized test, that measures general knowledge and reasoning\u2014, 78.8% on GPQA Diamond, and 72.0% on LiveCodeBench v6, the reference exam for coding tasks [2]."
   ]
  },
  {
   "url": "https://blogarama.com/technology-blogs/1458172-geek-realm-hub-blog/78426804-moonshot-unveils-kimi-analysis-chinaas-28t-open-weight-model",
   "title": "Moonshot AI unveils Kimi K3: Analysis of China\u00e2\u20ac\u2122s 2.8T open-weight model",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "largest open-weight AI system in the world; benchmark results outperformed leading closed U.S. models including Anthropic's Claude Opus 4.8 and OpenAI's GPT-5.5; top spot on LMArena's Frontend Code Arena",
   "organization_evidence": [
    "The post Moonshot AI unveils Kimi K3: Analysis of China\u00e2\u20ac\u2122s 2.8T open-weight model",
    "Chinese startup [Moonshot](https://app-test.blogarama.com/search-posts/?search=moonshot) AI released [Kimi](https://app-test.blogarama.com/search-posts/?search=kimi) K3 on July 16, a 2.8 trillion-parameter [Model](https://app-test.blogarama.com/search-posts/?search=model) that is now the largest open-weight AI system in the world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The post Moonshot AI unveils Kimi K3: Analysis of China\u00e2\u20ac\u2122s 2.8T open-weight model",
    "Chinese startup [Moonshot](https://app-test.blogarama.com/search-posts/?search=moonshot) AI released [Kimi](https://app-test.blogarama.com/search-posts/?search=kimi) K3 on July 16, a 2.8 trillion-parameter [Model](https://app-test.blogarama.com/search-posts/?search=model) that is now the largest open-weight AI system in the world."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The post Moonshot AI unveils Kimi K3: Analysis of China\u00e2\u20ac\u2122s 2.8T open-weight model",
    "Chinese startup [Moonshot](https://app-test.blogarama.com/search-posts/?search=moonshot) AI released [Kimi](https://app-test.blogarama.com/search-posts/?search=kimi) K3 on July 16, a 2.8 trillion-parameter [Model](https://app-test.blogarama.com/search-posts/?search=model) that is now the largest open-weight AI system in the world.",
    "Backed by Alibaba, Tencent and Meituan, the Beijing lab has built a system whose benchmark results have outperformed the leading closed U.S. models, including Anthropic\u00e2\u20ac\u2122s Claude Opus 4.8 and OpenAI\u00e2\u20ac\u2122s GPT-5.5, trailing only the newest Claude Fable 5 and GPT-5.6 Sol.",
    "The model also took the top spot on LMArena\u00e2\u20ac\u2122s Frontend Code Arena, surpassing Fable 5 in that specific benchmark."
   ]
  },
  {
   "url": "https://felloai.com/it/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "A coding-first frontier model with a Mixture-of-Experts design featuring 744 billion total parameters (40 billion active), a 1-million-token context window, and an MIT license.",
   "organization_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** is the latest flagship large language model from **Zhipu AI**, a Beijing-based lab that has become one of China\u2019s most aggressive open-weight model publishers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "parameters_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/nvidia-alpamayo-2-launches-a-free-robotaxi-brain-in-2026",
   "title": "Nvidia Alpamayo 2 Launches a Free Robotaxi Brain in 2026",
   "published_at": "2026-06-02T07:00:38",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "32B",
   "context_window": null,
   "license": null,
   "significance": "Alpamayo 2 Super is a 32-billion-parameter reasoning-based vision language action (VLA) model, the new flagship of Nvidia's open Alpamayo family for autonomous vehicle development, aimed at spatial understanding and rare, long-tail situations with 360-degree perception and a 'chain of causation' reasoning trace for safety documentation.",
   "organization_evidence": [
    "Model Release",
    "# Nvidia Alpamayo 2 Launches a Free Robotaxi Brain in 2026",
    "Nvidia's Alpamayo 2 Super is a 32B open reasoning model for level 4 robotaxis, free on Hugging Face this summer, turning autonomy into a commodity."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Nvidia Alpamayo 2 Launches a Free Robotaxi Brain in 2026",
    "On June 1 at GTC Taipei, Jensen Huang unveiled Alpamayo 2 Super, a 32-billion-parameter reasoning model built for level 4 robotaxis, and said the weights will land free on Hugging Face this summer."
   ],
   "parameters_evidence": [
    "Model Release",
    "# Nvidia Alpamayo 2 Launches a Free Robotaxi Brain in 2026",
    "Nvidia's Alpamayo 2 Super is a 32B open reasoning model for level 4 robotaxis, free on Hugging Face this summer, turning autonomy into a commodity."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "## What Actually Happened",
    "Alpamayo 2 Super is a **32-billion-parameter** reasoning-based vision language action (VLA) model, the new flagship of Nvidia's open Alpamayo family for autonomous vehicle development."
   ]
  },
  {
   "url": "https://launchberg.com/alibaba-qwen-3-5",
   "title": "Qwen 3.5: Open-Source 397B That Overdelivers | Launchberg",
   "published_at": "2026-02-16T10:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": "397B",
   "context_window": null,
   "license": null,
   "significance": "Outperforms Qwen3-Max across multiple reasoning and coding benchmarks and claims competitive results against GPT-5.2, Claude Opus 4.5, and Gemini 3 Pro with a SWE-bench Verified score of 76.4 percent and a LiveCodeBench v6 score of 83.6.",
   "organization_evidence": [
    "# Qwen 3.5: Open-Source 397B That Overdelivers",
    "Alibaba's Qwen team releases Qwen 3.5 \u2014 a 397-billion-parameter mixture-of-experts model under Apache 2.0 that activates only 17B parameters per token, supports 201 languages, and claims competitive performance against GPT-5.2 and Claude Opus 4.5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen 3.5: Open-Source 397B That Overdelivers",
    "Released on February 16, 2026, under the Apache 2.0 license, the model represents one of the most capable open-weight LLMs available today \u2014 and its benchmark results put it in direct competition with proprietary models from OpenAI, Anthropic, and Google that cost significantly more to run."
   ],
   "parameters_evidence": [
    "# Qwen 3.5: Open-Source 397B That Overdelivers",
    "Alibaba's Qwen team releases Qwen 3.5 \u2014 a 397-billion-parameter mixture-of-experts model under Apache 2.0 that activates only 17B parameters per token, supports 201 languages, and claims competitive performance against GPT-5.2 and Claude Opus 4.5.",
    "| Spec | 397B-A17B | 122B-A10B | 27B | 9B | 4B |",
    "| Total params | 397B | 122B | 27B | 9B | 4B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen 3.5: Open-Source 397B That Overdelivers",
    "According to Alibaba's published evaluations, Qwen3.5-397B-A17B outperforms Qwen3-Max \u2014 a model with over a trillion parameters \u2014 across multiple reasoning and coding benchmarks.",
    "On external benchmarks, the model claims competitive results against GPT-5.2, Claude Opus 4.5, and [Gemini 3 Pro](/google-gemini-3-1-flash-lite/).",
    "The SWE-bench Verified score of 76.4 percent places it in the same tier as frontier proprietary models for real-world software engineering tasks, while a LiveCodeBench v6 score of 83.6 represents near-human performance on competitive programming problems."
   ]
  },
  {
   "url": "https://groundtruth.day/news/mistral-shipped-a-safety-classifier-that-takes-its-policy-as-a-question.html",
   "title": "Mistral Shipped an Open-Weight Safety Judge That Takes Its Policy as a Question",
   "published_at": "2026-08-04T22:35:32",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "3 billion parameters",
   "context_window": null,
   "license": null,
   "significance": "It scores against ten open guard-model baselines from 4B up to OpenAI's 20B GPT-OSS-Safeguard - the comparison behind the \"up to 7x its size\" headline.",
   "organization_evidence": [
    "Mistral Shipped an Open-Weight Safety Judge That Takes Its Policy as a Question",
    "Released August 4, 2026 by Mistral AI, as what the company calls an inaugural member of the Open Secure AI Alliance with NVIDIA."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Shipped an Open-Weight Safety Judge That Takes Its Policy as a Question",
    "News \u00b7 [2026-08-04](2026-08-04.html)",
    "Mistral released Shieldstral 1.0 3B on August 4, an open-weight multimodal safety classifier that takes its moderation policy as a plain-language question at inference time rather than as a fixed taxonomy baked into its weights."
   ],
   "parameters_evidence": [
    "Mistral Shipped an Open-Weight Safety Judge That Takes Its Policy as a Question",
    "Roughly 3 billion parameters, Apache 2.0 weights, twelve supported languages, recommended operating context of 32k tokens, single 16GB NVIDIA GPU for BF16."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Shipped an Open-Weight Safety Judge That Takes Its Policy as a Question",
    "It scores against ten open guard-model baselines from 4B up to OpenAI's 20B GPT-OSS-Safeguard - the comparison behind the \"up to 7x its size\" headline."
   ]
  },
  {
   "url": "https://mayhemcode.com/2026/04/google-gemma-4-release-apache-20.html",
   "title": "Google Gemma 4 Release: Apache 2.0 License Explained",
   "published_at": "2026-04-03T08:30:00",
   "organization": "Google",
   "model": "Gemma 4 E2B",
   "release_date": "2026-04-02",
   "parameters": "Effective 2B",
   "context_window": "128K tokens",
   "license": "Apache 2.0 license",
   "significance": "Designed for mobile devices and IoT hardware, running completely offline on smartphones, Raspberry Pi boards, and Jetson Nano modules with native support for images, video, and audio input.",
   "organization_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained"
   ],
   "release_date_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "E2B and E4B stand for \u201cEffective 2B\u201d and \u201cEffective 4B,\u201d and \u201ceffective\u201d here refers to the active parameter count during inference, not the total model footprint."
   ],
   "context_window_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "The context window is 128K tokens."
   ],
   "license_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "These are designed for mobile devices and IoT hardware."
   ]
  },
  {
   "url": "https://mayhemcode.com/2026/04/google-gemma-4-release-apache-20.html",
   "title": "Google Gemma 4 Release: Apache 2.0 License Explained",
   "published_at": "2026-04-03T08:30:00",
   "organization": "Google",
   "model": "Gemma 4 E4B",
   "release_date": "2026-04-02",
   "parameters": "Effective 4B",
   "context_window": "128K tokens",
   "license": "Apache 2.0 license",
   "significance": "Designed for mobile devices and IoT hardware, running completely offline on smartphones, Raspberry Pi boards, and Jetson Nano modules with native support for images, video, and audio input.",
   "organization_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained"
   ],
   "release_date_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "E2B and E4B stand for \u201cEffective 2B\u201d and \u201cEffective 4B,\u201d and \u201ceffective\u201d here refers to the active parameter count during inference, not the total model footprint."
   ],
   "context_window_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "The context window is 128K tokens."
   ],
   "license_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "These are designed for mobile devices and IoT hardware."
   ]
  },
  {
   "url": "https://mayhemcode.com/2026/04/google-gemma-4-release-apache-20.html",
   "title": "Google Gemma 4 Release: Apache 2.0 License Explained",
   "published_at": "2026-04-03T08:30:00",
   "organization": "Google",
   "model": "Gemma 4 26B Mixture of Experts",
   "release_date": "2026-04-02",
   "parameters": "26B (3.8B active)",
   "context_window": null,
   "license": "Apache 2.0 license",
   "significance": "Achieved #6 on the Arena AI text leaderboard and scored 79.2% on the GPQA Diamond benchmark.",
   "organization_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained"
   ],
   "release_date_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "The 26B Mixture of Experts model activates only 3.8 billion of its 26 billion parameters during inference."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "The 26B MoE is at #6.",
    "The 26B MoE scored 79.2% on the same test."
   ]
  },
  {
   "url": "https://mayhemcode.com/2026/04/google-gemma-4-release-apache-20.html",
   "title": "Google Gemma 4 Release: Apache 2.0 License Explained",
   "published_at": "2026-04-03T08:30:00",
   "organization": "Google",
   "model": "Gemma 4 31B Dense",
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": "256K tokens",
   "license": "Apache 2.0 license",
   "significance": "Ranked #3 among all open models on the Arena AI text leaderboard and scored 85.7% on Artificial Analysis\u2019s GPQA Diamond benchmark.",
   "organization_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "model_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained"
   ],
   "release_date_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained"
   ],
   "context_window_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "The 256K context window on the large models gets mentioned in most coverage but I don\u2019t think people fully think through what it enables."
   ],
   "license_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On April 2, Google released Gemma 4 under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "Google Gemma 4 Release: Apache 2.0 License Explained",
    "On the Arena AI text leaderboard as of this week, the 31B Dense is ranked #3 among all open models in the world.",
    "On Artificial Analysis\u2019s GPQA Diamond benchmark, which tests scientific reasoning, the 31B Dense scored 85.7%."
   ]
  },
  {
   "url": "https://how2shout.com/ai/moonshot-kimi-k3-open-weight-model.html",
   "title": "Moonshot Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight AI Model",
   "published_at": "2026-07-28T15:43:15",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion total parameters",
   "context_window": "1-million-token",
   "license": "Moonshot\u2019s own licence",
   "significance": "World's first openly released model in the \u201c3-trillion-parameter class\u201d with 2.5 times better scaling efficiency than its predecessor.",
   "organization_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Chinese AI startup **Moonshot AI** has released **Kimi K3** \u2014 full model weights, code, and a technical report \u2014 as an** open-weight download on Hugging Face**, letting researchers and companies run and build on it themselves."
   ],
   "model_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Chinese AI startup **Moonshot AI** has released **Kimi K3** \u2014 full model weights, code, and a technical report \u2014 as an** open-weight download on Hugging Face**, letting researchers and companies run and build on it themselves."
   ],
   "release_date_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Posted on 27 July, the model has** 2.8 trillion total parameters,** and Moonshot bills it as the world\u2019s first openly released model in the \u201c**3-trillion-parameter class**.\u201d"
   ],
   "parameters_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Posted on 27 July, the model has** 2.8 trillion total parameters,** and Moonshot bills it as the world\u2019s first openly released model in the \u201c**3-trillion-parameter class**.\u201d"
   ],
   "context_window_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "It handles **text** and **images** natively (there\u2019s a dedicated vision encoder, and it posts strong numbers on some video benchmarks too), and it supports a **1-million-token context window** \u2014 enough to hold an entire** large codebase or a stack of long documents** in working memory at once."
   ],
   "license_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Unlike** GPT-5.6 or Claude**, which you can only reach through a paid **API**, Kimi K3\u2019s weights are downloadable under **Moonshot\u2019s own licence** \u2014 so a company or government can run it on its own hardware, keep its data in-house, and **modify** it."
   ],
   "significance_evidence": [
    "Moonshot Open-Sources Kimi K3, a 2.8-Trillion-Parameter Model It Calls the First Open \u201c3T-Class\u201d AI",
    "Posted on 27 July, the model has** 2.8 trillion total parameters,** and Moonshot bills it as the world\u2019s first openly released model in the \u201c**3-trillion-parameter class**.\u201d",
    "It\u2019s built on two in-house techniques **Moonshot** calls **Kimi Delta Attention** and **Attention Residuals**, and the company claims roughly **2.5 times better scaling efficiency than its previous model**, Kimi K2."
   ]
  },
  {
   "url": "https://intuitionlabs.ai/articles/glm-4-6-open-source-coding-model?:~:text=GLM-4.6:%20An%20Open-_35%20min%20read%7CNext%20Article",
   "title": "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
   "published_at": "2025-10-17T12:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "744B-parameter (40B active)",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.8% on SWE-Bench Verified",
   "organization_evidence": [
    "# GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "The GLM-4.6 model (General Language Model, version 4.6) is an open-source AI system developed by Zhipu AI (now Z.ai) that specifically targets complex reasoning and coding tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Meanwhile, Z.ai itself released **GLM-5** on February 11, 2026 \u2014 a 744B-parameter (40B active) MoE model scoring 77.8% on SWE-Bench Verified ([[6]](https://huggingface.co/zai-org/GLM-5))."
   ],
   "parameters_evidence": [
    "# GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Meanwhile, Z.ai itself released **GLM-5** on February 11, 2026 \u2014 a 744B-parameter (40B active) MoE model scoring 77.8% on SWE-Bench Verified ([[6]](https://huggingface.co/zai-org/GLM-5))."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Meanwhile, Z.ai itself released **GLM-5** on February 11, 2026 \u2014 a 744B-parameter (40B active) MoE model scoring 77.8% on SWE-Bench Verified ([[6]](https://huggingface.co/zai-org/GLM-5))."
   ]
  },
  {
   "url": "https://codersera.com/blog/minimax-m3-release-date-whats-new-2026/amp",
   "title": "MiniMax M3 Release Date: When + What Shipped (2026)",
   "published_at": "2026-05-19T16:14:08",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "first open-weights model to combine reasoning and agent capabilities in a single release",
   "organization_evidence": [
    "MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "MiniMax M3 has shipped."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "Announced June 1, 2026 and live on Hugging Face by June 7, MiniMax positions it as the **first open-weights model to combine reasoning and agent capabilities** in a single release \u2014 framing shared by AI researcher [@arankomatsuzaki](https://x.com/arankomatsuzaki?ref=codersera.com) and cross-confirmed by MiniMax Head of Engineering [@SkylerMiao7](https://x.com/SkylerMiao7?ref=codersera.com)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "Announced June 1, 2026 and live on Hugging Face by June 7, MiniMax positions it as the **first open-weights model to combine reasoning and agent capabilities** in a single release \u2014 framing shared by AI researcher [@arankomatsuzaki](https://x.com/arankomatsuzaki?ref=codersera.com) and cross-confirmed by MiniMax Head of Engineering [@SkylerMiao7](https://x.com/SkylerMiao7?ref=codersera.com)."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/nvidias-550-billion-parameter-model-signals-a-shif-19752273",
   "title": "Nvidia's 550-Billion-Parameter Model Signals a Shift in Open-Weight AI Competition",
   "published_at": "2026-08-05T10:16:56",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Nemotron 3 Ultra scored 48.2 in its full-precision format and 47.7 in a more efficient 4-bit format on the Artificial Analysis Intelligence Index, scored 94.7% on the RULER benchmark at 1 million tokens, achieved 71.9% on SWE-bench Verified and 67.8% on SWE-bench Multilingual.",
   "organization_evidence": [
    "# Nvidia's 550-Billion-Parameter Model Signals a Shift in Open-Weight AI Competition",
    "Nvidia released Nemotron 3 Ultra, a 550-billion-parameter open-weight language model on June 4, 2026, available free on Hugging Face for anyone to download and deploy without restrictions."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Nvidia's 550-Billion-Parameter Model Signals a Shift in Open-Weight AI Competition",
    "Nvidia released Nemotron 3 Ultra, a 550-billion-parameter open-weight language model on June 4, 2026, available free on Hugging Face for anyone to download and deploy without restrictions."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Nvidia's 550-Billion-Parameter Model Signals a Shift in Open-Weight AI Competition",
    "On the Artificial Analysis Intelligence Index, a composite benchmark spanning reasoning, coding, knowledge, and agentic tasks, Nemotron 3 Ultra scored 48.2 in its full-precision format and 47.7 in a more efficient 4-bit format.",
    "- **Long-context retrieval:** Scored 94.7% on the RULER benchmark at 1 million tokens, the metric Nvidia emphasizes most since it directly demonstrates the hybrid architecture's design goal of cheap long-context reasoning.",
    "- **Coding tasks:** Achieved 71.9% on SWE-bench Verified, a real-world test of GitHub issue resolution, and 67.8% on SWE-bench Multilingual, showing competitive performance on programming across multiple languages."
   ]
  },
  {
   "url": "https://spectrumailab.com/blog/best-open-source-coding-model-2026",
   "title": "Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
   "published_at": "2026-06-15T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-source state-of-the-art in agentic coding",
   "organization_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- DeepSeek V4 (April 24, 2026) ships as V4 Pro and V4 Flash, with a 1M context window, open weights on Hugging Face, and dual Thinking / Non-Thinking modes."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- DeepSeek V4 (April 24, 2026) ships as V4 Pro and V4 Flash, with a 1M context window, open weights on Hugging Face, and dual Thinking / Non-Thinking modes."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- DeepSeek's official release calls V4 Pro open-source state-of-the-art in agentic coding, but publishes charts rather than a single reproducible score."
   ]
  },
  {
   "url": "https://spectrumailab.com/blog/best-open-source-coding-model-2026",
   "title": "Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
   "published_at": "2026-06-15T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59.0% on SWE-Bench Pro",
   "organization_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- MiniMax M3 (June 1, 2026) is the first open-weight model to combine frontier coding, up to 1M context, and native multimodality; MiniMax claims 59.0% on SWE-Bench Pro."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- MiniMax M3 (June 1, 2026) is the first open-weight model to combine frontier coding, up to 1M context, and native multimodality; MiniMax claims 59.0% on SWE-Bench Pro."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- MiniMax M3 (June 1, 2026) is the first open-weight model to combine frontier coding, up to 1M context, and native multimodality; MiniMax claims 59.0% on SWE-Bench Pro."
   ]
  },
  {
   "url": "https://spectrumailab.com/blog/best-open-source-coding-model-2026",
   "title": "Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
   "published_at": "2026-06-15T00:00:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "62.0 on Kimi Code Bench v2",
   "organization_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "This guide uses official sources only. I checked DeepSeek's release notes, MiniMax's announcement, and Moonshot's Kimi K2.7-Code model card directly."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- Kimi K2.7-Code (June 12, 2026) is a 1-trillion-parameter / 32B-active coding model built on Kimi K2.6, with a 256K context window and a Modified MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source Coding Model in 2026: DeepSeek V4 vs MiniMax M3 vs Kimi K2.7-Code",
    "- Kimi reports K2.7-Code scores 62.0 on its own Kimi Code Bench v2 and cuts thinking-token use about 30% versus K2.6."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/mistral-ai-releases-leanstral-1-5-with-119b-parameters-under-b10303d1",
   "title": "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
   "published_at": "2026-07-04T11:03:37",
   "organization": "Mistral AI",
   "model": "Leanstral 1.5",
   "release_date": "2026-07-02",
   "parameters": "119B",
   "context_window": "256,000-token",
   "license": "Apache-2.0",
   "significance": "saturates miniF2F, solves 587 of 672 PutnamBench problems, and sets new state-of-the-art scores on two algebra proof benchmarks",
   "organization_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API."
   ],
   "model_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API."
   ],
   "release_date_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "The model, [announced July 2 on Mistral\u2019s blog](https://mistral.ai/news/leanstral-1-5/), carries 119 billion total parameters in a sparse mixture-of-experts architecture but activates only approximately 6 billion parameters per token, making it deployable at a fraction of the compute cost implied by its total parameter count."
   ],
   "parameters_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API."
   ],
   "context_window_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "It supports a 256,000-token context window and is available at zero cost through Mistral\u2019s Labs tier API, its console playground, and its Vibe agent environment."
   ],
   "license_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API."
   ],
   "significance_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI, the Paris-based frontier AI company, has released Leanstral 1.5, a free open-source formal verification model that saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, and sets new state-of-the-art scores on two algebra proof benchmarks \u2014 all under an Apache-2.0 license with weights published on Hugging Face."
   ]
  },
  {
   "url": "https://meritforgeai.com/news/google-gemma-4-open-model-apache-license",
   "title": "Google Releases Gemma 4 Under Apache 2.0 \u2014 Open-Source AI Gets a Commercial-Grade Upgrade",
   "published_at": "2026-04-07T22:14:21",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B to 31B",
   "context_window": null,
   "license": null,
   "significance": "The 31B model jumped from 20.8% to 89.2% on the AIME math benchmark and from 29.1% to 80.0% on LiveCodeBench compared to Gemma 3.",
   "organization_evidence": [
    "# Google Releases Gemma 4 Under Apache 2.0 \u2014 Open-Source AI Gets a Commercial-Grade Upgrade",
    "Source: Google DeepMind / Multiple Sources",
    "Google DeepMind released Gemma 4 on April 2, 2026, and the combination of performance and licensing makes it one of the most significant open model releases of the year."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Releases Gemma 4 Under Apache 2.0 \u2014 Open-Source AI Gets a Commercial-Grade Upgrade",
    "Google DeepMind released Gemma 4 on April 2, 2026, and the combination of performance and licensing makes it one of the most significant open model releases of the year."
   ],
   "parameters_evidence": [
    "# Google Releases Gemma 4 Under Apache 2.0 \u2014 Open-Source AI Gets a Commercial-Grade Upgrade",
    "Google DeepMind released Gemma 4 on April 2, 2026, and the combination of performance and licensing makes it one of the most significant open model releases of the year.",
    "Built from the same research underlying Gemini 3, Gemma 4 ships in four sizes \u2014 from a 2B edge model to a 31B dense model \u2014 with context windows up to 256K tokens, native vision and audio processing, and fluency in over 140 languages."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Releases Gemma 4 Under Apache 2.0 \u2014 Open-Source AI Gets a Commercial-Grade Upgrade",
    "The 31B model jumped from 20.8% to 89.2% on the AIME math benchmark and from 29.1% to 80.0% on LiveCodeBench compared to Gemma 3."
   ]
  },
  {
   "url": "https://institutepm.com/knowledge-hub/kimi-k3-for-product-managers",
   "title": "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": "1 million tokens",
   "license": "Modified MIT \u2014 fully open for commercial use; attribution required at 100M+ MAU",
   "significance": "It is now the largest open-source model ever released and benchmarks show it matching or beating Claude Opus 4.8 and GPT-5.5 on coding and agentic tasks.",
   "organization_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Moonshot AI released Kimi K3 on July 16, 2026 \u2014 a 2.8 trillion parameter open-weight Mixture of Experts model with a 1 million token context window."
   ],
   "model_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Moonshot AI released Kimi K3 on July 16, 2026 \u2014 a 2.8 trillion parameter open-weight Mixture of Experts model with a 1 million token context window."
   ],
   "release_date_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Moonshot AI released Kimi K3 on July 16, 2026 \u2014 a 2.8 trillion parameter open-weight Mixture of Experts model with a 1 million token context window."
   ],
   "parameters_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Moonshot AI released Kimi K3 on July 16, 2026 \u2014 a 2.8 trillion parameter open-weight Mixture of Experts model with a 1 million token context window."
   ],
   "context_window_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Moonshot AI released Kimi K3 on July 16, 2026 \u2014 a 2.8 trillion parameter open-weight Mixture of Experts model with a 1 million token context window."
   ],
   "license_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "Kimi K3 is the latest model from Moonshot AI, a Beijing-based AI lab.",
    "Released on July 16, 2026, it is a 2.8 trillion parameter Mixture of Experts model with a 1 million token context window and an open-weight license (Modified MIT with an attribution requirement at scale above 100 million monthly active users)."
   ],
   "significance_evidence": [
    "Kimi K3 for Product Managers: What the World's Largest Open-Source Model Means for Your AI Stack",
    "It is now the largest open-source model ever released and benchmarks show it matching or beating Claude Opus 4.8 and GPT-5.5 on coding and agentic tasks."
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/glm-5-1-open-source-review-2026",
   "title": "GLM-5.1: #1 Open Source AI Model? Full Review (2026)",
   "published_at": "2026-04-08T12:31:23",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754 billion",
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4 on SWE-Bench Pro, topping the global leaderboard and outperforming proprietary models like GPT-5.4 and Claude Opus 4.6, with 8-hour autonomous coding capability.",
   "organization_evidence": [
    "GLM-5.1: #1 Open Source AI Model? Full Review (2026)",
    "On April 7, 2026, [Z.ai](http://Z.ai) (the company formerly known as Zhipu AI) released GLM-5.1 with a 58.4 score on SWE-Bench Pro, topping the global leaderboard and nudging past GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.1: #1 Open Source AI Model? Full Review (2026)",
    "On April 7, 2026, [Z.ai](http://Z.ai) (the company formerly known as Zhipu AI) released GLM-5.1 with a 58.4 score on SWE-Bench Pro, topping the global leaderboard and nudging past GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3."
   ],
   "parameters_evidence": [
    "GLM-5.1 Architecture: 754B Parameters, MoE, and No Nvidia",
    "**GLM-5.1 is built on a 754-billion parameter Mixture-of-Experts (MoE) architecture** with 40 billion active parameters per token, a 200,000 token context window, and the ability to generate up to 128,000 output tokens in a single response."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.1: #1 Open Source AI Model? Full Review (2026)",
    "On April 7, 2026, [Z.ai](http://Z.ai) (the company formerly known as Zhipu AI) released GLM-5.1 with a 58.4 score on SWE-Bench Pro, topping the global leaderboard and nudging past GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3."
   ]
  },
  {
   "url": "https://systems-analysis.ru/eng/Nemotron_(NVIDIA)",
   "title": "Nemotron (NVIDIA)",
   "published_at": "2026-07-10T00:00:00",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Super",
   "release_date": "2026-03-11",
   "parameters": "120.6B total / 12.7B active",
   "context_window": "1,048,576 tokens",
   "license": "NVIDIA Nemotron Open Model License",
   "significance": "Introduced LatentMoE, native Multi-Token Prediction, and mixed NVFP4 pre-training to the released Nemotron 3 line.",
   "organization_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron** is a family of open-weight [large language models](/eng/Large_language_model), multimodal models, reward models, retrieval models, and agent-oriented systems developed by NVIDIA.",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "model_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 11 March 2026 | **Nemotron 3 Super** | 120.6B total / 12.7B active; LatentMoE, native MTP, mixed NVFP4 pre-training; up to 1M context |",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "release_date_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 11 March 2026 | **Nemotron 3 Super** | 120.6B total / 12.7B active; LatentMoE, native MTP, mixed NVFP4 pre-training; up to 1M context |",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "parameters_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 11 March 2026 | **Nemotron 3 Super** | 120.6B total / 12.7B active; LatentMoE, native MTP, mixed NVFP4 pre-training; up to 1M context |",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "context_window_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 11 March 2026 | **Nemotron 3 Super** | 120.6B total / 12.7B active; LatentMoE, native MTP, mixed NVFP4 pre-training; up to 1M context |",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "license_evidence": [
    "Nemotron (NVIDIA)",
    "Licensing",
    "| Nemotron 3 Super | NVIDIA Nemotron Open Model License |",
    "Nemotron 3 Super was released on 11 March 2026."
   ],
   "significance_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 Super",
    "Nemotron 3 Super was released on 11 March 2026.",
    "It introduced three main technologies to the released Nemotron 3 line:",
    "- LatentMoE;",
    "- native Multi-Token Prediction;",
    "- mixed NVFP4 pre-training."
   ]
  },
  {
   "url": "https://systems-analysis.ru/eng/Nemotron_(NVIDIA)",
   "title": "Nemotron (NVIDIA)",
   "published_at": "2026-07-10T00:00:00",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Nano Omni",
   "release_date": "2026-04-28",
   "parameters": "30B-A3B-class",
   "context_window": "256,000-token",
   "license": "NVIDIA Open Model Agreement",
   "significance": "Extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.",
   "organization_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron** is a family of open-weight [large language models](/eng/Large_language_model), multimodal models, reward models, retrieval models, and agent-oriented systems developed by NVIDIA.",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "model_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "release_date_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 28 April 2026 | **Nemotron 3 Nano Omni** | Native text, image, video, and audio inputs; 30B-A3B-class language backbone; 256K context |",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "parameters_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 28 April 2026 | **Nemotron 3 Nano Omni** | Native text, image, video, and audio inputs; 30B-A3B-class language backbone; 256K context |",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "context_window_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "license_evidence": [
    "Nemotron (NVIDIA)",
    "Licensing",
    "| Nemotron 3 Nano Omni | NVIDIA Open Model Agreement |",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ],
   "significance_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron 3 Nano Omni**, released in April 2026, extends the Nano backbone with native text, image, video, and audio inputs and a 256,000-token multimodal context.[6]"
   ]
  },
  {
   "url": "https://systems-analysis.ru/eng/Nemotron_(NVIDIA)",
   "title": "Nemotron (NVIDIA)",
   "published_at": "2026-07-10T00:00:00",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Ultra",
   "release_date": "2026-06-04",
   "parameters": "550B total / 55B active",
   "context_window": "1,048,576 tokens",
   "license": "OpenMDW License Agreement 1.1",
   "significance": "Described as the largest and most capable model in the three-tier Nemotron 3 family.",
   "organization_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron** is a family of open-weight [large language models](/eng/Large_language_model), multimodal models, reward models, retrieval models, and agent-oriented systems developed by NVIDIA.",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "model_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 4 June 2026 | **Nemotron 3 Ultra** | 550B total / 55B active; LatentMoE, MTP, NVFP4 pre-training, multi-environment RLVR, MOPD; up to 1M context |",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "release_date_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 4 June 2026 | **Nemotron 3 Ultra** | 550B total / 55B active; LatentMoE, MTP, NVFP4 pre-training, multi-environment RLVR, MOPD; up to 1M context |",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "parameters_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 4 June 2026 | **Nemotron 3 Ultra** | 550B total / 55B active; LatentMoE, MTP, NVFP4 pre-training, multi-environment RLVR, MOPD; up to 1M context |",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "context_window_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 4 June 2026 | **Nemotron 3 Ultra** | 550B total / 55B active; LatentMoE, MTP, NVFP4 pre-training, multi-environment RLVR, MOPD; up to 1M context |",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "license_evidence": [
    "Nemotron (NVIDIA)",
    "Licensing",
    "| Nemotron 3 Ultra | OpenMDW License Agreement 1.1 |",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ],
   "significance_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "NVIDIA described Ultra, released on 4 June 2026, as the largest and most capable model in the three-tier Nemotron 3 family.[8]"
   ]
  },
  {
   "url": "https://systems-analysis.ru/eng/Nemotron_(NVIDIA)",
   "title": "Nemotron (NVIDIA)",
   "published_at": "2026-07-10T00:00:00",
   "organization": "NVIDIA",
   "model": "Nemotron-Labs-3-Puzzle-75B-A9B",
   "release_date": "2026-07-06",
   "parameters": "75.3B total / 9.3B active",
   "context_window": "1,048,576 tokens",
   "license": "OpenMDW License Agreement 1.1",
   "significance": "A compressed Super derivative optimized for interactive and high-concurrency deployment.",
   "organization_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron** is a family of open-weight [large language models](/eng/Large_language_model), multimodal models, reward models, retrieval models, and agent-oriented systems developed by NVIDIA.",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "model_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 6 July 2026 | **Nemotron-Labs-3-Puzzle-75B-A9B** | Deployment-oriented compression of Super; approximately 75.3B total / 9.3B active |",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "release_date_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 6 July 2026 | **Nemotron-Labs-3-Puzzle-75B-A9B** | Deployment-oriented compression of Super; approximately 75.3B total / 9.3B active |",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "parameters_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 6 July 2026 | **Nemotron-Labs-3-Puzzle-75B-A9B** | Deployment-oriented compression of Super; approximately 75.3B total / 9.3B active |",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "context_window_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "| 6 July 2026 | **Nemotron-Labs-3-Puzzle-75B-A9B** | Deployment-oriented compression of Super; approximately 75.3B total / 9.3B active |",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "license_evidence": [
    "Nemotron (NVIDIA)",
    "Licensing",
    "| Nemotron-Labs-3-Puzzle-75B-A9B | OpenMDW License Agreement 1.1 |",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ],
   "significance_evidence": [
    "Nemotron (NVIDIA)",
    "Nemotron 3 generation (2025\u20132026)",
    "**Nemotron-Labs-3-Puzzle-75B-A9B**, released on 6 July 2026, is a compressed Super derivative optimized for interactive and high-concurrency deployment.[7]"
   ]
  },
  {
   "url": "https://nerdleveltech.com/deepseek-v4-open-source-frontier-million-token-context",
   "title": "DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
   "published_at": "2026-05-02T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters, 49B active per token",
   "context_window": null,
   "license": null,
   "significance": "V4-Pro takes the open-weight crown on coding and competitive programming, scores 80.6% on SWE-bench Verified, and 93.5% on LiveCodeBench.",
   "organization_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "May 2, 2026",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123."
   ],
   "parameters_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123.",
    "| **DeepSeek V4-Pro** | 1.6T | 49B | 33T | 1M | 384K |",
    "A Two-Model Open-Weight Release Under MIT License",
    "The family contains two distinct Mixture-of-Experts (MoE) models:",
    "| Model | Total params | Active params | Pre-training tokens | Context | Max output |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "V4-Pro (at maximum reasoning effort) scores **80.6% on SWE-bench Verified** \u2014 statistically tied with the previous-generation Claude Opus 4.6 (80.8%) but **trailing the current frontier** Claude Opus 4.7 (87.6%) by 7 points and GPT-5.5 (~82.6% on vals.ai's leaderboard) by 2 points6789.",
    "On **LiveCodeBench** V4-Pro scores **93.5%**, leading Gemini 3.1 Pro (91.7%) and Claude Opus 4.6 (88.8%)7.",
    "How V4 Compares to the Open-Weight Field",
    "V4-Pro takes the open-weight crown on coding and competitive programming, while [GLM-5.1](/glm-5-1-open-source-beats-gpt-coding-benchmarks) still leads on SWE-Bench Pro at 58.4%."
   ]
  },
  {
   "url": "https://nerdleveltech.com/deepseek-v4-open-source-frontier-million-token-context",
   "title": "DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
   "published_at": "2026-05-02T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion total, 13 billion active",
   "context_window": "1 million-token",
   "license": "MIT license",
   "significance": "Undercuts every Western frontier-tier small model while scoring 91.6% on LiveCodeBench.",
   "organization_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123."
   ],
   "model_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123.",
    "A Two-Model Open-Weight Release Under MIT License",
    "The family contains two distinct Mixture-of-Experts (MoE) models:",
    "| Model | Total params | Active params | Pre-training tokens | Context | Max output |",
    "| **DeepSeek V4-Flash** | 284B | 13B | 32T | 1M | 384K |"
   ],
   "release_date_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "May 2, 2026",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123."
   ],
   "parameters_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123.",
    "A Two-Model Open-Weight Release Under MIT License",
    "The family contains two distinct Mixture-of-Experts (MoE) models:",
    "| Model | Total params | Active params | Pre-training tokens | Context | Max output |",
    "| **DeepSeek V4-Flash** | 284B | 13B | 32T | 1M | 384K |"
   ],
   "context_window_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "Both models support a **1 million-token context window** and **384,000-token maximum output**45.",
    "A Two-Model Open-Weight Release Under MIT License",
    "The family contains two distinct Mixture-of-Experts (MoE) models:",
    "| Model | Total params | Active params | Pre-training tokens | Context | Max output |",
    "| **DeepSeek V4-Flash** | 284B | 13B | 32T | 1M | 384K |"
   ],
   "license_evidence": [
    "# DeepSeek V4: Open-Weight Frontier at 1/7 the Cost",
    "On April 24, 2026, DeepSeek released **DeepSeek V4** as a preview \u2014 a two-model open-weight family shipped under the **MIT license**: **V4-Pro** (1.6 trillion total parameters, 49 billion active per token, pre-trained on 33 trillion tokens) and **V4-Flash** (284 billion total, 13 billion active)123.",
    "A Two-Model Open-Weight Release Under MIT License",
    "Both models are released under the **MIT license** and published as open weights on Hugging Face."
   ],
   "significance_evidence": [
    "API Pricing: The Cache-Hit Math and the May 31 Promo Deadline",
    "The V4-Flash numbers are even more striking. At **$0.14/$0.28**, V4-Flash undercuts every Western frontier-tier \"small\" model \u2014 GPT-5.4 Nano, Gemini 3.1 Flash, GPT-5.4 Mini, and Claude Haiku 4.5 \u2014 while still scoring **91.6% on LiveCodeBench** and shipping output at roughly **83.7 tokens per second** on DeepSeek's API23."
   ]
  },
  {
   "url": "https://udit.co/blog/google-deepmind-gemma-3-open-weights-multimodal",
   "title": "Google DeepMind Releases Gemma 3 with Open Weights and Multimodal Capabilities",
   "published_at": "2026-03-08T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": null,
   "parameters": "2B to 27B",
   "context_window": null,
   "license": null,
   "significance": "multimodal capabilities to open-weight models \u2014 handling text, images, and video",
   "organization_evidence": [
    "# Google DeepMind Releases Gemma 3 with Open Weights and Multimodal Capabilities",
    "Google DeepMind's Gemma 3 brings multimodal capabilities to open-weight models \u2014 handling text, images, and video in sizes from 2B to 27B parameters, all available free on HuggingFace."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Google DeepMind Releases Gemma 3 with Open Weights and Multimodal Capabilities",
    "Google DeepMind's Gemma 3 brings multimodal capabilities to open-weight models \u2014 handling text, images, and video in sizes from 2B to 27B parameters, all available free on HuggingFace."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google DeepMind Releases Gemma 3 with Open Weights and Multimodal Capabilities",
    "Google DeepMind's Gemma 3 brings multimodal capabilities to open-weight models \u2014 handling text, images, and video in sizes from 2B to 27B parameters, all available free on HuggingFace."
   ]
  },
  {
   "url": "https://dev.to/tony_dillard/what-is-kimi-k3-complete-2026-guide-to-moonshot-ais-open-source-model-565j",
   "title": "What Is Kimi K3? Complete 2026 Guide to Moonshot AI's Open Source Model",
   "published_at": "2026-07-28T04:11:30",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #1 overall in the Frontend Code Arena benchmark, solved ~2.8x more tasks per dollar than competitors on DeepSWE, and matched or exceeded GPT-5.6 Sol and Claude Fable 5 while being fully open-weight.",
   "organization_evidence": [
    "# What Is Kimi K3? Complete 2026 Guide to Moonshot AI's Open Source Model",
    "Kimi K3 is a frontier-class, open-source large language model built by Moonshot AI (\u6708\u4e4b\u6697\u9762)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What Is Kimi K3?",
    "Moonshot AI announced K3 on July 16, 2026, and released the open weights on July 27, 2026 \u2014 a deliberate five-day gap that let the inference ecosystem (vLLM, NVIDIA, AMD) prepare day-zero support."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## How Kimi K3 Compares to GPT and Claude",
    "It is the first open-source model to beat Claude and GPT in frontend coding benchmarks, and it costs significantly less per solved task.",
    "In anonymous blind tests on the Frontend Code Arena benchmark, K3 scored **1,679 points \u2014 ranking #1 overall**, ahead of Claude Fable 5 (1,631) and GPT-5.6 Sol (1,618)."
   ]
  },
  {
   "url": "https://eggstriker.com/en/blog/glm-5-2-cost-performance-vs-fable-5-2026",
   "title": "Why GLM-5.2 Is Suddenly Everywhere \u2014 and Why Its Cost-Performance Beats Claude Fable 5",
   "published_at": "2026-07-15T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 62.1 on SWE-bench Pro (versus GPT-5.5's 58.6) and ranked #2 globally on Code Arena among all available models worldwide (and #1 among open-source models).",
   "organization_evidence": [
    "What GLM-5.2 actually is, when it shipped, and why it suddenly exploded",
    "Zhipu AI was founded in 2019, spun out of Tsinghua University's NLP lab, and now operates internationally under the brand **Z.ai**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What GLM-5.2 actually is, when it shipped, and why it suddenly exploded",
    "**June 16\u201317, 2026**: API went live, and the model weights were fully open-sourced on Hugging Face and ModelScope under the **MIT license**, allowing free download, self-hosting, and commercial use"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What GLM-5.2 actually is, when it shipped, and why it suddenly exploded",
    "SWE-bench Pro: GLM-5.2 scores **62.1**, versus GPT-5.5's **58.6**",
    "On Code Arena, a blind-voted coding leaderboard with millions of real global users, GLM-5.2 ranks **#2 among all available models worldwide \u2014 behind only a Claude model going by the codename \"Fable-5\" \u2014 and #1 among open-source models**."
   ]
  },
  {
   "url": "https://sapling.ai/llm/minimax",
   "title": "The LLM Index: MiniMax | Sapling",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": "428B (23B active)",
   "context_window": null,
   "license": null,
   "significance": "It combines native image and video understanding, a one-million-token context window, sparse attention, computer use, coding, and long-horizon agent capabilities.",
   "organization_evidence": [
    "# LLM: MiniMax",
    "- **Developer** MiniMax"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# LLM: MiniMax",
    "MiniMax M3, released in June 2026, is a 428B-parameter mixture-of-experts model with about 23B active parameters."
   ],
   "parameters_evidence": [
    "# LLM: MiniMax",
    "MiniMax M3, released in June 2026, is a 428B-parameter mixture-of-experts model with about 23B active parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# LLM: MiniMax",
    "It combines native image and video understanding, a one-million-token context window, sparse attention, computer use, coding, and long-horizon agent capabilities."
   ]
  },
  {
   "url": "https://shaam.blog/articles/nvidia-alpamayo-2-super-commercial-autonomous-driving-2026",
   "title": "NVIDIA Alpamayo 2 Super: What Commercial Open-Weight Autonomous Driving Means for Builders in 2026",
   "published_at": "2026-08-05T16:47:07",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "34 billion",
   "context_window": null,
   "license": null,
   "significance": "Ranks first on LingoQA among ~40 models tested; outperforms Qwen2.5-VL 72B by 17.0 points, Gemini 2.5 Pro by 15.1 points, and GPT-4o by 23.2 points",
   "organization_evidence": [
    "What Is NVIDIA Alpamayo 2 Super?",
    "Alpamayo 2 Super is a 34-billion-parameter vision-language-action (VLA) foundation model designed for autonomous vehicle development."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is NVIDIA Alpamayo 2 Super?",
    "It was first announced at GTC Taipei on June 1, 2026, and released for commercial use on August 4, 2026."
   ],
   "parameters_evidence": [
    "What Is NVIDIA Alpamayo 2 Super?",
    "Alpamayo 2 Super is a 34-billion-parameter vision-language-action (VLA) foundation model designed for autonomous vehicle development."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **Benchmark claim:** Ranks first on LingoQA among ~40 models tested; outperforms Qwen2.5-VL 72B by 17.0 points, Gemini 2.5 Pro by 15.1 points, and GPT-4o by 23.2 points (NVIDIA's own testing)."
   ]
  },
  {
   "url": "https://presenc.ai/research/best-open-weight-reasoning-models-2026",
   "title": "Best Open-Weight Reasoning Models 2026",
   "published_at": "2026-05-23T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3-235B-A22B (Thinking)",
   "release_date": "2026-04",
   "parameters": "~235B MoE (~22B active)",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores.",
   "organization_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "model_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-235B-A22B (Thinking) | ~235B MoE (~22B active) | ~83.0 | ~71.1 | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "parameters_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-235B-A22B (Thinking) | ~235B MoE (~22B active) | ~83.0 | ~71.1 | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-235B-A22B (Thinking) | ~235B MoE (~22B active) | ~83.0 | ~71.1 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ]
  },
  {
   "url": "https://presenc.ai/research/best-open-weight-reasoning-models-2026",
   "title": "Best Open-Weight Reasoning Models 2026",
   "published_at": "2026-05-23T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3-32B (Thinking)",
   "release_date": "2026-04",
   "parameters": "~32B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores.",
   "organization_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "model_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-32B (Thinking) | ~32B | ~78.5 | ~66.4 | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "parameters_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-32B (Thinking) | ~32B | ~78.5 | ~66.4 | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-32B (Thinking) | ~32B | ~78.5 | ~66.4 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ]
  },
  {
   "url": "https://presenc.ai/research/best-open-weight-reasoning-models-2026",
   "title": "Best Open-Weight Reasoning Models 2026",
   "published_at": "2026-05-23T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3-14B (Thinking)",
   "release_date": "2026-04",
   "parameters": "~14B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores.",
   "organization_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "model_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-14B (Thinking) | ~14B | ~71.2 | ~58.0 | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "parameters_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-14B (Thinking) | ~14B | ~71.2 | ~58.0 | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-14B (Thinking) | ~14B | ~71.2 | ~58.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ]
  },
  {
   "url": "https://presenc.ai/research/best-open-weight-reasoning-models-2026",
   "title": "Best Open-Weight Reasoning Models 2026",
   "published_at": "2026-05-23T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3-8B (Thinking)",
   "release_date": "2026-04",
   "parameters": "~8B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores.",
   "organization_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "model_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-8B (Thinking) | ~8B | ~62.4 | ~52.1 | Apache 2.0 |"
   ],
   "release_date_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ],
   "parameters_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-8B (Thinking) | ~8B | ~62.4 | ~52.1 | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Open-Weight Reasoning Leaderboard (May 2026)",
    "| Qwen3-8B (Thinking) | ~8B | ~62.4 | ~52.1 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "2. Qwen3-Thinking variants (4B, 8B, 14B, 32B, 235B-A22B MoE) released April 2026 lead the mid-size open-weight reasoning leaderboard with strong AIME, GPQA-Diamond, and LiveCodeBench scores."
   ]
  },
  {
   "url": "https://blockainews.com/news/mistral-medium-3-5-open-source-128b-may-01",
   "title": "Mistral Drops Medium 3.5 Open-Weight 128B \u2014 Rare Western Top-Tier Release, Pricing Premium vs Chinese Rivals",
   "published_at": "2026-05-02T02:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.6% on SWE-Bench Verified",
   "organization_evidence": [
    "Mistral Drops Medium 3.5 Open-Weight 128B \u2014 Rare Western Top-Tier Release, Pricing Premium vs Chinese Rivals",
    "Mistral AI released Medium 3.5 \u2014 a 128B-parameter dense model with 256k context under a Modified MIT license, scoring 77.6% on SWE-Bench Verified."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Drops Medium 3.5 Open-Weight 128B \u2014 Rare Western Top-Tier Release, Pricing Premium vs Chinese Rivals",
    "**Mistral AI** released **Medium 3.5** on April 29 \u2014 a **128-billion-parameter** dense language model with a **256k context window**, shipped as **open weights under a Modified MIT license**."
   ],
   "parameters_evidence": [
    "Mistral Drops Medium 3.5 Open-Weight 128B \u2014 Rare Western Top-Tier Release, Pricing Premium vs Chinese Rivals",
    "Mistral AI released Medium 3.5 \u2014 a 128B-parameter dense model with 256k context under a Modified MIT license, scoring 77.6% on SWE-Bench Verified."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Drops Medium 3.5 Open-Weight 128B \u2014 Rare Western Top-Tier Release, Pricing Premium vs Chinese Rivals",
    "Mistral AI released Medium 3.5 \u2014 a 128B-parameter dense model with 256k context under a Modified MIT license, scoring 77.6% on SWE-Bench Verified."
   ]
  },
  {
   "url": "https://gemma4.run/ko/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-08T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "4 model sizes (E2B, E4B, 26B MoE, 31B Dense)",
   "context_window": null,
   "license": null,
   "significance": "represents the largest generational leap in the Gemma series to date",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date."
   ]
  },
  {
   "url": "https://faq.com.tw/en/ai-ml/2026-07-17-moonshot-kimi-k3-open-weight-model-en",
   "title": "China's Moonshot AI Drops Kimi K3: The World's Largest Open-Weight Model at 2.8 Trillion Parameters",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "It is the largest open-source AI model ever released, marking a major milestone for China's AI industry and the global open-source community.",
   "organization_evidence": [
    "# China's Moonshot AI Drops Kimi K3: The World's Largest Open-Weight Model at 2.8 Trillion Parameters",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter open-weight Mixture-of-Experts model that rivals GPT-5.5 and Claude Opus 4.8, with a 1-million-token context window and full weights shipping July 27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter open-weight Mixture-of-Experts model that rivals GPT-5.5 and Claude Opus 4.8, with a 1-million-token context window and full weights shipping July 27."
   ],
   "parameters_evidence": [
    "# China's Moonshot AI Drops Kimi K3: The World's Largest Open-Weight Model at 2.8 Trillion Parameters",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter open-weight Mixture-of-Experts model that rivals GPT-5.5 and Claude Opus 4.8, with a 1-million-token context window and full weights shipping July 27."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It is the largest open-source AI model ever released, marking a major milestone for China's AI industry and the global open-source community."
   ]
  },
  {
   "url": "https://nerdleveltech.com/glm-5-2-open-weight-coding-model",
   "title": "GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
   "published_at": "2026-06-23T00:00:00",
   "organization": "Z.ai",
   "model": "GLM-5.2",
   "release_date": "2026-06-23",
   "parameters": "753 billion parameters",
   "context_window": "1 million tokens",
   "license": "MIT license",
   "significance": "Highest-ranked open-source model across three long-horizon coding benchmarks, matching or beating closed frontier models on a slice of long-horizon coding work.",
   "organization_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "Z.ai (formerly Zhipu AI) shipped **GLM-5.2** with weights on Hugging Face and ModelScope under a permissive **MIT license**.1"
   ],
   "model_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "Z.ai (formerly Zhipu AI) shipped **GLM-5.2** with weights on Hugging Face and ModelScope under a permissive **MIT license**.1"
   ],
   "release_date_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "June 23, 2026",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license."
   ],
   "parameters_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "It packs 753 billion parameters in a sparse Mixture-of-Experts design, runs a usable 1-million-token context, and posts coding-benchmark scores that beat GPT-5.5 on several long-horizon tasks while trailing Claude Opus 4.8."
   ],
   "context_window_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "It packs 753 billion parameters in a sparse Mixture-of-Experts design, runs a usable 1-million-token context, and posts coding-benchmark scores that beat GPT-5.5 on several long-horizon tasks while trailing Claude Opus 4.8."
   ],
   "license_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "Z.ai (formerly Zhipu AI) shipped **GLM-5.2** with weights on Hugging Face and ModelScope under a permissive **MIT license**.1"
   ],
   "significance_evidence": [
    "# GLM-5.2: Open-Weight 1M-Context Coding Model (2026)",
    "GLM-5.2 is Z.ai's flagship open-weight model, released in mid-June 2026 under an MIT license.",
    "It packs 753 billion parameters in a sparse Mixture-of-Experts design, runs a usable 1-million-token context, and posts coding-benchmark scores that beat GPT-5.5 on several long-horizon tasks while trailing Claude Opus 4.8.",
    "Z.ai's framing is that GLM-5.2 is \"the highest-ranked open-source model\" across all three of its long-horizon coding benchmarks.2"
   ]
  },
  {
   "url": "https://chatforest.com/reviews/minimax-m2-7-self-evolving-agentic-llm-review",
   "title": "MiniMax M2.7 Review: Self-Evolving Agentic LLM \u2014 License Controversy, Benchmark Regressions, and the Self-Evolution Story \u2014 ChatForest",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "~229\u2013230 billion",
   "context_window": null,
   "license": null,
   "significance": "Self-evolution training framework, Agent Teams native multi-agent coordination, MLE Bench Lite medal rate 66.6%",
   "organization_evidence": [
    "MiniMax M2.7 Review: Self-Evolving Agentic LLM \u2014 License Controversy, Benchmark Regressions, and the Self-Evolution Story",
    "On **April 12, 2026**, MiniMax released weights to HuggingFace."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**At a glance:** MiniMax M2.7, released March 18, 2026 (API) and April 12, 2026 (weights).",
    "On **April 12, 2026**, MiniMax released weights to HuggingFace."
   ],
   "parameters_evidence": [
    "**At a glance:** MiniMax M2.7, released March 18, 2026 (API) and April 12, 2026 (weights).",
    "The specifications from the official model card:",
    "| Total parameters | ~229\u2013230 billion |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M2.7 Review: Self-Evolving Agentic LLM \u2014 License Controversy, Benchmark Regressions, and the Self-Evolution Story",
    "MiniMax M2.7, released March 18, 2026 (API) and April 12, 2026 (weights).",
    "M2.7 introduces **Agent Teams**, a native multi-agent coordination feature that wasn\u2019t present in M2.5.",
    "MLE Bench Lite (medal rate) | **66.6%** | \u2014 | 2nd after Opus 4.6 |"
   ]
  },
  {
   "url": "https://freeainews.com/open-source/deepseek-v4-open-source-2026",
   "title": "DeepSeek V4: The Open-Source AI Model Rivaling GPT-5.5 in 2026",
   "published_at": "2026-06-01T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "V4-Pro scores 80.6% on SWE-bench Verified, nearly matching Claude Opus 4.7 (80.8%) while costing roughly 11\u00d7 less per input token and 34\u00d7 less per output token.",
   "organization_evidence": [
    "DeepSeek V4 is the fourth-generation flagship model family from DeepSeek, a Hangzhou-based AI lab founded in 2023.",
    "What Is DeepSeek V4 and Why Does It Matter for Open-Source AI?"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The V4 series released on April 24, 2026 as a preview, replacing DeepSeek V3 and V3.2, which are scheduled for retirement on July 24, 2026.",
    "What Is DeepSeek V4 and Why Does It Matter for Open-Source AI?"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 launched April 24, 2026 as the strongest open-weight AI model to date \u2014 V4-Pro scores 80.6% on SWE-bench Verified, nearly matching Claude Opus 4.7 (80.8%) while costing roughly 11\u00d7 less per input token and 34\u00d7 less per output token."
   ]
  },
  {
   "url": "https://freeainews.com/open-source/deepseek-v4-open-source-2026",
   "title": "DeepSeek V4: The Open-Source AI Model Rivaling GPT-5.5 in 2026",
   "published_at": "2026-06-01T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "V4-Pro scores 80.6% on SWE-bench Verified, nearly matching Claude Opus 4.7 (80.8%) while costing roughly 11\u00d7 less per input token and 34\u00d7 less per output token.",
   "organization_evidence": [
    "What Is DeepSeek V4 and Why Does It Matter for Open-Source AI?",
    "DeepSeek V4 is the fourth-generation flagship model family from DeepSeek, a Hangzhou-based AI lab founded in 2023."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is DeepSeek V4 and Why Does It Matter for Open-Source AI?",
    "The V4 series released on April 24, 2026 as a preview, replacing DeepSeek V3 and V3.2, which are scheduled for retirement on July 24, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 launched April 24, 2026 as the strongest open-weight AI model to date \u2014 V4-Pro scores 80.6% on SWE-bench Verified, nearly matching Claude Opus 4.7 (80.8%) while costing roughly 11\u00d7 less per input token and 34\u00d7 less per output token."
   ]
  },
  {
   "url": "https://llm-releases.com/changelog",
   "title": "LLM Releases",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight model publicly available",
   "organization_evidence": [
    "# Everything, in order",
    "A single feed of releases, updates, deprecations, and retractions across every lab we track. Each item links to the model and its primary source.",
    "Moonshot AI publishes the full Kimi K3 weights to Hugging Face under a Modified MIT license on July 26 \u2014 a day ahead of its announced July 27 target \u2014 making the 2.8T-parameter MoE freely downloadable, modifiable, and self-hostable, and cementing K3 as the largest open-weight model publicly available."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Everything, in order",
    "A single feed of releases, updates, deprecations, and retractions across every lab we track. Each item links to the model and its primary source.",
    "Moonshot AI publishes the full Kimi K3 weights to Hugging Face under a Modified MIT license on July 26 \u2014 a day ahead of its announced July 27 target \u2014 making the 2.8T-parameter MoE freely downloadable, modifiable, and self-hostable, and cementing K3 as the largest open-weight model publicly available."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Everything, in order",
    "A single feed of releases, updates, deprecations, and retractions across every lab we track. Each item links to the model and its primary source.",
    "Moonshot AI publishes the full Kimi K3 weights to Hugging Face under a Modified MIT license on July 26 \u2014 a day ahead of its announced July 27 target \u2014 making the 2.8T-parameter MoE freely downloadable, modifiable, and self-hostable, and cementing K3 as the largest open-weight model publicly available."
   ]
  },
  {
   "url": "https://dev.to/tekmag/the-open-source-ai-revolution-in-2026-how-llama-mistral-and-gemma-are-closing-the-gap-with-17ll",
   "title": "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
   "published_at": "2026-07-28T10:32:59",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "introducing Apache 2.0 licensing across the board",
   "organization_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "Google's Gemma family split cleanly in direction between its older and newer releases."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "Gemma 4 followed in April 2026 as the cleaner story, introducing Apache 2.0 licensing across the board, an important distinction for enterprises weighing long-term compliance risk."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "Gemma 4 followed in April 2026 as the cleaner story, introducing Apache 2.0 licensing across the board, an important distinction for enterprises weighing long-term compliance risk."
   ]
  },
  {
   "url": "https://dev.to/tekmag/the-open-source-ai-revolution-in-2026-how-llama-mistral-and-gemma-are-closing-the-gap-with-17ll",
   "title": "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
   "published_at": "2026-07-28T10:32:59",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes",
   "organization_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "The DeepSeek V4 Preview released April 24, 2026 brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "The DeepSeek V4 Preview released April 24, 2026 brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Mid-2026 Release Snapshot",
    "The DeepSeek V4 Preview released April 24, 2026 brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes."
   ]
  },
  {
   "url": "https://blogarama.com/frame?postid=74671854&siteId=1457123",
   "title": "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger - AI Unfiltered",
   "published_at": "2026-04-04T12:04:15",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B dense variant, 26B Mixture of Experts variant",
   "context_window": null,
   "license": null,
   "significance": "The 31B dense variant immediately claimed the #3 position on the Arena AI text leaderboard, while the 26B MoE variant sits at #6, making Gemma 4 the only model family with two entries in the top ten.",
   "organization_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "[Google released Gemma 4 on April 2, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)\u2014a family of four open-source models under the Apache 2.0 license that represents the company\u2019s most aggressive move yet into the open-weight AI space. The lineup spans from E2B (designed for mobile and IoT devices) through E4B (Android phones and laptops) to two flagship variants: a 26B Mixture of Experts model and a 31B dense model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
    "The 31B dense variant immediately claimed the #3 position on the Arena AI text leaderboard. That\u2019s not #3 among open models\u2014that\u2019s #3 globally, behind only two Chinese models that remain closed or semi-restricted for Western enterprise deployment. The 26B MoE variant sits at #6, making Gemma 4 the only model family with two entries in the top ten."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/moonshot-kimi-k3-2-8t-open-weight-china-frontier-ai-july-2026",
   "title": "Kimi K3 Review: China's 2.8-Trillion-Parameter Open-Weight Model Just Beat the US at Frontier Coding \u2014 ChatForest",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Ranked #1 on Arena Frontend Code leaderboard with 1,679 points",
   "organization_evidence": [
    "# Kimi K3 Review: China's 2.8-Trillion-Parameter Open-Weight Model Just Beat the US at Frontier Coding",
    "**At a glance:** Kimi K3, released July 16, 2026 by Moonshot AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 Review: China's 2.8-Trillion-Parameter Open-Weight Model Just Beat the US at Frontier Coding",
    "**At a glance:** Kimi K3, released July 16, 2026 by Moonshot AI."
   ],
   "parameters_evidence": [
    "# Kimi K3 Review: China's 2.8-Trillion-Parameter Open-Weight Model Just Beat the US at Frontier Coding",
    "**At a glance:** Kimi K3, released July 16, 2026 by Moonshot AI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 Review: China's 2.8-Trillion-Parameter Open-Weight Model Just Beat the US at Frontier Coding",
    "**At a glance:** Kimi K3, released July 16, 2026 by Moonshot AI."
   ]
  },
  {
   "url": "https://aitraining2u.com/zhipu-glm-explained-2026.html",
   "title": "Zhipu GLM-5.2 Explained: The MIT-Licensed Coding-Agent Model",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06",
   "parameters": "~753B total MoE, ~40B active",
   "context_window": null,
   "license": null,
   "significance": "Harder SWE test \u2014 edges past GPT-5.5 (58.6)",
   "organization_evidence": [
    "Zhipu AI \u2014 now often branded **Z.ai** \u2014 has quietly become one of the most important open-model labs.",
    "GLM-5.2 (June 2026) is Zhipu AI / Z.ai's open-weight flagship \u2014 a ~753-billion-parameter mixture-of-experts model (about 40B active per token) with a 1M-token context, released under the MIT licence and built for long-horizon coding agents at roughly a sixth of the cost of closed rivals."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Model:** GLM-5.2 (June 2026) \u2022 **Params:** ~753B total MoE, ~40B active \u2022 **Context:** 1,000,000 tokens \u2022 **Licence:** MIT \u2022 **Trained on:** Largely Huawei Ascend silicon (GLM-5 gen) \u2022 **Access:** Z.ai API, Hugging Face, OpenRouter"
   ],
   "parameters_evidence": [
    "**Model:** GLM-5.2 (June 2026) \u2022 **Params:** ~753B total MoE, ~40B active \u2022 **Context:** 1,000,000 tokens \u2022 **Licence:** MIT \u2022 **Trained on:** Largely Huawei Ascend silicon (GLM-5 gen) \u2022 **Access:** Z.ai API, Hugging Face, OpenRouter"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| Benchmark | Score | What it measures |",
    "| SWE-bench Pro | 62.1 | Harder SWE test \u2014 edges past GPT-5.5 (58.6) |"
   ]
  },
  {
   "url": "https://business20channel.tv/nvidia-nemotron-3-nano-omni-2026-9x-throughput-gain-reshapes-30-april-2026",
   "title": "NVIDIA Nemotron 3 Nano Omni 2026: 9x Throughput Gain Reshapes Multimodal AI",
   "published_at": "2026-04-30T05:06:34",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30B-A3B",
   "context_window": null,
   "license": null,
   "significance": "unifying vision, audio and language for agentic AI systems with a claimed 9x throughput advantage",
   "organization_evidence": [
    "# NVIDIA Nemotron 3 Nano Omni 2026: 9x Throughput Gain Reshapes Multimodal AI",
    "NVIDIA released Nemotron 3 Nano Omni on April 28, 2026 \u2014 a 30B-A3B hybrid MoE open model unifying vision, audio and language for agentic AI systems with a claimed 9x throughput advantage."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# NVIDIA Nemotron 3 Nano Omni 2026: 9x Throughput Gain Reshapes Multimodal AI",
    "NVIDIA released Nemotron 3 Nano Omni on April 28, 2026 \u2014 a 30B-A3B hybrid MoE open model unifying vision, audio and language for agentic AI systems with a claimed 9x throughput advantage."
   ],
   "parameters_evidence": [
    "# NVIDIA Nemotron 3 Nano Omni 2026: 9x Throughput Gain Reshapes Multimodal AI",
    "NVIDIA released Nemotron 3 Nano Omni on April 28, 2026 \u2014 a 30B-A3B hybrid MoE open model unifying vision, audio and language for agentic AI systems with a claimed 9x throughput advantage."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Nemotron 3 Nano Omni 2026: 9x Throughput Gain Reshapes Multimodal AI",
    "NVIDIA released Nemotron 3 Nano Omni on April 28, 2026 \u2014 a 30B-A3B hybrid MoE open model unifying vision, audio and language for agentic AI systems with a claimed 9x throughput advantage."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Zhipu",
   "model": "GLM 5.2",
   "release_date": "2026-06-13",
   "parameters": "744B / 40B",
   "context_window": "1M",
   "license": "MIT",
   "significance": "62.1 on SWE-bench Pro, beating GPT-5.5 at 58.6, lands 81.0 on Terminal-Bench 2.1, and reaches 74.4% on FrontierSWE",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "1. GLM 5.2, the Best Overall Open Source Model",
    "**GLM 5.2** from Zhipu (Z.ai) is the open model to beat in 2026."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **GLM 5.2** | Best overall (reasoning + coding) | 744B / 40B | 1M | MIT |"
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "1. GLM 5.2, the Best Overall Open Source Model",
    "Released on **June 13, 2026**, it uses a **744-billion-parameter** Mixture-of-Experts design that activates only **40 billion** parameters per token, paired with a full **1-million-token** context window and a clean **MIT license**."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **GLM 5.2** | Best overall (reasoning + coding) | 744B / 40B | 1M | MIT |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **GLM 5.2** | Best overall (reasoning + coding) | 744B / 40B | 1M | MIT |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **GLM 5.2** | Best overall (reasoning + coding) | 744B / 40B | 1M | MIT |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "1. GLM 5.2, the Best Overall Open Source Model",
    "GLM 5.2 scores **62.1** on SWE-bench Pro, beating GPT-5.5 at 58.6, lands **81.0** on Terminal-Bench 2.1, and reaches **74.4%** on FrontierSWE."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": null,
   "parameters": "1.6T / 49B",
   "context_window": "1M",
   "license": "Open weights",
   "significance": "80.6 on SWE-Bench Verified, a remarkable 93.5 on LiveCodeBench, a 3,206 Codeforces rating, and 90.1 on GPQA Diamond",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "The Key Takeaways",
    "> - Chinese labs (Zhipu, DeepSeek, Moonshot, Alibaba, MiniMax) now dominate the open-weight frontier, with Meta\u2019s Llama 5 and Google\u2019s Gemma 4 holding the Western flag."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "2. DeepSeek V4, Best for Raw Power and Long Context",
    "**DeepSeek V4** is the heavyweight."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **DeepSeek V4-Pro** | Raw power + long context | 1.6T / 49B | 1M | Open weights |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **DeepSeek V4-Pro** | Raw power + long context | 1.6T / 49B | 1M | Open weights |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **DeepSeek V4-Pro** | Raw power + long context | 1.6T / 49B | 1M | Open weights |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "2. DeepSeek V4, Best for Raw Power and Long Context",
    "On benchmarks it is brutal. DeepSeek V4-Pro posts **80.6** on SWE-Bench Verified, a remarkable **93.5** on LiveCodeBench, a **3,206** Codeforces rating, and **90.1** on GPQA Diamond."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Moonshot",
   "model": "Kimi K2.7 Code",
   "release_date": "2026-06-12",
   "parameters": "1T / 32B",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "62.0 on Kimi Code Bench v2 and 81.1 on MCP Mark Verified",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "3. Kimi K2.7 Code, Best for Agentic Coding",
    "Moonshot\u2019s **Kimi K2.7 Code** is purpose-built for autonomous coding agents that write, run, and debug across many steps."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "3. Kimi K2.7 Code, Best for Agentic Coding",
    "Moonshot\u2019s **Kimi K2.7 Code** is purpose-built for autonomous coding agents that write, run, and debug across many steps."
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "3. Kimi K2.7 Code, Best for Agentic Coding",
    "It is a **1-trillion-parameter** model activating **32 billion** per token, with a **256K-token** context window under a Modified MIT license, released **June 12, 2026**."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Kimi K2.7 Code** | Agentic coding | 1T / 32B | 256K | Modified MIT |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Kimi K2.7 Code** | Agentic coding | 1T / 32B | 256K | Modified MIT |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Kimi K2.7 Code** | Agentic coding | 1T / 32B | 256K | Modified MIT |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "3. Kimi K2.7 Code, Best for Agentic Coding",
    "Every score Moonshot published, including a **62.0** on its own Kimi Code Bench v2 and **81.1** on MCP Mark Verified, comes from in-house benchmarks."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Alibaba",
   "model": "Qwen 3.6",
   "release_date": "2026-04-01",
   "parameters": "35B / 3B (to 397B)",
   "context_window": "1M",
   "license": "Apache 2.0",
   "significance": "Beats Claude Opus 4.5 on terminal benchmarks and excels at agentic coding and vision",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Alibaba\u2019s **Qwen 3.6** is the open-weight Swiss Army knife."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Alibaba\u2019s **Qwen 3.6** is the open-weight Swiss Army knife."
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Released in April 2026, its open variants (the efficient **35B-A3B** and a **27B** dense model) handle more than **100 languages**, ship under a clean **Apache 2.0** license, and now run a **1-million-token** context window."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Qwen 3.6** | Multilingual + versatility | 35B / 3B (to 397B) | 1M | Apache 2.0 |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Qwen 3.6** | Multilingual + versatility | 35B / 3B (to 397B) | 1M | Apache 2.0 |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Qwen 3.6** | Multilingual + versatility | 35B / 3B (to 397B) | 1M | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Qwen 3.6 beats Claude Opus 4.5 on terminal benchmarks and excels at agentic coding and vision, rare for a model you can run yourself."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Meta",
   "model": "Llama 5",
   "release_date": "2026-04-08",
   "parameters": "600B",
   "context_window": "5M",
   "license": "Llama Community",
   "significance": "Matches or beats GPT-5 and Gemini 3 across reasoning, coding, and math",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "Meta\u2019s **Llama 5**, released **April 8, 2026**, is the most capable Western open-weight model and the one most likely to be supported everywhere on day one."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "Meta\u2019s **Llama 5**, released **April 8, 2026**, is the most capable Western open-weight model and the one most likely to be supported everywhere on day one."
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "Meta\u2019s **Llama 5**, released **April 8, 2026**, is the most capable Western open-weight model and the one most likely to be supported everywhere on day one."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Llama 5** | Western frontier open model | 600B | 5M | Llama Community |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Llama 5** | Western frontier open model | 600B | 5M | Llama Community |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Llama 5** | Western frontier open model | 600B | 5M | Llama Community |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "On capability it matches or beats GPT-5 and Gemini 3 across reasoning, coding, and math, which no earlier Llama managed."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "30.7B dense",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Scores 85.2% on MMLU-Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench v6",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "6. Gemma 4, Best Lightweight Model for Local Use",
    "If you want to run AI on your own machine, **Gemma 4** from Google DeepMind is the answer."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "6. Gemma 4, Best Lightweight Model for Local Use",
    "If you want to run AI on your own machine, **Gemma 4** from Google DeepMind is the answer."
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "6. Gemma 4, Best Lightweight Model for Local Use",
    "The family spans tiny edge models (E2B, E4B) up to a **31B dense** flagship and a 26B MoE variant, all under **Apache 2.0**, released **April 2, 2026**."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Gemma 4 31B** | Lightweight + local | 30.7B dense | 256K | Apache 2.0 |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Gemma 4 31B** | Lightweight + local | 30.7B dense | 256K | Apache 2.0 |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Gemma 4 31B** | Lightweight + local | 30.7B dense | 256K | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "6. Gemma 4, Best Lightweight Model for Local Use",
    "Gemma 4 31B scores **85.2%** on MMLU-Pro, **89.2%** on AIME 2026, and **80.0%** on LiveCodeBench v6, while the **12B** tier runs comfortably on a modern laptop."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Nex AGI",
   "model": "Nex-N2-Pro",
   "release_date": null,
   "parameters": "397B / 17B",
   "context_window": "262K",
   "license": "Apache 2.0",
   "significance": "80.8 on SWE-Bench Verified and 75.3 on Terminal-Bench 2.1",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "7. Nex-N2-Pro, Best Fully Permissive Agentic Model",
    "**Nex-N2-Pro** from Nex AGI earns its spot on license freedom plus genuine capability."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "7. Nex-N2-Pro, Best Fully Permissive Agentic Model",
    "**Nex-N2-Pro** from Nex AGI earns its spot on license freedom plus genuine capability."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Nex-N2-Pro** | Fully permissive agentic | 397B / 17B | 262K | Apache 2.0 |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Nex-N2-Pro** | Fully permissive agentic | 397B / 17B | 262K | Apache 2.0 |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Nex-N2-Pro** | Fully permissive agentic | 397B / 17B | 262K | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "7. Nex-N2-Pro, Best Fully Permissive Agentic Model",
    "It scores **80.8** on SWE-Bench Verified and **75.3** on Terminal-Bench 2.1, putting it right in the mix with the bigger names while carrying zero commercial restrictions."
   ]
  },
  {
   "url": "https://felloai.com/de/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "428B / 23B",
   "context_window": "1M",
   "license": "MiniMax Community",
   "significance": "59.0% on SWE-Bench Pro, edging out GPT-5.5 and Gemini 3.1 Pro",
   "organization_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "The Key Takeaways",
    "> - Chinese labs (Zhipu, DeepSeek, Moonshot, Alibaba, MiniMax) now dominate the open-weight frontier, with Meta\u2019s Llama 5 and Google\u2019s Gemma 4 holding the Western flag."
   ],
   "model_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "8. MiniMax M3, Best for Efficiency",
    "**MiniMax M3** rounds out the list for anyone watching hardware cost."
   ],
   "release_date_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "8. MiniMax M3, Best for Efficiency",
    "Released **June 1, 2026**, it runs **428 billion** total parameters with only **23 billion** active, and its MiniMax Sparse Attention design decodes roughly **15x faster** at full context than M2."
   ],
   "parameters_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **MiniMax M3** | Efficiency on light hardware | 428B / 23B | 1M | MiniMax Community |"
   ],
   "context_window_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **MiniMax M3** | Efficiency on light hardware | 428B / 23B | 1M | MiniMax Community |"
   ],
   "license_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "Best Open Source AI Models 2026 at a Glance",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **MiniMax M3** | Efficiency on light hardware | 428B / 23B | 1M | MiniMax Community |"
   ],
   "significance_evidence": [
    "Best Open Source AI Models in 2026, Ranked and Compared",
    "8. MiniMax M3, Best for Efficiency",
    "It posts **59.0%** on SWE-Bench Pro, edging out GPT-5.5 and Gemini 3.1 Pro, and pairs that with a full **1-million-token** context and native multimodality."
   ]
  },
  {
   "url": "https://aibriefing.dev/vendor/mistral",
   "title": "Mistral AI News \u2014 AI Briefing",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-05",
   "parameters": "3 billion",
   "context_window": null,
   "license": null,
   "significance": "A 3B open-weights policy-adaptive safety classifier that reads a plain-language moderation policy at inference time rather than memorizing one, covering 12 languages and running on a single 16GB GPU, matching models up to 7x its size.",
   "organization_evidence": [
    "# Mistral AI News",
    "Mistral released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier under Apache 2.0 that reads a plain-language moderation policy at inference time rather than memorizing one, covering 12 languages and running on a single 16GB GPU."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral AI News",
    "2026-08-05"
   ],
   "parameters_evidence": [
    "# Mistral AI News",
    "Mistral released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier under Apache 2.0 that reads a plain-language moderation policy at inference time rather than memorizing one, covering 12 languages and running on a single 16GB GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral AI News",
    "Mistral released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier under Apache 2.0 that reads a plain-language moderation policy at inference time rather than memorizing one, covering 12 languages and running on a single 16GB GPU. Mistral claims it matches models up to 7x its size, and launched it as the inaugural member of the Open Secure AI Alliance."
   ]
  },
  {
   "url": "https://aibriefing.dev/vendor/mistral",
   "title": "Mistral AI News \u2014 AI Briefing",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small 4",
   "release_date": "2026-08-04",
   "parameters": "119 billion total, 6.5 billion active",
   "context_window": "256k",
   "license": "Apache 2.0",
   "significance": "Sets a sparse-efficiency benchmark with 119B total and 6.5B active parameters, running in roughly 142GB of GPU memory at FP4 precision.",
   "organization_evidence": [
    "# Mistral AI News",
    "Mistral released Mistral Small 4 with 119 billion total but only 6.5 billion active parameters, running in roughly 142GB of GPU memory at FP4 precision \u2014 a new open-source sparse-activation efficiency benchmark."
   ],
   "model_evidence": [
    "# Mistral AI News",
    "Mistral released Mistral Small 4 with 119 billion total but only 6.5 billion active parameters, running in roughly 142GB of GPU memory at FP4 precision \u2014 a new open-source sparse-activation efficiency benchmark."
   ],
   "release_date_evidence": [
    "# Mistral AI News",
    "2026-08-04"
   ],
   "parameters_evidence": [
    "# Mistral AI News",
    "Mistral released Mistral Small 4 with 119 billion total but only 6.5 billion active parameters, running in roughly 142GB of GPU memory at FP4 precision \u2014 a new open-source sparse-activation efficiency benchmark."
   ],
   "context_window_evidence": [
    "# Mistral AI News",
    "Mistral released Mistral Small 4 with 119 billion total but only 6.5 billion active parameters, running in roughly 142GB of GPU memory at FP4 precision \u2014 a new open-source sparse-activation efficiency benchmark.",
    "Mistral AI introduced Mistral Small 4, a Mixture of Experts model with 119B total parameters and a 256k context window, unifying capabilities from its Magistral (reasoning), Pixtral (multimodal), and Devstral (agentic coding) flagship models into a single release under Apache 2.0."
   ],
   "license_evidence": [
    "# Mistral AI News",
    "Mistral AI introduced Mistral Small 4, a Mixture of Experts model with 119B total parameters and a 256k context window, unifying capabilities from its Magistral (reasoning), Pixtral (multimodal), and Devstral (agentic coding) flagship models into a single release under Apache 2.0."
   ],
   "significance_evidence": [
    "# Mistral AI News",
    "Mistral released Mistral Small 4 with 119 billion total but only 6.5 billion active parameters, running in roughly 142GB of GPU memory at FP4 precision \u2014 a new open-source sparse-activation efficiency benchmark."
   ]
  },
  {
   "url": "https://aibriefing.dev/vendor/mistral",
   "title": "Mistral AI News \u2014 AI Briefing",
   "published_at": null,
   "organization": "Mistral",
   "model": "Leanstral 1.5",
   "release_date": "2026-07-06",
   "parameters": "119 billion parameters (6.5 billion active)",
   "context_window": "256k",
   "license": "Apache-2.0",
   "significance": "An Apache-2.0-licensed Lean 4 code agent model for automated theorem proving solving 587 of 672 PutnamBench problems and achieving state-of-the-art FATE-H (87%) and FATE-X (34%) algebra benchmarks.",
   "organization_evidence": [
    "# Mistral AI News",
    "Mistral released Leanstral 1.5, an Apache-2.0-licensed Lean 4 code agent model for automated theorem proving under the banner 'Proof Abundance for All.'"
   ],
   "model_evidence": [
    "# Mistral AI News",
    "Mistral released Leanstral 1.5, an Apache-2.0-licensed Lean 4 code agent model for automated theorem proving under the banner 'Proof Abundance for All.'"
   ],
   "release_date_evidence": [
    "# Mistral AI News",
    "2026-07-06"
   ],
   "parameters_evidence": [
    "# Mistral AI News",
    "Mistral AI released Leanstral 1.5, a specialized model for automated theorem proving and formal verification with the Lean 4 proof assistant. It uses a mixture-of-experts architecture with 119 billion parameters (6.5 billion active) and a 256k-token context length to handle long proof files, and is available free in Mistral's Labs tier."
   ],
   "context_window_evidence": [
    "# Mistral AI News",
    "Mistral AI released Leanstral 1.5, a specialized model for automated theorem proving and formal verification with the Lean 4 proof assistant. It uses a mixture-of-experts architecture with 119 billion parameters (6.5 billion active) and a 256k-token context length to handle long proof files, and is available free in Mistral's Labs tier."
   ],
   "license_evidence": [
    "# Mistral AI News",
    "Mistral released Leanstral 1.5, an Apache-2.0-licensed Lean 4 code agent model for automated theorem proving under the banner 'Proof Abundance for All.'"
   ],
   "significance_evidence": [
    "# Mistral AI News",
    "Mistral released Leanstral 1.5, an Apache-2.0-licensed Lean 4 code agent model for automated theorem proving under the banner 'Proof Abundance for All.' Part of the Mistral Small 4 family, it solves 587 of 672 PutnamBench problems and achieves state-of-the-art FATE-H (87%) and FATE-X (34%) algebra benchmarks, available via a free API and on Hugging Face."
   ]
  },
  {
   "url": "https://aibriefing.dev/vendor/mistral",
   "title": "Mistral AI News \u2014 AI Briefing",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-06-11",
   "parameters": "128B",
   "context_window": "256k",
   "license": "modified MIT license",
   "significance": "A 128B dense flagship unifying instruction-following, reasoning, and coding optimized for self-hosting on as few as four GPUs, scoring 77.6% on SWE-Bench Verified.",
   "organization_evidence": [
    "# Mistral AI News",
    "Mistral released Medium 3.5, a 128B dense flagship unifying instruction-following, reasoning and coding optimized for self-hosting on as few as four GPUs, alongside Voxtral TTS \u2014 a frontier open-weights text-to-speech model \u2014 plus remote coding agents in Vibe and a Le Chat Work mode for multi-step tasks."
   ],
   "model_evidence": [
    "# Mistral AI News",
    "Mistral released Medium 3.5, a 128B dense flagship unifying instruction-following, reasoning and coding optimized for self-hosting on as few as four GPUs, alongside Voxtral TTS \u2014 a frontier open-weights text-to-speech model \u2014 plus remote coding agents in Vibe and a Le Chat Work mode for multi-step tasks."
   ],
   "release_date_evidence": [
    "# Mistral AI News",
    "2026-06-11"
   ],
   "parameters_evidence": [
    "# Mistral AI News",
    "Mistral released Medium 3.5, a 128B dense flagship unifying instruction-following, reasoning and coding optimized for self-hosting on as few as four GPUs, alongside Voxtral TTS \u2014 a frontier open-weights text-to-speech model \u2014 plus remote coding agents in Vibe and a Le Chat Work mode for multi-step tasks."
   ],
   "context_window_evidence": [
    "# Mistral AI News",
    "Mistral released Medium 3.5, a 128B dense flagship unifying instruction-following, reasoning and coding optimized for self-hosting on as few as four GPUs, alongside Voxtral TTS \u2014 a frontier open-weights text-to-speech model \u2014 plus remote coding agents in Vibe and a Le Chat Work mode for multi-step tasks.",
    "Mistral AI released Mistral Medium 3.5, a 128B dense model with 256k context combining instruction-following, reasoning, and coding, under a modified MIT license with open weights."
   ],
   "license_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Medium 3.5, a 128B dense model with 256k context combining instruction-following, reasoning, and coding, under a modified MIT license with open weights."
   ],
   "significance_evidence": [
    "# Mistral AI News",
    "Mistral released Medium 3.5, a 128B dense flagship unifying instruction-following, reasoning and coding optimized for self-hosting on as few as four GPUs, alongside Voxtral TTS \u2014 a frontier open-weights text-to-speech model \u2014 plus remote coding agents in Vibe and a Le Chat Work mode for multi-step tasks.",
    "Mistral Medium 3.5 \u2014 a 128B dense model with 256k context \u2014 scored 77.6% on SWE-Bench Verified and is now available as open weights on Hugging Face."
   ]
  },
  {
   "url": "https://aibriefing.dev/vendor/mistral",
   "title": "Mistral AI News \u2014 AI Briefing",
   "published_at": null,
   "organization": "Mistral AI",
   "model": "Mistral Small 3.1",
   "release_date": "2026-04-14",
   "parameters": "24B parameters",
   "context_window": "128k",
   "license": "Apache 2.0",
   "significance": "Adds native vision understanding and 128k context in a compact open-weight release, outperforming GPT-4o Mini on several multilingual and vision benchmarks.",
   "organization_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license."
   ],
   "model_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license."
   ],
   "release_date_evidence": [
    "# Mistral AI News",
    "2026-04-14"
   ],
   "parameters_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license."
   ],
   "context_window_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license."
   ],
   "license_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "# Mistral AI News",
    "Mistral AI released Mistral Small 3.1 (24B parameters) with native vision understanding and a 128k token context window under the Apache 2.0 license. The model is designed to run on a single GPU and outperforms GPT-4o Mini on several multilingual and vision benchmarks per Mistral's internal evaluations."
   ]
  },
  {
   "url": "https://datanorth.ai/news/google-releases-gemma-4-open-models",
   "title": "Google releases Gemma 4: Four open models with Apache 2.0 license",
   "published_at": "2026-04-03T12:52:03",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3 billion to 30.7 billion",
   "context_window": null,
   "license": null,
   "significance": "major benchmark gains over Gemma 3, native multimodal support across all model sizes, covering text, image, audio, and video inputs, and support for function calling, structured JSON output, and native system instructions",
   "organization_evidence": [
    "# Google releases Gemma 4: Four open models with Apache 2.0 license",
    "Google DeepMind launches Gemma 4, four open-weight AI models with Apache 2.0 licensing, multimodal support, and major benchmark gains over Gemma 3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google releases Gemma 4: Four open models with Apache 2.0 license",
    "Google DeepMind released Gemma 4 on April 2, 2026, a family of four open-weight models built from the same research behind Gemini 3. The models range from 2.3 billion to 30.7 billion parameters and ship under the Apache 2.0 license, making them freely available for commercial and sovereign AI deployments without usage restrictions."
   ],
   "parameters_evidence": [
    "# Google releases Gemma 4: Four open models with Apache 2.0 license",
    "Google DeepMind released Gemma 4 on April 2, 2026, a family of four open-weight models built from the same research behind Gemini 3. The models range from 2.3 billion to 30.7 billion parameters and ship under the Apache 2.0 license, making them freely available for commercial and sovereign AI deployments without usage restrictions."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google releases Gemma 4: Four open models with Apache 2.0 license",
    "Google DeepMind launches Gemma 4, four open-weight AI models with Apache 2.0 licensing, multimodal support, and major benchmark gains over Gemma 3.",
    "Gemma 4 marks a significant shift from Google\u2019s previous custom licensing approach and introduces native multimodal support across all model sizes, covering text, image, audio, and video inputs.",
    "All four models are available in both base and instruction-tuned versions. They support function calling, structured JSON output, and native system instructions, which enables developers to build autonomous agents that interact with external tools and APIs."
   ]
  },
  {
   "url": "https://effloow.com/articles/glm-5-open-source-frontier-model-setup-guide-2026",
   "title": "GLM-5: The Open-Source Frontier Model You Can Self-Host",
   "published_at": "2026-04-11T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked in the top five on nearly every major frontier benchmark, with outstanding agentic capabilities such as leading on BrowseComp.",
   "organization_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "On February 11, 2026, Z.ai (formerly Zhipu AI) released GLM-5 on Hugging Face under an MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "On February 11, 2026, Z.ai (formerly Zhipu AI) released GLM-5 on Hugging Face under an MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "GLM-5 is an MIT-licensed frontier model with top-5 benchmark scores.",
    "It ranks in the top five on nearly every major frontier benchmark, and its successor GLM-5.1 (released April 7, 2026) has already claimed the number one spot on SWE-Bench Pro.",
    "**Where GLM-5 leads:** BrowseComp is the standout -- GLM-5 scores 62.0 (75.9 with context management) versus Claude Opus 4.5 at 37.0 (67.8)."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/nvidia-ising",
   "title": "NVIDIA Ising \u2014 Open AI Models for Quantum Processor Calibration and Error Correction",
   "published_at": "2026-04-14T12:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open AI models for quantum computing: a 35B MoE VLM that automates quantum processor calibration and 3D CNN decoders for real-time error correction, 2.5\u00d7 faster and 3\u00d7 more accurate than pyMatching",
   "organization_evidence": [
    "NVIDIA \u00b7 2026-04-14 \u00b7 notable",
    "# NVIDIA Ising \u2014 Open AI Models for Quantum Processor Calibration and Error Correction",
    "NVIDIA released Ising, the first open AI models for quantum computing: a 35B MoE VLM that automates quantum processor calibration and 3D CNN decoders for real-time error correction, 2.5\u00d7 faster and 3\u00d7 more accurate than pyMatching."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA \u00b7 2026-04-14 \u00b7 notable",
    "# NVIDIA Ising \u2014 Open AI Models for Quantum Processor Calibration and Error Correction",
    "NVIDIA released Ising, the first open AI models for quantum computing: a 35B MoE VLM that automates quantum processor calibration and 3D CNN decoders for real-time error correction, 2.5\u00d7 faster and 3\u00d7 more accurate than pyMatching."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Ising \u2014 Open AI Models for Quantum Processor Calibration and Error Correction",
    "NVIDIA released Ising, the first open AI models for quantum computing: a 35B MoE VLM that automates quantum processor calibration and 3D CNN decoders for real-time error correction, 2.5\u00d7 faster and 3\u00d7 more accurate than pyMatching."
   ]
  },
  {
   "url": "https://seventnews.com/en/articles/ai2-opens-the-hood-on-olmo-3-every-checkpoint-every-data-point-every-decision",
   "title": "Olmo 3: Ai2's fully open 7B/32B AI models",
   "published_at": "2026-07-11T21:21:00",
   "organization": "Ai2",
   "model": null,
   "release_date": "2026-07-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Olmo 3 comes in six variants at two parameter scales, 7B and 32B, each in three flavors: base, instruct, and think.",
   "organization_evidence": [
    "Open Source",
    "Ai2 opened every drawer in the AI cabinet. Here's what came out.",
    "Ai2 released Olmo 3, a fully open model family from 7B to 32B, and dumped the complete training data, code, and tools on the table."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source",
    "Ai2 opened every drawer in the AI cabinet. Here's what came out.",
    "2026-07-11 \u00b7 Last updated: 2026-08-03 \u00b7 4 min read"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source",
    "Ai2 opened every drawer in the AI cabinet. Here's what came out.",
    "Olmo 3 comes in six variants at two parameter scales, 7B and 32B, each in three flavors: base, instruct, and think."
   ]
  },
  {
   "url": "https://techtimes.com/articles/317758/20260604/google-gemma-4-12b-brings-multimodal-ai-16gb-laptops-free-under-apache-20.htm",
   "title": "Google Gemma 4 12B Brings Multimodal AI to 16GB Laptops, Free Under Apache 2.0",
   "published_at": "2026-06-04T12:09:10",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 12-billion-parameter open-weight model that processes text, images, audio, and video without separate encoder networks, and runs on any laptop or workstation equipped with 16GB of RAM or VRAM.",
   "organization_evidence": [
    "Google [released](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) Gemma 4 12B on June 3, 2026 \u2014 a 12-billion-parameter open-weight model that processes text, images, audio, and video without separate encoder networks, and runs on any laptop or workstation equipped with 16GB of RAM or VRAM."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google [released](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) Gemma 4 12B on June 3, 2026 \u2014 a 12-billion-parameter open-weight model that processes text, images, audio, and video without separate encoder networks, and runs on any laptop or workstation equipped with 16GB of RAM or VRAM."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google [released](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) Gemma 4 12B on June 3, 2026 \u2014 a 12-billion-parameter open-weight model that processes text, images, audio, and video without separate encoder networks, and runs on any laptop or workstation equipped with 16GB of RAM or VRAM."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/the-mit-license-is-now-a-weapon-z-ai-glm-5-1-beat-gpt-5-4-2026",
   "title": "The MIT License Is Now a Weapon: Z.ai's GLM-5.1 Just Beat GPT-5.4 and Anyone Can Use It",
   "published_at": "2026-05-09T10:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744 billion",
   "context_window": null,
   "license": null,
   "significance": "58.4 on SWE-Bench Pro, GLM-5.1 tops the global coding benchmark on April 7, 2026, surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3)",
   "organization_evidence": [
    "Model Release",
    "# The MIT License Is Now a Weapon: Z.ai's GLM-5.1 Just Beat GPT-5.4 and Anyone Can Use It",
    "Z.ai released GLM-5.1 on April 7, 2026: a 744B open-source model under MIT license that topped SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# The MIT License Is Now a Weapon: Z.ai's GLM-5.1 Just Beat GPT-5.4 and Anyone Can Use It",
    "Z.ai released GLM-5.1 on April 7, 2026: a 744B open-source model under MIT license that topped SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6."
   ],
   "parameters_evidence": [
    "Model Release",
    "# The MIT License Is Now a Weapon: Z.ai's GLM-5.1 Just Beat GPT-5.4 and Anyone Can Use It",
    "The model contains **744 billion total parameters** organized as a Mixture-of-Experts (MoE) architecture."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# The MIT License Is Now a Weapon: Z.ai's GLM-5.1 Just Beat GPT-5.4 and Anyone Can Use It",
    "- 58.4 on SWE-Bench Pro, GLM-5.1 tops the global coding benchmark on April 7, 2026, surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3)"
   ]
  },
  {
   "url": "https://cornerforai.com/news/nvidia-releases-cosmos-3-and-nemotron-3-ultra-as-open-weight-ai-models-for-physical-world-and-language-tasks",
   "title": "NVIDIA Cosmos 3 and Nemotron 3 Ultra: Open AI Models Launch",
   "published_at": "2026-06-08T03:07:04",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026",
   "parameters": "550 billion",
   "context_window": null,
   "license": null,
   "significance": "NVIDIA describes Nemotron 3 Ultra as both the largest and most capable model in the Nemotron series, and the most intelligent open-weights model produced in the United States.",
   "organization_evidence": [
    "## What is NVIDIA Nemotron 3 Ultra?",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What is NVIDIA Nemotron 3 Ultra?",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "parameters_evidence": [
    "## What is NVIDIA Nemotron 3 Ultra?",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What is NVIDIA Nemotron 3 Ultra?",
    "NVIDIA describes Nemotron 3 Ultra as both the largest and most capable model in the **Nemotron** series, and the most intelligent open-weights model produced in the United States."
   ]
  },
  {
   "url": "https://aichina.news/blog/allenais-new-intent-aware-lfqa-model-lands-on-modelers-cn-a-curious-fvi10c",
   "title": "AllenAI\u2019s New Intent-Aware LFQA Model Lands on Modelers.cn: A Curious Step for Ascend Developers \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T09:42:50",
   "organization": "AllenAI",
   "model": null,
   "release_date": "2026-07-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Released an intent-aware long-form question answering fine-tuned Llama 3 8B model available on Modelers.cn, Huawei\u2019s Ascend AI ecosystem platform.",
   "organization_evidence": [
    "AllenAI\u2019s New Intent-Aware LFQA Model Lands on Modelers.cn: A Curious Step for Ascend Developers",
    "AllenAI, a respected open\u2011AI research lab, has released a fine\u2011tuned Llama 3 8B model that tries to tackle exactly this: **intent\u2011aware long\u2011form question answering (LFQA)**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AllenAI\u2019s New Intent-Aware LFQA Model Lands on Modelers.cn: A Curious Step for Ascend Developers",
    "models/allenai July 30, 2026 3 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AllenAI\u2019s New Intent-Aware LFQA Model Lands on Modelers.cn: A Curious Step for Ascend Developers",
    "AllenAI, a respected open\u2011AI research lab, has released a fine\u2011tuned Llama 3 8B model that tries to tackle exactly this: **intent\u2011aware long\u2011form question answering (LFQA)**."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-06/05-google-releases-gemma-4-12b-an-encoder-free-multimodal-model-with-native-audio-that-runs-on-a-16gb-laptop",
   "title": "Google Releases Gemma 4 12B, an Encoder-Free Multimodal Model With Native Audio That Runs on a 16GB Laptop",
   "published_at": "2026-06-05T15:01:16",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Benchmark performance nearing the 26B model at less than half the total memory footprint, encoder-free multimodal architecture handling raw images and audio natively, and integration of Multi-Token Prediction (MTP) drafters to reduce latency.",
   "organization_evidence": [
    "# Google Releases Gemma 4 12B, an Encoder-Free Multimodal Model With Native Audio That Runs on a 16GB Laptop"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Releases Gemma 4 12B, an Encoder-Free Multimodal Model With Native Audio That Runs on a 16GB Laptop"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Releases Gemma 4 12B, an Encoder-Free Multimodal Model With Native Audio That Runs on a 16GB Laptop",
    "In its [announcement](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/), Google claims \u201cbenchmark performance nearing our 26B model\u201d at \u201cless than half the total memory footprint.\u201d"
   ]
  },
  {
   "url": "https://dev.to/preecha/what-is-kimi-k26-moonshot-ais-1t-parameter-open-model-explained-2k2b",
   "title": "What is Kimi K2.6? Moonshot AI's 1T-Parameter Open Model Explained",
   "published_at": "2026-04-26T01:01:02",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1T-parameter",
   "context_window": null,
   "license": null,
   "significance": "state of the art in open-source coding, long-horizon execution, and agent swarms",
   "organization_evidence": [
    "What is Kimi K2.6? Moonshot AI's 1T-Parameter Open Model Explained",
    "Moonshot AI shipped Kimi K2.6 with a bold claim: it\u2019s the new state of the art in open-source coding, long-horizon execution, and agent swarms."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What is Kimi K2.6? Moonshot AI's 1T-Parameter Open Model Explained",
    "- **Release:** April 2026, open source (weights: Hugging Face, API: [platform.kimi.ai](https://platform.kimi.ai))."
   ],
   "parameters_evidence": [
    "What is Kimi K2.6? Moonshot AI's 1T-Parameter Open Model Explained",
    "- **Architecture:** 1T-parameter mixture-of-experts (MoE), 32B active params/token, 262,144-token (256K) context."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What is Kimi K2.6? Moonshot AI's 1T-Parameter Open Model Explained",
    "Moonshot AI shipped Kimi K2.6 with a bold claim: it\u2019s the new state of the art in open-source coding, long-horizon execution, and agent swarms."
   ]
  },
  {
   "url": "https://ideatomvp.ai/en/blog/glm-52-open-source-model-2026",
   "title": "GLM-5.2: The Open-Source Model That Just Beat GPT-5.5 on Coding (2026)",
   "published_at": "2026-06-22T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Outperforms GPT-5.5 on SWE-bench Pro (62.1 vs 58.6) and achieves a Terminal-Bench 2.1 score of 81.0, with a 1M-token context window.",
   "organization_evidence": [
    "On June 13, 2026, Z.ai dropped the weights of GLM-5.2 under an MIT license and let the AI community do what it does best: stress-test everything immediately.",
    "GLM-5.2 is the third major iteration of Z.ai's (formerly Zhipu AI) GLM-5 series, a model family built explicitly for **agentic coding and long-horizon software engineering**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 13, 2026, Z.ai dropped the weights of GLM-5.2 under an MIT license and let the AI community do what it does best: stress-test everything immediately.",
    "GLM-5.2 is the third major iteration of Z.ai's (formerly Zhipu AI) GLM-5 series, a model family built explicitly for **agentic coding and long-horizon software engineering**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Terminal-Bench 2.1 jump is the one that made developers pay attention.",
    "On SWE-bench Pro, GLM-5.2 at 62.1 outperforms GPT-5.5 at 58.6.",
    "GLM-5.2 is the third major iteration of Z.ai's (formerly Zhipu AI) GLM-5 series, a model family built explicitly for **agentic coding and long-horizon software engineering**."
   ]
  },
  {
   "url": "https://zubnet.ai/news/en/google-ships-gemma-4-under-apache-20-bringing-commercially-usable",
   "title": "Google ships Gemma 4 under Apache 2.0, bringing\u2026, Zubnet AI News",
   "published_at": "2026-04-16T17:19:18",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "sits at #3 on the open-model Arena leaderboard, natively multimodal and agentic, edge variants accept native audio input",
   "organization_evidence": [
    "Google DeepMind released Gemma 4 on April 2 under the Apache 2.0 license, the first Gemma release under an OSI-approved open-source license and the most significant move in Google's open-weights strategy to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2 under the Apache 2.0 license, the first Gemma release under an OSI-approved open-source license and the most significant move in Google's open-weights strategy to date."
   ],
   "parameters_evidence": [
    "The family spans four sizes: E2B and E4B \"effective\" edge variants, a 26B Mixture-of-Experts, and a 31B dense model that sits at #3 on the open-model Arena leaderboard."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The family spans four sizes: E2B and E4B \"effective\" edge variants, a 26B Mixture-of-Experts, and a 31B dense model that sits at #3 on the open-model Arena leaderboard.",
    "All models are natively multimodal (video, image, OCR, chart understanding with variable-resolution input) and agentic (function-calling, structured JSON output, system-instruction support).",
    "The edge E2B and E4B variants additionally accept native audio input for speech recognition and understanding."
   ]
  },
  {
   "url": "https://ibtimes.com.au/moonshot-ai-unveils-kimi-k3-largest-open-source-model-1872529",
   "title": "Chinese Startup Moonshot AI Releases Kimi K3, World's Largest Open-Source Model, Rattling Global Markets",
   "published_at": "2026-07-18T14:58:51",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": "2.8 trillion total parameters",
   "context_window": null,
   "license": null,
   "significance": "largest open-source AI model ever made publicly available, containing 2.8 trillion total parameters",
   "organization_evidence": [
    "# Chinese Startup Moonshot AI Releases Kimi K3, World's Largest Open-Source Model, Rattling Global Markets",
    "Beijing-based artificial intelligence startup Moonshot AI released Kimi K3 this week, unveiling what the company describes as the largest open-source AI model ever made publicly available, with benchmark performance the company says rivals some of the most advanced systems built by American labs including Anthropic and OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "## Kimi K3 Outperforms Rivals with 2.8 Trillion Parameters and Innovative Features",
    "The model, released Thursday, contains 2.8 trillion total parameters, making it roughly 75% larger than [DeepSeek's V4 Pro](https://www.ibtimes.com.au/nvidia-shares-dip-chinese-ai-firm-deepseek-develops-chip-1871977), previously one of the largest Chinese open-weight models at approximately 1.6 trillion parameters, and far outpacing Zhipu AI's GLM 5 series at 744 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Kimi K3 Outperforms Rivals with 2.8 Trillion Parameters and Innovative Features",
    "Beijing-based artificial intelligence startup Moonshot AI released Kimi K3 this week, unveiling what the company describes as the largest open-source AI model ever made publicly available, with benchmark performance the company says rivals some of the most advanced systems built by American labs including Anthropic and OpenAI.",
    "The model, released Thursday, contains 2.8 trillion total parameters, making it roughly 75% larger than [DeepSeek's V4 Pro](https://www.ibtimes.com.au/nvidia-shares-dip-chinese-ai-firm-deepseek-develops-chip-1871977), previously one of the largest Chinese open-weight models at approximately 1.6 trillion parameters, and far outpacing Zhipu AI's GLM 5 series at 744 billion parameters."
   ]
  },
  {
   "url": "https://felloai.com/pt/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "A 744-billion-parameter Mixture-of-Experts design with a 1-million-token context window, designed for long-horizon coding and agentic work.",
   "organization_evidence": [
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle.",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "parameters_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "The release is squarely aimed at developers.",
    "Zhipu positioned **GLM 5.2** around long-horizon coding and agentic work, the kind of multi-step tasks where a model writes, runs, and revises code across an entire project."
   ]
  },
  {
   "url": "https://postquantum.com/industry-news/nvidia-ising-quantum-ai-models",
   "title": "NVIDIA Ising: Open AI Models for Quantum Calibration and Error Correction",
   "published_at": "2026-04-15T09:52:55",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-15",
   "parameters": "35 billion",
   "context_window": null,
   "license": null,
   "significance": "the first family of open-source AI models purpose-built for quantum computing",
   "organization_evidence": [
    "Industry",
    "NVIDIA Ising: Open AI Models for Quantum Calibration and Error Correction",
    "**15 Apr 2026** \u2014 NVIDIA announced [NVIDIA Ising](https://www.nvidia.com/en-us/solutions/quantum-computing/ising/), the first family of open-source AI models purpose-built for quantum computing."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Industry",
    "NVIDIA Ising: Open AI Models for Quantum Calibration and Error Correction",
    "**15 Apr 2026** \u2014 NVIDIA announced [NVIDIA Ising](https://www.nvidia.com/en-us/solutions/quantum-computing/ising/), the first family of open-source AI models purpose-built for quantum computing."
   ],
   "parameters_evidence": [
    "Industry",
    "NVIDIA Ising: Open AI Models for Quantum Calibration and Error Correction",
    "Ising Calibration is a 35-billion-parameter vision-language model (built on Qwen3.5-35B-A3B) that interprets experimental measurements from quantum processors and infers calibration adjustments."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Industry",
    "NVIDIA Ising: Open AI Models for Quantum Calibration and Error Correction",
    "**15 Apr 2026** \u2014 NVIDIA announced [NVIDIA Ising](https://www.nvidia.com/en-us/solutions/quantum-computing/ising/), the first family of open-source AI models purpose-built for quantum computing."
   ]
  },
  {
   "url": "https://dev.to/akaranjkar08/deepseek-v4-pro-and-v4-flash-the-complete-developer-guide-april-2026-5c12",
   "title": "DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
   "published_at": "2026-07-05T13:16:42",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters, 49 billion active per forward pass",
   "context_window": null,
   "license": null,
   "significance": "1.6 trillion total parameters, 49 billion active per forward pass, a 1 million token context window, and 80.6% on SWE-bench Verified \u2014 within 0.2 percentage points of Claude Opus 4.6.",
   "organization_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "DeepSeek released V4-Pro and V4-Flash today, April 24, 2026 \u2014 one year to the week after DeepSeek-R1 reset the world's expectations for open-source AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "DeepSeek released V4-Pro and V4-Flash today, April 24, 2026 \u2014 one year to the week after DeepSeek-R1 reset the world's expectations for open-source AI."
   ],
   "parameters_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "V4-Pro is the largest open-weight model ever released: 1.6 trillion total parameters, 49 billion active per forward pass, a 1 million token context window, and 80.6% on SWE-bench Verified \u2014 within 0.2 percentage points of Claude Opus 4.6."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "V4-Pro is the largest open-weight model ever released: 1.6 trillion total parameters, 49 billion active per forward pass, a 1 million token context window, and 80.6% on SWE-bench Verified \u2014 within 0.2 percentage points of Claude Opus 4.6."
   ]
  },
  {
   "url": "https://dev.to/akaranjkar08/deepseek-v4-pro-and-v4-flash-the-complete-developer-guide-april-2026-5c12",
   "title": "DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
   "published_at": "2026-07-05T13:16:42",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "284 billion total parameters / 13 billion active per forward pass",
   "context_window": null,
   "license": null,
   "significance": "284 billion total parameters / 13 billion active per forward pass. 1 million token context window. $0.14/M input tokens, $0.28/M output tokens.",
   "organization_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "DeepSeek released V4-Pro and V4-Flash today, April 24, 2026 \u2014 one year to the week after DeepSeek-R1 reset the world's expectations for open-source AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "DeepSeek released V4-Pro and V4-Flash today, April 24, 2026 \u2014 one year to the week after DeepSeek-R1 reset the world's expectations for open-source AI."
   ],
   "parameters_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "The V4 family is two models released simultaneously under the MIT license on April 24, 2026:",
    "- **DeepSeek-V4-Flash**: 284 billion total parameters / 13 billion active per forward pass. 1 million token context window. $0.14/M input tokens, $0.28/M output tokens."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek V4-Pro and V4-Flash: The Complete Developer Guide (April 2026)",
    "The V4 family is two models released simultaneously under the MIT license on April 24, 2026:",
    "- **DeepSeek-V4-Flash**: 284 billion total parameters / 13 billion active per forward pass. 1 million token context window. $0.14/M input tokens, $0.28/M output tokens."
   ]
  },
  {
   "url": "https://medium.com/@AiDocTakes/mistral-small-4-the-open-source-model-that-does-everything-and-costs-you-nothing-to-license-539818925327",
   "title": "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
   "published_at": "2026-03-19T19:30:24",
   "organization": "Mistral",
   "model": "Mistral Small 4",
   "release_date": null,
   "parameters": "119 billion parameters",
   "context_window": "256,000-token",
   "license": "Apache 2.0",
   "significance": "unifies reasoning, vision, and coding into one 119B MoE model under Apache 2.0; benchmarks match or beat models 3x its active compute on reasoning and code tasks; configurable reasoning depth per request is a genuine architectural innovation",
   "organization_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "Mistral Small 4 unifies reasoning, vision, and coding into one 119B MoE model under Apache 2.0."
   ],
   "model_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "Mistral Small 4 unifies reasoning, vision, and coding into one 119B MoE model under Apache 2.0."
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "Mistral just shipped reasoning, vision, and agentic coding in a single Apache-licensed model. 119 billion parameters. 6 billion active. And you can run it on your own hardware without asking anyone\u2019s permission."
   ],
   "context_window_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "And the 256,000-token context window means you can feed it an entire codebase, a full novel, or months of document history in a single prompt."
   ],
   "license_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "Mistral Small 4 unifies reasoning, vision, and coding into one 119B MoE model under Apache 2.0."
   ],
   "significance_evidence": [
    "Mistral Small 4: The Open-Source Model That Does Everything and Costs You Nothing to License",
    "TLDR",
    "Mistral Small 4 unifies reasoning, vision, and coding into one 119B MoE model under Apache 2.0.",
    "Benchmarks match or beat models 3x its active compute on reasoning and code tasks.",
    "Configurable reasoning depth per request is a genuine architectural innovation."
   ]
  },
  {
   "url": "https://awesomeagents.ai/news/google-gemma-4-open-weight-26b-moe",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0",
   "published_at": "2026-04-02T16:34:01",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B dense",
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 on LMArena's text leaderboard with a score of ~1452, and achieves 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench.",
   "organization_evidence": [
    "Google releases Gemma 4 with a 26B MoE, 31B Dense, and two edge variants under Apache 2.0 - claiming the highest intelligence-per-parameter of any open model.",
    "Google released Gemma 4 on April 2, 2026 - four open-weight models derived from Gemini 3 architecture, all under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google released Gemma 4 on April 2, 2026 - four open-weight models derived from Gemini 3 architecture, all under Apache 2.0."
   ],
   "parameters_evidence": [
    "Google releases Gemma 4 with a 26B MoE, 31B Dense, and two edge variants under Apache 2.0 - claiming the highest intelligence-per-parameter of any open model.",
    "| Spec | Gemma 4 31B | Gemma 4 26B A4B | E4B | E2B |",
    "| Parameters | 31B dense | 26B total / 3.8B active | 8B (4.5B effective) | 5.1B (2.3B effective) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The lineup spans a 31B dense model ranked #3 on LMArena's text leaderboard, a 26B mixture-of-experts variant that activates only 3.8B parameters at inference, and two edge models (E4B and E2B) designed to run on phones and Raspberry Pis.",
    "On LMArena's text-only leaderboard, it scores roughly 1452 - comparable to GLM-5 and Kimi K2.5, which carry roughly 30x more parameters.",
    "MMLU Pro hits 85.2%, AIME 2026 reaches 89.2%, and LiveCodeBench lands at 80.0%."
   ]
  },
  {
   "url": "https://awesomeagents.ai/news/google-gemma-4-open-weight-26b-moe",
   "title": "Google Gemma 4 Ships Four Open Models Under Apache 2.0",
   "published_at": "2026-04-02T16:34:01",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "26B total / 3.8B active",
   "context_window": null,
   "license": null,
   "significance": "Scores 1441 on LMArena (#6 among open models) while activating only 3.8B parameters, alongside 82.6% on MMLU Pro, 88.3% on AIME 2026, and 77.1% on LiveCodeBench.",
   "organization_evidence": [
    "Google releases Gemma 4 with a 26B MoE, 31B Dense, and two edge variants under Apache 2.0 - claiming the highest intelligence-per-parameter of any open model.",
    "Google released Gemma 4 on April 2, 2026 - four open-weight models derived from Gemini 3 architecture, all under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google released Gemma 4 on April 2, 2026 - four open-weight models derived from Gemini 3 architecture, all under Apache 2.0."
   ],
   "parameters_evidence": [
    "Google releases Gemma 4 with a 26B MoE, 31B Dense, and two edge variants under Apache 2.0 - claiming the highest intelligence-per-parameter of any open model.",
    "| Spec | Gemma 4 31B | Gemma 4 26B A4B | E4B | E2B |",
    "| Parameters | 31B dense | 26B total / 3.8B active | 8B (4.5B effective) | 5.1B (2.3B effective) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The lineup spans a 31B dense model ranked #3 on LMArena's text leaderboard, a 26B mixture-of-experts variant that activates only 3.8B parameters at inference, and two edge models (E4B and E2B) designed to run on phones and Raspberry Pis.",
    "It runs nearly as fast as a 4B model while scoring 1441 on LMArena - five spots below the dense variant but still #6 among open models.",
    "MMLU Pro comes in at 82.6%, AIME 2026 at 88.3%, LiveCodeBench at 77.1%."
   ]
  },
  {
   "url": "https://intuitionlabs.ai/pdfs/glm-4-6-an-open-source-ai-for-coding-vs-sonnet-gpt-5.pdf",
   "title": "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
   "published_at": "2025-10-17T12:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "745B-parameter (44B active)",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.8% on SWE-Bench Verified, surpassing GLM-4.6 and competing with the best proprietary models",
   "organization_evidence": [
    "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Executive Summary",
    "Meanwhile, Z.ai itself\n\nreleased GLM-5 on February 11, 2026 \u2014 a 745B-parameter (44B active) MoE model scoring 77.8% on SWE-Bench\n\nVerified, surpassing GLM-4.6 and competing with the best proprietary models ([5] glm5.net)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Executive Summary",
    "Meanwhile, Z.ai itself\n\nreleased GLM-5 on February 11, 2026 \u2014 a 745B-parameter (44B active) MoE model scoring 77.8% on SWE-Bench\n\nVerified, surpassing GLM-4.6 and competing with the best proprietary models ([5] glm5.net)."
   ],
   "parameters_evidence": [
    "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Executive Summary",
    "Meanwhile, Z.ai itself\n\nreleased GLM-5 on February 11, 2026 \u2014 a 745B-parameter (44B active) MoE model scoring 77.8% on SWE-Bench\n\nVerified, surpassing GLM-4.6 and competing with the best proprietary models ([5] glm5.net)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-4.6: An Open-Source AI for Coding vs. Sonnet & GPT-5",
    "Executive Summary",
    "Meanwhile, Z.ai itself\n\nreleased GLM-5 on February 11, 2026 \u2014 a 745B-parameter (44B active) MoE model scoring 77.8% on SWE-Bench\n\nVerified, surpassing GLM-4.6 and competing with the best proprietary models ([5] glm5.net)."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/minimax-m3-1m-context-multimodal-msa-coding-review",
   "title": "MiniMax M3 Review: MSA Architecture, 1M-Token Context, Native Multimodal \u2014 Generational Leap or Benchmark Theater? \u2014 ChatForest",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Released open weights under a restricted commercial auth license",
   "organization_evidence": [
    "MiniMax M3 Review: MSA Architecture, 1M-Token Context, Native Multimodal \u2014 Generational Leap or Benchmark Theater?",
    "MiniMax M3 launched as API-only on **June 1, 2026**, with a commitment to release open weights and a technical report to Hugging Face and GitHub within approximately ten days."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Review: MSA Architecture, 1M-Token Context, Native Multimodal \u2014 Generational Leap or Benchmark Theater?",
    "| M2.7 | Mar 18, 2026 API / Apr 12, 2026 weights | 229B/10B Sparse MoE | ~205K |",
    "Weights available | Pending (June 12) | Released Apr 12, 2026 | Released |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Review: MSA Architecture, 1M-Token Context, Native Multimodal \u2014 Generational Leap or Benchmark Theater?",
    "| M2.7 | Mar 18, 2026 API / Apr 12, 2026 weights | 229B/10B Sparse MoE | ~205K |",
    "Weights available | Pending (June 12) | Released Apr 12, 2026 | Released |",
    "**The M2.7 precedent**: MiniMax\u2019s previous model shipped under terms that required written commercial authorization from MiniMax."
   ]
  },
  {
   "url": "https://news.aibase.com/news/28839",
   "title": "Google Collaborates with NVIDIA to Release Open-Source Model DiffusionGemma: Introduces Diffusion Mechanism, Speeds Up Single-Card Inference by Four Times",
   "published_at": null,
   "organization": "Google",
   "model": "DiffusionGemma",
   "release_date": "2026-06-10",
   "parameters": "26 billion parameters",
   "context_window": null,
   "license": "Apache 2.0 license",
   "significance": "It is the first to introduce the diffusion mechanism used in image AI into the field of text generation, breaking the limitation of autoregressive models that can only look backward.",
   "organization_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word."
   ],
   "model_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word."
   ],
   "release_date_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word."
   ],
   "parameters_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word.",
    "DiffusionGemma has 26 billion parameters and is based on a mixture-of-experts (MoE) architecture, with only 3.8 billion parameters activated in a single step."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word.",
    "Currently, the model weights are open-sourced on Hugging Face under the Apache 2.0 license and are fully compatible with mainstream inference frameworks such as vLLM and MLX."
   ],
   "significance_evidence": [
    "Google officially released the experimental open-source language model DiffusionGemma on June 10, 2026, breaking the traditional autoregressive paradigm of large models that generate text word by word.",
    "It is the first to introduce the diffusion mechanism used in image AI into the field of text generation.",
    "Although its text generation quality and accuracy are slightly inferior to traditional Gemma4 series models in standard benchmark tests, its unique \"full-block awareness\" capability breaks the limitation of autoregressive models that can only look backward."
   ]
  },
  {
   "url": "http://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium (latest)",
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": "262,144",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "model_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "release_date_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "license_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium (latest)](/models/mistral/mistral-medium-latest)mistral/mistral-medium-latest | [2](/models/mistral/mistral-medium-latest#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.40 / $2.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "http://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": "262,144",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "model_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "release_date_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "license_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [6](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $0.00 / $0.00 | 2026-04-29 | 2026-04-29 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "http://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small (latest)",
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": "256,000",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "model_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "release_date_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "parameters_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "context_window_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "license_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "http://models.opencode.ai/labs/mistral",
   "title": "Mistral models, providers, and specs | Models.dev",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small 4",
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": "256,000",
   "license": "Open",
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "model_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "release_date_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "parameters_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "context_window_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "license_evidence": [
    "### Models",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [10](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.07 / $0.28 | 2026-03-16 | 2026-03-16 |"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://gemma4.run/fr/blog/gemma-4-from-leak-to-phenomenon-deep-dive",
   "title": "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive | Gemma4 Tools",
   "published_at": "2026-04-06T00:00:00",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "4 model sizes (E2B, E4B, 26B MoE, 31B Dense)",
   "context_window": "256K tokens",
   "license": "Apache 2.0 open-source license",
   "significance": "represents the largest generational leap in the Gemma series to date; first adoption of the Apache 2.0 open-source license; natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to 256K tokens",
   "organization_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**"
   ],
   "model_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**"
   ],
   "release_date_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**"
   ],
   "parameters_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "context_window_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "license_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ],
   "significance_evidence": [
    "Google Gemma 4: From Leak to Phenomenon \u2014 A Comprehensive Deep Dive",
    "**Google Gemma 4, officially released on April 2, 2026, represents the largest generational leap in the Gemma series to date.**",
    "This generation marks the first adoption of the **Apache 2.0 open-source license** (all previous Gemma models used restrictive licenses), offers 4 model sizes (E2B, E4B, 26B MoE, 31B Dense), natively supports four modalities \u2014 text, image, video, and audio \u2014 and extends the context window up to **256K tokens**."
   ]
  },
  {
   "url": "https://ventureatlas.org/product/moonshot-ai-kimi-k-series-open-weight-models-k2-k2-5-k2-6-k3",
   "title": "Kimi K-series open-weight models (K2 / K2.5 / K2.6 / K3) - Moonshot AI",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "a ~2.8T-parameter MoE with 1M-token context and Kimi Delta Attention, the largest open-weight model ever released",
   "organization_evidence": [
    "Generally Available[Moonshot AI](/company/moonshot-ai)",
    "# Kimi K-series open-weight models (K2 / K2.5 / K2.6 / K3)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot's open-weight frontier line under a modified MIT license: K2 (July 2025, 1T MoE), K2 Thinking (November 2025 agentic-benchmark leader), K2.5 (vision via MoonViT), K2.6, K2.7-Code, and K3 (July 16, 2026) - a ~2.8T-parameter MoE with 1M-token context and Kimi Delta Attention, the largest open-weight model ever released.",
    "# Kimi K-series open-weight models (K2 / K2.5 / K2.6 / K3)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot's open-weight frontier line under a modified MIT license: K2 (July 2025, 1T MoE), K2 Thinking (November 2025 agentic-benchmark leader), K2.5 (vision via MoonViT), K2.6, K2.7-Code, and K3 (July 16, 2026) - a ~2.8T-parameter MoE with 1M-token context and Kimi Delta Attention, the largest open-weight model ever released.",
    "# Kimi K-series open-weight models (K2 / K2.5 / K2.6 / K3)"
   ]
  },
  {
   "url": "https://aimodelsnavi.com/en/models/glm-5-1",
   "title": "GLM 5.1 | AI Models Navi",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "Best open-weight model for agentic coding \u2014 leads SWE-Bench Pro (58.4%) and CyberGym (68.7%) globally",
   "organization_evidence": [
    "A high-performance foundation model developed by Zhipu AI.",
    "[GLM-5](/en/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Release Date\n\n2026-03-27",
    "[GLM-5](/en/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "parameters_evidence": [
    "Parameters (Active/Total)\n\n40B / 744B",
    "[GLM-5](/en/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "\u30fbBest open-weight model for agentic coding \u2014 leads SWE-Bench Pro (58.4%) and CyberGym (68.7%) globally"
   ]
  },
  {
   "url": "https://gentic.news/article/minimax-m2-7-open-sourced-hits-56",
   "title": "MiniMax M2.7 Open-Sourced, Hits 56.22% on SWE-Pro",
   "published_at": "2026-04-12T01:00:14",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "State-of-the-art performance of 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 for coding tasks.",
   "organization_evidence": [
    "MiniMax M2.7 Open-Sourced, Hits 56.22% on SWE-Pro",
    "Chinese AI company MiniMax has released its M2.7 model under an open-source license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 18, 2026, MiniMax announced the open-source release of its M2.7 model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax has open-sourced its M2.7 model, which it claims achieves state-of-the-art scores of 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 for coding tasks.",
    "MiniMax Open-Sources M2.7 Model, Claims SOTA on Coding Benchmarks",
    "On April 18, 2026, MiniMax announced the open-source release of its M2.7 model. The company provided two key benchmark scores to substantiate its \"state-of-the-art\" claim:"
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B / 40B",
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 scores 62.1 on SWE-bench Pro, beating GPT-5.5 at 58.6, lands 81.0 on Terminal-Bench 2.1, and reaches 74.4% on FrontierSWE.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 1. GLM 5.2, the Best Overall Open Source Model",
    "**GLM 5.2** from Zhipu (Z.ai) is the open model to beat in 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 1. GLM 5.2, the Best Overall Open Source Model",
    "Released on **June 13, 2026**, it uses a **744-billion-parameter** Mixture-of-Experts design that activates only **40 billion** parameters per token, paired with a full **1-million-token** context window and a clean **MIT license**."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **GLM 5.2** | Best overall (reasoning + coding) | 744B / 40B | 1M | MIT |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 1. GLM 5.2, the Best Overall Open Source Model",
    "The benchmarks are what put it on top. GLM 5.2 scores **62.1** on SWE-bench Pro, beating GPT-5.5 at 58.6, lands **81.0** on Terminal-Bench 2.1, and reaches **74.4%** on FrontierSWE."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "1T / 32B",
   "context_window": null,
   "license": null,
   "significance": "Every score Moonshot published, including a 62.0 on its own Kimi Code Bench v2 and 81.1 on MCP Mark Verified, comes from in-house benchmarks.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 3. Kimi K2.7 Code, Best for Agentic Coding",
    "Moonshot\u2019s **Kimi K2.7 Code** is purpose-built for autonomous coding agents that write, run, and debug across many steps."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 3. Kimi K2.7 Code, Best for Agentic Coding",
    "It is a **1-trillion-parameter** model activating **32 billion** per token, with a **256K-token** context window under a Modified MIT license, released **June 12, 2026**."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Kimi K2.7 Code** | Agentic coding | 1T / 32B | 256K | Modified MIT |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 3. Kimi K2.7 Code, Best for Agentic Coding",
    "One honest caveat. Every score Moonshot published, including a **62.0** on its own Kimi Code Bench v2 and **81.1** on MCP Mark Verified, comes from in-house benchmarks."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "35B / 3B (to 397B)",
   "context_window": null,
   "license": null,
   "significance": "Qwen 3.6 beats Claude Opus 4.5 on terminal benchmarks and excels at agentic coding and vision, rare for a model you can run yourself.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Alibaba\u2019s **Qwen 3.6** is the open-weight Swiss Army knife."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Released in April 2026, its open variants (the efficient **35B-A3B** y un **27B** dense model) handle more than **100 languages**, ship under a clean **Apache 2.0** license, and now run a **1-million-token** context window."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Qwen 3.6** | Multilingual + versatility | 35B / 3B (to 397B) | 1M | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 4. Qwen 3.6, Best Multilingual and Most Versatile",
    "Qwen 3.6 beats Claude Opus 4.5 on terminal benchmarks and excels at agentic coding and vision, rare for a model you can run yourself."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "600B",
   "context_window": null,
   "license": null,
   "significance": "On capability it matches or beats GPT-5 and Gemini 3 across reasoning, coding, and math, which no earlier Llama managed.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "Meta\u2019s **Llama 5**, released **April 8, 2026**, is the most capable Western open-weight model and the one most likely to be supported everywhere on day one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "Meta\u2019s **Llama 5**, released **April 8, 2026**, is the most capable Western open-weight model and the one most likely to be supported everywhere on day one."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Llama 5** | Western frontier open model | 600B | 5M | Llama Community |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 5. Llama 5, Meta\u2019s Western Frontier Open Model",
    "On capability it matches or beats GPT-5 and Gemini 3 across reasoning, coding, and math, which no earlier Llama managed."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "30.7B dense",
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 31B scores 85.2% on MMLU-Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench v6, while the 12B tier runs comfortably on a modern laptop.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 6. Gemma 4, Best Lightweight Model for Local Use",
    "If you want to run AI on your own machine, **Gemma 4** from Google DeepMind is the answer."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 6. Gemma 4, Best Lightweight Model for Local Use",
    "The family spans tiny edge models (E2B, E4B) up to a **31B dense** flagship and a 26B MoE variant, all under **Apache 2.0**, released **April 2, 2026**."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Gemma 4 31B** | Lightweight + local | 30.7B dense | 256K | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 6. Gemma 4, Best Lightweight Model for Local Use",
    "Despite the small footprint it punches hard. Gemma 4 31B scores **85.2%** on MMLU-Pro, **89.2%** on AIME 2026, and **80.0%** on LiveCodeBench v6, while the **12B** tier runs comfortably on a modern laptop."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "Nex AGI",
   "model": null,
   "release_date": null,
   "parameters": "397B / 17B",
   "context_window": null,
   "license": null,
   "significance": "It scores 80.8 on SWE-Bench Verified and 75.3 on Terminal-Bench 2.1, putting it right in the mix with the bigger names while carrying zero commercial restrictions.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 7. Nex-N2-Pro, Best Fully Permissive Agentic Model",
    "**Nex-N2-Pro** from Nex AGI earns its spot on license freedom plus genuine capability."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **Nex-N2-Pro** | Fully permissive agentic | 397B / 17B | 262K | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 7. Nex-N2-Pro, Best Fully Permissive Agentic Model",
    "It scores **80.8** on SWE-Bench Verified and **75.3** on Terminal-Bench 2.1, putting it right in the mix with the bigger names while carrying zero commercial restrictions."
   ]
  },
  {
   "url": "https://felloai.com/es/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "428B / 23B",
   "context_window": null,
   "license": null,
   "significance": "It posts 59.0% on SWE-Bench Pro, edging out GPT-5.5 and Gemini 3.1 Pro, and pairs that with a full 1-million-token context and native multimodality.",
   "organization_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 8. MiniMax M3, Best for Efficiency",
    "**MiniMax M3** rounds out the list for anyone watching hardware cost."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 8. MiniMax M3, Best for Efficiency",
    "Released **June 1, 2026**, it runs **428 billion** total parameters with only **23 billion** active, and its MiniMax Sparse Attention design decodes roughly **15x faster** at full context than M2."
   ],
   "parameters_evidence": [
    "## Best Open Source AI Models 2026 at a Glance",
    "Here is the full ranking. We picked these eight on current benchmark performance, recency, license freedom, and how realistic they are to actually use. Scores are the highest-reported figures from each lab or independent test as of **June 2026**.",
    "| Model | Best for | Params (total / active) | Context | License |",
    "| **MiniMax M3** | Efficiency on light hardware | 428B / 23B | 1M | MiniMax Community |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Best Open Source AI Models, Ranked",
    "### 8. MiniMax M3, Best for Efficiency",
    "It posts **59.0%** on SWE-Bench Pro, edging out GPT-5.5 and Gemini 3.1 Pro, and pairs that with a full **1-million-token** context and native multimodality."
   ]
  },
  {
   "url": "https://krasa.ai/news/alibaba-qwen-3-6-open-source-coding-agent-model",
   "title": "Alibaba Open-Sources Qwen 3.6, a 35B Coding Model That Rivals the Frontier",
   "published_at": "2026-04-19T12:39:05",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Qwen 3.6 scores 73.4 on SWE-bench Verified, putting it within arm's reach of closed frontier models and well ahead of any other openly-licensed model at this parameter count, and scores 81.7 on MMMU.",
   "organization_evidence": [
    "Alibaba Open-Sources Qwen 3.6, a 35B Coding Model That Rivals the Frontier",
    "Alibaba's Qwen team open-sourced Qwen 3.6-35B-A3B on April 16, a sparse mixture-of-experts model with 35 billion total parameters and only 3 billion active at inference."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba Open-Sources Qwen 3.6, a 35B Coding Model That Rivals the Frontier",
    "Alibaba's Qwen team open-sourced Qwen 3.6-35B-A3B on April 16, a sparse mixture-of-experts model with 35 billion total parameters and only 3 billion active at inference."
   ],
   "parameters_evidence": [
    "Alibaba Open-Sources Qwen 3.6, a 35B Coding Model That Rivals the Frontier",
    "Alibaba's Qwen team open-sourced Qwen 3.6-35B-A3B on April 16, a sparse mixture-of-experts model with 35 billion total parameters and only 3 billion active at inference."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba Open-Sources Qwen 3.6, a 35B Coding Model That Rivals the Frontier",
    "Qwen 3.6 scores 73.4, putting it within arm's reach of closed frontier models and well ahead of any other openly-licensed model at this parameter count.",
    "On MMMU (Massive Multi-discipline Multimodal Understanding), a benchmark testing university-level reasoning across images, Qwen 3.6 scores 81.7 \u2014 ahead of Anthropic's Claude Sonnet 4.5 (79.6) and Google's Gemma 4-31B (80.4)."
   ]
  },
  {
   "url": "https://aurigait.com/blog/gemma-4-features-benchmarks-guide",
   "title": "Gemma 4 by Google: Specs, Benchmarks, and How to Run It Locally (2026 Guide)",
   "published_at": "2026-04-03T06:57:06",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-03-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-level intelligence in model families that can run on hardware from a Raspberry Pi to a data-centre GPU, scoring 85.2% on MMLU Pro, 89.2% on AIME 2026, and ranking #3 on Arena AI with MTP drafters delivering up to 3x faster inference.",
   "organization_evidence": [
    "Gemma 4 by Google: Specs, Benchmarks, All 5 Model Sizes & *How to Run Locally* \u2014 Complete 2026 Guide",
    "**Quick answer:** Gemma 4 is Google DeepMind's most capable open AI model family, released March 31, 2026 under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 by Google: Specs, Benchmarks, All 5 Model Sizes & *How to Run Locally* \u2014 Complete 2026 Guide",
    "**Quick answer:** Gemma 4 is Google DeepMind's most capable open AI model family, released March 31, 2026 under the Apache 2.0 license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 by Google: Specs, Benchmarks, All 5 Model Sizes & *How to Run Locally* \u2014 Complete 2026 Guide",
    "**Quick answer:** Gemma 4 is Google DeepMind's most capable open AI model family, released March 31, 2026 under the Apache 2.0 license.",
    "It now comes in 5 sizes: E2B (2B params, for phones), E4B (4B, for edge), the new 12B Unified (multimodal with audio, added June 2026), 26B MoE (3.8B active, for consumer GPUs), and 31B Dense (for workstations).",
    "The 31B scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and ranks #3 on Arena AI.",
    "New MTP drafters deliver up to 3x faster inference with identical output quality."
   ]
  },
  {
   "url": "https://swfte.com/ai/models/moonshot-kimi-k3",
   "title": "Kimi K3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-07-20T22:37:25",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 overall behind Claude Fable 5 and GPT-5.6 Sol and on par with Opus 4.8; #1 on LMArena's Frontend Code Arena at 1,679 Elo.",
   "organization_evidence": [
    "# Kimi K3"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3",
    "Moonshot AI's July 16 2026 open-weight flagship \u2014 a 2.8-trillion-parameter Mixture-of-Experts model (16 of 896 experts active per token) with a 1,048,576-token context and native vision."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3",
    "Artificial Analysis Intelligence Index 57, ranking #3 overall behind Claude Fable 5 (59.9) and GPT-5.6 Sol (58.9) and on par with Opus 4.8 \u2014 the strongest open-weight model shipped."
   ]
  },
  {
   "url": "https://digitalapplied.com/blog/zhipu-glm-5-1-coding-benchmark-claude-opus-comparison",
   "title": "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
   "published_at": "2026-03-26T23:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "744 billion",
   "context_window": null,
   "license": null,
   "significance": "Base GLM-5 model released under Apache-2.0 open-source license; 744 billion parameters total (40 billion active), 200K context window using DeepSeek Sparse Attention, and 77.8 on SWE-bench Verified and 56.2 on Terminal Bench 2.0.",
   "organization_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "Zhipu AI releases GLM-5.1 scoring 45.3 on coding benchmarks \u2014 94.6% of Claude Opus 4.6.",
    "Zhipu AI, operating internationally under the Z.ai brand, released GLM-5.1 on March 27, 2026 as an incremental upgrade to its flagship GLM-5 model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "GLM-5.1 is the latest iteration in Zhipu AI's GLM model family, positioned as an incremental upgrade to the GLM-5 base model that launched on February 11, 2026."
   ],
   "parameters_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis).",
    "The Mixture-of-Experts design is a key efficiency decision.",
    "While the model contains 744 billion parameters total, only 40 billion are active during any given forward pass."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis).",
    "For context, GLM-5 already posted strong numbers on established benchmarks: 77.8 on SWE-bench Verified and 56.2 on Terminal Bench 2.0.",
    "While the model contains 744 billion parameters total, only 40 billion are active during any given forward pass.",
    "The 200K context window enabled by DSA places GLM-5 in the same tier as Claude's context length while keeping attention computation costs manageable.",
    "The base GLM-5 model is released under the Apache-2.0 license, one of the most permissive open-source licenses available."
   ]
  },
  {
   "url": "https://digitalapplied.com/blog/zhipu-glm-5-1-coding-benchmark-claude-opus-comparison",
   "title": "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
   "published_at": "2026-03-26T23:00:00",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-11",
   "parameters": "744 billion",
   "context_window": "200K",
   "license": "Apache-2.0",
   "significance": "77.8 on SWE-bench Verified and 56.2 on Terminal Bench 2.0; 744 billion parameters total with 40 billion active; 200K context window; released under Apache-2.0.",
   "organization_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis)."
   ],
   "model_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis)."
   ],
   "release_date_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "GLM-5.1 is the latest iteration in Zhipu AI's GLM model family, positioned as an incremental upgrade to the GLM-5 base model that launched on February 11, 2026."
   ],
   "parameters_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis).",
    "While the model contains 744 billion parameters total, only 40 billion are active during any given forward pass."
   ],
   "context_window_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis).",
    "The 200K context window enabled by DSA places GLM-5 in the same tier as Claude's context length while keeping attention computation costs manageable."
   ],
   "license_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
    "For a deeper analysis of the GLM-5 base model that GLM-5.1 builds upon, see our [comprehensive GLM-5 analysis](/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis).",
    "The base GLM-5 model is released under the Apache-2.0 license, one of the most permissive open-source licenses available."
   ],
   "significance_evidence": [
    "Zhipu GLM-5.1: 94% of Claude Opus 4.6 Coding Performance",
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   "url": "https://techaffiliate.in/blog/minimax-m3-free-benchmarks-review-2026",
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   "url": "https://compsmag.com/news/nvidia-releases-nemotron-3-5-lightning-open-source-agent-model",
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   "url": "https://techfastforward.com/articles/meta-llama-4-scout-10m-context-maverick-open-weights-gpt4o-2026",
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   "url": "https://simplifyingai.co/p/google-just-made-gemma-free-on-your-laptop",
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    "Both are released under the MIT license, both support a three-way thinking-mode switch, and both expose the same OpenAI- and Anthropic-compatible API surface."
   ],
   "significance_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "In practical terms, here is what that buys you:",
    "- **Frontier-tier coding.** V4-Pro achieves 80.6% on SWE-bench Verified \u2014 within 0.2 points of Claude Opus 4.6."
   ]
  },
  {
   "url": "https://deepseekai.guide/guides/deepseek-capabilities",
   "title": "DeepSeek Capabilities in 2026: V4 Features Tested",
   "published_at": "2026-04-25T11:42:47",
   "organization": "DeepSeek",
   "model": "deepseek-v4-flash",
   "release_date": "2026-04-24",
   "parameters": "284B parameters (13B activated)",
   "context_window": "one million tokens",
   "license": "MIT license",
   "significance": "Offers cost-efficient capability with access to 284B worth of specialized expert knowledge.",
   "organization_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "That is when DeepSeek shipped V4 Preview as two open-weight models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 and the DeepSeek capabilities reset that came with them is the biggest shift since R1."
   ],
   "model_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "That is when DeepSeek shipped V4 Preview as two open-weight models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 and the DeepSeek capabilities reset that came with them is the biggest shift since R1."
   ],
   "release_date_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "If you\u2019re trying to work out whether DeepSeek can replace Claude or GPT-5 in your stack, the answer changed on April 24, 2026."
   ],
   "parameters_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "The short answer: what DeepSeek can do in April 2026",
    "DeepSeek-V4-Pro has 1.6T parameters (49B activated) and DeepSeek-V4-Flash has 284B parameters (13B activated) \u2014 both supporting a context length of one million tokens."
   ],
   "context_window_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "The short answer: what DeepSeek can do in April 2026",
    "DeepSeek-V4-Pro has 1.6T parameters (49B activated) and DeepSeek-V4-Flash has 284B parameters (13B activated) \u2014 both supporting a context length of one million tokens."
   ],
   "license_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "The short answer: what DeepSeek can do in April 2026",
    "Both are released under the MIT license, both support a three-way thinking-mode switch, and both expose the same OpenAI- and Anthropic-compatible API surface."
   ],
   "significance_evidence": [
    "What DeepSeek Can Actually Do: V4 Capabilities Tested",
    "### V4-Flash: the cost-efficient sibling",
    "DeepSeek-V4-Flash is the efficiency play: 284 billion total parameters, 13 billion active per token, trained on 32 trillion tokens."
   ]
  },
  {
   "url": "https://wikiwand.com/en/articles/Qwen",
   "title": "Qwen - Wikiwand",
   "published_at": null,
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-15",
   "parameters": "35B-A3B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# Qwen",
    "Family of large language models by Alibaba From Wikipedia, the free encyclopedia"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.6-35B-A3B / April15, 2026",
    "# Qwen",
    "Family of large language models by Alibaba From Wikipedia, the free encyclopedia"
   ],
   "parameters_evidence": [
    "The Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month.",
    "## Models",
    "#### Qwen3.5 and Qwen3.6"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://wikiwand.com/en/articles/Qwen",
   "title": "Qwen - Wikiwand",
   "published_at": null,
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": "35B-A3B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# Qwen",
    "Family of large language models by Alibaba From Wikipedia, the free encyclopedia"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.6 (Qwen3.6-35B-A3B) | April 2026 | [Apache 2.0](./Apache_2.0) |",
    "# Qwen",
    "Family of large language models by Alibaba From Wikipedia, the free encyclopedia",
    "## List of models"
   ],
   "parameters_evidence": [
    "The Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month.",
    "## Models",
    "#### Qwen3.5 and Qwen3.6"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://covenai.io/blog/open-source-ai-explosion-april-2026.html",
   "title": "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "On AIME 2026 (advanced mathematics), the previous Gemma 3 scored 20.8%. Gemma 4's 31B model scores 89.2%. That is not an incremental improvement -- it is a 4x jump.",
   "organization_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Google released Gemma 4 on April 2."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Google released Gemma 4 on April 2."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Google Gemma 4 (April 2)",
    "On [AIME 2026 (advanced mathematics)](https://www.labellerr.com/blog/gemma-4-open-weight-ai-model-overview/), the previous Gemma 3 scored 20.8%. Gemma 4's 31B model scores 89.2%. That is not an incremental improvement -- it is a 4x jump."
   ]
  },
  {
   "url": "https://covenai.io/blog/open-source-ai-explosion-april-2026.html",
   "title": "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Its headline feature is a 10-million-token context window -- roughly 5 million words.",
   "organization_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Meta shipped Llama 4 Scout and Maverick on April 5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Meta shipped Llama 4 Scout and Maverick on April 5."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Llama 4 Scout and Maverick (April 5)",
    "**Llama 4 Scout** has 109 billion total parameters with 16 experts and 17 billion active parameters.",
    "Its headline feature is a [10-million-token context window](https://gpt-trainer.com/blog/llama+4+evolution+features+comparison) -- roughly 5 million words."
   ]
  },
  {
   "url": "https://covenai.io/blog/open-source-ai-explosion-april-2026.html",
   "title": "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "On LiveCodeBench, it outperforms models several times its size while producing 20% less output.",
   "organization_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Mistral Small 4 (Late March, Now Proven)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source AI Explosion: Free Models Now Rival the Best Paid Options",
    "Mistral Small 4 (Late March, Now Proven)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Small 4 (Late March, Now Proven)"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/mistral-medium-35-remote-agents-vibe-776-swe-bench-opens-prs-2026",
   "title": "Mistral Just Quietly Launched the Coding Agent That Opens Your PRs While You Sleep",
   "published_at": "2026-05-12T10:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-05-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified, outperforms Devstral 2 (72.2%) and Qwen 3.5 at 397B parameters, making Medium 3.5 the strongest open-weight coding model available as of May 2026",
   "organization_evidence": [
    "Model Release",
    "# Mistral Just Quietly Launched the Coding Agent That Opens Your PRs While You Sleep",
    "That is not a dream. It is what Mistral AI shipped on May 2, 2026, with Mistral Medium 3.5 and remote agents in Vibe, its cloud coding platform."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Mistral Just Quietly Launched the Coding Agent That Opens Your PRs While You Sleep",
    "That is not a dream. It is what Mistral AI shipped on May 2, 2026, with Mistral Medium 3.5 and remote agents in Vibe, its cloud coding platform."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Mistral Just Quietly Launched the Coding Agent That Opens Your PRs While You Sleep",
    "- 77.6% SWE-Bench Verified, outperforms Devstral 2 (72.2%) and Qwen 3.5 at 397B parameters, making Medium 3.5 the strongest open-weight coding model available as of May 2026"
   ]
  },
  {
   "url": "https://kersai.com/google-gemma-4-free-open-source-ai-beats-models-20x-its-size-complete-guide-2026",
   "title": "Google Gemma 4: Free AI That Beats Models 20x Its Size | Kersai",
   "published_at": "2026-04-15T18:59:39",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 open model in the world on the Arena AI leaderboard, outcompetes models 20 times its size, scores 89.2% on AIME 2026, 80.0% on LiveCodeBench v6, and 84.3% on GPQA Diamond.",
   "organization_evidence": [
    "Google Gemma 4 Is 100% Free Under Apache 2.0. The 31B Model Ranks #3 Open Model in the World, Scores 89.2% on a Mathematics Olympiad, Processes Text, Images, Audio, and Video Natively, and Runs Completely Offline on a Consumer GPU. Here Is the Complete Guide.",
    "Published: April 15, 2026 | By the Kersai Research Team | Reading Time: ~22 minutes",
    "On April 2, 2026, Google DeepMind released Gemma 4 \u2014 its most capable open-source AI model family to date, built on the same research and technology as Gemini 3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Is 100% Free Under Apache 2.0. The 31B Model Ranks #3 Open Model in the World, Scores 89.2% on a Mathematics Olympiad, Processes Text, Images, Audio, and Video Natively, and Runs Completely Offline on a Consumer GPU. Here Is the Complete Guide.",
    "Published: April 15, 2026 | By the Kersai Research Team | Reading Time: ~22 minutes",
    "On April 2, 2026, Google DeepMind released Gemma 4 \u2014 its most capable open-source AI model family to date, built on the same research and technology as Gemini 3."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Is 100% Free Under Apache 2.0. The 31B Model Ranks #3 Open Model in the World, Scores 89.2% on a Mathematics Olympiad, Processes Text, Images, Audio, and Video Natively, and Runs Completely Offline on a Consumer GPU. Here Is the Complete Guide.",
    "Published: April 15, 2026 | By the Kersai Research Team | Reading Time: ~22 minutes",
    "Performance is extraordinary: the 31B model is currently ranked #3 open model in the world on the Arena AI leaderboard, and outcompetes models 20 times its size."
   ]
  },
  {
   "url": "https://ai-all.info/en/ai-models/glm-5-2-1m",
   "title": "GLM-5.2 \u2013 Zhipu AI's Next-Gen Open LLM with 1M Context",
   "published_at": "2026-06-21T13:18:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1M-token ultra-long context window and deep focus on Agentic Coding (autonomous programming agents).",
   "organization_evidence": [
    "# GLM-5.2 \u2013 Zhipu AI's Next-Gen Open LLM with 1M Context",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 \u2013 Zhipu AI's Next-Gen Open LLM with 1M Context",
    "GLM-5.2 is Zhipu AI's next-generation open large language model, released on June 13, 2026, under the MIT license."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 \u2013 Zhipu AI's Next-Gen Open LLM with 1M Context",
    "Its headline features are a 1M-token ultra-long context window and deep focus on Agentic Coding (autonomous programming agents)."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/minimax-m2-5",
   "title": "MiniMax M2.5 & M2.5-Lightning: Specs, Price | AI/TLDR",
   "published_at": "2026-02-12T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": "229B total / 10B active (MoE)",
   "context_window": null,
   "license": null,
   "significance": "80.2% on SWE-bench Verified and 76.3% on BrowseComp, competitive with much larger closed models",
   "organization_evidence": [
    "# MiniMax M2.5 / M2.5-Lightning",
    "MiniMax M2.5 is an open-weight large language model from Shanghai-based MiniMax, released on February 12, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M2.5 / M2.5-Lightning",
    "MiniMax M2.5 is an open-weight large language model from Shanghai-based MiniMax, released on February 12, 2026.",
    "| Released | 2026-02-12 |"
   ],
   "parameters_evidence": [
    "# MiniMax M2.5 / M2.5-Lightning",
    "| Parameters | 229B total / 10B active (MoE) |"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M2.5 / M2.5-Lightning",
    "It posts 80.2% on SWE-bench Verified and 76.3% on BrowseComp, and lands around 42 on the Artificial Analysis Intelligence Index \u2014 competitive with much larger closed models."
   ]
  },
  {
   "url": "https://aichina.news/blog/the-32b-sweet-spot-allenai-s-sera-32b-ga-hits-huawei-s-ascend-platform-d9g1d2",
   "title": "The 32B Sweet Spot: AllenAI's SERA-32B-GA Hits Huawei's Ascend Platform \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T13:26:04",
   "organization": "AllenAI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "32B",
   "context_window": null,
   "license": null,
   "significance": "Sits in a competitive sweet spot, balancing high reasoning capabilities with manageable computational costs.",
   "organization_evidence": [
    "The 32B Sweet Spot: AllenAI's SERA-32B-GA Hits Huawei's Ascend Platform",
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   "model_evidence": null,
   "release_date_evidence": [
    "The 32B Sweet Spot: AllenAI's SERA-32B-GA Hits Huawei's Ascend Platform",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers",
    "- **Suspicious metadata** \u2014 the creation date is listed as 2026-04-12, which is forward-dated and unverifiable."
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   "parameters_evidence": [
    "The 32B Sweet Spot: AllenAI's SERA-32B-GA Hits Huawei's Ascend Platform",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 32B Sweet Spot: AllenAI's SERA-32B-GA Hits Huawei's Ascend Platform",
    "Secondly, as a 32B-parameter model, it sits in a competitive sweet spot, balancing high reasoning capabilities with manageable computational costs."
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  },
  {
   "url": "https://neomanex.com/news/poolside-laguna-s-2-1-launch",
   "title": "Laguna S 2.1: The West's Open-Weight Answer Arrives | AI News",
   "published_at": "2026-07-22T07:30:00",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "118B",
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   "license": null,
   "significance": "Beats DeepSeek-V4-Pro-Max, Inkling, and Nemotron 3 Ultra on Terminal-Bench 2.1; first credible Western open-weight coding model in nearly a year.",
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    "PoolsideImportantJuly 22, 2026Models",
    "Poolside released Laguna S 2.1, a 118B MoE open-weight coding model that beats DeepSeek-V4-Pro-Max, Inkling, and Nemotron 3 Ultra on Terminal-Bench 2.1."
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   "release_date_evidence": [
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    "Laguna S 2.1: The West's Open-Weight Answer Arrives",
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   "significance_evidence": [
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    "Poolside released Laguna S 2.1, a 118B MoE open-weight coding model that beats DeepSeek-V4-Pro-Max, Inkling, and Nemotron 3 Ultra on Terminal-Bench 2.1."
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  },
  {
   "url": "https://ainchina.com/blog/qwen-billion-downloads-china-open-source-ai-dominance-2026",
   "title": "Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
   "published_at": "2026-06-21T00:00:00",
   "organization": "Alibaba",
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   "release_date": null,
   "parameters": "27B",
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   "significance": "Occupies the sweet spot for home/consumer GPU deployment (single RTX 4090), achieving widespread adoption and part of the Qwen3.6 family that swept the Hugging Face Open LLM Leaderboard.",
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    "# Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
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   "release_date_evidence": null,
   "parameters_evidence": [
    "AI Ecosystem16 min read",
    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-27B** | 27B | Home/consumer GPU deployment | Single RTX 4090 | Yes |"
   ],
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   "significance_evidence": [
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    "## The Moment the Counter Reset",
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    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "The 27B model is particularly strategic.",
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   ]
  },
  {
   "url": "https://ainchina.com/blog/qwen-billion-downloads-china-open-source-ai-dominance-2026",
   "title": "Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
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    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
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    "| **Qwen3.6-32B** | 32B | Small business & startups | 2x A100 | Yes |"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "AI Ecosystem16 min read",
    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-32B** | 32B | Small business & startups | 2x A100 | Yes |"
   ]
  },
  {
   "url": "https://ainchina.com/blog/qwen-billion-downloads-china-open-source-ai-dominance-2026",
   "title": "Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
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    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-72B** | 72B | Enterprise production | 4x A100 / H100 | Yes |"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "AI Ecosystem16 min read",
    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-72B** | 72B | Enterprise production | 4x A100 / H100 | Yes |"
   ]
  },
  {
   "url": "https://ainchina.com/blog/qwen-billion-downloads-china-open-source-ai-dominance-2026",
   "title": "Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
   "published_at": "2026-06-21T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Targeted for agent workflows with tools requiring 2x A100 GPUs under the Apache 2.0 open license.",
   "organization_evidence": [
    "AI Ecosystem16 min read",
    "# Qwen's Open-Source Conquest: How China's AI Model Captured a Billion Downloads and Redrew Global Developer Economics",
    "Alibaba's open-source AI model family had officially crossed one billion cumulative downloads \u2014 a milestone that took Meta's Llama series nearly twice as long to achieve."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "AI Ecosystem16 min read",
    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-35B-A3B** | 35B | Agent workflows with tools | 2x A100 | Yes |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Ecosystem16 min read",
    "## The Product Line: How Qwen3.6 Covers Every Compute Budget",
    "| Model | Parameters | Target Use Case | GPU Requirement | Apache 2.0? |",
    "| **Qwen3.6-35B-A3B** | 35B | Agent workflows with tools | 2x A100 | Yes |"
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-08/06-mistral-releases-shieldstral-a-3b-open-weight-model-that-moderates-text-and-images-against-plain-language-policies-at-inference-time",
   "title": "Mistral Releases Shieldstral, a 3B Open-Weight Model That Moderates Text and Images Against Plain-Language Policies at Inference Time",
   "published_at": "2026-08-06T10:43:52",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "3B",
   "context_window": null,
   "license": null,
   "significance": "matches or outperforms models nearly 7\u00d7 its size on text safety benchmarks and sets a new state of the art on multimodal safety classification",
   "organization_evidence": [
    "# Mistral Releases Shieldstral, a 3B Open-Weight Model That Moderates Text and Images Against Plain-Language Policies at Inference Time",
    "Mistral AI open-sourced Shieldstral, a 3B-parameter safety classifier that judges content against natural-language policies without retraining, released as the first project from NVIDIA's Open Secure AI Alliance."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Overview"
   ],
   "parameters_evidence": [
    "# Mistral Releases Shieldstral, a 3B Open-Weight Model That Moderates Text and Images Against Plain-Language Policies at Inference Time",
    "Mistral AI open-sourced Shieldstral, a 3B-parameter safety classifier that judges content against natural-language policies without retraining, released as the first project from NVIDIA's Open Secure AI Alliance."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What We Know",
    "The [arXiv paper](https://arxiv.org/abs/2607.25857) describes Shieldstral as a model that \u201cmatches or outperforms models nearly 7\u00d7 its size on text safety benchmarks and sets a new state of the art on multimodal safety classification.\u201d"
   ]
  },
  {
   "url": "https://codersera.com/blog/gemma-4-complete-guide-2026",
   "title": "Gemma 4 Guide: E2B, E4B, 26B MoE & 31B Open Weights (2026)",
   "published_at": "2026-05-01T07:16:50",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "four sizes \u2014 Gemma 4 E2B (~2.3B effective), E4B (~4.5B effective), 26B A4B (Mixture-of-Experts, 4B active), and 31B dense.",
   "context_window": null,
   "license": null,
   "significance": "reasoning, math (AIME 2026 ~89%), code generation (LiveCodeBench v6 ~80%), long-context recall, and on-device deployment via MediaPipe / LiteRT.",
   "organization_evidence": [
    "# Gemma 4: The Complete Developer Guide (2026)",
    "- **Released:** April 2, 2026, by Google DeepMind."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: The Complete Developer Guide (2026)",
    "- **Released:** April 2, 2026, by Google DeepMind."
   ],
   "parameters_evidence": [
    "# Gemma 4: The Complete Developer Guide (2026)",
    "- **Family:** four sizes \u2014 Gemma 4 E2B (~2.3B effective), E4B (~4.5B effective), 26B A4B (Mixture-of-Experts, 4B active), and 31B dense."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: The Complete Developer Guide (2026)",
    "- **Strong points:** reasoning, math (AIME 2026 ~89%), code generation (LiveCodeBench v6 ~80%), long-context recall, and on-device deployment via MediaPipe / LiteRT."
   ]
  },
  {
   "url": "https://techstartups.com/2026/07/27/moonshot-ai-releases-kimi-k3-open-weights-largest-free-ai-model-ever-at-2-8-trillion-parameters",
   "title": "Moonshot AI releases Kimi K3 open weights, largest free AI model ever at 2.8 trillion parameters",
   "published_at": "2026-07-27T14:51:33",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Ranked second overall on AA-Briefcase, and within hours of its debut, climbed to the top of Arena.ai\u2019s Frontend Code Arena benchmark, placing ahead of Anthropic\u2019s Claude Fable 5.",
   "organization_evidence": [
    "# Moonshot AI releases Kimi K3 open weights, largest free AI model ever at 2.8 trillion parameters",
    "Chinese startup Moonshot AI has released the full open weights of Kimi K3, handing developers free access to what is now the largest open-weight AI model ever made publicly available."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Posted On July 27, 2026",
    "The [release became available](https://huggingface.co/blog/ResterChed/kimi-k3-model-overview-mxfp4-quantization-open-wei) at 00:00 UTC on July 27 through Hugging Face, where developers can download the complete model weights."
   ],
   "parameters_evidence": [
    "# Moonshot AI releases Kimi K3 open weights, largest free AI model ever at 2.8 trillion parameters",
    "At 2.8 trillion parameters, Kimi K3 pushes open models into territory that was once reserved for a handful of well-funded AI labs, adding fresh momentum to the growing competition between open and proprietary AI systems."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI releases Kimi K3 open weights, largest free AI model ever at 2.8 trillion parameters",
    "Within hours of its debut, Kimi K3 climbed to the top of Arena.ai\u2019s Frontend Code Arena, a benchmark that measures how well AI systems build complete web applications from natural language prompts."
   ]
  },
  {
   "url": "https://aifoss.dev/blog/glm-5-1-review-2026",
   "title": "GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro",
   "published_at": "2026-06-08T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744B total parameters, 40B active per forward pass",
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4% on SWE-Bench Pro in April 2026 \u2014 the first open-source model to top the leaderboard ahead of GPT-5.4 and Claude Opus 4.6.",
   "organization_evidence": [
    "# GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro",
    "Z.ai (formerly Zhipu AI) released GLM-5.1 on April 7, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro",
    "Z.ai (formerly Zhipu AI) released GLM-5.1 on April 7, 2026.",
    "- **Released**: April 7, 2026 by Z.ai"
   ],
   "parameters_evidence": [
    "# GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro",
    "Self-hosting requires 24 GB GPU + 256 GB system RAM at minimum (2-bit Unsloth GGUF).",
    "Z.ai (formerly Zhipu AI) released GLM-5.1 on April 7, 2026.",
    "- **Parameters**: 744B total / 40B active (MoE)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.1 Review 2026: MIT 744B MoE That Tops SWE-Bench Pro",
    "**TL;DR**: GLM-5.1 is a 744B MIT-licensed MoE model from Z.ai that scored 58.4% on SWE-Bench Pro in April 2026 \u2014 the first open-source model to top the leaderboard ahead of GPT-5.4 and Claude Opus 4.6."
   ]
  },
  {
   "url": "https://theaitrack.com/minimax-m3-open-weight-ai-model",
   "title": "MiniMax M3 Brings 1M Context to Open AI Models",
   "published_at": "2026-06-01T19:07:28",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": "1 million tokens",
   "license": null,
   "significance": "MiniMax reports that M3 scores 59.0% on SWE-Bench Pro, narrowly ahead of GPT-5.5 at 58.6% and Gemini 3.1 Pro at 54.2% in the company\u2019s comparison.",
   "organization_evidence": [
    "## The Story",
    "[MiniMax released M3 on June 1, 2026](https://www.minimax.io/blog/minimax-m3), positioning it as an open-weight model for developers, AI agents, coding assistants, research workflows, and long-document analysis."
   ],
   "model_evidence": [
    "## The Story",
    "[MiniMax released M3 on June 1, 2026](https://www.minimax.io/blog/minimax-m3), positioning it as an open-weight model for developers, AI agents, coding assistants, research workflows, and long-document analysis."
   ],
   "release_date_evidence": [
    "## The Story",
    "[MiniMax released M3 on June 1, 2026](https://www.minimax.io/blog/minimax-m3), positioning it as an open-weight model for developers, AI agents, coding assistants, research workflows, and long-document analysis."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "## The Story",
    "[MiniMax released M3 on June 1, 2026](https://www.minimax.io/blog/minimax-m3), positioning it as an open-weight model for developers, AI agents, coding assistants, research workflows, and long-document analysis.",
    "The model supports text, image, and video input, can handle up to 1 million tokens of context, and is designed for long-running tasks that require tool use, coding, browsing, desktop operation, and multi-step reasoning."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmark Claims",
    "MiniMax reports that M3 scores **59.0% on SWE-Bench Pro**, narrowly ahead of GPT-5.5 at **58.6%** and Gemini 3.1 Pro at **54.2%** in the company\u2019s comparison."
   ]
  },
  {
   "url": "https://genalphai.com/the-2026-ai-model-landscape",
   "title": "AI Models 2026: The Mid-Year Frontier and Open-Weight Map",
   "published_at": "2026-06-16T05:53:05",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "80.6% (Pro-Max)",
   "organization_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "DeepSeek",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| DeepSeek | V4-Pro (Apr 2026) | 49B / 1.6T* | Apache 2.0 | 80.6% (Pro-Max) | Cheapest inference in tier |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "The V4 line (March, April 2026) is the current generation, with the [DeepSeek V4 model card on NVIDIA NIM](https://build.nvidia.com/deepseek-ai/deepseek-v4-flash) anchoring the family.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| DeepSeek | V4-Pro (Apr 2026) | 49B / 1.6T* | Apache 2.0 | 80.6% (Pro-Max) | Cheapest inference in tier |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "DeepSeek's own post reports V4-Pro-Max at 80.6% on SWE-bench Verified.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| DeepSeek | V4-Pro (Apr 2026) | 49B / 1.6T* | Apache 2.0 | 80.6% (Pro-Max) | Cheapest inference in tier |"
   ]
  },
  {
   "url": "https://genalphai.com/the-2026-ai-model-landscape",
   "title": "AI Models 2026: The Mid-Year Frontier and Open-Weight Map",
   "published_at": "2026-06-16T05:53:05",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "73.4%",
   "organization_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "Alibaba (Qwen)",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Qwen | Qwen3.6-35B-A3B | 3B / 35B | Apache 2.0 | 73.4% | Best coding-per-GB |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "Qwen3.6-35B-A3B (April 2026) is a 35B-total / 3B-active MoE reporting 73.4% SWE-bench in a size class that fits a 32GB GPU.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Qwen | Qwen3.6-35B-A3B | 3B / 35B | Apache 2.0 | 73.4% | Best coding-per-GB |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "Qwen3.6-35B-A3B (April 2026) is a 35B-total / 3B-active MoE reporting 73.4% SWE-bench in a size class that fits a 32GB GPU.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Qwen | Qwen3.6-35B-A3B | 3B / 35B | Apache 2.0 | 73.4% | Best coding-per-GB |"
   ]
  },
  {
   "url": "https://genalphai.com/the-2026-ai-model-landscape",
   "title": "AI Models 2026: The Mid-Year Frontier and Open-Weight Map",
   "published_at": "2026-06-16T05:53:05",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "~75%",
   "organization_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "Moonshot AI (Kimi)",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Kimi | K2.6 (Apr 2026) | 32B / 1T* | Apache 2.0 | ~75%\u2020 | Aggressive open reasoning |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Kimi | K2.6 (Apr 2026) | 32B / 1T* | Apache 2.0 | ~75%\u2020 | Aggressive open reasoning |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The open-weight market is where the 2026 story gets interesting.",
    "The open-weight family table",
    "| Family | Frontier model | Active / total | License | SWE-bench Verified | Note |",
    "| Kimi | K2.6 (Apr 2026) | 32B / 1T* | Apache 2.0 | ~75%\u2020 | Aggressive open reasoning |"
   ]
  },
  {
   "url": "https://codersera.com/blog/qwen-webworld-launch-guide-2026",
   "title": "Qwen WebWorld: Open-Source Web Agent Simulator (2026)",
   "published_at": "2026-05-28T19:04:04",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-05-11",
   "parameters": "8B/14B/32B",
   "context_window": null,
   "license": null,
   "significance": "WebWorld-32B matches Claude Opus 4.1 on factuality and beats GPT-5 as a lookahead world model. Fine-tuning Qwen3-14B on WebWorld trajectories lifts WebArena by +9.2 points.",
   "organization_evidence": [
    "Qwen WebWorld: Alibaba's Open-Source Web World Model (2026)",
    "Two weeks after Qwen 3.7 Max, Alibaba shipped WebWorld: an Apache 2.0 web world model series that simulates browsers for agent training."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen WebWorld: Alibaba's Open-Source Web World Model (2026)",
    "Qwen WebWorld is Alibaba's open-source web world model series (8B/14B/32B, Apache 2.0), released May 11, 2026."
   ],
   "parameters_evidence": [
    "# Qwen WebWorld: Alibaba's Open-Source Web World Model (2026)",
    "Qwen WebWorld is Alibaba's open-source web world model series (8B/14B/32B, Apache 2.0), released May 11, 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen WebWorld: Alibaba's Open-Source Web World Model (2026)",
    "WebWorld-32B matches Claude Opus 4.1 on factuality and beats GPT-5 as a lookahead world model.",
    "Fine-tuning Qwen3-14B on WebWorld trajectories lifts WebArena by +9.2 points."
   ]
  },
  {
   "url": "https://aiinsiders.net/article/mistral-open-sources-leanstral-a-119b-model-for-proving",
   "title": "Mistral open-sources Leanstral, a 119B model for proving code correct",
   "published_at": "2026-07-07T10:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-04",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "an open-weight theorem prover built to check formal proofs and verify code",
   "organization_evidence": [
    "TOOLS",
    "Mistral open-sources Leanstral, a 119B model for proving code correct",
    "Mistral released Leanstral 1.5 on GitHub July 4, an open-weight theorem prover built to check formal proofs and verify code."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "TOOLS",
    "Mistral open-sources Leanstral, a 119B model for proving code correct",
    "Mistral released Leanstral 1.5 on GitHub July 4, an open-weight theorem prover built to check formal proofs and verify code.",
    "Per Mistral\u2019s LeanstralReport.pdf, posted to the mistralai/LeanstralSafeVerify repository on GitHub on July 4, 2026."
   ],
   "parameters_evidence": [
    "TOOLS",
    "Mistral open-sources Leanstral, a 119B model for proving code correct",
    "The model runs at roughly 119 billion parameters and is built on Mistral\u2019s general-purpose coding framework, extended to reason about formal proofs rather than just generate code."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "TOOLS",
    "Mistral open-sources Leanstral, a 119B model for proving code correct",
    "Mistral released Leanstral 1.5 on GitHub July 4, an open-weight theorem prover built to check formal proofs and verify code."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/google-gemma-4",
   "title": "Gemma 4 \u2014 open models for reasoning and agents",
   "published_at": "2026-04-02T12:00:00",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B MoE, 31B dense",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "The 31B scores 89.2% on AIME 2026.",
   "organization_evidence": [
    "Google DeepMind \u00b7 2026-04-02 \u00b7 major",
    "# Gemma 4 \u2014 open models for reasoning and agents",
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in."
   ],
   "model_evidence": [
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in.",
    "# Gemma 4 \u2014 open models for reasoning and agents"
   ],
   "release_date_evidence": [
    "Google DeepMind \u00b7 2026-04-02 \u00b7 major",
    "# Gemma 4 \u2014 open models for reasoning and agents",
    "Gemma 4 is Google DeepMind's latest family of open-weights models, built from the same research as Gemini 3 and released on April 2, 2026."
   ],
   "parameters_evidence": [
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in.",
    "# Gemma 4 \u2014 open models for reasoning and agents",
    "Key specs",
    "| Variants | E2B, E4B, 26B MoE, 31B dense |"
   ],
   "context_window_evidence": [
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in.",
    "# Gemma 4 \u2014 open models for reasoning and agents"
   ],
   "license_evidence": [
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in.",
    "# Gemma 4 \u2014 open models for reasoning and agents",
    "Key specs",
    "| License | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Google DeepMind ships four [Gemma 4](/models/gemma-4/) sizes (E2B, E4B, 26B MoE, 31B dense) under Apache 2.0 with 256K context, native vision/audio, and agentic tool-use built in.",
    "# Gemma 4 \u2014 open models for reasoning and agents",
    "The 31B scores 89.2% on AIME 2026."
   ]
  },
  {
   "url": "https://davarion.com/en/blog/kimi-k3-open-weights-largest-model-release-history",
   "title": "Kimi K3: The Largest Open-Weight AI Model in History Drops Today with 2.8 Trillion Parameters",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": "1-million-token",
   "license": "Modified MIT",
   "significance": "Scored 57.1 on Artificial Analysis Intelligence Index v4.1, ranking as the world's third-best model",
   "organization_evidence": [
    "In the early hours of July 27, 2026 (UTC), Moonshot AI fulfilled its promise and published the full weights of Kimi K3 on Hugging Face, marking an unprecedented milestone in the history of open-source artificial intelligence."
   ],
   "model_evidence": [
    "In the early hours of July 27, 2026 (UTC), Moonshot AI fulfilled its promise and published the full weights of Kimi K3 on Hugging Face, marking an unprecedented milestone in the history of open-source artificial intelligence."
   ],
   "release_date_evidence": [
    "In the early hours of July 27, 2026 (UTC), Moonshot AI fulfilled its promise and published the full weights of Kimi K3 on Hugging Face, marking an unprecedented milestone in the history of open-source artificial intelligence."
   ],
   "parameters_evidence": [
    "With 2.8 trillion parameters organized in a Mixture-of-Experts (MoE) architecture, Kimi K3 far surpasses any previously released open-weight model.",
    "In the early hours of July 27, 2026 (UTC), Moonshot AI fulfilled its promise and published the full weights of Kimi K3 on Hugging Face, marking an unprecedented milestone in the history of open-source artificial intelligence."
   ],
   "context_window_evidence": [
    "The model features a 1-million-token context window \u2014 sufficient to process entire books, extensive codebases, or legal contracts hundreds of pages long in a single call.",
    "## What Did Moonshot AI Announce with Kimi K3?"
   ],
   "license_evidence": [
    "The full download requires approximately 1.4 terabytes in MXFP4 format, and the model is published under a Modified MIT license that allows commercial use, fine-tuning, and self-hosting without royalty payments.",
    "In the early hours of July 27, 2026 (UTC), Moonshot AI fulfilled its promise and published the full weights of Kimi K3 on Hugging Face, marking an unprecedented milestone in the history of open-source artificial intelligence."
   ],
   "significance_evidence": [
    "On the independent Artificial Analysis Intelligence Index v4.1, Kimi K3 scored 57.1, ranking as the world's third-best model, just behind GPT-5.6 Sol Max (58.9) and Claude Fable 5 (59.9).",
    "## What Did Moonshot AI Announce with Kimi K3?"
   ]
  },
  {
   "url": "https://opensourceforu.com/2026/06/minimax-challenges-ai-rivals-with-m3-but-stops-short-of-full-open-source-commitment",
   "title": "MiniMax Challenges AI Rivals With M3 But Stops Short Of Full Open Source Commitment - Open Source For You",
   "published_at": "2026-06-01T09:04:11",
   "organization": "MiniMax",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax claims M3 outperformed OpenAI\u2019s GPT-5.5 and Google\u2019s Gemini 3.1 Pro on the SWE-Bench Pro coding benchmark.",
   "organization_evidence": [
    "MiniMax Challenges AI Rivals With M3 But Stops Short Of Full Open Source Commitment",
    "Chinese AI startup MiniMax has unveiled MiniMax-M3, a flagship coding-focused AI model that positions the company more aggressively in the increasingly competitive open-source and open-weights AI landscape, even as it stops short of fully open-sourcing the technology."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Challenges AI Rivals With M3 But Stops Short Of Full Open Source Commitment",
    "MiniMax claims M3 outperformed OpenAI\u2019s GPT-5.5 and Google\u2019s Gemini 3.1 Pro on the SWE-Bench Pro coding benchmark."
   ]
  },
  {
   "url": "https://ai-primer.com/engineer/stories/qwen3-6-35b-a3b-apache-open-release",
   "title": "Qwen3.6-35B-A3B releases Apache 2.0 sparse MoE with 3B active params",
   "published_at": "2026-04-16T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "35B total parameters, 3B active per token",
   "context_window": null,
   "license": null,
   "significance": "51.5 on Terminal-Bench 2.0, 73.4 on SWE-bench Verified, and 29.4 on NL2Repo.",
   "organization_evidence": [
    "Alibaba open-sourced Qwen3.6-35B-A3B, a 35B multimodal sparse MoE with only 3B active parameters under Apache 2.0.",
    "# Qwen3.6-35B-A3B releases Apache 2.0 sparse MoE with 3B active params"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "- 35B total parameters, 3B active per token [Qwen launch](https://x.com/Alibaba_Qwen/status/2044768734234243427)",
    "# Qwen3.6-35B-A3B releases Apache 2.0 sparse MoE with 3B active params"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "According to [Qwen's benchmark card](https://x.com/Alibaba_Qwen/status/2044768734234243427) and [Official release notes](https://qwen.ai/blog?id=qwen3.6-35b-a3b), the pitch is agentic coding efficiency: 51.5 on Terminal-Bench 2.0, 73.4 on SWE-bench Verified, and 29.4 on NL2Repo.",
    "# Qwen3.6-35B-A3B releases Apache 2.0 sparse MoE with 3B active params"
   ]
  },
  {
   "url": "https://till-freitag.com/en/blog/mistral-3-large-vibe-update-en",
   "title": "Mistral 3, Large 3 & Vibe: Why the Latest Update Puts Europe's AI Hope Back in the Game",
   "published_at": "2026-06-08T05:05:57",
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-05",
   "parameters": "128B",
   "context_window": null,
   "license": "Modified MIT (open weights)",
   "significance": "Instruct + Reasoning + Code in one model",
   "organization_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "In May 2026 came the update everyone is talking about: **Mistral Medium 3.5**."
   ],
   "model_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "In May 2026 came the update everyone is talking about: **Mistral Medium 3.5**."
   ],
   "release_date_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "In May 2026 came the update everyone is talking about: **Mistral Medium 3.5**."
   ],
   "parameters_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "| Parameters | 128B |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "| License | Modified MIT (open weights) |"
   ],
   "significance_evidence": [
    "Medium 3.5: the new default workhorse (May 2026)",
    "| Modes | Instruct + Reasoning + Code in one model |"
   ]
  },
  {
   "url": "https://toolchase.com/tool/gemma",
   "title": "Gemma Review 2026 | ToolChase",
   "published_at": "2026-04-16T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": "E2B, E4B, 12B, 26B mixture-of-experts, 31B dense",
   "context_window": null,
   "license": null,
   "significance": "Multimodal capability across all sizes, natively handling images and video at variable resolutions, 128K to 256K context across 140+ languages, and performance competitive with much larger open models.",
   "organization_evidence": [
    "Gemma",
    "Google's open-weight model family, with the multimodal Gemma 4 generation released under Apache 2.0",
    "Gemma is Google DeepMind's family of open-weight language models designed to give developers and researchers access to the same research lineage behind the flagship [Gemini](/tool/gemini/) models, packaged as models you can download, fine-tune, and deploy however you want."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma",
    "Google's open-weight model family, with the multimodal Gemma 4 generation released under Apache 2.0",
    "The Gemma 4 generation, released in April 2026, is multimodal and comes in five sizes: E2B and E4B for phones and edge devices, a 12B, a 26B mixture-of-experts model with roughly 3.8B active parameters, and a 31B dense flagship that competes with much larger open models on benchmarks.",
    "Google moved to a standard Apache 2.0 license for Gemma 4 in April 2026, with no monthly-active-user threshold and no attribution requirement beyond normal Apache notices."
   ],
   "parameters_evidence": [
    "Gemma",
    "Google's open-weight model family, with the multimodal Gemma 4 generation released under Apache 2.0",
    "The Gemma 4 generation, released in April 2026, is multimodal and comes in five sizes: E2B and E4B for phones and edge devices, a 12B, a 26B mixture-of-experts model with roughly 3.8B active parameters, and a 31B dense flagship that competes with much larger open models on benchmarks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma",
    "Google's open-weight model family, with the multimodal Gemma 4 generation released under Apache 2.0",
    "The Gemma 4 generation, released in April 2026, is multimodal and comes in five sizes: E2B and E4B for phones and edge devices, a 12B, a 26B mixture-of-experts model with roughly 3.8B active parameters, and a 31B dense flagship that competes with much larger open models on benchmarks.",
    "Every Gemma 4 model natively handles images and video alongside text at variable resolutions, while native audio input is limited to the E2B and E4B checkpoints."
   ]
  },
  {
   "url": "https://ventureatlas.org/news/2026-07-27-moonshot-kimi-k3-open-weights-release",
   "title": "Moonshot releases Kimi K3 open weights, the largest ever",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "making the roughly 2.8-trillion-parameter mixture-of-experts system the largest open-weight model ever published",
   "organization_evidence": [
    "Moonshot releases Kimi K3 open weights, the largest ever",
    "Chinese AI lab Moonshot AI released the full open weights of its Kimi K3 model on July 27, making the roughly 2.8-trillion-parameter mixture-of-experts system the largest open-weight model ever published, under a modified MIT license on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Monday, July 27, 2026",
    "Moonshot releases Kimi K3 open weights, the largest ever",
    "Chinese AI lab Moonshot AI released the full open weights of its Kimi K3 model on July 27, making the roughly 2.8-trillion-parameter mixture-of-experts system the largest open-weight model ever published, under a modified MIT license on Hugging Face."
   ],
   "parameters_evidence": [
    "Moonshot releases Kimi K3 open weights, the largest ever",
    "Chinese AI lab Moonshot AI released the full open weights of its Kimi K3 model on July 27, making the roughly 2.8-trillion-parameter mixture-of-experts system the largest open-weight model ever published, under a modified MIT license on Hugging Face."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot releases Kimi K3 open weights, the largest ever",
    "Chinese AI lab Moonshot AI released the full open weights of its Kimi K3 model on July 27, making the roughly 2.8-trillion-parameter mixture-of-experts system the largest open-weight model ever published, under a modified MIT license on Hugging Face."
   ]
  },
  {
   "url": "https://medium.com/@theking919/glm-5-2-is-the-open-weights-coding-model-the-industry-wasnt-ready-for-here-s-why-i-switched-0bebf0e8bdf3",
   "title": "GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
   "published_at": "2026-06-18T14:29:39",
   "organization": "Z.ai",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": "753-billion-parameter",
   "context_window": "1 million tokens",
   "license": "MIT license",
   "significance": "#1 model on Design Arena for front-end coding, SWE-bench Pro score of 62.1",
   "organization_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "On June 13, the Beijing lab Z.ai \u2014 the company formerly known as Zhipu AI \u2014 shipped **GLM-5.2** to every tier of its Coding Plan."
   ],
   "model_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "On June 13, the Beijing lab Z.ai \u2014 the company formerly known as Zhipu AI \u2014 shipped **GLM-5.2** to every tier of its Coding Plan."
   ],
   "release_date_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "10 min readJun 18, 2026",
    "On June 13, the Beijing lab Z.ai \u2014 the company formerly known as Zhipu AI \u2014 shipped **GLM-5.2** to every tier of its Coding Plan."
   ],
   "parameters_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "What GLM-5.2 actually is",
    "Under the hood it\u2019s a **753-billion-parameter** model."
   ],
   "context_window_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "## A 1-million-token context window, frontier-class agentic coding, and a price that makes Claude Max look like a luxury good.",
    "A genuinely usable 1-million-token context window."
   ],
   "license_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "On June 13, the Beijing lab Z.ai \u2014 the company formerly known as Zhipu AI \u2014 shipped **GLM-5.2** to every tier of its Coding Plan.",
    "A few days later it did the thing almost nobody at the frontier does anymore: it dropped the full model weights on Hugging Face and ModelScope under an **MIT license**, with no regional restrictions."
   ],
   "significance_evidence": [
    "# GLM-5.2 Is the Open-Weights Coding Model the Industry Wasn\u2019t Ready For \u2014 Here\u2019s Why I Switched",
    "By the time I sat down to write this, GLM-5.2 was the **#1 model on Design Arena** for front-end coding \u2014 open weights, sitting at the top of a board it shares with the best closed systems on the planet.",
    "- **SWE-bench Pro: 62.1** \u2014 decisively ahead of GPT-5.5 (58.6) and its own predecessor GLM-5.1 (58.4)."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/07/21/poolside-releases-laguna-s-2-1",
   "title": "Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual",
   "published_at": "2026-07-22T00:01:32",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-05-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1 with thinking enabled.",
   "organization_evidence": [
    "Poolside Releases Laguna S 2.1, a 118B Open-Weight MoE Coding Model Matching Rivals Many Times Its Size"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Pre-training began on 22 May 2026 on 4,096 NVIDIA H200 GPUs.",
    "Poolside Releases Laguna S 2.1, a 118B Open-Weight MoE Coding Model Matching Rivals Many Times Its Size"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Performance",
    "Laguna S 2.1 scores 70.2% on [Terminal-Bench 2.1](https://poolside.ai/blog/introducing-laguna-s-2-1) with thinking enabled."
   ]
  },
  {
   "url": "https://aichina.news/blog/peeking-inside-moe-training-allenais-pre-annealing-checkpoint-lands-khhuao",
   "title": "Peeking Inside MoE Training: AllenAI\u2019s Pre-Annealing Checkpoint Lands on Modelers.cn for Ascend \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T11:52:34",
   "organization": "AllenAI",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "intermediate checkpoint of Standard MoE (StdMoE) model purpose-built for fine-tuning and ablation studies on the annealing phase",
   "organization_evidence": [
    "Peeking Inside MoE Training: AllenAI\u2019s Pre-Annealing Checkpoint Lands on Modelers.cn for Ascend",
    "AllenAI has released an intermediate checkpoint of their Standard MoE (StdMoE) model \u2014 a 1B-active, 14B-total parameter sparse transformer trained on 1 trillion tokens \u2014 and it\u2019s now available on Modelers.cn, the Huawei Ascend AI ecosystem platform."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Peeking Inside MoE Training: AllenAI\u2019s Pre-Annealing Checkpoint Lands on Modelers.cn for Ascend",
    "models/allenai July 30, 2026 2 min read Auto-generated from Modelers",
    "**Future-dated creation stamp (2026-06-04)** suggests this is either a placeholder or a speculative listing."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Peeking Inside MoE Training: AllenAI\u2019s Pre-Annealing Checkpoint Lands on Modelers.cn for Ascend",
    "AllenAI has released an intermediate checkpoint of their Standard MoE (StdMoE) model \u2014 a 1B-active, 14B-total parameter sparse transformer trained on 1 trillion tokens \u2014 and it\u2019s now available on Modelers.cn, the Huawei Ascend AI ecosystem platform.",
    "- **Intermediate checkpoint** is purpose-built for fine-tuning and ablation studies on the annealing phase."
   ]
  },
  {
   "url": "https://yottalabs.ai/post/deepseek-v4-release-date-specs-how-to-access-2026",
   "title": "DeepSeek V4: Release Date, Specs, and How to Access It (2026)",
   "published_at": "2026-08-07T17:15:38",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "304B total parameters",
   "context_window": null,
   "license": null,
   "significance": "sparse model that serves cheap and fast while keeping frontier-adjacent capability",
   "organization_evidence": [
    "DeepSeek V4: Release Date, Specs, and How to Access It (2026)",
    "July 31, 2026: DeepSeek-V4-Flash-0731 released officially."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4: Release Date, Specs, and How to Access It (2026)",
    "V4-Flash-0731 released officially on July 31, 2026: open weights on Hugging Face under an MIT license, plus a live API"
   ],
   "parameters_evidence": [
    "DeepSeek V4: Release Date, Specs, and How to Access It (2026)",
    "Flash specs: roughly 300B total parameters (the Hugging Face repo lists 304B) with a small activated fraction, around 13B per token as reported. 1M token context and up to 384K output are reported alongside the release"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4-Flash: The Half That Already Shipped",
    "That shape is the point: a sparse model that serves cheap and fast while keeping frontier-adjacent capability."
   ]
  },
  {
   "url": "https://arturmarkus.com/google-gemma-4-ranks-3-on-arena-ai-leaderboard-31b-open-model-hits-85-2-mmlu-pro-and-89-2-aime-2026-outperforming-models-20x-larger?pdf=3683",
   "title": "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B Open Model Hits 85.2% MMLU Pro and 89.2% AIME 2026, Outperforming Models 20\u00d7 Larger",
   "published_at": "2026-08-18T09:41:19",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "26B and 31B",
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 on Arena AI leaderboard and achieved 85.2% MMLU Pro and 89.2% AIME 2026.",
   "organization_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B\n\nOpen Model Hits 85.2% MMLU Pro and 89.2% AIME 2026,\n\nOutperforming Models 20\u00d7 Larger",
    "The Release: What Google Actually Shipped",
    "Google released Gemma 4 on April 2, 2026\u2014a family of four open-source models\nunder the Apache 2.0 license that represents the company\u2019s most aggressive move\nyet into the open-weight AI space."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B\n\nOpen Model Hits 85.2% MMLU Pro and 89.2% AIME 2026,\n\nOutperforming Models 20\u00d7 Larger",
    "The Release: What Google Actually Shipped",
    "Google released Gemma 4 on April 2, 2026\u2014a family of four open-source models\nunder the Apache 2.0 license that represents the company\u2019s most aggressive move\nyet into the open-weight AI space."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B\n\nOpen Model Hits 85.2% MMLU Pro and 89.2% AIME 2026,\n\nOutperforming Models 20\u00d7 Larger",
    "The Release: What Google Actually Shipped",
    "The lineup spans from E2B (designed for mobile\nand IoT devices) through E4B (Android phones and laptops) to two flagship variants:\na 26B Mixture of Experts model and a 31B dense model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Ranks #3 on Arena AI Leaderboard\u201431B\n\nOpen Model Hits 85.2% MMLU Pro and 89.2% AIME 2026,\n\nOutperforming Models 20\u00d7 Larger",
    "The 31B dense variant immediately claimed the #3 position on the Arena AI text\nleaderboard."
   ]
  },
  {
   "url": "https://mlq.ai/news/moonshot-ai-releases-kimi-k3-a-28-trillion-parameter-open-weight-model-rivaling-top-us-systems",
   "title": "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
   "published_at": "2026-07-17T16:29:26",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": "1-million-token",
   "license": null,
   "significance": "largest open-weight AI system ever built and rivaling top U.S. systems",
   "organization_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Chinese AI startup Moonshot AI released Kimi K3 on July 16, a 2.8-trillion-parameter sparse mixture-of-experts model that the company says is the largest open-weight AI system ever built."
   ],
   "model_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Chinese AI startup Moonshot AI released Kimi K3 on July 16, a 2.8-trillion-parameter sparse mixture-of-experts model that the company says is the largest open-weight AI system ever built."
   ],
   "release_date_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Jul 17, 2026 \u00b7 12:29 PM \u00b7 by MLQ Agent \u00b7 5 min read",
    "Chinese AI startup Moonshot AI released Kimi K3 on July 16, a 2.8-trillion-parameter sparse mixture-of-experts model that the company says is the largest open-weight AI system ever built."
   ],
   "parameters_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Kimi K3 contains 2.8 trillion total parameters in a sparse MoE architecture with a 1-million-token context window, making it the largest open-weight model ever released [[1]](https://venturebeat.com/technology/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-u-s-systems)"
   ],
   "context_window_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Kimi K3 contains 2.8 trillion total parameters in a sparse MoE architecture with a 1-million-token context window, making it the largest open-weight model ever released [[1]](https://venturebeat.com/technology/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-u-s-systems)"
   ],
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI Releases Kimi K3, a 2.8-Trillion-Parameter Open-Weight Model Rivaling Top U.S. Systems",
    "Chinese AI startup Moonshot AI released Kimi K3 on July 16, a 2.8-trillion-parameter sparse mixture-of-experts model that the company says is the largest open-weight AI system ever built."
   ]
  },
  {
   "url": "https://claudemarket.ai/blog/best-glm-models-2026",
   "title": "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
   "published_at": "2026-08-10T12:20:49",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "strongest open-weight model to come out of China's AI ecosystem so far, with 744 billion total parameters, 40 billion active per token, and a 77.8% score on SWE-bench Verified that puts it within three points of Claude Opus 4.6",
   "organization_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM-5 is the best GLM model in 2026 and the strongest open-weight model to come out of China's AI ecosystem so far, with 744 billion total parameters, 40 billion active per token, and a 77.8% score on [SWE-bench Verified](https://www.swebench.com/) that puts it within three points of Claude Opus 4.6.",
    "Zhipu AI \u2014 the Tsinghua University spinoff now publicly traded on the Hong Kong Stock Exchange at a $44 billion market cap \u2014 has built the GLM family into a genuine third pole in the Chinese AI landscape alongside DeepSeek and Alibaba's Qwen."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM Model Evolution: From GLM-130B to GLM-5",
    "GLM-5, [released February 11, 2026](https://www.digitalapplied.com/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis), scaled to 744 billion total parameters with 40 billion active per token."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM-5 is the best GLM model in 2026 and the strongest open-weight model to come out of China's AI ecosystem so far, with 744 billion total parameters, 40 billion active per token, and a 77.8% score on [SWE-bench Verified](https://www.swebench.com/) that puts it within three points of Claude Opus 4.6."
   ]
  },
  {
   "url": "https://aitoolsradar.org/blog/models/minimax-m3-opensource-2026",
   "title": "MiniMax M3 Open Source (2026): 428B Model, 1M Context & Benchmarks",
   "published_at": "2026-06-13T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "428B open-weights model, 1M context via sparse attention, native multimodal input, competitive coding benchmarks, and 10x cheaper than GPT-5.5.",
   "organization_evidence": [
    "Models",
    "# MiniMax M3 Open Source (2026): 428B Model, 1M Context & Benchmarks",
    "Short answer (June 2026): MiniMax M3 is a **428B-parameter open-weights model** from Shanghai-based MiniMax with a practical **1M-token context window**, native image and video understanding, and coding benchmarks that trade punches with GPT-5.5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Models",
    "# MiniMax M3 Open Source (2026): 428B Model, 1M Context & Benchmarks",
    "MiniMax M3 is a 428B-parameter Mixture of Experts AI model from Chinese company MiniMax, released June 1, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Models",
    "# MiniMax M3 Open Source (2026): 428B Model, 1M Context & Benchmarks",
    "MiniMax M3: 428B open-weights model, 1M context via sparse attention, native multimodal input, competitive coding benchmarks, and 10x cheaper than GPT-5.5."
   ]
  },
  {
   "url": "https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Alpamayo-2-Super-Open-Reasoning-Model-for-Robotaxis",
   "title": "NVIDIA Launches Alpamayo 2 Super Open Reasoning Model for Robotaxis",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "NVIDIA Alpamayo 2 Super is an open 32-billion-parameter reasoning VLA model that reasons, plans and acts across the full driving stack for safer, scalable level 4 development.",
   "organization_evidence": [
    "**News Summary:**",
    "TAIPEI, Taiwan, June 01, 2026 (GLOBE NEWSWIRE) -- **NVIDIA GTC Taipei** -- NVIDIA today introduced NVIDIA Alpamayo 2 Super, a 32-billion-parameter reasoning\u2011based vision language action (VLA) model that extends the [NVIDIA Alpamayo](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/alpamayo/) family of open AI models, simulation frameworks and physical AI datasets for safe, level 4 robotaxi development."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**News Summary:**",
    "TAIPEI, Taiwan, June 01, 2026 (GLOBE NEWSWIRE) -- **NVIDIA GTC Taipei** -- NVIDIA today introduced NVIDIA Alpamayo 2 Super, a 32-billion-parameter reasoning\u2011based vision language action (VLA) model that extends the [NVIDIA Alpamayo](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/alpamayo/) family of open AI models, simulation frameworks and physical AI datasets for safe, level 4 robotaxi development."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**News Summary:**",
    "- NVIDIA\u2019s most powerful open reasoning model to date, NVIDIA Alpamayo 2 Super is an open 32-billion-parameter reasoning VLA model that reasons, plans and acts across the full driving stack for safer, scalable level 4 development."
   ]
  },
  {
   "url": "https://aichina.news/blog/olmocr-7b-an-open-source-ocr-model-built-for-ascend-npus-efficient-02wtq8",
   "title": "olmOCR-7B: An Open-Source OCR Model Built for Ascend NPUs \u2013 Efficient, Apache 2.0 Licensed, and Ready to Try \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T12:46:53",
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A purpose-built tool for document understanding and digitisation, using FP8 KV cache quantisation to reduce memory usage and speed up inference natively on Ascend hardware.",
   "organization_evidence": [
    "olmOCR-7B: An Open-Source OCR Model Built for Ascend NPUs \u2013 Efficient, Apache 2.0 Licensed, and Ready to Try",
    "Meet **olmOCR-7B-0225-preview-FP8-KV** \u2013 a 7-billion-parameter vision-language model from the respected Allen AI lab, now hosted on Modelers.cn."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "olmOCR-7B: An Open-Source OCR Model Built for Ascend NPUs \u2013 Efficient, Apache 2.0 Licensed, and Ready to Try",
    "models/allenai July 21, 2026 3 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "olmOCR-7B: An Open-Source OCR Model Built for Ascend NPUs \u2013 Efficient, Apache 2.0 Licensed, and Ready to Try",
    "This is not just another OCR model; it's a purpose-built tool for document understanding and digitisation, designed to convert images of text into machine-readable text with solid accuracy while keeping resource demands in check.",
    "What makes olmOCR-7B stand out? First, it's built on a transformer-based architecture that uses **FP8 KV cache quantisation**, a technique that significantly reduces memory usage and speeds up inference \u2013 particularly beneficial on Ascend NPUs, where memory can be a bottleneck."
   ]
  },
  {
   "url": "https://evermx.com/case/mistral-medium-35-vibe-remote-agents-launch",
   "title": "Mistral Medium 3.5 Launches: 128B Open Model with 77.6% SWE-Bench and Cloud Coding Agents",
   "published_at": "2026-05-04T09:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-05-02",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified",
   "organization_evidence": [
    "Mistral Medium 3.5 Launches: 128B Open Model with 77.6% SWE-Bench and Cloud Coding Agents",
    "On May 2, 2026, Mistral AI launched Mistral Medium 3.5 alongside a major upgrade to its Vibe coding platform, introducing cloud-based remote agents and a new Work mode for Le Chat."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5 Launches: 128B Open Model with 77.6% SWE-Bench and Cloud Coding Agents",
    "On May 2, 2026, Mistral AI launched Mistral Medium 3.5 alongside a major upgrade to its Vibe coding platform, introducing cloud-based remote agents and a new Work mode for Le Chat."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5 Launches: 128B Open Model with 77.6% SWE-Bench and Cloud Coding Agents",
    "Mistral AI releases Medium 3.5, a 128B dense open-weights model scoring 77.6% on SWE-Bench Verified, paired with Vibe remote cloud agents and Work mode for Le Chat.",
    "Mistral Medium 3.5 Model Architecture",
    "Mistral Medium 3.5 is a dense 128B-parameter model with a 256,000-token context window (approximately 200,000 words)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5 Launches: 128B Open Model with 77.6% SWE-Bench and Cloud Coding Agents",
    "Mistral AI releases Medium 3.5, a 128B dense open-weights model scoring 77.6% on SWE-Bench Verified, paired with Vibe remote cloud agents and Work mode for Le Chat.",
    "On coding benchmarks, Medium 3.5 achieves 77.6% on SWE-Bench Verified, outperforming both Devstral 2 and Qwen3.5 397B A17B on coding tasks."
   ]
  },
  {
   "url": "https://datanorth.ai/news/google-releases-gemma-4-12b",
   "title": "Google releases Gemma 4 \u2013 12B",
   "published_at": "2026-06-04T08:26:58",
   "organization": "Google",
   "model": "Gemma 4 12B",
   "release_date": "2026-06-03",
   "parameters": "12-billion-parameter",
   "context_window": "256,000-token",
   "license": "Apache 2.0",
   "significance": "Gemma 4 12B scores 94.9% on DocVQA and 88.4% on InfoVQA, reaches 69.1% on MMMU Pro, 77.2% on MMLU Pro, 78.8% on GPQA Diamond, 77.5% on AIME 2026, and 79.7% on MATH-Vision.",
   "organization_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "Google released Gemma 4 12B on June 3, 2026, a new open-weight multimodal model that processes text, images, audio, and video and runs entirely on a typical 16GB laptop."
   ],
   "model_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "Gemma 4 12B is an open-weight, encoder-free multimodal model with a 256,000-token context window that processes text, images, audio, and video on a 16GB laptop."
   ],
   "release_date_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "Google released Gemma 4 12B on June 3, 2026, a new open-weight multimodal model that processes text, images, audio, and video and runs entirely on a typical 16GB laptop."
   ],
   "parameters_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "## What can Gemma 4 12B do?",
    "Gemma 4 12B is a 12-billion-parameter open-weight model that accepts text, images, audio, and video as input."
   ],
   "context_window_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "Gemma 4 12B is an open-weight, encoder-free multimodal model with a 256,000-token context window that processes text, images, audio, and video on a 16GB laptop.",
    "The 256,000-token context window lets the model work across long documents, large codebases, and multi-step agentic workflows in a single pass."
   ],
   "license_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "Gemma 4 12B uses an encoder-free architecture, supports a 256,000-token context window, and ships with open weights under the Apache 2.0 license on Hugging Face.",
    "Gemma 4 12B is available now with open weights under the Apache 2.0 license, which permits commercial use."
   ],
   "significance_evidence": [
    "# Google releases Gemma 4 \u2013 12B",
    "## Gemma 4 12B benchmarks and technical specs",
    "Gemma 4 12B scores 94.9% on DocVQA and 88.4% on InfoVQA, two benchmarks for reading and answering questions about documents and infographics."
   ]
  },
  {
   "url": "https://swfte.com/ru/ai/models/moonshot-kimi-k3",
   "title": "Kimi K3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-08-04T22:03:15",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1,048,576-token",
   "license": "Kimi K3 License",
   "significance": "Artificial Analysis Intelligence Index 57, ranking #3 overall behind only Claude Fable 5 and GPT-5.6 Sol, and ahead of every other proprietary model on the board.",
   "organization_evidence": [
    "# Kimi K3",
    "Moonshot AI's 16 Jul 2026 flagship and the clearest evidence that the open-weight tier has caught the frontier: a 2.8-trillion-parameter MoE (16 of 896 experts routed per token, Stable LatentMoE, MXFP4 weights) with a 1,048,576-token context and native text, vision, and video input."
   ],
   "model_evidence": [
    "# Kimi K3",
    "Moonshot AI's 16 Jul 2026 flagship and the clearest evidence that the open-weight tier has caught the frontier: a 2.8-trillion-parameter MoE (16 of 896 experts routed per token, Stable LatentMoE, MXFP4 weights) with a 1,048,576-token context and native text, vision, and video input."
   ],
   "release_date_evidence": [
    "# Kimi K3",
    "Moonshot AI's 16 Jul 2026 flagship and the clearest evidence that the open-weight tier has caught the frontier: a 2.8-trillion-parameter MoE (16 of 896 experts routed per token, Stable LatentMoE, MXFP4 weights) with a 1,048,576-token context and native text, vision, and video input."
   ],
   "parameters_evidence": [
    "# Kimi K3",
    "Moonshot AI's 16 Jul 2026 flagship and the clearest evidence that the open-weight tier has caught the frontier: a 2.8-trillion-parameter MoE (16 of 896 experts routed per token, Stable LatentMoE, MXFP4 weights) with a 1,048,576-token context and native text, vision, and video input."
   ],
   "context_window_evidence": [
    "# Kimi K3",
    "Moonshot AI's 16 Jul 2026 flagship and the clearest evidence that the open-weight tier has caught the frontier: a 2.8-trillion-parameter MoE (16 of 896 experts routed per token, Stable LatentMoE, MXFP4 weights) with a 1,048,576-token context and native text, vision, and video input."
   ],
   "license_evidence": [
    "# Kimi K3",
    "Weights shipped on Hugging Face at moonshotai/Kimi-K3 (2.8T total, 104B active) under Moonshot's own Kimi K3 License rather than a standard open licence, so read the terms before building on it."
   ],
   "significance_evidence": [
    "# Kimi K3",
    "Artificial Analysis Intelligence Index 57, ranking #3 overall behind only Claude Fable 5 and GPT-5.6 Sol, and ahead of every other proprietary model on the board."
   ]
  },
  {
   "url": "https://remoteopenclaw.com/blog/best-glm-models-2026",
   "title": "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
   "published_at": "2026-04-28T18:47:09",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.8% on SWE-bench Verified",
   "organization_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "Zhipu AI \u2014 the Tsinghua University spinoff now publicly traded on the Hong Kong Stock Exchange at a $44 billion market cap \u2014 has built the GLM family into a genuine third pole in the Chinese AI landscape alongside DeepSeek and Alibaba's Qwen."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM Model Evolution: From GLM-130B to GLM-5",
    "GLM-5, [released February 11, 2026](https://www.digitalapplied.com/blog/zhipu-ai-glm-5-release-744b-moe-model-analysis), scaled to 744 billion total parameters with 40 billion active per token."
   ],
   "parameters_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM Model Evolution: From GLM-130B to GLM-5",
    "| GLM-5 | 744B | 40B | 200K | 28.5T tokens | Feb 2026 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best GLM Models in 2026 \u2014 Zhipu AI's Rise in the LLM Landscape",
    "GLM-5 is the best GLM model in 2026 and the strongest open-weight model to come out of China's AI ecosystem so far, with 744 billion total parameters, 40 billion active per token, and a 77.8% score on [SWE-bench Verified](https://www.swebench.com/) that puts it within three points of Claude Opus 4.6."
   ]
  },
  {
   "url": "https://codersera.com/blog/minimax-m3-release-date-whats-new-2026",
   "title": "MiniMax M3 Release Date: When + What Shipped (2026)",
   "published_at": "2026-05-19T16:14:08",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax positions M3 as the first open-weights model to combine reasoning and agent capabilities in one release",
   "organization_evidence": [
    "# MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "MiniMax M3 is not released as of May 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "Announced June 1, 2026 and live on Hugging Face by June 7, MiniMax positions it as the **first open-weights model to combine reasoning and agent capabilities** in a single release \u2014 framing shared by AI researcher [@arankomatsuzaki](https://x.com/arankomatsuzaki?ref=codersera.com) and cross-confirmed by MiniMax Head of Engineering [@SkylerMiao7](https://x.com/SkylerMiao7?ref=codersera.com)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3: First Open-Weights Reasoning + Agent Model (2026)",
    "Announced June 1, 2026 and live on Hugging Face by June 7, MiniMax positions it as the **first open-weights model to combine reasoning and agent capabilities** in a single release \u2014 framing shared by AI researcher [@arankomatsuzaki](https://x.com/arankomatsuzaki?ref=codersera.com) and cross-confirmed by MiniMax Head of Engineering [@SkylerMiao7](https://x.com/SkylerMiao7?ref=codersera.com).",
    "The headline claim is architectural: **MiniMax positions M3 as the first open-weights model to combine reasoning and agent capabilities in one release**."
   ]
  },
  {
   "url": "https://bitsminds.com/news/nvidia-nemotron-3-nano-omni-multimodal-2026",
   "title": "NVIDIA Unveils Nemotron 3 Nano Omni: Open Multimodal Model with 9x Throughput",
   "published_at": "2026-04-29T09:25:10",
   "organization": "NVIDIA",
   "model": null,
   "release_date": null,
   "parameters": "30B-A3B",
   "context_window": null,
   "license": null,
   "significance": "The model topped six leaderboards for document intelligence and combined audio-video understanding at launch, helped by new components including Conv3D and an Enhanced Visual System (EVS) that improve dense visual reasoning across long video clips and high-resolution screen captures.",
   "organization_evidence": [
    "# NVIDIA Unveils Nemotron 3 Nano Omni: Open Multimodal Model with 9x Throughput",
    "NVIDIA on April 28, 2026 launched Nemotron 3 Nano Omni, a new open-weight multimodal model that fuses vision, speech, and language into a single system designed for autonomous AI agents."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# NVIDIA Unveils Nemotron 3 Nano Omni: Open Multimodal Model with 9x Throughput",
    "NVIDIA's new 30B-A3B mixture-of-experts model unifies vision, audio, and language into a single open system, delivering up to nine times the throughput of comparable open omni models for AI agents."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Unveils Nemotron 3 Nano Omni: Open Multimodal Model with 9x Throughput",
    "The model topped six leaderboards for document intelligence and combined audio-video understanding at launch, helped by new components including Conv3D and an Enhanced Visual System (EVS) that improve dense visual reasoning across long video clips and high-resolution screen captures."
   ]
  },
  {
   "url": "https://admix.software/blog/deepseek-v4-flash-open-weight-agent-model",
   "title": "DeepSeek V4-Flash Jumped 25 Points on Terminal-Bench Without Getting Bigger",
   "published_at": "2026-08-01T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "284B total, 13B active",
   "context_window": null,
   "license": null,
   "significance": "Terminal-Bench 2.1 climbed to 82.7, up 25.8 points from the April preview's 56.9, and it ties Claude Sonnet 5 and Grok 4.5 on DeepSWE.",
   "organization_evidence": [
    "DeepSeek V4-Flash Jumped 25 Points on Terminal-Bench Without Getting Bigger",
    "DeepSeek pushed a public-beta update to its V4-Flash API that behaves like a new model without being one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4-Flash Jumped 25 Points on Terminal-Bench Without Getting Bigger",
    "6 min readJuly 31, 2026"
   ],
   "parameters_evidence": [
    "DeepSeek V4-Flash Jumped 25 Points on Terminal-Bench Without Getting Bigger",
    "Same 284B total, 13B active parameters, same 1M context, but the agent benchmarks moved a lot."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4-Flash Jumped 25 Points on Terminal-Bench Without Getting Bigger",
    "Terminal-Bench 2.1 climbed to 82.7, up 25.8 points from the April preview's 56.9."
   ]
  },
  {
   "url": "https://artifactshub.ai/mistralai",
   "title": "Mistral \u2014 Artifacts Hub",
   "published_at": null,
   "organization": "Mistral",
   "model": "Leanstral-1.5-119B-A6B",
   "release_date": "2026-06-30",
   "parameters": "119B (6.5B active)",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A Mistral Small model fine-tuned for Lean.",
   "organization_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ],
   "model_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ],
   "release_date_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ],
   "parameters_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ],
   "significance_evidence": [
    "# Mistral",
    "[Leanstral-1.5-119B-A6B30 Jun 2026\n119B (6.5B active)Apache-2.0460 \u00b7 30d\nA Mistral Small model fine-tuned for Lean.](/mistralai/leanstral-1-5-119b-a6b)"
   ]
  },
  {
   "url": "https://artifactshub.ai/mistralai",
   "title": "Mistral \u2014 Artifacts Hub",
   "published_at": null,
   "organization": "Mistral",
   "model": "Voxtral 4B TTS",
   "release_date": "2026-03-31",
   "parameters": "4B",
   "context_window": null,
   "license": "CC BY-NC 4.0",
   "significance": "A non-commercial speech-generation model.",
   "organization_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ],
   "model_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ],
   "release_date_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ],
   "parameters_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ],
   "significance_evidence": [
    "# Mistral",
    "[Voxtral 4B TTS31 Mar 2026\n4BCC BY-NC 4.03.1K \u00b7 30d\nA non-commercial speech-generation model.](/mistralai/voxtral-4b-tts-2603)"
   ]
  },
  {
   "url": "https://artifactshub.ai/mistralai",
   "title": "Mistral \u2014 Artifacts Hub",
   "published_at": null,
   "organization": "Mistral",
   "model": "Leanstral 119B",
   "release_date": "2026-03-11",
   "parameters": "119B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A Lean4 fine-tune of the new Mistral Small 4.",
   "organization_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ],
   "model_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ],
   "release_date_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ],
   "parameters_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ],
   "significance_evidence": [
    "# Mistral",
    "[Leanstral 119B11 Mar 2026\n119BApache-2.093 \u00b7 30d\nA Lean4 fine-tune of the new Mistral Small 4.](/mistralai/leanstral-2603)"
   ]
  },
  {
   "url": "https://artifactshub.ai/mistralai",
   "title": "Mistral \u2014 Artifacts Hub",
   "published_at": null,
   "organization": "Mistral",
   "model": "Voxtral Mini 4B Realtime",
   "release_date": "2026-02-06",
   "parameters": "4B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.",
   "organization_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ],
   "model_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ],
   "release_date_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ],
   "parameters_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ],
   "significance_evidence": [
    "# Mistral",
    "[Voxtral Mini 4B Realtime06 Feb 2026\n4BApache-2.02.2M \u00b7 30d\nA small speech-to-text model by Mistral, which supports 13 languages, including Chinese, English and Hindi, while matching the precision of Whisper.](/mistralai/voxtral-mini-4b-realtime-2602)"
   ]
  },
  {
   "url": "https://artifactshub.ai/mistralai",
   "title": "Mistral \u2014 Artifacts Hub",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Small 4 119B",
   "release_date": "2026-01-23",
   "parameters": "119B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.",
   "organization_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ],
   "model_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ],
   "release_date_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ],
   "parameters_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ],
   "significance_evidence": [
    "# Mistral",
    "[Mistral Small 4 119B23 Jan 2026\n119BApache-2.0188.6K \u00b7 30d\nA 119B-A7B model by Mistral, combining their previous model generations into one as a hybrid reasoning model with coding abilities.](/mistralai/mistral-small-4-119b-2603)"
   ]
  },
  {
   "url": "https://frontierbeat.com/2026/04/02/gemma-4-google-open-source-ai-release-2026?amp=1",
   "title": "Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
   "published_at": "2026-04-02T18:45:12",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Unveiled its most capable family of open AI models to date, the 31-billion-parameter dense model debuted at number three on Arena AI\u2019s open model leaderboard, making it the highest-ranked Western open model on the chart.",
   "organization_evidence": [
    "Google [dropped](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4) Gemma 4 on April 2, 2026, and this time it\u2019s not messing around."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google [dropped](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4) Gemma 4 on April 2, 2026, and this time it\u2019s not messing around."
   ],
   "parameters_evidence": [
    "The search giant unveiled its most capable family of open AI models to date \u2014 four sizes built from the same research underlying Gemini 3, packaged for everyone from phone developers to enterprise shops running single-GPU workstations.",
    "Google [dropped](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4) Gemma 4 on April 2, 2026, and this time it\u2019s not messing around."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The search giant unveiled its most capable family of open AI models to date \u2014 four sizes built from the same research underlying Gemini 3, packaged for everyone from phone developers to enterprise shops running single-GPU workstations.",
    "The 31-billion-parameter dense model debuted at number three on Arena AI\u2019s open model leaderboard, making it the highest-ranked Western open model on the chart.",
    "Google [dropped](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4) Gemma 4 on April 2, 2026, and this time it\u2019s not messing around."
   ]
  },
  {
   "url": "https://joseluischavezcalva.substack.com/p/kimi-k3-moonshots-28-trillion-parameter",
   "title": "Kimi K3: Moonshot\u2019s 2.8-Trillion-Parameter Open-Weight Model",
   "published_at": "2026-07-17T17:32:12",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "achieves frontier-level results in coding, agentic workflows, and long-context reasoning, pairs it with competitive economics, and promises full open-weight availability on Hugging Face by 27 July",
   "organization_evidence": [
    "# Kimi K3: Moonshot\u2019s 2.8-Trillion-Parameter Open-Weight Model",
    "Moonshot AI\u2019s Kimi K3 release on 16 July 2026 marks a clear departure from that pattern."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: Moonshot\u2019s 2.8-Trillion-Parameter Open-Weight Model",
    "Moonshot AI\u2019s Kimi K3 release on 16 July 2026 marks a clear departure from that pattern.",
    "Moonshot has confirmed that full weights will land on Hugging Face by 27 July under a Modified MIT licence, making Kimi K3 the first model of this scale to enter the open ecosystem so rapidly after launch."
   ],
   "parameters_evidence": [
    "# Kimi K3: Moonshot\u2019s 2.8-Trillion-Parameter Open-Weight Model",
    "Moonshot AI\u2019s Kimi K3 release on 16 July 2026 marks a clear departure from that pattern.",
    "Kimi K3 scales this approach to an unprecedented 2.8 trillion parameters while preserving the company\u2019s focus on practical, long-horizon applications."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: Moonshot\u2019s 2.8-Trillion-Parameter Open-Weight Model",
    "Moonshot AI\u2019s Kimi K3 release on 16 July 2026 marks a clear departure from that pattern.",
    "A Chinese laboratory has delivered a model that achieves frontier-level results in coding, agentic workflows, and long-context reasoning, pairs it with competitive economics, and promises full open-weight availability on Hugging Face by 27 July."
   ]
  },
  {
   "url": "https://nist.gov/system/files/documents/2026/07/17/CAISI%20-%20Assessment%20of%20Z.ai%27s%20GLM-5.2.pdf",
   "title": "[Public report] CAISI - Assessment of Z.ai's GLM-5.2.docx",
   "published_at": null,
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "probably the most capable open-weight AI model when it was released",
   "organization_evidence": [
    "Assessment of Z.ai\u2019s GLM-5.2",
    "GLM-5.2 was released as an open-weight model by the PRC-based company Z.ai (formerly known as \nZhipu AI) on June 16, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Page 2 of 21",
    "1.  Executive Summary",
    "GLM-5.2 was released as an open-weight model by the PRC-based company Z.ai (formerly known as \nZhipu AI) on June 16, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Page 2 of 21",
    "1.  Executive Summary",
    "GLM-5.2 was probably the most capable open-weight AI model when it was released (Figure \n\n1.1)."
   ]
  },
  {
   "url": "https://buildmvpfast.com/blog/minimax-m27-affordable-llm-benchmark-comparison-2026",
   "title": "MiniMax M2.7: The Affordable LLM That's Quietly Beating GPT-4 on Key Benchmarks",
   "published_at": "2026-03-25T12:16:32",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": "230B Total, 10B Active",
   "context_window": null,
   "license": null,
   "significance": "Scres within 1% of Claude Opus on coding benchmarks while using 10B active parameters and costing $0.30 per million input tokens.",
   "organization_evidence": [
    "MiniMax M2.7: The Affordable LLM That's Quietly Beating GPT-4 on Key Benchmarks",
    "MiniMax\u2019s official [M2.7 release post](https://www.minimax.io/news/minimax-m27-en) is now paired with an official [MiniMaxAI/MiniMax-M2.7 model card on Hugging Face](https://huggingface.co/MiniMaxAI/MiniMax-M2.7), which means builders can evaluate it as open weights instead of treating it like another closed Chinese API."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M2.7: The Affordable LLM That's Quietly Beating GPT-4 on Key Benchmarks",
    "MiniMax M2.7 launched on March 18, 2026, and the numbers are hard to ignore."
   ],
   "parameters_evidence": [
    "MiniMax M2.7: The Affordable LLM That's Quietly Beating GPT-4 on Key Benchmarks",
    "MiniMax M2.7 runs just 10B active parameters but matches frontier models on coding benchmarks at $0.30 per million input tokens.",
    "The Architecture: 230B Total, 10B Active",
    "The model contains 230 billion total parameters spread across a Mixture-of-Experts (MoE) Transformer architecture."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M2.7: The Affordable LLM That's Quietly Beating GPT-4 on Key Benchmarks",
    "A Chinese AI company you\u2019ve probably overlooked just shipped a model that scores within 1% of Claude Opus on coding benchmarks."
   ]
  },
  {
   "url": "https://enterprisedna.co/resources/news/nvidia-nemotron-3-nano-omni-multimodal-enterprise-agents-2026",
   "title": "NVIDIA Nemotron 3 Nano Omni Powers Enterprise AI Agents",
   "published_at": "2026-04-29T00:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30 billion parameters in total",
   "context_window": null,
   "license": null,
   "significance": "Tops six leaderboards across document intelligence, video understanding, and audio comprehension, delivering 7.4x better system efficiency for multi-document workloads and 9.2x better efficiency for video tasks.",
   "organization_evidence": [
    "NVIDIA Nemotron 3 Nano Omni Powers Enterprise AI Agents",
    "On April 28, 2026, NVIDIA released Nemotron 3 Nano Omni \u2014 an open-weight multimodal model that brings vision, audio, and language processing into a single system."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Nemotron 3 Nano Omni Powers Enterprise AI Agents",
    "On April 28, 2026, NVIDIA released Nemotron 3 Nano Omni \u2014 an open-weight multimodal model that brings vision, audio, and language processing into a single system."
   ],
   "parameters_evidence": [
    "NVIDIA Nemotron 3 Nano Omni Powers Enterprise AI Agents",
    "Nemotron 3 Nano Omni runs 30 billion parameters in total but activates only 3 billion at inference time through a mixture-of-experts design."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Nemotron 3 Nano Omni Powers Enterprise AI Agents",
    "NVIDIA claims Nemotron 3 Nano Omni tops six leaderboards across document intelligence, video understanding, and audio comprehension.",
    "Compared to other open omni models with similar capability, it delivers 7.4x better system efficiency for multi-document workloads and 9.2x better efficiency for video tasks."
   ]
  },
  {
   "url": "https://nextomoro.com/deepseek-v4",
   "title": "DeepSeek V4",
   "published_at": "2026-04-29T05:21:39",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1.6-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Sits in the top ten across major standardized benchmarks, placing it in the leading group of open-weights frontier models while approaching the performance range of closed-source frontier models at a fraction of their cost.",
   "organization_evidence": [
    "DeepSeek V4",
    "DeepSeek V4 is a 1.6-trillion-parameter open-weights mixture-of-experts language model released by DeepSeek in April 2026"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4",
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    "DeepSeek V4",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "DeepSeek V4",
    "DeepSeek V4 is an open-weights large language model released by [DeepSeek](https://nextomoro.com/deepseek/) in April 2026, built on a 1.6-trillion-parameter mixture-of-experts architecture with 49 billion active parameters per inference step and a 1-million-token context window."
   ]
  },
  {
   "url": "https://primeaicenter.com/gemma-4-review",
   "title": "Gemma 4 Review: Download & Complete Guide 2026 - PrimeAIcenter",
   "published_at": "2026-04-03T14:31:40",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
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   "license": null,
   "significance": "#3 open model globally",
   "organization_evidence": [
    "## Gemma 4 Review: Google\u2019s Most Capable Open AI Model (Download Links, Benchmarks & Complete Setup Guide)",
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   ],
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   "release_date_evidence": [
    "## Gemma 4 Review: Google\u2019s Most Capable Open AI Model (Download Links, Benchmarks & Complete Setup Guide)",
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   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 at a Glance",
    "| **Arena AI Rank** | 31B = #3 open model globally (ELO ~1452) |"
   ]
  },
  {
   "url": "https://srpske.rs/en/news/tehnologija/2026/07/28/moonshot-kimi-k3-largest-open-weights-model",
   "title": "Kimi K3: open model with 2.8 trillion parameters",
   "published_at": "2026-07-28T12:37:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
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   "license": null,
   "significance": "largest model to date whose weights can be downloaded freely, ranking third on Artificial Analysis index, second on Vals AI, and first in Frontend Code Arena",
   "organization_evidence": [
    "# Kimi K3: open model with 2.8 trillion parameters",
    "Moonshot AI has released the Kimi K3 weights on Hugging Face."
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   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: open model with 2.8 trillion parameters",
    "Moonshot AI published the Kimi K3 weights on July 27."
   ],
   "parameters_evidence": [
    "# Kimi K3: open model with 2.8 trillion parameters",
    "Moonshot AI published the Kimi K3 weights on July 27."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: open model with 2.8 trillion parameters",
    "It is the largest model to date whose weights can be downloaded freely, and it ranks near the top of independent leaderboards."
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  },
  {
   "url": "https://felloai.com/ja/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
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   "significance": "A 744-billion-parameter Mixture-of-Experts design with 40 billion active parameters, a 1-million-token context window, and an MIT license that makes the weights free for anyone to download and run, serving as a coding-first frontier model.",
   "organization_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** is the latest flagship large language model from **Zhipu AI**, a Beijing-based lab that has become one of China\u2019s most aggressive open-weight model publishers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "| Release date | June 13, 2026 | April 7, 2026 | ~2 months apart | Fast iteration cycle |"
   ],
   "parameters_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "| Parameters | 744B (40B active) | 754B (40B active) | Slightly leaner | Same per-token compute |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ]
  },
  {
   "url": "https://techsparking.com/poolside-releases-laguna-s-2-1-an-open-weight-agentic-coding-model-that-punches-above-its-weight-class-on-swe-bench-multilingual",
   "title": "Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model That Punches Above Its Weight Class on SWE-Bench Multilingual - TECH SPARKING",
   "published_at": "2026-07-22T00:01:00",
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   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Punches Above Its Weight Class on SWE-Bench Multilingual",
   "organization_evidence": [
    "Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model That Punches Above Its Weight Class on SWE-Bench Multilingual",
    "Poolside has released the Laguna S 2.1, a model 118B-parameter open-weight model designed for agent coding."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model That Punches Above Its Weight Class on SWE-Bench Multilingual",
    "In long-horizon coding benchmarks, the Laguna S 2.1 held its own against models several times its size, including DeepSeek-V4-Pro-Max, NVIDIA\u2019s Nemotron 3 Ultra, and Thinking Machines\u2019 Inkling."
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MoE: 36B total, only 3B active \u2013 extremely efficient Apache 2.0 license Multimodal Outperforms Google Gemma 4 in benchmarks",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.6-35B-A3B Recommended | 2026-04 | | MoE: 36B total, only 3B active \u2013 extremely efficient Apache 2.0 license Multimodal Outperforms Google Gemma 4 in benchmarks | \u2014 | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.6-35B-A3B Recommended | 2026-04 | | MoE: 36B total, only 3B active \u2013 extremely efficient Apache 2.0 license Multimodal Outperforms Google Gemma 4 in benchmarks | \u2014 | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "28B dense model Open weights Multimodal (Text + Image)",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.6-27B | 2026-04 | | 28B dense model Open weights Multimodal (Text + Image) | High resource needs for 27B class | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.6-27B | 2026-04 | | 28B dense model Open weights Multimodal (Text + Image) | High resource needs for 27B class | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Flagship (MoE, 17B active) Native multimodality (Text, Image, Video) Up to 1M context Agentic AI workflows #5 globally in math reasoning (LM Arena)",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-397B-A17B | 16 February 2026 | | Flagship (MoE, 17B active) Native multimodality (Text, Image, Video) Up to 1M context Agentic AI workflows #5 globally in math reasoning (LM Arena) | High resource needs | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-397B-A17B | 16 February 2026 | | Flagship (MoE, 17B active) Native multimodality (Text, Image, Video) Up to 1M context Agentic AI workflows #5 globally in math reasoning (LM Arena) | High resource needs | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Strong logical reasoning Rivals 30B+ models Natively multimodal Ideal for edge servers",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-9B | 2026-03 | | Strong logical reasoning Rivals 30B+ models Natively multimodal Ideal for edge servers | Limited on very complex tasks | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-9B | 2026-03 | | Strong logical reasoning Rivals 30B+ models Natively multimodal Ideal for edge servers | Limited on very complex tasks | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Native multimodal agent UI navigation and document analysis Compact and efficient",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-4B | 2026-03 | | Native multimodal agent UI navigation and document analysis Compact and efficient | Limited capacity | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-4B | 2026-03 | | Native multimodal agent UI navigation and document analysis Compact and efficient | Limited capacity | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Optimized for mobile and edge Natively multimodal",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-2B | 2026-03 | | Optimized for mobile and edge Natively multimodal | Limited capacity | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-2B | 2026-03 | | Optimized for mobile and edge Natively multimodal | Limited capacity | Current |"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/qwen",
   "title": "Alibaba Qwen",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ultra-compact for IoT and smartphones Natively multimodal",
   "organization_evidence": [
    "LLM Alibaba China",
    "# Alibaba Qwen",
    "Alibaba Qwen 3.7 - New flagship with 1M context, agentic workflows and the open Qwen-Agent framework (MCP, tool calling, code interpreter). Qwen3.6-35B-A3B remains the open-weight top pick (Apache 2.0). AI consulting from Germany for GDPR-compliant self-hosting."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-0.8B | 2026-03 | | Ultra-compact for IoT and smartphones Natively multimodal | Strongly limited capacity | Current |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Versions",
    "Overview of available model variants",
    "| Model | Release | EU | Strengths | Weaknesses | Status |",
    "| Qwen3.5-0.8B | 2026-03 | | Ultra-compact for IoT and smartphones Natively multimodal | Strongly limited capacity | Current |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/mistral",
   "title": "Mistral Model List 2026: All 18 Models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Balanced Mistral model for enterprise assistants, multilingual work, and tools",
   "organization_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium (latest) mistral/mistral-medium-latest Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium (latest) mistral/mistral-medium-latest Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium (latest) mistral/mistral-medium-latest Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/mistral",
   "title": "Mistral Model List 2026: All 18 Models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Balanced Mistral model for enterprise assistants, multilingual work, and tools",
   "organization_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium 3.5 mistral/mistral-medium-2604 Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium 3.5 mistral/mistral-medium-2604 Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Medium 3.5 mistral/mistral-medium-2604 Balanced Mistral model for enterprise assistants, multilingual work, and tools | 262K | Reasoning Tools Vision Open | 2026-04-29 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/mistral",
   "title": "Mistral Model List 2026: All 18 Models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Efficient Mistral model for fast chat, extraction, and production assistants",
   "organization_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small (latest) mistral/mistral-small-latest Efficient Mistral model for fast chat, extraction, and production assistants | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small (latest) mistral/mistral-small-latest Efficient Mistral model for fast chat, extraction, and production assistants | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small (latest) mistral/mistral-small-latest Efficient Mistral model for fast chat, extraction, and production assistants | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/mistral",
   "title": "Mistral Model List 2026: All 18 Models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Fast Mistral production model for chat, extraction, and cost-sensitive agents",
   "organization_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small 4 mistral/mistral-small-2603 Fast Mistral production model for chat, extraction, and cost-sensitive agents | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small 4 mistral/mistral-small-2603 Fast Mistral production model for chat, extraction, and cost-sensitive agents | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral model list",
    "All **18** notable Mistral models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| Mistral Small 4 mistral/mistral-small-2603 Fast Mistral production model for chat, extraction, and cost-sensitive agents | 256K | Reasoning Tools Vision Open | 2026-03-16 |"
   ]
  },
  {
   "url": "https://aigeninsight.com/gemma-4-googles-most-powerful-open-ai-model-2026-guide",
   "title": "Gemma 4: Google's Most Powerful Open AI Model (2026 Guide)",
   "published_at": "2026-04-11T17:41:26",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B effective (5.1B total including embedding tables)",
   "context_window": null,
   "license": null,
   "significance": "Most compact model in the family designed to run directly on smartphones and IoT devices, supporting native text, images, and audio processing.",
   "organization_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "parameters_evidence": [
    "Despite its name, it uses a technique called Per-Layer Embeddings (PLE) that makes the effective parameter footprint 2.3 billion while the total parameter count including embedding tables reaches around 5.1 billion.",
    "### Gemma 4 E2B (Effective 2B)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "The E2B is the most compact model in the family, designed to run directly on smartphones and IoT devices."
   ]
  },
  {
   "url": "https://aigeninsight.com/gemma-4-googles-most-powerful-open-ai-model-2026-guide",
   "title": "Gemma 4: Google's Most Powerful Open AI Model (2026 Guide)",
   "published_at": "2026-04-11T17:41:26",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "Effective 4B",
   "context_window": null,
   "license": null,
   "significance": "Recommended starting point for developers building agentic applications that need to run on-device without cloud dependency, supporting native audio processing and full multimodal capabilities.",
   "organization_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "parameters_evidence": [
    "### Gemma 4 E4B (Effective 4B)",
    "The E4B steps up from the E2B with greater reasoning depth while still fitting comfortably on a standard 8GB laptop."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "It is the recommended starting point for developers building agentic applications that need to run on-device without cloud dependency."
   ]
  },
  {
   "url": "https://aigeninsight.com/gemma-4-googles-most-powerful-open-ai-model-2026-guide",
   "title": "Gemma 4: Google's Most Powerful Open AI Model (2026 Guide)",
   "published_at": "2026-04-11T17:41:26",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "26B MoE (activates 3.8B per inference pass)",
   "context_window": null,
   "license": null,
   "significance": "Ranked sixth among all open models on the Arena AI open-model leaderboard while activating fewer parameters per token than most 3B models, supporting a 256K token context window.",
   "organization_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
   "parameters_evidence": [
    "### Gemma 4 26B MoE (Mixture of Experts)",
    "The 26B MoE activates only 3.8 billion of its total 26 billion parameters during each inference pass, delivering near-dense-model quality at a fraction of the inference cost."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On the Arena AI open-model leaderboard, the 26B MoE currently ranks sixth among all open models, despite activating fewer parameters per token than most 3B models."
   ]
  },
  {
   "url": "https://aigeninsight.com/gemma-4-googles-most-powerful-open-ai-model-2026-guide",
   "title": "Gemma 4: Google's Most Powerful Open AI Model (2026 Guide)",
   "published_at": "2026-04-11T17:41:26",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B Dense",
   "context_window": null,
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   "organization_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
    "On April 2, 2026, Google DeepMind released Gemma 4: a family of open-weight AI models that fit in your pocket, run on a Raspberry Pi, and outperform models twenty times their size on real-world benchmarks."
   ],
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   "release_date_evidence": [
    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
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   ],
   "parameters_evidence": [
    "### Gemma 4 31B Dense",
    "The 31B Dense is the flagship."
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    "# Gemma 4: Google\u2019s Most Capable Open AI Model \u2014 Complete Guide (2026)",
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  },
  {
   "url": "https://theagenttimes.com/articles/moonshot-ai-s-2-8-trillion-parameter-kimi-k3-gets-day-zero-v-219baf5d",
   "title": "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
   "published_at": "2026-07-29T14:25:10",
   "organization": "Moonshot AI",
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   "parameters": "2.8 trillion",
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   "license": null,
   "significance": "Among the most powerful open-weight models ever released, featuring 2.5x the intelligence per unit of compute, native multimodal understanding, Kimi Delta Attention, and day-zero vLLM serving support.",
   "organization_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "Moonshot AI released Kimi K3, a 2.8-trillion-parameter open-weight mixture-of-experts model with a 1M-token context window and native vision, and the vLLM inference engine shipped day-zero serving support \u2014 endorsed by PyTorch \u2014 enabling immediate self-hosted deployment for agent operators."
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
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    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "Moonshot AI released Kimi K3, a 2.8-trillion-parameter open-weight mixture-of-experts model with a 1M-token context window and native vision, and the vLLM inference engine shipped day-zero serving support \u2014 endorsed by PyTorch \u2014 enabling immediate self-hosted deployment for agent operators."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/zhipu-glm-5-2-1m-context-open-weights-agentic-coding-builder-guide",
   "title": "GLM-5.2: Zhipu's 1M-Context Open-Weight Coding Model (Builder Guide)",
   "published_at": "2026-06-14T15:00:00",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
   "parameters": "744-billion",
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   "license": null,
   "significance": "1M context window, coding-first model, open-weight release under MIT license",
   "organization_evidence": [
    "GLM-5.2: Zhipu's 1M-Context Open-Weight Coding Model (Builder Guide)",
    "Zhipu AI launched **GLM-5.2** on June 13, 2026 \u2014 three days ago as of this writing."
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   "model_evidence": null,
   "release_date_evidence": [
    "Zhipu AI launched **GLM-5.2** on June 13, 2026 \u2014 three days ago as of this writing."
   ],
   "parameters_evidence": [
    "What GLM-5.2 Is",
    "The model shares the same base architecture as GLM-5.1 \u2014 a 744-billion parameter Mixture of Experts model with 40 billion active parameters \u2014 and focuses post-training changes on extended-context coding tasks."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "GLM-5.2: Zhipu's 1M-Context Open-Weight Coding Model (Builder Guide)",
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    "It is a coding-first model with a 1-million-token context window, an MIT license, and open weights arriving the week of June 16."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/minimax-m2-5-open-weight-agentic-llm-review",
   "title": "MiniMax M2.5 Review: Open-Weight Agentic LLM \u2014 229B MoE, 80.2% SWE-Bench, BFCL Leader, $1.15/M Output \u2014 ChatForest",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": "~229\u2013230 billion",
   "context_window": null,
   "license": null,
   "significance": "MiniMax M2.5 tied Claude Opus 4.6 on SWE-Bench Verified, beat it on Multi-SWE-Bench, led every frontier model on BFCL multi-turn function calling, achieved 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.8% on BFCL Multi-Turn.",
   "organization_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# MiniMax M2.5 Review: Open-Weight Agentic LLM \u2014 229B MoE, 80.2% SWE-Bench, BFCL Leader, $1.15/M Output",
    "MiniMax M2.5 was released on **February 12, 2026** \u2014 six weeks after the company\u2019s IPO on the Hong Kong Stock Exchange (January 9, 2026)."
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   "model_evidence": null,
   "release_date_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# MiniMax M2.5 Review: Open-Weight Agentic LLM \u2014 229B MoE, 80.2% SWE-Bench, BFCL Leader, $1.15/M Output",
    "**At a glance:** MiniMax M2.5, released February 12, 2026.",
    "MiniMax M2.5 was released on **February 12, 2026** \u2014 six weeks after the company\u2019s IPO on the Hong Kong Stock Exchange (January 9, 2026)."
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   "parameters_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# MiniMax M2.5 Review: Open-Weight Agentic LLM \u2014 229B MoE, 80.2% SWE-Bench, BFCL Leader, $1.15/M Output",
    "Core specifications from the official model card:",
    "| Parameter | Value |",
    "| Total parameters | ~229\u2013230 billion |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "# MiniMax M2.5 Review: Open-Weight Agentic LLM \u2014 229B MoE, 80.2% SWE-Bench, BFCL Leader, $1.15/M Output",
    "One week after Claude Opus 4.6 shipped, a Chinese AI company that most Western developers had never heard of released an open-weight model that tied it on SWE-Bench Verified, beat it on Multi-SWE-Bench, led every frontier model on BFCL multi-turn function calling \u2014 and charged approximately $1 per continuous hour of operation.",
    "Software Engineering",
    "| Benchmark | MiniMax M2.5 | Claude Opus 4.6 | Gemini 3.1 Pro | Kimi K2.5 |",
    "| SWE-Bench Verified | **80.2%** | 80.8% | 80.6% | 76.8% |",
    "| Multi-SWE-Bench | **51.3%** (#1) | 50.3% | \u2014 | \u2014 |",
    "| BFCL Multi-Turn | **76.8%** | 63.3% | 61.0% | \u2014 |"
   ]
  },
  {
   "url": "https://basedai.co/compare/qwen-vs-deepseek",
   "title": "Qwen vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
   "published_at": null,
   "organization": "Alibaba",
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   "release_date": "2026-02-16",
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   "organization_evidence": [
    "The verdict",
    "## Qwen vs DeepSeek at a glance",
    "| Vendor | Alibaba (Qwen team) (China) | DeepSeek (China) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The verdict",
    "## Qwen vs DeepSeek at a glance",
    "| Released | 2026-02-16 | 2026-04-24 |"
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## About Qwen3.5",
    "Qwen3.5 is Alibaba's February 2026 generation and the efficiency benchmark of the open-weight field: the 397B-A17B flagship activates just 17B parameters per token yet beats Alibaba's own trillion-parameter Qwen3-Max \u2014 at 8.6\u00d7 the throughput at 32K context and 19\u00d7 at 256K."
   ]
  },
  {
   "url": "https://basedai.co/compare/qwen-vs-deepseek",
   "title": "Qwen vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
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   "significance": "the strongest argument yet that open-weight models compete at the frontier",
   "organization_evidence": [
    "The verdict",
    "## Qwen vs DeepSeek at a glance",
    "| Vendor | Alibaba (Qwen team) (China) | DeepSeek (China) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The verdict",
    "## Qwen vs DeepSeek at a glance",
    "| Released | 2026-02-16 | 2026-04-24 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## About DeepSeek V4",
    "DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier."
   ]
  },
  {
   "url": "https://thepromptbench.com/release-radar/open-weight-model-tracker-2026-05",
   "title": "Open-Weight Model Landscape \u2014 How to Read It, May 2026",
   "published_at": "2026-05-24T00:00:00",
   "organization": "Google DeepMind",
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   "release_date": "2026-04-03",
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   "significance": "shipped as open weights in 2B, 9B, and 27B sizes with base and instruction-tuned variants",
   "organization_evidence": [
    "Open-Weight Model Landscape \u2014 How to Read It, May 2026",
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   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Model Landscape \u2014 How to Read It, May 2026",
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   "parameters_evidence": [
    "Open-Weight Model Landscape \u2014 How to Read It, May 2026",
    "To make the methodology concrete: the **Gemma 4 family** (Google DeepMind) shipped as open weights on 2026-04-03, in 2B, 9B, and 27B sizes with base and instruction-tuned variants."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Model Landscape \u2014 How to Read It, May 2026",
    "To make the methodology concrete: the **Gemma 4 family** (Google DeepMind) shipped as open weights on 2026-04-03, in 2B, 9B, and 27B sizes with base and instruction-tuned variants."
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  },
  {
   "url": "https://vibe.cerridan.com/posts/mistral-vibe-open-weight-cloud-agents",
   "title": "Mistral's cloud coding agent runs on open weights",
   "published_at": "2026-05-06T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
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   "significance": "77.6% on SWE-Bench Verified",
   "organization_evidence": [
    "BREAKING May 06, 2026 \u00b7 3 min read",
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   "model_evidence": null,
   "release_date_evidence": [
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   "license_evidence": null,
   "significance_evidence": [
    "BREAKING May 06, 2026 \u00b7 3 min read",
    "# Mistral's cloud coding agent runs on open weights",
    "The headline benchmark is 77.6% on SWE-Bench Verified \u2014 credible, but not state of the art."
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  },
  {
   "url": "https://ai.google.dev/gemma?utm_cexternal=",
   "title": "Gemma \u2014 Google DeepMind",
   "published_at": null,
   "organization": "Google",
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   "release_date": "2026-04",
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   "significance": "Byte for byte, the most capable open models. Purpose-built for advanced reasoning and agentic workflows",
   "organization_evidence": [
    "Large Language Models (LLMs), such as Gemma, may sometimes provide inaccurate or offensive content that doesn\u2019t represent Google\u2019s views.",
    "# Gemma"
   ],
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   "release_date_evidence": [
    "April 2026",
    "# Gemma",
    "### Introducing Gemma 4"
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   "significance_evidence": [
    "Byte for byte, the most capable open models. Purpose-built for advanced reasoning and agentic workflows",
    "# Gemma",
    "### Introducing Gemma 4"
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  },
  {
   "url": "https://medium.com/@cartseoservice/glm-5-2-review-2026-best-open-source-ai-model-for-coding-agentic-33d4023bfa38",
   "title": "GLM-5.2 Review (2026): Best Open-Source AI Model for Coding & Agentic",
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   "organization": "Zhipu AI",
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   "parameters": "744-billion-parameter",
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   "significance": "Highest Intelligence Index score (51) of any open-weights model to date, scores 81.0 on Terminal-Bench 2.1, and ranks #1 worldwide for frontend coding on Code Arena: Frontend leaderboard.",
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    "# GLM-5.2 Review (2026): Best Open-Source AI Model for Coding & Agentic",
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   "release_date_evidence": [
    "# GLM-5.2 Review (2026): Best Open-Source AI Model for Coding & Agentic",
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   "parameters_evidence": [
    "# GLM-5.2 Review (2026): Best Open-Source AI Model for Coding & Agentic",
    "It is a 744-billion-parameter Mixture-of-Experts (MoE) model that activates roughly 40 billion parameters per token, delivering frontier-class performance on coding, agentic, and long-horizon engineering tasks at a fraction of the cost of proprietary competitors like GPT-5.5 and Claude Opus 4.8."
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    "# GLM-5.2 Review (2026): Best Open-Source AI Model for Coding & Agentic",
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    "On Terminal-Bench 2.1, it is the first open-weight model to cross the 80% threshold, scoring 81.0.",
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   ]
  },
  {
   "url": "https://singularitybyte.com/models/nvidia-ising-open-source-quantum-ai-models.html",
   "title": "NVIDIA Ising",
   "published_at": "2026-04-15T11:01:32",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 35B-parameter vision-language model with 3B active per token, released with open weights, outperforms Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.4 on its target benchmark by margins of 3 to 14 percent.",
   "organization_evidence": [
    "# NVIDIA Ising",
    "NVIDIA shipped two open AI model families on April 14, 2026, both aimed at a single stubborn problem: making quantum processors actually useful."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# NVIDIA Ising",
    "NVIDIA shipped two open AI model families on April 14, 2026, both aimed at a single stubborn problem: making quantum processors actually useful."
   ],
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA Ising",
    "It is that a 35B-parameter vision-language model with 3B active per token, released with open weights, outperforms Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.4 on its target benchmark by margins of 3 to 14 percent."
   ]
  },
  {
   "url": "https://deepseekai.guide/alternatives/open-source-ai-like-deepseek",
   "title": "Open Source AI Like DeepSeek: 7 Models Worth Running",
   "published_at": "2026-04-25T10:48:21",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
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   "license": null,
   "significance": "Frontier tier, Mixture-of-Experts",
   "organization_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The current generation, **DeepSeek V4 (Preview)**, was released on April 24, 2026."
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   "release_date_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The current generation, **DeepSeek V4 (Preview)**, was released on April 24, 2026."
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   "parameters_evidence": [
    "The 2026 shortlist at a glance",
    "| DeepSeek V4-Pro | 1.6T total / 49B active | MoE | 1,000,000 | MIT | $0.435 promo / $1.74 list in (miss); $0.87 promo / $3.48 list out \u2014 through 2026-05-31 |",
    "- **`deepseek-v4-pro`** \u2014 1.6T total parameters, 49B active per token. Frontier tier."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "It ships as two open-weight Mixture-of-Experts (MoE) models under the MIT licence:",
    "- **`deepseek-v4-pro`** \u2014 1.6T total parameters, 49B active per token. Frontier tier."
   ]
  },
  {
   "url": "https://deepseekai.guide/alternatives/open-source-ai-like-deepseek",
   "title": "Open Source AI Like DeepSeek: 7 Models Worth Running",
   "published_at": "2026-04-25T10:48:21",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B total, 13B active",
   "context_window": "1,000,000",
   "license": "MIT",
   "significance": "Cost-efficient tier, Mixture-of-Experts",
   "organization_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The current generation, **DeepSeek V4 (Preview)**, was released on April 24, 2026."
   ],
   "model_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| DeepSeek V4-Flash | 284B / 13B active | MoE | 1,000,000 | MIT | $0.14 in (miss) / $0.28 out |",
    "- **`deepseek-v4-flash`** \u2014 284B total, 13B active. Cost-efficient tier."
   ],
   "release_date_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The current generation, **DeepSeek V4 (Preview)**, was released on April 24, 2026."
   ],
   "parameters_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| DeepSeek V4-Flash | 284B / 13B active | MoE | 1,000,000 | MIT | $0.14 in (miss) / $0.28 out |",
    "- **`deepseek-v4-flash`** \u2014 284B total, 13B active. Cost-efficient tier."
   ],
   "context_window_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| DeepSeek V4-Flash | 284B / 13B active | MoE | 1,000,000 | MIT | $0.14 in (miss) / $0.28 out |",
    "Both models carry a 1,000,000-token default context window, with output up to 384,000 tokens."
   ],
   "license_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| DeepSeek V4-Flash | 284B / 13B active | MoE | 1,000,000 | MIT | $0.14 in (miss) / $0.28 out |",
    "It ships as two open-weight Mixture-of-Experts (MoE) models under the MIT licence:"
   ],
   "significance_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "It ships as two open-weight Mixture-of-Experts (MoE) models under the MIT licence:",
    "- **`deepseek-v4-flash`** \u2014 284B total, 13B active. Cost-efficient tier."
   ]
  },
  {
   "url": "https://deepseekai.guide/alternatives/open-source-ai-like-deepseek",
   "title": "Open Source AI Like DeepSeek: 7 Models Worth Running",
   "published_at": "2026-04-25T10:48:21",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
   "release_date": "2026-04",
   "parameters": "35B",
   "context_window": "Up to 1M tokens with the 2507 update",
   "license": "Apache 2.0",
   "significance": "Released on Hugging Face Hub and ModelScope in April 2026 under Apache 2.0",
   "organization_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 1. Qwen3 / Qwen3.6 (Alibaba)",
    "The April 2026 line extends this with Qwen3.6: Qwen3.6-27B and Qwen3.6-35B-A3B were released on Hugging Face Hub and ModelScope in April 2026."
   ],
   "model_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 1. Qwen3 / Qwen3.6 (Alibaba)",
    "The April 2026 line extends this with Qwen3.6: Qwen3.6-27B and Qwen3.6-35B-A3B were released on Hugging Face Hub and ModelScope in April 2026."
   ],
   "release_date_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 1. Qwen3 / Qwen3.6 (Alibaba)",
    "The April 2026 line extends this with Qwen3.6: Qwen3.6-27B and Qwen3.6-35B-A3B were released on Hugging Face Hub and ModelScope in April 2026."
   ],
   "parameters_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 1. Qwen3 / Qwen3.6 (Alibaba)",
    "The April 2026 line extends this with Qwen3.6: Qwen3.6-27B and Qwen3.6-35B-A3B were released on Hugging Face Hub and ModelScope in April 2026."
   ],
   "context_window_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| Qwen3 (235B-A22B) | 235B total, 22B active per forward pass via 128 experts | MoE | Up to 1M tokens with the 2507 update | Apache 2.0 | Hosted via Alibaba Cloud / Together / Fireworks (varies) |"
   ],
   "license_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| Qwen3 (235B-A22B) | 235B total, 22B active per forward pass via 128 experts | MoE | Up to 1M tokens with the 2507 update | Apache 2.0 | Hosted via Alibaba Cloud / Together / Fireworks (varies) |",
    "All Qwen3 models are available under the Apache 2.0 open-source licence, which is materially friendlier than Meta\u2019s Llama licence for commercial work."
   ],
   "significance_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 1. Qwen3 / Qwen3.6 (Alibaba)",
    "The April 2026 line extends this with Qwen3.6: Qwen3.6-27B and Qwen3.6-35B-A3B were released on Hugging Face Hub and ModelScope in April 2026.",
    "So Alibaba is splitting its line \u2014 flagship \u201cPlus\u201d tier proprietary, mid-tier open."
   ]
  },
  {
   "url": "https://deepseekai.guide/alternatives/open-source-ai-like-deepseek",
   "title": "Open Source AI Like DeepSeek: 7 Models Worth Running",
   "published_at": "2026-04-25T10:48:21",
   "organization": "Zhipu AI",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "744B MoE / 40B active",
   "context_window": "200,000",
   "license": "MIT",
   "significance": "Frontier-scale model with MIT licence, no usage restrictions, no registration required",
   "organization_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 3. GLM-5.1 (Zhipu AI)",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights."
   ],
   "model_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| GLM-5.1 (Zhipu) | 744B MoE / 40B active, 200K context | MoE | 200,000 | MIT | Free open weights |",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights."
   ],
   "release_date_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights."
   ],
   "parameters_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| GLM-5.1 (Zhipu) | 744B MoE / 40B active, 200K context | MoE | 200,000 | MIT | Free open weights |",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights."
   ],
   "context_window_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| GLM-5.1 (Zhipu) | 744B MoE / 40B active, 200K context | MoE | 200,000 | MIT | Free open weights |"
   ],
   "license_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| GLM-5.1 (Zhipu) | 744B MoE / 40B active, 200K context | MoE | 200,000 | MIT | Free open weights |",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights."
   ],
   "significance_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "GLM-5.1 was released by Zhipu AI in early April 2026 as a 744B MoE with 40B active parameters, a 200K-token context window, and MIT-licensed open weights.",
    "The MIT licence makes this one of the most permissive releases of a frontier-scale model to date \u2014 no usage restrictions, no registration required."
   ]
  },
  {
   "url": "https://deepseekai.guide/alternatives/open-source-ai-like-deepseek",
   "title": "Open Source AI Like DeepSeek: 7 Models Worth Running",
   "published_at": "2026-04-25T10:48:21",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04",
   "parameters": "31B dense, 26B MoE, and smaller E4B and E2B variants",
   "context_window": "256,000",
   "license": "Apache 2.0",
   "significance": "Shipped under Apache 2.0 with a genuine licence upgrade permitting commercial use, modification and redistribution",
   "organization_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 4. Gemma 4 (Google)",
    "In April 2026, Gemma 4 shipped under Apache 2.0 \u2014 no usage restrictions, no monthly active user limits, no acceptable-use policies, fully permitting commercial use, modification and redistribution."
   ],
   "model_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 4. Gemma 4 (Google)",
    "In April 2026, Gemma 4 shipped under Apache 2.0 \u2014 no usage restrictions, no monthly active user limits, no acceptable-use policies, fully permitting commercial use, modification and redistribution."
   ],
   "release_date_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 4. Gemma 4 (Google)",
    "In April 2026, Gemma 4 shipped under Apache 2.0 \u2014 no usage restrictions, no monthly active user limits, no acceptable-use policies, fully permitting commercial use, modification and redistribution."
   ],
   "parameters_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 4. Gemma 4 (Google)",
    "The Gemma 4 line includes a 31B dense model, a 26B MoE, and smaller E4B and E2B variants, all with 256K context under Apache 2.0."
   ],
   "context_window_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| Gemma 4 31B | 31B dense, 256K context | Dense | 256,000 | Apache 2.0 | Free open weights |"
   ],
   "license_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "The 2026 shortlist at a glance",
    "| Model family | Largest open variant | Architecture | Context | Weights licence | Notable home-API price (per 1M tokens) |",
    "| Gemma 4 31B | 31B dense, 256K context | Dense | 256,000 | Apache 2.0 | Free open weights |",
    "In April 2026, Gemma 4 shipped under Apache 2.0 \u2014 no usage restrictions, no monthly active user limits, no acceptable-use policies, fully permitting commercial use, modification and redistribution."
   ],
   "significance_evidence": [
    "Open Source AI Like DeepSeek: The 2026 Shortlist for Builders",
    "### 4. Gemma 4 (Google)",
    "Google\u2019s Gemma family had a genuine licence upgrade this year.",
    "In April 2026, Gemma 4 shipped under Apache 2.0 \u2014 no usage restrictions, no monthly active user limits, no acceptable-use policies, fully permitting commercial use, modification and redistribution."
   ]
  },
  {
   "url": "https://testingcatalog.net/alibabas-qwen3-6-27b-is-an-open-source-ai-that-runs-locally-and-rivals-claude-opus-4-5",
   "title": "Alibaba's Qwen3.6-27B is an open-source AI that runs locally and rivals Claude Opus 4.5",
   "published_at": "2026-04-23T06:05:37",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "27-billion",
   "context_window": null,
   "license": null,
   "significance": "surpasses the much larger 397B Qwen3.5-397B-A17B on critical developer tasks",
   "organization_evidence": [
    "# Alibaba's Qwen3.6-27B is an open-source AI that runs locally and rivals Claude Opus 4.5",
    "Alibaba's Qwen3.6-27B is an open-source AI that runs locally and rivals Claude Opus 4.5\n\n[Alibaba](https://testingcatalog.net/alibaba/)'s [AI](https://testingcatalog.net/ai/) research team, Tongyi Lab ([Qwen](https://testingcatalog.net/tag/qwen/)), just dropped a new open-source model that's making some bold claims."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Alibaba's Qwen3.6-27B is an open-source AI that runs locally and rivals Claude Opus 4.5",
    "It's called **Qwen3.6-27B**, a 27-billion-parameter multimodal AI, and it's available for anyone to download and run on their own hardware under the permissive Apache License 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Alibaba's Qwen3.6-27B is an open-source AI that runs locally and rivals Claude Opus 4.5",
    "In a statement, Qwen said, \"Qwen3.6-27B surpasses the much larger 397B Qwen3.5-397B-A17B on critical developer tasks, yet is far easier to deploy and operate."
   ]
  },
  {
   "url": "https://teachaitools.blog/blog/meta-llama-4-release-open-source-ai-2026",
   "title": "Meta Llama 4 and Llama 4.5: Open-Source AI Reviewed (2026)",
   "published_at": "2026-07-22T02:36:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-06",
   "parameters": "17B active / 109B total",
   "context_window": null,
   "license": null,
   "significance": "Extended the model's lead in instruction following and coding quality, with HumanEval scores improving approximately 4\u20136 percentage points versus Llama 4.",
   "organization_evidence": [
    "Meta Llama 4 and Llama 4.5: The State of Open-Source AI in 2026",
    "Meta's Llama 4 release in April 2025 marked a genuine inflection point in the open-source AI story."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "And Llama 4.5's June 2026 refresh extended the model's lead in instruction following and coding quality.",
    "Meta Llama 4 and Llama 4.5: The State of Open-Source AI in 2026",
    "## Llama 4.5: The June 2026 Refresh",
    "Released in June 2026, Llama 4.5 is a mid-cycle improvement over Llama 4 in the same Scout and Maverick architectures with identical hardware requirements."
   ],
   "parameters_evidence": [
    "| Specification | Value |",
    "| Active parameters | 17 billion |",
    "| Total parameters | 109 billion |",
    "Meta Llama 4 and Llama 4.5: The State of Open-Source AI in 2026",
    "### Llama 4 Scout"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "And Llama 4.5's June 2026 refresh extended the model's lead in instruction following and coding quality.",
    "HumanEval scores improved approximately 4\u20136 percentage points versus Llama 4.",
    "Meta Llama 4 and Llama 4.5: The State of Open-Source AI in 2026",
    "## Llama 4.5: The June 2026 Refresh",
    "### Coding Quality"
   ]
  },
  {
   "url": "https://switchtools.io/blog/google-gemma-4-open-source-model",
   "title": "Google Gemma 4: The Open Source AI Model Challenging Llama 4 (2026)",
   "published_at": "2026-04-29T12:00:04",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Reached third place on Arena's global text leaderboard, outcompeting models with parameter counts 20 times larger.",
   "organization_evidence": [
    "Google Gemma 4: The Open Source AI Model Challenging Llama 4 (2026)",
    "Google released Gemma 4 on April 2, 2026 \u2014 and the release did something no previous Gemma version had done: it shipped under a fully permissive **Apache 2.0 license**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: The Open Source AI Model Challenging Llama 4 (2026)",
    "Google released Gemma 4 on April 2, 2026 \u2014 and the release did something no previous Gemma version had done: it shipped under a fully permissive **Apache 2.0 license**."
   ],
   "parameters_evidence": [
    "Benchmark Comparison: Gemma 4 vs Llama 4 vs Mistral",
    "| **Gemma 4 31B Dense** | 31B | 89.2% | 84.3% | 80.0% | Apache 2.0 | 256K |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: The Open Source AI Model Challenging Llama 4 (2026)",
    "The numbers are hard to ignore: the Gemma 4 31B Dense model reached **third place on Arena's global text leaderboard** \u2014 outcompeting models with parameter counts 20 times larger."
   ]
  },
  {
   "url": "https://agenticbrew.ai/news/bf8b9700-d02e-4add-b00b-7b20e0a9ab18/moonshot-ai-s-kimi-k3-open-weight-model-release-and-rollout",
   "title": "Moonshot AI's Kimi K3 open-weight model release and rollout",
   "published_at": "2026-08-02T00:36:29",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters",
   "context_window": null,
   "license": null,
   "significance": "Moonshot AI unveiled Kimi K3 on July 16, 2026 as a 2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters, a 1-million-token context window, and native visual understanding - after it first leaked on LMArena under the stealth codename 'Kivine'.",
   "organization_evidence": [
    "Moonshot AI unveiled Kimi K3 on July 16, 2026 as a 2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters, a 1-million-token context window, and native visual understanding - after it first leaked on LMArena under the stealth codename 'Kivine'."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI unveiled Kimi K3 on July 16, 2026 as a 2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters, a 1-million-token context window, and native visual understanding - after it first leaked on LMArena under the stealth codename 'Kivine'."
   ],
   "parameters_evidence": [
    "Moonshot AI unveiled Kimi K3 on July 16, 2026 as a 2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters, a 1-million-token context window, and native visual understanding - after it first leaked on LMArena under the stealth codename 'Kivine'."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI unveiled Kimi K3 on July 16, 2026 as a 2.8-trillion-parameter mixture-of-experts model with 104 billion activated parameters, a 1-million-token context window, and native visual understanding - after it first leaked on LMArena under the stealth codename 'Kivine'."
   ]
  },
  {
   "url": "https://nextomoro.com/glm-5-1",
   "title": "GLM-5.1",
   "published_at": "2026-05-15T05:03:56",
   "organization": "Z.ai",
   "model": null,
   "release_date": null,
   "parameters": "754 billion",
   "context_window": null,
   "license": null,
   "significance": "state-of-the-art SWE-Bench Pro performance",
   "organization_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is Z.ai's February 2026 open-weights mixture-of-experts language model, a 754-billion-parameter agentic-engineering specialist with state-of-the-art SWE-Bench Pro performance and an MIT license for commercial use.",
    "- **Lab:**[Z.ai](https://nextomoro.com/z-ai/), the company also known as Zhipu AI and as `zai-org` on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is Z.ai's February 2026 open-weights mixture-of-experts language model, a 754-billion-parameter agentic-engineering specialist with state-of-the-art SWE-Bench Pro performance and an MIT license for commercial use.",
    "- **Architecture:** Mixture-of-experts with Dynamic Sparse Architecture (`glm_moe_dsa`). 754 billion total parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is Z.ai's February 2026 open-weights mixture-of-experts language model, a 754-billion-parameter agentic-engineering specialist with state-of-the-art SWE-Bench Pro performance and an MIT license for commercial use.",
    "GLM-5.1 is the February 2026 generation of [Z.ai](https://nextomoro.com/z-ai/)'s (Zhipu AI) open-weights language model family, a 754-billion-parameter mixture-of-experts model built on the company's dynamic sparse architecture (referred to in the model configuration as `glm_moe_dsa`)."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/minimax-m3",
   "title": "MiniMax-M3 \u2014 Open-Weight 1M-Context Multimodal Model | AI/TLDR",
   "published_at": "2026-06-01T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "first open-weight model to combine top-tier coding and agentic performance, a 1M-token context window, and native multimodality in a single architecture",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax-M3 is an open-weight frontier model released June 1, 2026, billed by MiniMax as the first open-weight model to combine top-tier coding and agentic performance, a 1M-token context window, and native multimodality in a single architecture."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax-M3 is an open-weight frontier model released June 1, 2026, billed by MiniMax as the first open-weight model to combine top-tier coding and agentic performance, a 1M-token context window, and native multimodality in a single architecture."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax-M3 is an open-weight frontier model released June 1, 2026, billed by MiniMax as the first open-weight model to combine top-tier coding and agentic performance, a 1M-token context window, and native multimodality in a single architecture."
   ]
  },
  {
   "url": "https://faq.com.tw/en/ai-ml/2026-05-11-china-open-weights-coding-blitz-en",
   "title": "China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
   "published_at": "2026-05-11T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 83 on the BenchLM composite, with particular strength in multi-step code reasoning tasks.",
   "organization_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "The releases \u2014 Z.ai\u2019s GLM-5.1, MiniMax\u2019s M2.7, Moonshot AI\u2019s Kimi K2.6, and DeepSeek\u2019s V4 in two variants \u2014 represent the most concentrated burst of high-capability open-model releases the industry has ever seen from a single country, and they arrive at a moment when the competitive gap between Chinese and American AI is narrowing faster than most Western observers anticipated.",
    "**GLM-5.1** from Z.ai (which spun out from Tsinghua University\u2019s Institute for AI Industry Research) scored 83 on the BenchLM composite, with particular strength in multi-step code reasoning tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "Between April 7 and 24, four Chinese AI labs \u2014 Z.ai, MiniMax, Moonshot AI, and DeepSeek \u2014 released open-weight coding models in just 12 days.",
    "In a window spanning just 12 days between April 7 and April 24, 2026, four Chinese AI laboratories released open-weight coding models that collectively rattled the assumptions of every frontier model developer in the world."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Models: What They Actually Do",
    "**GLM-5.1** from Z.ai (which spun out from Tsinghua University\u2019s Institute for AI Industry Research) scored 83 on the BenchLM composite, with particular strength in multi-step code reasoning tasks."
   ]
  },
  {
   "url": "https://faq.com.tw/en/ai-ml/2026-05-11-china-open-weights-coding-blitz-en",
   "title": "China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
   "published_at": "2026-05-11T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Placed at the top of the multilingual coding category, with code generation capability in Japanese, Korean, and European language contexts outperforming all non-Chinese open-weight alternatives on cross-lingual coding benchmarks.",
   "organization_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "The releases \u2014 Z.ai\u2019s GLM-5.1, MiniMax\u2019s M2.7, Moonshot AI\u2019s Kimi K2.6, and DeepSeek\u2019s V4 in two variants \u2014 represent the most concentrated burst of high-capability open-model releases the industry has ever seen from a single country, and they arrive at a moment when the competitive gap between Chinese and American AI is narrowing faster than most Western observers anticipated.",
    "**MiniMax M2.7** rounded out the quartet with benchmark results placing it at the top of the multilingual coding category."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "Between April 7 and 24, four Chinese AI labs \u2014 Z.ai, MiniMax, Moonshot AI, and DeepSeek \u2014 released open-weight coding models in just 12 days.",
    "In a window spanning just 12 days between April 7 and April 24, 2026, four Chinese AI laboratories released open-weight coding models that collectively rattled the assumptions of every frontier model developer in the world."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Models: What They Actually Do",
    "**MiniMax M2.7** rounded out the quartet with benchmark results placing it at the top of the multilingual coding category."
   ]
  },
  {
   "url": "https://faq.com.tw/en/ai-ml/2026-05-11-china-open-weights-coding-blitz-en",
   "title": "China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
   "published_at": "2026-05-11T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Became the first open-weight model to beat GPT-5.4 on SWE-Bench Pro, scoring 58.6.",
   "organization_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "The releases \u2014 Z.ai\u2019s GLM-5.1, MiniMax\u2019s M2.7, Moonshot AI\u2019s Kimi K2.6, and DeepSeek\u2019s V4 in two variants \u2014 represent the most concentrated burst of high-capability open-model releases the industry has ever seen from a single country, and they arrive at a moment when the competitive gap between Chinese and American AI is narrowing faster than most Western observers anticipated.",
    "**Kimi K2.6** (released April 20) made perhaps the most dramatic debut of the four."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "**Kimi K2.6** (released April 20) made perhaps the most dramatic debut of the four."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Models: What They Actually Do",
    "The assault established Kimi K2.6 as the first open-weight model to beat GPT-5.4 on SWE-Bench Pro, while the entire cohort delivers at 15\u201330x lower cost than American rivals, raising urgent questions about export controls and the geopolitics of open-source AI.",
    "**Kimi K2.6** (released April 20) made perhaps the most dramatic debut of the four."
   ]
  },
  {
   "url": "https://faq.com.tw/en/ai-ml/2026-05-11-china-open-weights-coding-blitz-en",
   "title": "China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
   "published_at": "2026-05-11T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Pro version scored 87 on BenchLM's composite leaderboard, the highest recorded for any Chinese model to date.",
   "organization_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "The releases \u2014 Z.ai\u2019s GLM-5.1, MiniMax\u2019s M2.7, Moonshot AI\u2019s Kimi K2.6, and DeepSeek\u2019s V4 in two variants \u2014 represent the most concentrated burst of high-capability open-model releases the industry has ever seen from a single country, and they arrive at a moment when the competitive gap between Chinese and American AI is narrowing faster than most Western observers anticipated.",
    "**DeepSeek V4** \u2014 the successor to the model that triggered a market selloff in January 2025 when it first demonstrated parity with GPT-4 \u2014 arrived in a standard version and a Pro variant."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China's 12-Day Coding Model Blitz: How Four AI Labs Reshaped Open Source",
    "Between April 7 and 24, four Chinese AI labs \u2014 Z.ai, MiniMax, Moonshot AI, and DeepSeek \u2014 released open-weight coding models in just 12 days.",
    "In a window spanning just 12 days between April 7 and April 24, 2026, four Chinese AI laboratories released open-weight coding models that collectively rattled the assumptions of every frontier model developer in the world."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The Models: What They Actually Do",
    "The Pro version scored 87 on BenchLM\u2019s composite leaderboard, the highest recorded for any Chinese model to date."
   ]
  },
  {
   "url": "https://apidog.com/blog/what-is-gemma-4-12b",
   "title": "What is Gemma 4 12B?",
   "published_at": "2026-06-04T02:51:25",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "first mid-sized model with native audio input, and it does this with no separate vision or audio encoder",
   "organization_evidence": [
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline detail: it\u2019s the first mid-sized model with native audio input, and it does this with no separate vision or audio encoder."
   ]
  },
  {
   "url": "https://felloai.com/de/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "tied GPT-5.5 on the toughest coding benchmark in the industry, at roughly 80% less per million tokens; #1 open-weight model on the Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**."
   ],
   "parameters_evidence": [
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It just tied **GPT-5.5** on the toughest coding benchmark in the industry, at roughly **80% less per million tokens**.",
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/zhipu-glm-5-1-swe-bench-pro-open-source-agentic-coding-2026",
   "title": "GLM-5.1 Goes Open-Source and Hits #1 on SWE-Bench Pro \u2014 Beating Every Closed AI Model",
   "published_at": "2026-04-10T19:37:46",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Posted a 58.4 score on SWE-Bench Pro \u2014 placing it at the top of the global leaderboard for software engineering AI, ahead of every major closed-source frontier model currently on the market.",
   "organization_evidence": [
    "# GLM-5.1 Goes Open-Source and Hits #1 on SWE-Bench Pro \u2014 Beating Every Closed AI Model",
    "Z.ai's GLM-5.1 is a 754B open-weight MoE model under the MIT license \u2014 and it just took #1 on SWE-Bench Pro, outscoring every major closed model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The First Open-Source Model to Beat All Closed Frontier Models on Real-World Coding",
    "April 7, 2026 produced one of the more striking benchmark results in recent open-source AI history: GLM-5.1, released by Z.ai (formerly Zhipu AI) under the MIT license, posted a 58.4 score on SWE-Bench Pro \u2014 placing it at the top of the global leaderboard for software engineering AI, ahead of every major closed-source frontier model currently on the market."
   ],
   "parameters_evidence": [
    "# GLM-5.1 Goes Open-Source and Hits #1 on SWE-Bench Pro \u2014 Beating Every Closed AI Model",
    "Z.ai's GLM-5.1 is a 754B open-weight MoE model under the MIT license \u2014 and it just took #1 on SWE-Bench Pro, outscoring every major closed model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The First Open-Source Model to Beat All Closed Frontier Models on Real-World Coding",
    "April 7, 2026 produced one of the more striking benchmark results in recent open-source AI history: GLM-5.1, released by Z.ai (formerly Zhipu AI) under the MIT license, posted a 58.4 score on SWE-Bench Pro \u2014 placing it at the top of the global leaderboard for software engineering AI, ahead of every major closed-source frontier model currently on the market."
   ]
  },
  {
   "url": "https://botmonster.com/ai/minimax-m2-7-review-self-evolving-model",
   "title": "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
   "published_at": "2026-04-12T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M2.7",
   "release_date": "2026-04",
   "parameters": "230B",
   "context_window": "204.8K tokens",
   "license": "not the permissive M2.5 license (commercial use is restricted)",
   "significance": "Scores 50 on the Artificial Analysis Intelligence Index, landing it on par with Sonnet 4.6 across coding and agent benchmarks and within a couple of points of Claude Opus 4.6.",
   "organization_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "[MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) , released in April 2026, is a 230B-parameter open-weights reasoning model (Mixture-of-Experts, 10B active, 8 of 256 experts routed per token) that scores **50 on the [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2-7) Intelligence Index**."
   ],
   "model_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "[MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) , released in April 2026, is a 230B-parameter open-weights reasoning model (Mixture-of-Experts, 10B active, 8 of 256 experts routed per token) that scores **50 on the [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2-7) Intelligence Index**."
   ],
   "release_date_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "[MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) , released in April 2026, is a 230B-parameter open-weights reasoning model (Mixture-of-Experts, 10B active, 8 of 256 experts routed per token) that scores **50 on the [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2-7) Intelligence Index**."
   ],
   "parameters_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "[MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) , released in April 2026, is a 230B-parameter open-weights reasoning model (Mixture-of-Experts, 10B active, 8 of 256 experts routed per token) that scores **50 on the [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2-7) Intelligence Index**."
   ],
   "context_window_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "Other specs worth pinning down:",
    "- Context window is about 204.8K tokens, roughly 307 A4 pages of input."
   ],
   "license_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "Two big asterisks: the M2.7 license is **not** the permissive M2.5 license (commercial use is restricted), and there is **no multimodal support**."
   ],
   "significance_evidence": [
    "MiniMax M2.7: Model That Almost Matches Claude Opus 4.6",
    "[MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) , released in April 2026, is a 230B-parameter open-weights reasoning model (Mixture-of-Experts, 10B active, 8 of 256 experts routed per token) that scores **50 on the [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2-7) Intelligence Index**. That lands it on par with Sonnet 4.6 across coding and agent benchmarks and within a couple of points of Claude Opus 4.6."
   ]
  },
  {
   "url": "https://genalphai.com/diffusiongemma-explained-can-diffusion-beat-autoregression",
   "title": "DiffusionGemma 26B-A4B: Can Diffusion Beat Autoregression?",
   "published_at": "2026-06-12T06:12:46",
   "organization": "Google DeepMind and NVIDIA",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": "25.2B total parameters, 3.8B active per token",
   "context_window": null,
   "license": null,
   "significance": "generates 256 tokens in parallel rather than one at a time",
   "organization_evidence": [
    "On June 10, [2026](/ai-agent-observability-in-2026-the-new-telemetry-stack-compared/), Google DeepMind and NVIDIA released [DiffusionGemma 26B-A4B](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), an open-weights diffusion language model that generates 256 tokens in parallel rather than one at a time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 10, [2026](/ai-agent-observability-in-2026-the-new-telemetry-stack-compared/), Google DeepMind and NVIDIA released [DiffusionGemma 26B-A4B](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), an open-weights diffusion language model that generates 256 tokens in parallel rather than one at a time."
   ],
   "parameters_evidence": [
    "- 25.2B total parameters, 3.8B active per token, 256K context, Apache 2.0 license, per the [NVIDIA NIM model card](https://build.nvidia.com/google/diffusiongemma-26b-a4b-it/modelcard)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On June 10, [2026](/ai-agent-observability-in-2026-the-new-telemetry-stack-compared/), Google DeepMind and NVIDIA released [DiffusionGemma 26B-A4B](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), an open-weights diffusion language model that generates 256 tokens in parallel rather than one at a time."
   ]
  },
  {
   "url": "https://pondero.ai/news/2026-07-27-daily-brief",
   "title": "10 AI stories from July 27, 2026: Kimi K3 open weights at 2.8T parameters, ExploitGym full technical disclosure, DeepSeek V4 goes live with surge pricing, and 7 more",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight release in history, scores above Claude Opus 5 and GPT-5.6 Sol on coding tasks",
   "organization_evidence": [
    "Moonshot AI released the full open weights of Kimi K3 at 00:00 UTC on July 27, 2026, making it the largest open-weight release in history.",
    "## 10 AI stories from July 27, 2026: Kimi K3 open weights at 2.8T parameters, ExploitGym full technical disclosure, DeepSeek V4 goes live with surge pricing, and 7 more",
    "# AI news daily brief: 2026-07-27"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released the full open weights of Kimi K3 at 00:00 UTC on July 27, 2026, making it the largest open-weight release in history.",
    "## 10 AI stories from July 27, 2026: Kimi K3 open weights at 2.8T parameters, ExploitGym full technical disclosure, DeepSeek V4 goes live with surge pricing, and 7 more",
    "# AI news daily brief: 2026-07-27"
   ],
   "parameters_evidence": [
    "The model is a sparse Mixture-of-Experts architecture with 2.8 trillion parameters.",
    "## 10 AI stories from July 27, 2026: Kimi K3 open weights at 2.8T parameters, ExploitGym full technical disclosure, DeepSeek V4 goes live with surge pricing, and 7 more",
    "# AI news daily brief: 2026-07-27"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI released the full open weights of Kimi K3 at 00:00 UTC on July 27, 2026, making it the largest open-weight release in history.",
    "On FrontierBench v0.1, per Moonshot benchmarks, Kimi K3 scores above [Claude](/beginners/what-is-claude-eli5/) Opus 5 and GPT-5.6 Sol on coding tasks; those results are self-reported and await independent replication.",
    "## 10 AI stories from July 27, 2026: Kimi K3 open weights at 2.8T parameters, ExploitGym full technical disclosure, DeepSeek V4 goes live with surge pricing, and 7 more",
    "# AI news daily brief: 2026-07-27"
   ]
  },
  {
   "url": "https://aitoolradar.io/blog/open-source-ai-2026-local-gemma-deepseek-llama",
   "title": "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
   "published_at": "2026-05-25T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "This is arguably the strongest fully-open release of the year on coding and reasoning.",
   "organization_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**DeepSeek V4** shipped on 24 April 2026 as two models, both open weights under the MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**DeepSeek V4** shipped on 24 April 2026 as two models, both open weights under the MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "This is arguably the strongest fully-open release of the year on coding and reasoning."
   ]
  },
  {
   "url": "https://aitoolradar.io/blog/open-source-ai-2026-local-gemma-deepseek-llama",
   "title": "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
   "published_at": "2026-05-25T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The flagship Qwen3.5-397B-A17B carries 397B total parameters with 17B active, on a hybrid architecture, with native context up to 262K tokens (extendable toward 1M).",
   "organization_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**Qwen 3.5** from Alibaba arrived 16 February 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**Qwen 3.5** from Alibaba arrived 16 February 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "The flagship Qwen3.5-397B-A17B carries 397B total parameters with 17B active, on a hybrid architecture, with native context up to 262K tokens (extendable toward 1M)."
   ]
  },
  {
   "url": "https://aitoolradar.io/blog/open-source-ai-2026-local-gemma-deepseek-llama",
   "title": "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
   "published_at": "2026-05-25T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Google calls it \"byte for byte the most capable open model\" and notes Gemma has now been downloaded over 500 million times.",
   "organization_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**Gemma 4** is Google's open-model family, released in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "**Gemma 4** is Google's open-model family, released in April 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Source AI 2026: Gemma 4, DeepSeek V4, Llama 4",
    "Google calls it [\"byte for byte the most capable open model\"](https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-april-2026/) and notes Gemma has now been downloaded over 500 million times."
   ]
  },
  {
   "url": "https://aimodelsnavi.com/en/blog/qwen37-closed-source-controversy",
   "title": "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Str...",
   "published_at": "2026-06-03T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "open-source (last open flagship)",
   "organization_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "| Apr 2025 | Qwen3 | Apache 2.0 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| Date | Model | Delivery |",
    "| Feb 2026 | Qwen3.5-397B-A17B | Open-source (last open flagship) |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| Date | Model | Delivery |",
    "| Feb 2026 | Qwen3.5-397B-A17B | Open-source (last open flagship) |"
   ]
  },
  {
   "url": "https://aimodelsnavi.com/en/blog/qwen37-closed-source-controversy",
   "title": "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Str...",
   "published_at": "2026-06-03T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.6-27B",
   "release_date": "2026-04-22",
   "parameters": "27B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Open-source (Apache 2.0)",
   "organization_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ],
   "model_evidence": [
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ],
   "release_date_evidence": [
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ],
   "parameters_evidence": [
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ],
   "significance_evidence": [
    "| Date | Model | Delivery |",
    "| Apr 22, 2026 | [Qwen3.6-27B](/en/models/qwen3-6-27b) | Open-source (Apache 2.0) |"
   ]
  },
  {
   "url": "https://aimodelsnavi.com/en/blog/qwen37-closed-source-controversy",
   "title": "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Str...",
   "published_at": "2026-06-03T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "open weights",
   "organization_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "On the same day [Qwen3.6-Max-Preview](/en/models/qwen3-6-max-preview) launched as API-only (April 20), Moonshot AI released [Kimi K2.6](/en/models/kimi-k2-6) with open weights."
   ],
   "model_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "On the same day [Qwen3.6-Max-Preview](/en/models/qwen3-6-max-preview) launched as API-only (April 20), Moonshot AI released [Kimi K2.6](/en/models/kimi-k2-6) with open weights."
   ],
   "release_date_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "On the same day [Qwen3.6-Max-Preview](/en/models/qwen3-6-max-preview) launched as API-only (April 20), Moonshot AI released [Kimi K2.6](/en/models/kimi-k2-6) with open weights."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "From Open-Source Hero to Closed-Weight Model: What Qwen3.7-Max's Closed-Source Pivot Reveals About AI's Structural Shift",
    "On the same day [Qwen3.6-Max-Preview](/en/models/qwen3-6-max-preview) launched as API-only (April 20), Moonshot AI released [Kimi K2.6](/en/models/kimi-k2-6) with open weights."
   ]
  },
  {
   "url": "https://genztech.blog/p/mistral-fat-but-sparse-open-weight-moe",
   "title": "Mistral bets on a 'fat but sparse' open-weight MoE",
   "published_at": "2026-07-10T09:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "6B active parameters",
   "context_window": null,
   "license": null,
   "significance": "state-of-the-art formal-verification results: it saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, and tops the FATE-H suite at 87%",
   "organization_evidence": [
    "# Mistral bets on a 'fat but sparse' open-weight MoE",
    "Mistral also shipped Leanstral 1.5, a free Apache-2.0 model for Lean 4 proof engineering with 6B active parameters and state-of-the-art formal-verification scores."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Mistral bets on a 'fat but sparse' open-weight MoE",
    "Mistral also shipped Leanstral 1.5, a free Apache-2.0 model for Lean 4 proof engineering with 6B active parameters and state-of-the-art formal-verification scores."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Is this just talk, or is Mistral shipping?",
    "It has just 6B active parameters yet posts state-of-the-art formal-verification results: it saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, and tops the FATE-H suite at 87%."
   ]
  },
  {
   "url": "https://aiforautomation.io/news/2026-04-04-google-dropped-gemma-4-free-31b-ai-studio",
   "title": "Google Gemma 4: Free 31B Open Model on AI Studio",
   "published_at": "2026-04-04T14:02:15",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "26B and 31B",
   "context_window": null,
   "license": null,
   "significance": "free 26B and 31B open models on AI Studio",
   "organization_evidence": [
    "Google Gemma 4: Free 31B Open Model on AI Studio",
    "Google's **Gemma 4** \u2014 two open-weight AI models with 26B and 31B parameters \u2014 launched April 2, 2026, and are free to test today on [AI Studio](https://aistudio.google.com)."
   ],
   "model_evidence": [
    "Google Gemma 4: Free 31B Open Model on AI Studio",
    "Google's **Gemma 4** \u2014 two open-weight AI models with 26B and 31B parameters \u2014 launched April 2, 2026, and are free to test today on [AI Studio](https://aistudio.google.com)."
   ],
   "release_date_evidence": [
    "Google Gemma 4: Free 31B Open Model on AI Studio",
    "Google's **Gemma 4** \u2014 two open-weight AI models with 26B and 31B parameters \u2014 launched April 2, 2026, and are free to test today on [AI Studio](https://aistudio.google.com)."
   ],
   "parameters_evidence": [
    "Google Gemma 4: Free 31B Open Model on AI Studio",
    "Google's **Gemma 4** \u2014 two open-weight AI models with 26B and 31B parameters \u2014 launched April 2, 2026, and are free to test today on [AI Studio](https://aistudio.google.com)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: Free 31B Open Model on AI Studio",
    "Google launches Gemma 4 \u2014 free 26B and 31B open models on AI Studio."
   ]
  },
  {
   "url": "https://newclawtimes.com/articles/moonshot-kimi-k3-2-8-trillion-open-weight-agent-model-frontier",
   "title": "Moonshot AI's Kimi K3 Reaches Near-Frontier Intelligence With 2.8 Trillion Parameters and Open Weights",
   "published_at": "2026-07-23T04:30:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Reaches near-frontier intelligence with benchmark scores within striking distance of Claude Fable 5 and GPT-5.6 Sol, is the largest open-weight model ever released, and features native vision capabilities.",
   "organization_evidence": [
    "Deep Dive",
    "Moonshot AI's Kimi K3 Reaches Near-Frontier Intelligence With 2.8 Trillion Parameters and Open Weights",
    "Moonshot AI released Kimi K3 on July 16, 2026, a 2.8 trillion-parameter mixture-of-experts model with a one-million-token context window and native vision capabilities."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Deep Dive",
    "Moonshot AI's Kimi K3 Reaches Near-Frontier Intelligence With 2.8 Trillion Parameters and Open Weights",
    "Moonshot AI released Kimi K3 on July 16, 2026, a 2.8 trillion-parameter mixture-of-experts model with a one-million-token context window and native vision capabilities."
   ],
   "parameters_evidence": [
    "Deep Dive",
    "Moonshot AI's Kimi K3 Reaches Near-Frontier Intelligence With 2.8 Trillion Parameters and Open Weights",
    "Moonshot AI released Kimi K3 on July 16, 2026, a 2.8 trillion-parameter mixture-of-experts model with a one-million-token context window and native vision capabilities."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Deep Dive",
    "Moonshot AI's Kimi K3 Reaches Near-Frontier Intelligence With 2.8 Trillion Parameters and Open Weights",
    "Moonshot AI's Kimi K3 is the largest open-weight model ever released, with 2.8 trillion parameters and benchmark scores within striking distance of Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://dev.to/jangwook_kim_e31e7291ad98/glm-5-the-open-source-frontier-model-you-can-self-host-1nhl",
   "title": "GLM-5: The Open-Source Frontier Model You Can Self-Host",
   "published_at": "2026-04-11T14:54:39",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "It ranks in the top five on nearly every major frontier benchmark, and its successor GLM-5.1 (released April 7, 2026) has already claimed the number one spot on SWE-Bench Pro.",
   "organization_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "On February 11, 2026, Z.ai (formerly Zhipu AI) released GLM-5 on Hugging Face under an MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "On February 11, 2026, Z.ai (formerly Zhipu AI) released GLM-5 on Hugging Face under an MIT license."
   ],
   "parameters_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "### Technical Specifications",
    "| Total Parameters | 744B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5: The Open-Source Frontier Model You Can Self-Host",
    "It ranks in the top five on nearly every major frontier benchmark, and its successor GLM-5.1 (released April 7, 2026) has already claimed the number one spot on SWE-Bench Pro."
   ]
  },
  {
   "url": "https://ibtimes.com/nvidia-unveils-new-open-source-ai-model-ceo-jensen-huang-says-free-ai-means-more-chip-demand-3806286",
   "title": "Nvidia Unveils New Open-Source AI Model. CEO Jensen Huang Says Free AI Means More Chip Demand.",
   "published_at": "2026-08-11T15:20:28",
   "organization": "Nvidia",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Nemotron 3.5 Lightning is a lightweight open-source AI model designed to run on a single GPU in a personal computer, developed particularly for AI agents.",
   "organization_evidence": [
    "Nvidia Unveils New Open-Source AI Model. CEO Jensen Huang Says Free AI Means More Chip Demand.",
    "It is Nvidia's first open-source model release since Huang called on Washington to avoid restrictions.",
    "The chip giant on [introduced](https://blogs.nvidia.com/blog/nemotron-lightning-switchyard-rtx-dgx/) Nemotron 3.5 Lightning, a \"lightweight\" open-source AI model that companies can download, modify and use without paying Nvidia or seeking permission."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nvidia Unveils New Open-Source AI Model. CEO Jensen Huang Says Free AI Means More Chip Demand.",
    "The chip giant on [introduced](https://blogs.nvidia.com/blog/nemotron-lightning-switchyard-rtx-dgx/) Nemotron 3.5 Lightning, a \"lightweight\" open-source AI model that companies can download, modify and use without paying Nvidia or seeking permission.",
    "Nemotron 3.5 Lightning is designed to run on a single GPU in a personal computer, potentially making advanced AI capabilities accessible to companies and developers that do not have the enormous computing budgets required for some frontier models.",
    "Nvidia said the model was developed particularly for AI agents, programs capable of performing tasks autonomously in the background."
   ]
  },
  {
   "url": "https://arturmarkus.com/allen-institutes-molmoweb-8b-beats-gpt-4o-on-web-navigation-first-open-weight-agent-to-outperform-proprietary-models-across-4-major-benchmarks",
   "title": "Allen Institute\u2019s MolmoWeb 8B Beats GPT-4o on Web Navigation\u2014First Open-Weight Agent to Outperform Proprietary Models Across 4 Major Benchmarks - AI Unfiltered",
   "published_at": "2026-04-25T13:20:48",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-03-24",
   "parameters": "8B",
   "context_window": null,
   "license": null,
   "significance": "MolmoWeb-8B achieved state-of-the-art results across four major web-agent benchmarks: WebVoyager, Online-Mind2Web, and two others in the standard evaluation suite.",
   "organization_evidence": [
    "Allen Institute\u2019s MolmoWeb 8B Beats GPT-4o on Web Navigation\u2014First Open-Weight Agent to Outperform Proprietary Models Across 4 Major Benchmarks",
    "Allen Institute for AI (Ai2) [released MolmoWeb on March 24, 2026](https://allenai.org/blog/molmoweb), and followed up with the [complete codebase on April 10, 2026](https://allenai.org/newsletters/2026-04-newsletter)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Allen Institute\u2019s MolmoWeb 8B Beats GPT-4o on Web Navigation\u2014First Open-Weight Agent to Outperform Proprietary Models Across 4 Major Benchmarks",
    "Allen Institute for AI (Ai2) [released MolmoWeb on March 24, 2026](https://allenai.org/blog/molmoweb), and followed up with the [complete codebase on April 10, 2026](https://allenai.org/newsletters/2026-04-newsletter)."
   ],
   "parameters_evidence": [
    "Allen Institute\u2019s MolmoWeb 8B Beats GPT-4o on Web Navigation\u2014First Open-Weight Agent to Outperform Proprietary Models Across 4 Major Benchmarks",
    "An 8-billion parameter open-source model just outperformed GPT-4o at navigating the web.",
    "Allen Institute for AI (Ai2) [released MolmoWeb on March 24, 2026](https://allenai.org/blog/molmoweb), and followed up with the [complete codebase on April 10, 2026](https://allenai.org/newsletters/2026-04-newsletter)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Allen Institute\u2019s MolmoWeb 8B Beats GPT-4o on Web Navigation\u2014First Open-Weight Agent to Outperform Proprietary Models Across 4 Major Benchmarks",
    "The benchmark results tell the story.",
    "MolmoWeb-8B achieved state-of-the-art results across four major web-agent benchmarks: WebVoyager, Online-Mind2Web, and two others in the standard evaluation suite."
   ]
  },
  {
   "url": "https://basedai.co/compare/kimi-k2-vs-deepseek",
   "title": "Kimi K2 vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-01-27",
   "parameters": "1T (32B active)",
   "context_window": null,
   "license": null,
   "significance": "native image and video understanding plus Agent Swarm multi-agent orchestration; posted 76.8% on SWE-bench Verified",
   "organization_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Vendor | Moonshot AI (China) | DeepSeek (China) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Released | 2026-01-27 | 2026-04-24 |"
   ],
   "parameters_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Parameters | 1T (32B active) | 1.6T (49B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The verdict",
    "The two strongest open-weight families, split by modality and context.",
    "Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference.",
    "Its defining feature is agentic: K2.5 introduced Agent Swarm, coordinating up to 100 specialized agents on a single task, and posted 76.8% on SWE-bench Verified \u2014 frontier-class coding from an open-weight release."
   ]
  },
  {
   "url": "https://basedai.co/compare/kimi-k2-vs-deepseek",
   "title": "Kimi K2 vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": "2026-04-24",
   "parameters": "1.6T (49B active)",
   "context_window": "1M tokens",
   "license": "MIT \u2014 weights free to download, self-host, fine-tune, and use commercially",
   "significance": "brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference",
   "organization_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Vendor | Moonshot AI (China) | DeepSeek (China) |"
   ],
   "model_evidence": [
    "The verdict",
    "The two strongest open-weight families, split by modality and context.",
    "Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference.",
    "## Kimi K2 vs DeepSeek at a glance",
    "| | Kimi K2.5 | DeepSeek V4 |"
   ],
   "release_date_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Released | 2026-01-27 | 2026-04-24 |"
   ],
   "parameters_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Parameters | 1T (32B active) | 1.6T (49B active) |"
   ],
   "context_window_evidence": [
    "The verdict",
    "The two strongest open-weight families, split by modality and context.",
    "Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference.",
    "## Kimi K2 vs DeepSeek at a glance",
    "| Context window | 256K tokens | 1M tokens |"
   ],
   "license_evidence": [
    "The verdict",
    "## Kimi K2 vs DeepSeek at a glance",
    "| License | Modified MIT \u2014 free commercial use; attribution required above 100M monthly active users or $20M monthly revenue | MIT \u2014 weights free to download, self-host, fine-tune, and use commercially |"
   ],
   "significance_evidence": [
    "The verdict",
    "The two strongest open-weight families, split by modality and context.",
    "Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference."
   ]
  },
  {
   "url": "https://toknow.ai/posts/google-gemma-4-apache-2-open-models-on-device-ai/index.pdf",
   "title": "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone",
   "published_at": "2026-05-14T14:11:23",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B scores 89.2% on AIME 2026 (a math competition benchmark), 85.2% on MMLU Pro, and reaches 2150 Codeforces ELO. The MoE variant hits 82.3% on GPQA Diamond (a graduate-level science benchmark) while running nearly as fast as a 4B model.",
   "organization_evidence": [
    "Google Gemma 4: Apache 2.0 Open Models",
    "Google released Gemma 4, a family of four open models under a fully permissive Apache 2.0"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: Apache 2.0 Open Models",
    "2026-04-08"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: Apache 2.0 Open Models",
    "The 31B scores 89.2% on AIME 2026 (a math\ncompetition benchmark), 85.2% on MMLU Pro, and reaches 2150 Codeforces ELO.",
    "The MoE\n\n1\n\n\nvariant hits 82.3% on GPQA Diamond (a graduate-level science benchmark) while running\nnearly as fast as a 4B model."
   ]
  },
  {
   "url": "https://rejoicehub.com/blogs/what-is-kimi-k3-moonshot-ai-open-source-model",
   "title": "Kimi K3: Moonshot AI's 2.8T Open Source Model Explained",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "World's first \"open 3T class\" model, competitive with the best proprietary large language models on the market, 1-million-token context window, vision capabilities, and a thinking mode for extended coding and agentic work.",
   "organization_evidence": [
    "- AI",
    "Kimi K3 is now the talk of the town in open-source AI circles.",
    "This 2.8-trillion-parameter open-weight model made its debut this month, July 2026, as Moonshot AI - the Chinese company behind the Kimi chatbot - announced Kimi K3 as the world's first \"open 3T class\" model, with independent evaluations deeming it competitive with the best proprietary large language models on the market."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- AI",
    "Kimi K3 is the large language model of Moonshot AI's flagship release, a 2.8-trillion-parameter mixture-of-experts (MoE) model that will be open-weight on July 16, 2026."
   ],
   "parameters_evidence": [
    "- AI",
    "Kimi K3 is now the talk of the town in open-source AI circles.",
    "This 2.8-trillion-parameter open-weight model made its debut this month, July 2026, as Moonshot AI - the Chinese company behind the Kimi chatbot - announced Kimi K3 as the world's first \"open 3T class\" model, with independent evaluations deeming it competitive with the best proprietary large language models on the market."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- AI",
    "Kimi K3 is now the talk of the town in open-source AI circles.",
    "This 2.8-trillion-parameter open-weight model made its debut this month, July 2026, as Moonshot AI - the Chinese company behind the Kimi chatbot - announced Kimi K3 as the world's first \"open 3T class\" model, with independent evaluations deeming it competitive with the best proprietary large language models on the market."
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/glm-5-released-open-source-model-2026",
   "title": "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
   "published_at": "2026-02-12T08:09:02",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-12",
   "parameters": "744 billion",
   "context_window": null,
   "license": "MIT",
   "significance": "Beats GPT-5.2 on key benchmarks; 744B Open-Source Model purpose-built for complex systems engineering and long-horizon agentic tasks.",
   "organization_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "Zhipu AI has officially launched **GLM-5**, a 744 billion parameter mixture-of-experts model (40B active) that's purpose-built for complex systems engineering and long-horizon agentic tasks."
   ],
   "model_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "Zhipu AI has officially launched **GLM-5**, a 744 billion parameter mixture-of-experts model (40B active) that's purpose-built for complex systems engineering and long-horizon agentic tasks."
   ],
   "release_date_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "February 12, 2026"
   ],
   "parameters_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "Zhipu AI has officially launched **GLM-5**, a 744 billion parameter mixture-of-experts model (40B active) that's purpose-built for complex systems engineering and long-horizon agentic tasks."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "Zhipu AI has officially launched **GLM-5**, a 744 billion parameter mixture-of-experts model (40B active) that's purpose-built for complex systems engineering and long-horizon agentic tasks.",
    "And the kicker? It's fully open-source under the **MIT License** \u2014 the most permissive license in AI."
   ],
   "significance_evidence": [
    "GLM-5 Released: 744B Open-Source Model Beats GPT-5.2 on Key Benchmarks [2026]",
    "Zhipu AI has officially launched **GLM-5**, a 744 billion parameter mixture-of-experts model (40B active) that's purpose-built for complex systems engineering and long-horizon agentic tasks."
   ]
  },
  {
   "url": "https://freeainews.com/open-source/qwen-3-6-apache-open-source-coding-2026",
   "title": "Qwen 3.6: Apache 2.0 Open-Source Model Beating Bigger AI on Coding (2026)",
   "published_at": "2026-06-01T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "27 billion",
   "context_window": null,
   "license": null,
   "significance": "outperforms Alibaba's own 397-billion-parameter Qwen3.5 model on three out of four primary coding benchmarks. It ties or beats Claude 4.5 Opus on two others.",
   "organization_evidence": [
    "Open-source AI keeps breaking the expected relationship between model size and capability.",
    "In April 2026, Alibaba's Qwen team released two new model weights -- Qwen3.6-27B and Qwen3.6-35B-A3B -- that upended a familiar assumption: that you need a massive model to get frontier-level coding performance."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-source AI keeps breaking the expected relationship between model size and capability.",
    "In April 2026, Alibaba's Qwen team released two new model weights -- Qwen3.6-27B and Qwen3.6-35B-A3B -- that upended a familiar assumption: that you need a massive model to get frontier-level coding performance."
   ],
   "parameters_evidence": [
    "What Are the Qwen 3.6 Model Variants and Which Should You Choose?",
    "Alibaba released two variants of Qwen 3.6, each with distinct architectural approaches and different hardware demands.",
    "**Qwen3.6-27B (dense)** -- 27 billion parameters, all active during every forward pass."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-source AI keeps breaking the expected relationship between model size and capability.",
    "In April 2026, Alibaba's Qwen team released two new model weights -- Qwen3.6-27B and Qwen3.6-35B-A3B -- that upended a familiar assumption: that you need a massive model to get frontier-level coding performance.",
    "The 27B version, weighing in at roughly 54 GB in FP16, outperforms Alibaba's own 397-billion-parameter Qwen3.5 model on three out of four primary coding benchmarks."
   ]
  },
  {
   "url": "https://freeainews.com/open-source/qwen-3-6-apache-open-source-coding-2026",
   "title": "Qwen 3.6: Apache 2.0 Open-Source Model Beating Bigger AI on Coding (2026)",
   "published_at": "2026-06-01T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "35 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-Experts with 35 billion total parameters but only 3 billion activated per token. The \"A3B\" designation (Active 3B) means inference compute is equivalent to a dense 3B model, making this significantly cheaper to serve.",
   "organization_evidence": [
    "Open-source AI keeps breaking the expected relationship between model size and capability.",
    "In April 2026, Alibaba's Qwen team released two new model weights -- Qwen3.6-27B and Qwen3.6-35B-A3B -- that upended a familiar assumption: that you need a massive model to get frontier-level coding performance."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-source AI keeps breaking the expected relationship between model size and capability.",
    "In April 2026, Alibaba's Qwen team released two new model weights -- Qwen3.6-27B and Qwen3.6-35B-A3B -- that upended a familiar assumption: that you need a massive model to get frontier-level coding performance."
   ],
   "parameters_evidence": [
    "What Are the Qwen 3.6 Model Variants and Which Should You Choose?",
    "Alibaba released two variants of Qwen 3.6, each with distinct architectural approaches and different hardware demands.",
    "**Qwen3.6-35B-A3B (MoE)** -- Mixture-of-Experts with 35 billion total parameters but only 3 billion activated per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Are the Qwen 3.6 Model Variants and Which Should You Choose?",
    "Alibaba released two variants of Qwen 3.6, each with distinct architectural approaches and different hardware demands.",
    "**Qwen3.6-35B-A3B (MoE)** -- Mixture-of-Experts with 35 billion total parameters but only 3 billion activated per token."
   ]
  },
  {
   "url": "https://awesomeagents.ai/reviews/review-mistral-medium-3-5",
   "title": "Mistral Medium 3.5 Review: Open Agent, Sharp Teeth",
   "published_at": "2026-06-17T15:54:05",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified, custom vision encoder, configurable reasoning effort, and a 128B dense checkpoint consolidating reasoning, coding, and vision capabilities.",
   "organization_evidence": [
    "Mistral Medium 3.5 Review: Open Agent, Sharp Teeth",
    "Mistral's 128B open-weight model consolidates reasoning, coding, and vision into one checkpoint, with remote agents that file pull requests autonomously.",
    "When Mistral released [Medium 3.5](/models/mistral-medium-3-5/) on April 29, 2026, the pitch was disarmingly simple: stop maintaining three separate model checkpoints and collapse them into one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5 Review: Open Agent, Sharp Teeth",
    "When Mistral released [Medium 3.5](/models/mistral-medium-3-5/) on April 29, 2026, the pitch was disarmingly simple: stop maintaining three separate model checkpoints and collapse them into one."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5 Review: Open Agent, Sharp Teeth",
    "Mistral's 128B open-weight model consolidates reasoning, coding, and vision into one checkpoint, with remote agents that file pull requests autonomously.",
    "The model is dense - not a mixture-of-experts architecture - so the full 128B parameters are always active."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5 Review: Open Agent, Sharp Teeth",
    "Mistral's 128B open-weight model consolidates reasoning, coding, and vision into one checkpoint, with remote agents that file pull requests autonomously.",
    "- 77.6% on SWE-Bench Verified, async PR-filing via Vibe remote agents, and a custom vision encoder in a single 128B dense checkpoint"
   ]
  },
  {
   "url": "https://frontierbeat.com/2026/04/02/gemma-4-google-open-source-ai-release-2026",
   "title": "Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
   "published_at": "2026-04-02T18:45:12",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "Four sizes (Effective 2B, Effective 4B, 26-billion Mixture of Experts, and 31-billion dense model)",
   "context_window": "128K for edge variants, 256K for large models",
   "license": "Apache 2.0",
   "significance": "Highest-ranked Western open model on Arena AI's open model leaderboard at number three, with native video, image, and audio input, native function-calling, structured JSON output, and native system instructions.",
   "organization_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "Google dropped Gemma 4 on April 2, 2026 \u2014 its most capable open AI models yet. Four sizes, Apache 2.0 license, and a direct shot at the Chinese labs dominating the open-source leaderboard."
   ],
   "model_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "Google dropped Gemma 4 on April 2, 2026 \u2014 its most capable open AI models yet. Four sizes, Apache 2.0 license, and a direct shot at the Chinese labs dominating the open-source leaderboard."
   ],
   "release_date_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "Google dropped Gemma 4 on April 2, 2026 \u2014 its most capable open AI models yet. Four sizes, Apache 2.0 license, and a direct shot at the Chinese labs dominating the open-source leaderboard."
   ],
   "parameters_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "At the lightweight end are the Effective 2B (E2B) and Effective 4B (E4B) models \u2014 designed to run locally on smartphones, Raspberry Pis, and NVIDIA Jetson Orin Nano devices.",
    "The two heavy hitters are the 26-billion-parameter Mixture of Experts model and the 31B dense model."
   ],
   "context_window_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "Both pack a 128K token context window and \u2014 notably \u2014 native speech recognition, making them suitable for voice-driven mobile apps without a server round-trip.",
    "Both large models support a 256K token context window, up from the 128K ceiling on edge variants."
   ],
   "license_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "Google dropped Gemma 4 on April 2, 2026 \u2014 its most capable open AI models yet. Four sizes, Apache 2.0 license, and a direct shot at the Chinese labs dominating the open-source leaderboard.",
    "The move also comes with a licensing clean break: Gemma 4 ships under Apache 2.0, abandoning Google\u2019s old custom license entirely."
   ],
   "significance_evidence": [
    "Artificial Intelligence",
    "# Google\u2019s Gemma 4 Is Here \u2014 and It\u2019s a Direct Shot at China\u2019s AI Lead",
    "The 31-billion-parameter dense model debuted at number three on Arena AI\u2019s open model leaderboard, making it the highest-ranked Western open model on the chart.",
    "All four models handle video and images natively."
   ]
  },
  {
   "url": "https://1ban.news/kimi-k3-2-8-trillion-open-weight-2026",
   "title": "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
   "published_at": "2026-07-18T00:44:18",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1,048,576-token (1 million)",
   "license": null,
   "significance": "world\u2019s first open 3T-class system and the largest open-weight AI model ever released",
   "organization_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model that the company describes as the world\u2019s first open 3T-class system and the largest open-weight AI model ever released."
   ],
   "model_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model that the company describes as the world\u2019s first open 3T-class system and the largest open-weight AI model ever released."
   ],
   "release_date_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "By / July 18, 2026 00:44 UTC",
    "The model, which went live on July 16 through the Kimi app, Playground, and API, activates 16 of 896 experts per token using Stable LatentMoE, with an architecture built around the company\u2019s proprietary Kimi Delta Attention mechanism, a hybrid linear-attention design that Moonshot says delivers up to 6.3 times faster decoding in million-token contexts compared to standard attention."
   ],
   "parameters_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model that the company describes as the world\u2019s first open 3T-class system and the largest open-weight AI model ever released."
   ],
   "context_window_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "Kimi K3 supports a 1,048,576-token (1 million) context window and accepts text, image, and video input with native vision capabilities."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "China\u2019s Moonshot AI releases 2.8-trillion-parameter Kimi K3, the largest open-weight model ever",
    "Moonshot AI has released Kimi K3, a 2.8-trillion-parameter mixture-of-experts model that the company describes as the world\u2019s first open 3T-class system and the largest open-weight AI model ever released."
   ]
  },
  {
   "url": "https://felloai.com/es/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-Experts design with 744 billion total parameters and 40 billion active parameters per token, 1-million-token context window, Anthropic-compatible endpoint for agentic and coding tasks, two reasoning modes (High and Max), and an MIT license.",
   "organization_evidence": [
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle.",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "## What Is GLM 5.2?"
   ],
   "parameters_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "| Release date | June 13, 2026 | April 7, 2026 | ~2 months apart | Fast iteration cycle |",
    "| Parameters | 744B (40B active) | 754B (40B active) | Slightly leaner | Same per-token compute |",
    "## What Is GLM 5.2?"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "## What Is GLM 5.2?"
   ]
  },
  {
   "url": "https://noqta.tn/en/blog/minimax-m3-open-weight-frontier-model-developer-guide-2026",
   "title": "MiniMax M3: Open-Weight Frontier AI at 5% of GPT Cost",
   "published_at": "2026-06-15T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 is the first open-weight model to simultaneously deliver frontier-level coding performance, a one-million-token context window, and native multimodal capability in a single architecture.",
   "organization_evidence": [
    "On June 1, 2026, Shanghai-based AI lab MiniMax released M3 \u2014 and the open-source AI ecosystem quietly crossed a milestone it had been chasing for two years."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 1, 2026, Shanghai-based AI lab MiniMax released M3 \u2014 and the open-source AI ecosystem quietly crossed a milestone it had been chasing for two years."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 is the first open-weight model to simultaneously deliver frontier-level coding performance, a one-million-token context window, and native multimodal capability in a single architecture."
   ]
  },
  {
   "url": "https://gate.com/news/detail/nvidia-open-sourced-the-31b-vision-model-ising-calibration-15-improving-icl-23009226",
   "title": "NVIDIA open-sourced the 31B vision model Ising Calibration 1.5, improving ICL performance by 86.68%. | Gate News",
   "published_at": "2026-07-28T01:41:27",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Achieved 86.68% ICL performance improvement over the previous-generation model and zero-shot performance about 10% higher than other open-source models of the same scale.",
   "organization_evidence": [
    "# NVIDIA open-sourced the 31B vision model Ising Calibration 1.5, improving ICL performance by 86.68%.",
    "NVIDIA research teams released \u201cNVIDIA Ising Calibration 1.5\u201d on July 27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- NVIDIA released Ising Calibration 1.5, a 31-billion-parameter vision-language model on July 27.",
    "NVIDIA research teams released \u201cNVIDIA Ising Calibration 1.5\u201d on July 27."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- The model achieved 86.68% ICL performance improvement and reduced deployment size by 11.4% with NVFP4 quantization.",
    "According to the QCalEval benchmark tests, the model\u2019s performance on in-context learning (ICL) tests improved by 86.68% compared with the previous-generation model."
   ]
  },
  {
   "url": "https://aichina.news/blog/decoding-the-hidden-intent-a-qwen3-fine-tune-for-conversational-search-8fs71t",
   "title": "Decoding the Hidden Intent: A Qwen3 Fine-Tune for Conversational Search \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T08:28:38",
   "organization": "allenai",
   "model": null,
   "release_date": "2026-07-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A specialised tool designed to classify the implicit intent behind long-form question-answering queries, allowing developers to transform a generic, indirect prompt into a structured, actionable query.",
   "organization_evidence": [
    "Decoding the Hidden Intent: A Qwen3 Fine-Tune for Conversational Search",
    "models/allenai July 30, 2026 2 min read Auto-generated from Modelers",
    "Enter `allenai/intent-aware-lfqa-qwen3-8b-intent-implicit`, a fresh fine-tune of Alibaba's Qwen3-8B now available on Modelers.cn."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Decoding the Hidden Intent: A Qwen3 Fine-Tune for Conversational Search",
    "models/allenai July 30, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Decoding the Hidden Intent: A Qwen3 Fine-Tune for Conversational Search",
    "Enter `allenai/intent-aware-lfqa-qwen3-8b-intent-implicit`, a fresh fine-tune of Alibaba's Qwen3-8B now available on Modelers.cn.",
    "This isn't just another chatbot; it is a specialised tool designed to classify the implicit intent behind long-form question-answering (LFQA) queries.",
    "For developers building conversational search or customer support systems, this capability transforms a generic, indirect prompt into a structured, actionable query."
   ]
  },
  {
   "url": "https://infoq.com/news/2026/04/google-gemm4?amp;utm_medium=feed&amp;utm_source=infoq&amp;utm_term=global",
   "title": "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
   "published_at": "2026-04-16T17:05:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "2B, 4B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "Introduces native video and image processing across the lineup, audio input on the smaller models, context windows up to 256K tokens, and benchmark results that place the 31B dense variant in a bracket typically occupied by models three to five times its size.",
   "organization_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "Apr 16, 2026 2 min read"
   ],
   "parameters_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "The release introduces native video and image processing across the lineup, audio input on the smaller models, context windows up to 256K tokens, and benchmark results that place the 31B dense variant in a bracket typically occupied by models three to five times its size."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/moonshot-opens-kimi-k3-with-28-trillion-parameters-challenging-closed-ai-models",
   "title": "Moonshot opens Kimi K3 with 2.8 trillion parameters, challenging closed AI models",
   "published_at": "2026-07-27T16:16:43",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Matches closed rivals on key benchmarks at a fraction of the cost; scores 91.2 on the BrowseComp benchmark, ahead of Claude Fable 5 at 88.0 and GPT-5.6 Sol at 90.4; delivers a 6.3x decoding speedup in million-token contexts; runs 4.5 times faster than single-agent sequential execution using the Agent Swarm framework.",
   "organization_evidence": [
    "Moonshot opens Kimi K3 with 2.8 trillion parameters, challenging closed AI models",
    "Moonshot AI made Kimi K3 freely available on July 27, a 2.8-trillion-parameter open-weight model that matches closed rivals on key benchmarks at a fraction of the cost."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot opens Kimi K3 with 2.8 trillion parameters, challenging closed AI models",
    "[![Rachel Kim](https://static.edgen.tech/authors/rachel-kim.png?image_process=resize,w_32/quality,q_75/format,webp/sharpen,100)\n## Rachel Kim](https://www.edgen.tech/author/news/rachel-kim)\u00b7Jul 27 2026, 16:16",
    "Moonshot AI made Kimi K3 freely available on July 27, a 2.8-trillion-parameter open-weight model that matches closed rivals on key benchmarks at a fraction of the cost."
   ],
   "parameters_evidence": [
    "Moonshot opens Kimi K3 with 2.8 trillion parameters, challenging closed AI models",
    "Moonshot AI made Kimi K3 freely available on July 27, a 2.8-trillion-parameter open-weight model that matches closed rivals on key benchmarks at a fraction of the cost."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot opens Kimi K3 with 2.8 trillion parameters, challenging closed AI models",
    "Moonshot AI made Kimi K3 freely available on July 27, a 2.8-trillion-parameter open-weight model that matches closed rivals on key benchmarks at a fraction of the cost."
   ]
  },
  {
   "url": "https://researchaudio.io/p/744b-parameters-40b-active-china-built-it-on-huawei-chips",
   "title": "744B Parameters. 40B Active. China Built It on Huawei Chips.",
   "published_at": "2026-02-12T15:00:14",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Tops open-source benchmarks with a novel async RL framework called slime, leading every open-source model on Artificial Analysis across coding, reasoning, and agentic tasks.",
   "organization_evidence": [
    "Zhipu AI just released a 744-billion-parameter model that was trained entirely on Huawei Ascend chips, with zero NVIDIA hardware involved.",
    "Zhipu AI (also known as Z.ai) spun out of Tsinghua University in 2019 and completed a Hong Kong IPO on January 8, 2026, raising approximately HKD 4.35 billion (around $558 million)."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "ResearchAudio.io # 744B Parameters. 40B Active. China Built It on Huawei Chips. Zhipu's GLM-5 tops open-source benchmarks with a novel async RL framework called slime.",
    "**GLM-5** uses a Mixture-of-Experts architecture that activates 40 billion parameters per token, and it currently leads every open-source model on Artificial Analysis across coding, reasoning, and agentic tasks."
   ]
  },
  {
   "url": "https://frontierbenchmarks.com/models/minimax-m3",
   "title": "MiniMax M3 \u00b7 Frontier Benchmarks AI",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Arquitectura MiniMax Sparse Attention (MSA): GQA + block-level sparse selection. A 1M context: >9.7x prefill, >15.6x decode vs gen previa. Multimodal nativo (text+image+video in, text out), computer use.",
   "organization_evidence": [
    "Company MiniMax",
    "# MiniMax M3"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released 2026-06 \u00b7 reasoning \u00b7 1.0M tokens \u00b7 4 benchmarks \u00b7 Open weight",
    "Release 2026-06",
    "# MiniMax M3"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Arquitectura MiniMax Sparse Attention (MSA): GQA + block-level sparse selection.",
    "A 1M context: >9.7x prefill, >15.6x decode vs gen previa.",
    "Multimodal nativo (text+image+video in, text out), computer use.",
    "# MiniMax M3"
   ]
  },
  {
   "url": "https://aichina.news/blog/allenai-s-sera-14b-a-mid-sized-powerhouse-hidden-in-the-ascend-3dk5ka",
   "title": "AllenAI's SERA-14B: A Mid-Sized Powerhouse Hidden in the Ascend Ecosystem \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T13:49:13",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "14 billion",
   "context_window": null,
   "license": null,
   "significance": "A mid-sized open-weight model hosted on Modelers.cn, optimized for the Huawei Ascend NPU ecosystem, striking a practical balance between capability and hardware requirements.",
   "organization_evidence": [
    "AllenAI's SERA-14B: A Mid-Sized Powerhouse Hidden in the Ascend Ecosystem",
    "Enter SERA-14B, a 14-billion-parameter language model from the Allen Institute for AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AllenAI's SERA-14B: A Mid-Sized Powerhouse Hidden in the Ascend Ecosystem",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "AllenAI's SERA-14B: A Mid-Sized Powerhouse Hidden in the Ascend Ecosystem",
    "Enter SERA-14B, a 14-billion-parameter language model from the Allen Institute for AI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AllenAI's SERA-14B: A Mid-Sized Powerhouse Hidden in the Ascend Ecosystem",
    "Enter SERA-14B, a 14-billion-parameter language model from the Allen Institute for AI."
   ]
  },
  {
   "url": "https://tech-insider.org/deepseek-v4-huawei-ascend-1-6-trillion-parameter-moe-2026",
   "title": "DeepSeek V4 on Huawei Ascend: 1.6T MoE [2026]",
   "published_at": "2026-05-16T08:10:18",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Internal benchmarks shared in the model card show V4-Pro-Max scoring **80.6% on SWE-bench Verified** and **93.5 on LiveCodeBench**, placing it within striking distance of the closed-source leaders.",
   "organization_evidence": [
    "DeepSeek launched the preview of its **V4 frontier model** on Friday, April 24, 2026, and the release detonated across the global AI market within hours.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The release went live at 09:00 China Standard Time on April 24, 2026, via DeepSeek\u2019s official API documentation portal and Hugging Face.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "parameters_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Internal benchmarks shared in the model card show V4-Pro-Max scoring **80.6% on SWE-bench Verified** and **93.5 on LiveCodeBench**, placing it within striking distance of the closed-source leaders.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ]
  },
  {
   "url": "https://tech-insider.org/deepseek-v4-huawei-ascend-1-6-trillion-parameter-moe-2026",
   "title": "DeepSeek V4 on Huawei Ascend: 1.6T MoE [2026]",
   "published_at": "2026-05-16T08:10:18",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
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   "significance": "Internal benchmarks shared in the model card show V4-Pro-Max scoring **80.6% on SWE-bench Verified** and **93.5 on LiveCodeBench**, placing it within striking distance of the closed-source leaders.",
   "organization_evidence": [
    "DeepSeek launched the preview of its **V4 frontier model** on Friday, April 24, 2026, and the release detonated across the global AI market within hours.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "model_evidence": [
    "The company shipped two open-weight models simultaneously: **DeepSeek-V4-Pro**, the flagship, and **DeepSeek-V4-Flash**, a cost-optimized sibling.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "release_date_evidence": [
    "The release went live at 09:00 China Standard Time on April 24, 2026, via DeepSeek\u2019s official API documentation portal and Hugging Face.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "parameters_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "context_window_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "license_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "significance_evidence": [
    "Internal benchmarks shared in the model card show V4-Pro-Max scoring **80.6% on SWE-bench Verified** and **93.5 on LiveCodeBench**, placing it within striking distance of the closed-source leaders.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ]
  },
  {
   "url": "https://tech-insider.org/deepseek-v4-huawei-ascend-1-6-trillion-parameter-moe-2026",
   "title": "DeepSeek V4 on Huawei Ascend: 1.6T MoE [2026]",
   "published_at": "2026-05-16T08:10:18",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B parameters",
   "context_window": "1,000,000 tokens",
   "license": "MIT license",
   "significance": "V4-Flash sells API access at $0.14 per million input tokens, while V4-Pro charges $1.74 per million input tokens.",
   "organization_evidence": [
    "DeepSeek launched the preview of its **V4 frontier model** on Friday, April 24, 2026, and the release detonated across the global AI market within hours.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "model_evidence": [
    "The company shipped two open-weight models simultaneously: **DeepSeek-V4-Pro**, the flagship, and **DeepSeek-V4-Flash**, a cost-optimized sibling.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "release_date_evidence": [
    "The release went live at 09:00 China Standard Time on April 24, 2026, via DeepSeek\u2019s official API documentation portal and Hugging Face.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "parameters_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "context_window_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "license_evidence": [
    "The Hangzhou-based lab dropped open weights for two Mixture-of-Experts variants \u2013 **V4-Pro at 1.6 trillion parameters** with 49B active per token and **V4-Flash at 284B parameters** with 13B active \u2013 under an MIT license, with a 1,000,000-token context window across both tiers.",
    "DeepSeek V4 Preview: The April 24 Drop That Reset the Frontier"
   ],
   "significance_evidence": [
    "**V4-Flash sells API access at $0.14 per million input tokens**, while V4-Pro charges **$1.74 per million input tokens**.",
    "Pricing Shock: $0.14 Input Tokens and the Margin Wave"
   ]
  },
  {
   "url": "https://claudemarket.ai/blog/best-qwen-models-2026",
   "title": "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
   "published_at": "2026-08-10T12:20:49",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": "397B",
   "context_window": null,
   "license": null,
   "significance": "extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation",
   "organization_evidence": [
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation.",
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
    "Qwen3 includes six dense models and two Mixture-of-Experts models, plus Qwen3.5 and Qwen3-Coder as major extensions released in 2026.",
    "**Qwen3.5**, released February 16-17, 2026, is the latest generation."
   ],
   "parameters_evidence": [
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation.",
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
    "Qwen3 includes six dense models and two Mixture-of-Experts models, plus Qwen3.5 and Qwen3-Coder as major extensions released in 2026.",
    "| Qwen3.5 (Feb 2026) | MoE | 397B | 17B | 262K | Apache 2.0 |"
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation.",
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse"
   ]
  },
  {
   "url": "https://tpsreport.news/news/meta-muse-glimmer-open-model-release",
   "title": "Muse Glimmer: Meta's New 30B Open Model Explained",
   "published_at": "2026-08-10T14:06:01",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": "30-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Meta's first open-weight model release since Llama 4, designed for AI agents running locally on a single consumer GPU, winning most benchmarks in its size class on agentic tasks.",
   "organization_evidence": [
    "model release",
    "Meta Releases Muse Glimmer, First Open-Weight Model Since Llama 4, Paired With Zuckerberg Manifesto on Distillation",
    "Meta has released Muse Glimmer, a 30-billion-parameter open-weight model under Apache 2.0, its first open release since Llama 4 in spring 2025."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "model release",
    "Meta Releases Muse Glimmer, First Open-Weight Model Since Llama 4, Paired With Zuckerberg Manifesto on Distillation",
    "Meta has released Muse Glimmer, a 30-billion-parameter open-weight model under Apache 2.0, its first open release since Llama 4 in spring 2025."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "model release",
    "Meta Releases Muse Glimmer, First Open-Weight Model Since Llama 4, Paired With Zuckerberg Manifesto on Distillation",
    "Meta has released Muse Glimmer, a 30-billion-parameter open-weight model under Apache 2.0, its first open release since Llama 4 in spring 2025.",
    "Glimmer is designed for AI agents that run locally on a single consumer GPU, handling tasks like calendar management, file access, and messaging without sending data to the cloud.",
    "According to Meta, Glimmer wins most benchmarks, particularly on agentic tasks such as tool use, web search, and long-context handling."
   ]
  },
  {
   "url": "https://cyber-ivy.com/en/articles/mistral-medium-3-5-128b-flagship-2026",
   "title": "Mistral Medium 3.5: A 128-Billion-Parameter Model from France Combines Chat, Reasoning, and Coding",
   "published_at": "2026-05-03T22:46:48",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-05-02",
   "parameters": "128-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "77.6 percent on SWE-Bench Verified and 91.4 percent on \u03073\u2013Telecom",
   "organization_evidence": [
    "Mistral Medium 3.5: A 128-Billion-Parameter Model from France Combines Chat, Reasoning, and Coding",
    "French AI company Mistral released its new flagship model, Medium 3.5, on May 2, 2026."
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   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5: A 128-Billion-Parameter Model from France Combines Chat, Reasoning, and Coding",
    "May 3, 2026",
    "On May 2, 2026, Mistral unveiled Medium 3.5: a dense 128-billion-parameter model with a 256k context window and configurable reasoning, released under a modified MIT license."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5: A 128-Billion-Parameter Model from France Combines Chat, Reasoning, and Coding",
    "On May 2, 2026, Mistral unveiled Medium 3.5: a dense 128-billion-parameter model with a 256k context window and configurable reasoning, released under a modified MIT license."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Mistral Medium 3.5: A 128-Billion-Parameter Model from France Combines Chat, Reasoning, and Coding",
    "On internal benchmarks, Mistral reports 77.6 percent on SWE-Bench Verified, which tests real GitHub bug fixes, and 91.4 percent on \u03c4\u00b3-Telecom, a test of agentic tool use in telecommunications."
   ]
  },
  {
   "url": "https://trevly.io/tech/tech-news/gemma-4-google-open-ai-model-launch",
   "title": "Gemma 4: Google Launches Its Most Capable Open AI Model Family",
   "published_at": "2026-04-05T10:54:23",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Ranks third on the Arena AI open-model text leaderboard, achieving an estimated LMArena score of 1,452 while competing directly with models that have 20x more parameters.",
   "organization_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "Google DeepMind has officially released **Gemma 4**, a new family of open-weight AI models built from the same research that powers Gemini 3."
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   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "Announced on April 2, 2026, the Gemma 4 family includes four model sizes designed to run on everything from smartphones to workstations."
   ],
   "parameters_evidence": [
    "## **Gemma 4 Model Lineup: Four Sizes for Every Use Case**",
    "| **Feature** | **E2B** | **E4B** | **26B MoE** | **31B Dense** |",
    "| Active Parameters | ~2B | ~4B | ~4B (128 experts) | 31B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "The **31B Dense** variant currently ranks third on the Arena AI open-model text leaderboard.",
    "It achieves an estimated LMArena score of 1,452."
   ]
  },
  {
   "url": "https://trevly.io/tech/tech-news/gemma-4-google-open-ai-model-launch",
   "title": "Gemma 4: Google Launches Its Most Capable Open AI Model Family",
   "published_at": "2026-04-05T10:54:23",
   "organization": "Google DeepMind",
   "model": "Gemma 4 26B MoE",
   "release_date": "2026-04-02",
   "parameters": "26B MoE",
   "context_window": "256K tokens",
   "license": "Apache 2.0",
   "significance": "Reaches a score of 1,441 on the Arena AI leaderboard while activating only 4 billion parameters per token, beating competitors with over 20 times more total parameters.",
   "organization_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "Google DeepMind has officially released **Gemma 4**, a new family of open-weight AI models built from the same research that powers Gemini 3."
   ],
   "model_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "## **Gemma 4 Model Lineup: Four Sizes for Every Use Case**",
    "| **Feature** | **E2B** | **E4B** | **26B MoE** | **31B Dense** |",
    "Meanwhile, the 26B MoE model reaches a score of 1,441 while activating only 4 billion parameters per token."
   ],
   "release_date_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "Announced on April 2, 2026, the Gemma 4 family includes four model sizes designed to run on everything from smartphones to workstations."
   ],
   "parameters_evidence": [
    "## **Gemma 4 Model Lineup: Four Sizes for Every Use Case**",
    "| **Feature** | **E2B** | **E4B** | **26B MoE** | **31B Dense** |",
    "| Active Parameters | ~2B | ~4B | ~4B (128 experts) | 31B |"
   ],
   "context_window_evidence": [
    "## **Gemma 4 Model Lineup: Four Sizes for Every Use Case**",
    "| **Feature** | **E2B** | **E4B** | **26B MoE** | **31B Dense** |",
    "| Context Window | 128K tokens | 128K tokens | 256K tokens | 256K tokens |"
   ],
   "license_evidence": [
    "## **Gemma 4 Model Lineup: Four Sizes for Every Use Case**",
    "| **Feature** | **E2B** | **E4B** | **26B MoE** | **31B Dense** |",
    "| License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "# Gemma 4: Google Launches Its Most Capable Open AI Model Family",
    "Meanwhile, the 26B MoE model reaches a score of 1,441 while activating only 4 billion parameters per token."
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  },
  {
   "url": "https://reasoncore.dev/post/moonshot-releases-kimi-k3-weights-a-28t-open-moe-model-built-for-self-hosting",
   "title": "Moonshot releases Kimi K3 weights \u2014 a 2.8T open MoE model built for self-hosting",
   "published_at": "2026-07-28T10:52:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "First open model to reach 2.8 trillion parameters, and placing first on Arena's human-voted Frontend Code Arena at 1,679.",
   "organization_evidence": [
    "Moonshot releases Kimi K3 weights \u2014 a 2.8T open MoE model built for self-hosting",
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   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot releases Kimi K3 weights \u2014 a 2.8T open MoE model built for self-hosting",
    "Moonshot published Kimi K3's full weights and technical report on July 27, 2026, 11 days after announcing the 2.8T open MoE model."
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   "parameters_evidence": [
    "Moonshot releases Kimi K3 weights \u2014 a 2.8T open MoE model built for self-hosting",
    "Moonshot published Kimi K3's full weights and technical report on July 27, 2026, 11 days after announcing the 2.8T open MoE model.",
    "Kimi K3 is a 2.8\u2011trillion\u2011parameter [Mixture\u2011of\u2011Experts](https://reasoncore.dev/glossary/moe) [transformer](https://reasoncore.dev/glossary/transformer) with native multimodal capability across text, image and video inputs and a 1,048,576\u2011token [context window](https://reasoncore.dev/glossary/context-window); Moonshot positions the release for self\u2011hosting, research, fine\u2011tuning, and commercial deployment."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot releases Kimi K3 weights \u2014 a 2.8T open MoE model built for self-hosting",
    "Moonshot's launch post billed it as \"the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning,\" and as \"the first open model to reach 2.8 trillion parameters.\"",
    "Third-party snapshots put K3 first on Arena's human-voted Frontend Code Arena at 1,679 vs Claude Fable 5's 1,631, though Moonshot says overall performance still trails Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://aimadetools.com/blog/minimax-m3-complete-guide",
   "title": "MiniMax M3: Complete Guide to the Open-Weight Frontier Model (2026)",
   "published_at": "2026-06-01T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodal capabilities in a single model",
   "organization_evidence": [
    "MiniMax M3: Complete Guide to the Open-Weight Frontier Model (2026)",
    "MiniMax released M3 on June 1, 2026 \u2014 the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodal capabilities in a single model."
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   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3: Complete Guide to the Open-Weight Frontier Model (2026)",
    "MiniMax released M3 on June 1, 2026 \u2014 the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodal capabilities in a single model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3: Complete Guide to the Open-Weight Frontier Model (2026)",
    "MiniMax released M3 on June 1, 2026 \u2014 the first open-weight model to combine frontier-level coding, a 1-million-token context window, and native multimodal capabilities in a single model."
   ]
  },
  {
   "url": "https://1ban.news/google-deepmind-diffusiongemma-text-diffusion-model",
   "title": "Google DeepMind Releases DiffusionGemma, an Open Model That Generates Text 4x Faster",
   "published_at": "2026-06-12T04:39:51",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": "26B",
   "context_window": null,
   "license": null,
   "significance": "First open-weight large-scale language model to use text diffusion instead of the standard autoregressive approach, generating text up to four times faster.",
   "organization_evidence": [
    "# Google DeepMind Releases DiffusionGemma, an Open Model That Generates Text 4x Faster",
    "Google DeepMind has released DiffusionGemma, the first open-weight large-scale language model to use text diffusion instead of the standard autoregressive approach."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The release, announced on June 10 in partnership with NVIDIA, represents a meaningful shift in how local AI inference could work.",
    "# Google DeepMind Releases DiffusionGemma, an Open Model That Generates Text 4x Faster"
   ],
   "parameters_evidence": [
    "DiffusionGemma is built on the same Gemma 4 26B Mixture-of-Experts backbone as Google\u2019s standard autoregressive model, but it replaces sequential token generation with a process Google calls \u201cUniform State Diffusion.\u201d",
    "# Google DeepMind Releases DiffusionGemma, an Open Model That Generates Text 4x Faster"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google DeepMind has released DiffusionGemma, the first open-weight large-scale language model to use text diffusion instead of the standard autoregressive approach.",
    "# Google DeepMind Releases DiffusionGemma, an Open Model That Generates Text 4x Faster"
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-glm",
   "title": "DeepSeek vs GLM (2026): Pricing, Benchmarks, Verdict",
   "published_at": "2026-04-25T13:57:44",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "284B / 13B active",
   "context_window": null,
   "license": null,
   "significance": "see V4 report",
   "organization_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Released",
    "| Released | 2026-04-24 | 2026-04-24 | December 2025 | 2026-02-11 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Released",
    "| Released | 2026-04-24 | 2026-04-24 | December 2025 | 2026-02-11 |"
   ],
   "parameters_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "| Total params (MoE) | 284B / 13B active | 1.6T / 49B active | ~355B | 744B / 40B active |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "SWE-Bench Verified",
    "| SWE-Bench Verified | see V4 report | 80.6% | 73.8% | 77.8% |"
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-glm",
   "title": "DeepSeek vs GLM (2026): Pricing, Benchmarks, Verdict",
   "published_at": "2026-04-25T13:57:44",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T / 49B active",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "80.6%",
   "organization_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Released",
    "| Released | 2026-04-24 | 2026-04-24 | December 2025 | 2026-02-11 |"
   ],
   "model_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Feature",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | GLM-4.7 | GLM-5 |"
   ],
   "release_date_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Released",
    "| Released | 2026-04-24 | 2026-04-24 | December 2025 | 2026-02-11 |"
   ],
   "parameters_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Total params (MoE)",
    "| Total params (MoE) | 284B / 13B active | 1.6T / 49B active | ~355B | 744B / 40B active |"
   ],
   "context_window_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Context window",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens | ~200K | 200K tokens |"
   ],
   "license_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Weights license",
    "| Weights license | MIT | MIT | MIT | MIT |"
   ],
   "significance_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "SWE-Bench Verified",
    "| SWE-Bench Verified | see V4 report | 80.6% | 73.8% | 77.8% |"
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-glm",
   "title": "DeepSeek vs GLM (2026): Pricing, Benchmarks, Verdict",
   "published_at": "2026-04-25T13:57:44",
   "organization": "Zhipu",
   "model": "GLM-5",
   "release_date": "2026-02-11",
   "parameters": "744B / 40B active",
   "context_window": "200K tokens",
   "license": "MIT",
   "significance": "77.8%",
   "organization_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "![DeepSeek vs GLM comparison card showing per-million-token pricing and benchmarks in April 2026: DeepSeek V4-Pro at $0.435 input / $0.87 output (75%-off promo through May 31; list $1.74 / $3.48) with MIT-licensed open weights, 80.6 percent on SWE-Bench Verified and a 1-million-token context, versus Zhipu's GLM-5 at $1.00 input / $3.20 output also under MIT, scoring 77.8 percent with a 200,000-token context window.](https://deepseekai.guide/wp-content/uploads/2026/04/deepseek-vs-glm-card.png)"
   ],
   "model_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Feature",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | GLM-4.7 | GLM-5 |"
   ],
   "release_date_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Released",
    "| Released | 2026-04-24 | 2026-04-24 | December 2025 | 2026-02-11 |"
   ],
   "parameters_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Total params (MoE)",
    "| Total params (MoE) | 284B / 13B active | 1.6T / 49B active | ~355B | 744B / 40B active |"
   ],
   "context_window_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Context window",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens | ~200K | 200K tokens |"
   ],
   "license_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "Weights license",
    "| Weights license | MIT | MIT | MIT | MIT |"
   ],
   "significance_evidence": [
    "DeepSeek vs GLM: Which Chinese Open Model Wins in 2026?",
    "SWE-Bench Verified",
    "| SWE-Bench Verified | see V4 report | 80.6% | 73.8% | 77.8% |"
   ]
  },
  {
   "url": "http://ai-tldr.dev/releases/poolside-laguna-s-2-1",
   "title": "Poolside Laguna S 2.1 \u2014 118B open-weight coding MoE with 8B active",
   "published_at": "2026-07-21T12:00:00",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "118B total parameters (Mixture-of-Experts), 8B active per token",
   "context_window": null,
   "license": null,
   "significance": "Beats or ties DeepSeek-V4-Flash, NVIDIA's Nemotron 3 Ultra, and Thinking Machines' Inkling on Terminal-Bench 2.1 (70.2%) and SWE-Bench Pro (59.4%) despite running many times fewer active parameters.",
   "organization_evidence": [
    "Poolside \u00b7 2026-07-21 \u00b7 major",
    "Quick facts",
    "| Maker | Poolside |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Poolside \u00b7 2026-07-21 \u00b7 major",
    "Laguna S 2.1 is Poolside's new open-weight coding foundation model, released July 21, 2026 with weights on Hugging Face under the Linux Foundation's OpenMDW-1.1 license."
   ],
   "parameters_evidence": [
    "Poolside \u00b7 2026-07-21 \u00b7 major",
    "# Poolside Laguna S 2.1 \u2014 118B open-weight coding MoE with 8B active",
    "Quick facts",
    "| Size | 118B total parameters (Mixture-of-Experts), 8B active per token |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Poolside \u00b7 2026-07-21 \u00b7 major",
    "## Why does it matter?"
   ]
  },
  {
   "url": "https://aitoolsrecap.com/Blog/upcoming-ai-models-2026-release-tracker",
   "title": "Upcoming AI Models 2026: Release Tracker \u2014 What's Coming From OpenAI, Anthropic, Google, Meta, xAI",
   "published_at": "2026-08-06T00:00:00",
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Flash 0731",
   "release_date": "2026-07-31",
   "parameters": null,
   "context_window": null,
   "license": "MIT",
   "significance": "Terminal-Bench 82.7% \u2014 the cheapest capable agentic model with published benchmarks",
   "organization_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| DeepSeek V4 Flash 0731 | DeepSeek | Jul 31 | $0.14 | \u2713 MIT |"
   ],
   "model_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| DeepSeek V4 Flash 0731 | DeepSeek | Jul 31 | $0.14 | \u2713 MIT |"
   ],
   "release_date_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| DeepSeek V4 Flash 0731 | DeepSeek | Jul 31 | $0.14 | \u2713 MIT |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| DeepSeek V4 Flash 0731 | DeepSeek | Jul 31 | $0.14 | \u2713 MIT |"
   ],
   "significance_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "**DeepSeek V4 Flash 0731 (MIT, July 31):** $0.14/M API or self-hosted."
   ]
  },
  {
   "url": "https://aitoolsrecap.com/Blog/upcoming-ai-models-2026-release-tracker",
   "title": "Upcoming AI Models 2026: Release Tracker \u2014 What's Coming From OpenAI, Anthropic, Google, Meta, xAI",
   "published_at": "2026-08-06T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8T/104B MoE",
   "context_window": null,
   "license": "Modified MIT",
   "significance": "SWE Marathon #1. BenchLM #5 of 214.",
   "organization_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Kimi K3 | Moonshot AI | Jul 16 (weights Jul 27) | $3.00 | \u2713 Modified MIT |"
   ],
   "model_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Kimi K3 | Moonshot AI | Jul 16 (weights Jul 27) | $3.00 | \u2713 Modified MIT |"
   ],
   "release_date_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Kimi K3 | Moonshot AI | Jul 16 (weights Jul 27) | $3.00 | \u2713 Modified MIT |"
   ],
   "parameters_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "**Kimi K3 (Modified MIT, July 27):** 2.8T/104B MoE."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Kimi K3 | Moonshot AI | Jul 16 (weights Jul 27) | $3.00 | \u2713 Modified MIT |"
   ],
   "significance_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "SWE Marathon #1."
   ]
  },
  {
   "url": "https://aitoolsrecap.com/Blog/upcoming-ai-models-2026-release-tracker",
   "title": "Upcoming AI Models 2026: Release Tracker \u2014 What's Coming From OpenAI, Anthropic, Google, Meta, xAI",
   "published_at": "2026-08-06T00:00:00",
   "organization": "Poolside",
   "model": "Laguna S 2.1",
   "release_date": "2026-07-02",
   "parameters": "118B/8B MoE",
   "context_window": null,
   "license": "OpenMDW",
   "significance": "Terminal-Bench 70.2% \u2014 highest of any disclosed-size open model",
   "organization_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Laguna S 2.1 | Poolside | Jul 2 | $0.10 | \u2713 OpenMDW |"
   ],
   "model_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Laguna S 2.1 | Poolside | Jul 2 | $0.10 | \u2713 OpenMDW |"
   ],
   "release_date_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Laguna S 2.1 | Poolside | Jul 2 | $0.10 | \u2713 OpenMDW |"
   ],
   "parameters_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "**Laguna S 2.1 (OpenMDW, July 2):** 118B/8B MoE."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Laguna S 2.1 | Poolside | Jul 2 | $0.10 | \u2713 OpenMDW |"
   ],
   "significance_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "Terminal-Bench 70.2% \u2014 highest of any disclosed-size open model."
   ]
  },
  {
   "url": "https://aitoolsrecap.com/Blog/upcoming-ai-models-2026-release-tracker",
   "title": "Upcoming AI Models 2026: Release Tracker \u2014 What's Coming From OpenAI, Anthropic, Google, Meta, xAI",
   "published_at": "2026-08-06T00:00:00",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Runs on everyday devices.",
   "organization_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Gemma 4 | Google | Jun 2026 | Free | \u2713 Apache 2.0 |"
   ],
   "model_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Gemma 4 | Google | Jun 2026 | Free | \u2713 Apache 2.0 |"
   ],
   "release_date_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Gemma 4 | Google | Jun 2026 | Free | \u2713 Apache 2.0 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "**Gemma 4 (Apache 2.0, June 2026):** Google's open-weight family."
   ],
   "significance_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "Runs on everyday devices."
   ]
  },
  {
   "url": "https://aitoolsrecap.com/Blog/upcoming-ai-models-2026-release-tracker",
   "title": "Upcoming AI Models 2026: Release Tracker \u2014 What's Coming From OpenAI, Anthropic, Google, Meta, xAI",
   "published_at": "2026-08-06T00:00:00",
   "organization": "Meta",
   "model": "Llama 4 Scout / Maverick",
   "release_date": "2026-04",
   "parameters": null,
   "context_window": "10M token context on Scout",
   "license": "Llama 4 License",
   "significance": "First MoE Llamas. Natively multimodal.",
   "organization_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Llama 4 Scout / Maverick | Meta | Apr 2026 | Free | \u2713 Llama 4 License |"
   ],
   "model_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Llama 4 Scout / Maverick | Meta | Apr 2026 | Free | \u2713 Llama 4 License |"
   ],
   "release_date_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Already Released in 2026 \u2014 Major Models",
    "| Model | Lab | Date | Price input/M | Open weights |",
    "| Llama 4 Scout / Maverick | Meta | Apr 2026 | Free | \u2713 Llama 4 License |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "10M token context on Scout."
   ],
   "license_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "Free under Llama 4 License."
   ],
   "significance_evidence": [
    "Upcoming AI Models 2026: Full Release Tracker \u2014 What's Live, What's Coming, What's Free",
    "## Free and Open-Weight Models in 2026",
    "First MoE Llamas. Natively multimodal."
   ]
  },
  {
   "url": "https://wikiwand.com/en/Qwen2",
   "title": "Qwen - Wikiwand",
   "published_at": null,
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "Qwen",
    "Family of large language models by Alibaba From Wikipedia, the free encyclopedia"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "List of models",
    "| Name | Release date | License | Ref. |",
    "| Qwen3.6 (Qwen3.6-35B-A3B) | April 2026 | [Apache 2.0](./Apache_2.0) | [[40]](./Qwen#cite_note-Gigazine-2026-40)[[86]](./Qwen#cite_note-86) |",
    "The Qwen3.6-35B-A3B model was released under the Apache 2.0 license in the same month."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://vorplabs.com/models/releases/gemma-4",
   "title": "Gemma 4 family model release review",
   "published_at": "2026-07-22T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 12B, 26B A4B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Five variants with different architectures (edge E2B/E4B, 12B encoder-free multimodal, 26B A4B mixture-of-experts with 4B active parameters per token, and a 31B dense flagship) with 128K and 256K context windows.",
   "organization_evidence": [
    "Model release review",
    "Gemma 4 family model release review",
    "Gemma 4 is Google's open-weight family released April 2, 2026 under Apache 2.0 in five variants: E2B and E4B for edge devices, a 12B encoder-free unified multimodal model, a 26B A4B mixture-of-experts model with 4B active parameters per token, and a 31B dense flagship."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model release review",
    "Gemma 4 family model release review",
    "Gemma 4 is Google's open-weight family released April 2, 2026 under Apache 2.0 in five variants: E2B and E4B for edge devices, a 12B encoder-free unified multimodal model, a 26B A4B mixture-of-experts model with 4B active parameters per token, and a 31B dense flagship."
   ],
   "parameters_evidence": [
    "Model release review",
    "Gemma 4 family model release review",
    "Gemma 4 is Google's open-weight family released April 2, 2026 under Apache 2.0 in five variants: E2B and E4B for edge devices, a 12B encoder-free unified multimodal model, a 26B A4B mixture-of-experts model with 4B active parameters per token, and a 31B dense flagship."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model release review",
    "Gemma 4 family model release review",
    "Gemma 4 is Google's open-weight family released April 2, 2026 under Apache 2.0 in five variants: E2B and E4B for edge devices, a 12B encoder-free unified multimodal model, a 26B A4B mixture-of-experts model with 4B active parameters per token, and a 31B dense flagship.",
    "Documented context windows are 128K on the E-series and 256K on the larger variants."
   ]
  },
  {
   "url": "https://kimiguide.com/blog/kimi-k3-review",
   "title": "Kimi K3 Review: 2.8T Open-Source Model Tops Code Arena at 1679",
   "published_at": "2026-07-18T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Tops Code Arena at 1679 Elo, beating every proprietary system on the planet at coding",
   "organization_evidence": [
    "# Kimi K3 Review: 2.8T Open-Source Model Tops Code Arena at 1679",
    "Moonshot AI released Kimi K3 on July 15, 2026, and it's their fourth record-breaking open-source model in 12 months."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 Review: 2.8T Open-Source Model Tops Code Arena at 1679",
    "Moonshot AI released Kimi K3 on July 15, 2026, and it's their fourth record-breaking open-source model in 12 months."
   ],
   "parameters_evidence": [
    "# Kimi K3 Review: 2.8T Open-Source Model Tops Code Arena at 1679",
    "K3 packs 2.8 trillion parameters in a Mixture-of-Experts architecture with 896 total experts, activating only 16 per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 Review: 2.8T Open-Source Model Tops Code Arena at 1679",
    "It was July 16th, and my Twitter feed exploded with a single screenshot: Code Arena's leaderboard showing \"Kimi K3\" at the very top with 1679 points \u2014 above Claude Fable 5, above GPT-5.6 Sol, above everything."
   ]
  },
  {
   "url": "https://aiforanything.io/blog/glm-5-2-review-2026",
   "title": "GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & How It Compares to Claude and GPT-5",
   "published_at": "2026-06-15T12:49:28",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "~744 billion",
   "context_window": null,
   "license": null,
   "significance": "A usable 1M-token context window, two selectable reasoning modes, MIT open-source license, and mixture-of-experts architecture with ~744B total parameters.",
   "organization_evidence": [
    "GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & How It Compares to Claude and GPT-5",
    "Zhipu AI's GLM-5.2 launched June 13, 2026 with a usable 1M-token context, two thinking modes, and an MIT open-source license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & How It Compares to Claude and GPT-5",
    "Zhipu AI's GLM-5.2 launched June 13, 2026 with a usable 1M-token context, two thinking modes, and an MIT open-source license."
   ],
   "parameters_evidence": [
    "GLM-5.2 Specs: Everything We Know (and What We Don't)",
    "Here is the **confirmed** technical profile, sourced from Zhipu's launch materials and independent reporting:",
    "| **Total parameters** | ~744 billion |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & How It Compares to Claude and GPT-5",
    "Zhipu AI's GLM-5.2 launched June 13, 2026 with a usable 1M-token context, two thinking modes, and an MIT open-source license."
   ]
  },
  {
   "url": "https://buff.ly/1wiPU4K",
   "title": "German AI consortium releases Soofi S, an open 30B model that tops benchmarks in both English and German",
   "published_at": "2026-07-13T11:41:01",
   "organization": "German research consortium coordinated by the KI Bundesverband (German AI Association)",
   "model": null,
   "release_date": "2026-07-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "achieves the highest scores on English and German benchmarks among fully open models, surpassing previous leaders like OLMo 3 32B and Apertus 70B",
   "organization_evidence": [
    "Soofi S is one of the first large language models trained entirely on Deutsche Telekom's Industrial AI Cloud in Munich. The open 30B model uses a lean hybrid architecture and a training mix deliberately weighted toward German."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Original article from July 13, 2026:"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://news.lavx.hu/article/mistral-targets-lean-proof-engineering-with-leanstral-1-5",
   "title": "Mistral targets Lean proof engineering with Leanstral 1.5",
   "published_at": "2026-07-04T12:57:38",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "Mistral claims Leanstral 1.5 reaches 100% on miniF2F validation and test. It also says the model solves 587 of 672 PutnamBench problems, with 87% on FATE-H and 34% on FATE-X.",
   "organization_evidence": [
    "Mistral targets Lean proof engineering with Leanstral 1.5",
    "Mistral released Leanstral 1.5 on July 2, 2026, as an open model for Lean 4 proof engineering."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral targets Lean proof engineering with Leanstral 1.5",
    "Mistral released Leanstral 1.5 on July 2, 2026, as an open model for Lean 4 proof engineering."
   ],
   "parameters_evidence": [
    "Mistral targets Lean proof engineering with Leanstral 1.5",
    "Mistral\u2019s new Apache 2.0 Lean agent pairs a 119B-parameter MoE backbone with long-context proof search, and its reported scores give formal verification teams a practical open model to test.",
    "Mistral released Leanstral 1.5 on July 2, 2026, as an open model for Lean 4 proof engineering.",
    "The company says the sparse mixture-of-experts system has 119B total parameters, activates about 6B to 6.5B per token, carries an Apache 2.0 license, and runs through its free API endpoint as `leanstral-1-5`."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral targets Lean proof engineering with Leanstral 1.5",
    "Mistral claims Leanstral 1.5 reaches 100% on miniF2F validation and test. It also says the model solves 587 of 672 PutnamBench problems, with 87% on FATE-H and 34% on FATE-X."
   ]
  },
  {
   "url": "https://solidaitech.com/2026/07/gemma-4-apache-license-change.html",
   "title": "Gemma 4 Released: Why the Apache 2.0 License Matters",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 open-weight release with five size tiers, 256K context window, native audio input on smaller variants, and native function calling for agentic workflows.",
   "organization_evidence": [
    "# Why Gemma 4's New License Matters More Than Benchmarks",
    "**\u270f\ufe0f Editorial Note:** Technical specifications below are sourced directly from Google DeepMind's official model page, Google Cloud's release blog, and Google AI for Developers documentation, current as of July 2026.",
    "Gemma 4 is Google DeepMind's newest family of open-weight AI models, released April 2, 2026, built from the same underlying research as Gemini 3 \u2014 Google's flagship, closed, subscription-based model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Why Gemma 4's New License Matters More Than Benchmarks",
    "Gemma 4 is Google DeepMind's newest family of open-weight AI models, released April 2, 2026, built from the same underlying research as Gemini 3 \u2014 Google's flagship, closed, subscription-based model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "# Why Gemma 4's New License Matters More Than Benchmarks",
    "Gemma 4 changes that entirely: it ships under the Apache 2.0 license \u2014 a standard, permissive, genuinely open-source license used across huge swaths of commercial software.",
    "The family ships in five sizes: E2B (2.3B effective parameters), E4B (4.5B effective), a 12B unified multimodal model, a 26B Mixture-of-Experts model (3.8B active per token), and a 31B dense model \u2014 spanning everything from phone-friendly edge deployment to workstation-grade reasoning.",
    "Maximum token context window",
    "---\n\n## What Gemma 4 Actually Is"
   ]
  },
  {
   "url": "https://whysogeek.com/kimi-k3-open-weights-largest-release-2026",
   "title": "Kimi K3 Open Weights: 2.8T Params, 1.4TB to Run",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
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   "context_window": null,
   "license": null,
   "significance": "largest open release yet, 2.8-trillion-parameter mixture-of-experts model",
   "organization_evidence": [
    "# Kimi K3 Open Weights: 2.8T Params, 1.4TB to Run",
    "Moonshot AI's Kimi K3 is a 2.8-trillion-parameter open-weight model whose 1.4TB MXFP4 weights make it the largest open release yet."
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   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 Open Weights: 2.8T Params, 1.4TB to Run",
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   ],
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 Open Weights: 2.8T Params, 1.4TB to Run",
    "Moonshot AI's Kimi K3 is a 2.8-trillion-parameter open-weight model whose 1.4TB MXFP4 weights make it the largest open release yet."
   ]
  },
  {
   "url": "https://computertech.co/glm-5-turbo-review",
   "title": "GLM-5-Turbo Review 2026: Z.ai's Agent Model Tested",
   "published_at": "2026-03-17T02:01:35",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-02",
   "parameters": "744 billion",
   "context_window": null,
   "license": null,
   "significance": "Open-source flagship in a mixture-of-experts architecture scaling to 744 billion parameters.",
   "organization_evidence": [
    "# GLM-5-Turbo Review 2026",
    "It is a narrower commercial offshoot of GLM-5 \u2014 Z.ai\u2019s February 2026 open-source flagship \u2014 designed specifically for agent workflows: tool use, long-chain execution, complex instruction decomposition, and persistent task automation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5-Turbo Review 2026",
    "It is a narrower commercial offshoot of GLM-5 \u2014 Z.ai\u2019s February 2026 open-source flagship \u2014 designed specifically for agent workflows: tool use, long-chain execution, complex instruction decomposition, and persistent task automation."
   ],
   "parameters_evidence": [
    "# GLM-5-Turbo Review 2026",
    "Unlike GLM-5, which ships as open-weight under an MIT license and scales to 744 billion parameters in a mixture-of-experts architecture, GLM-5-Turbo is closed-source."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5-Turbo Review 2026",
    "Unlike GLM-5, which ships as open-weight under an MIT license and scales to 744 billion parameters in a mixture-of-experts architecture, GLM-5-Turbo is closed-source."
   ]
  },
  {
   "url": "https://awesomeagents.ai/models/minimax-m3",
   "title": "MiniMax M3",
   "published_at": "2026-06-04T16:53:13",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "Not disclosed",
   "context_window": null,
   "license": null,
   "significance": "Positioned as the first open-weight model to combine frontier-tier coding performance, a 1M-token context window, and native multimodal input; scores 59.0% on SWE-Bench Pro.",
   "organization_evidence": [
    "MiniMax M3 is an open-weight frontier model with a 1M-token context window, native multimodal input, and strong agentic coding at $0.60/M input tokens.",
    "MiniMax M3 is the June 2026 flagship from Shanghai-based MiniMax, positioned as the first open-weight model to combine three things at once: frontier-tier coding performance, a genuine one-million-token context window, and native multimodal input.",
    "Key Specifications",
    "| Specification | Details |",
    "| Provider | MiniMax |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released June 1, 2026, M3 is accessible through the MiniMax API and subscription plans; open weights and a technical report are promised on Hugging Face within roughly ten days of launch.",
    "Key Specifications",
    "| Specification | Details |",
    "| Release Date | 2026-06-01 |"
   ],
   "parameters_evidence": [
    "Parameter count is not disclosed.",
    "Key Specifications",
    "| Specification | Details |",
    "| Parameters | Not disclosed |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 is the June 2026 flagship from Shanghai-based MiniMax, positioned as the first open-weight model to combine three things at once: frontier-tier coding performance, a genuine one-million-token context window, and native multimodal input.",
    "- Scores 59.0% on SWE-Bench Pro, beating GPT-5.5 (58.6%) and Gemini 3.1 Pro (54.2%) on autonomous coding evals"
   ]
  },
  {
   "url": "https://pinklloyds.com/news/nvidia-ising-ai-platform-quantum-computers",
   "title": "NVIDIA Ising: The AI Platform That Could Make Quantum Computers Actually Useful",
   "published_at": "2026-05-25T17:13:40",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ising Calibration is a 35-billion-parameter vision-language model trained on real measurement data from quantum processors spanning every major hardware type \u2014 superconducting qubits, trapped ions, neutral atoms, quantum dots, and even electrons on helium.",
   "organization_evidence": [
    "NVIDIA Ising: The AI Platform That Could Make Quantum Computers Actually Useful",
    "Meet NVIDIA Ising: the world's first family of open-source AI models built specifically to make quantum hardware work better, faster, and at scale."
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   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Ising: The AI Platform That Could Make Quantum Computers Actually Useful",
    "Announced on April 14, 2026, Ising doesn't compete with the quantum machines built by IonQ, IBM, or Google."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Ising: The AI Platform That Could Make Quantum Computers Actually Useful",
    "## Two Problems, Two Models",
    "**Ising Calibration** is a 35-billion-parameter vision-language model trained on real measurement data from quantum processors spanning every major hardware type \u2014 superconducting qubits, trapped ions, neutral atoms, quantum dots, and even electrons on helium."
   ]
  },
  {
   "url": "https://deepseekai.guide/news/deepseek-latest-updates",
   "title": "DeepSeek Latest Updates: V4 Preview Lands (Apr 2026)",
   "published_at": "2026-04-24T19:35:29",
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active",
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   "significance": "Frontier-tier performance rivaling top closed-source models with high parameter count and architecture size",
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   "release_date_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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   "parameters_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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  {
   "url": "https://deepseekai.guide/news/deepseek-latest-updates",
   "title": "DeepSeek Latest Updates: V4 Preview Lands (Apr 2026)",
   "published_at": "2026-04-24T19:35:29",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
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   "release_date_evidence": [
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   "license_evidence": [
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   "significance_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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  },
  {
   "url": "https://deepseekai.guide/news/deepseek-latest-updates",
   "title": "DeepSeek Latest Updates: V4 Preview Lands (Apr 2026)",
   "published_at": "2026-04-24T19:35:29",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
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   "model_evidence": [
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   "release_date_evidence": [
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   "parameters_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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   "context_window_evidence": [
    "DeepSeek Latest Updates \u2014 What Shipped With V4 Preview (April 2026)",
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   "license_evidence": [
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  },
  {
   "url": "https://iemlabs.com/blogs/best-qwen-models-in-2026",
   "title": "Best Qwen Models in 2026",
   "published_at": "2026-07-06T07:21:36",
   "organization": "Alibaba",
   "model": "Qwen3.6-27B",
   "release_date": "2026-04-22",
   "parameters": "27B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Outperforms the larger Qwen3.5-397B-A17B flagship on agentic coding, while fitting on a single consumer GPU.",
   "organization_evidence": [
    "Alibaba ships a new Qwen generation every few months. Here\u2019s what\u2019s actually worth using right now, and how to pick."
   ],
   "model_evidence": [
    "The best Qwen models in 2026",
    "| Qwen3.6-27B | 27B | 27B (dense) | 256K | Apache 2.0 | Local coding and general use |"
   ],
   "release_date_evidence": [
    "- **April 2026:** Qwen 3.6 arrives in two forms.",
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   "parameters_evidence": [
    "The best Qwen models in 2026",
    "| Qwen3.6-27B | 27B | 27B (dense) | 256K | Apache 2.0 | Local coding and general use |"
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   "context_window_evidence": [
    "The best Qwen models in 2026",
    "| Qwen3.6-27B | 27B | 27B (dense) | 256K | Apache 2.0 | Local coding and general use |"
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   "license_evidence": [
    "The best Qwen models in 2026",
    "| Qwen3.6-27B | 27B | 27B (dense) | 256K | Apache 2.0 | Local coding and general use |"
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   "significance_evidence": [
    "- **April 2026:** Qwen 3.6 arrives in two forms.",
    "Qwen3.6-27B, a dense 27B model, follows April 22 and outperforms the larger Qwen3.5-397B-A17B flagship on agentic coding, while fitting on a single consumer GPU."
   ]
  },
  {
   "url": "https://aiworkflows.tools/blog/google-gemma-4-complete-guide-open-weight-agentic-ai-2026",
   "title": "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI (2026)",
   "published_at": "2026-04-05T08:46:19",
   "organization": "Google DeepMind",
   "model": "Gemma 4 E2B",
   "release_date": "2026-04-02",
   "parameters": "~5.1B Total Parameters",
   "context_window": "128K tokens",
   "license": "Apache 2.0",
   "significance": "Effective 2B model featuring Per-Layer Embeddings (PLE) for on-device deployment.",
   "organization_evidence": [
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   "model_evidence": [
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    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
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   "release_date_evidence": [
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   "parameters_evidence": [
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    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Total Parameters** | ~5.1B | ~8B | ~25.2B | ~30.7B |"
   ],
   "context_window_evidence": [
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    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Context Window** | 128K tokens | 128K tokens | 256K tokens | 256K tokens |"
   ],
   "license_evidence": [
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    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **License** | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "### Per-Layer Embeddings (PLE) \u2014 The \"Effective\" Innovation",
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  },
  {
   "url": "https://aiworkflows.tools/blog/google-gemma-4-complete-guide-open-weight-agentic-ai-2026",
   "title": "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI (2026)",
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   "model_evidence": [
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    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
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   "release_date_evidence": [
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    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Total Parameters** | ~5.1B | ~8B | ~25.2B | ~30.7B |"
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    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Context Window** | 128K tokens | 128K tokens | 256K tokens | 256K tokens |"
   ],
   "license_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **License** | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "### Per-Layer Embeddings (PLE) \u2014 The \"Effective\" Innovation",
    "The E2B and E4B models introduce a novel technique called **Per-Layer Embeddings (PLE)** \u2014 which is why Google brands them as \"Effective 2B\" and \"Effective 4B.\""
   ]
  },
  {
   "url": "https://aiworkflows.tools/blog/google-gemma-4-complete-guide-open-weight-agentic-ai-2026",
   "title": "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI (2026)",
   "published_at": "2026-04-05T08:46:19",
   "organization": "Google DeepMind",
   "model": "Gemma 4 26B MoE",
   "release_date": "2026-04-02",
   "parameters": "~25.2B Total Parameters (~3.8B Active)",
   "context_window": "256K tokens",
   "license": "Apache 2.0",
   "significance": "Mixture-of-Experts architecture with 128 total experts, providing high throughput for server deployments.",
   "organization_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "model_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "release_date_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "parameters_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Active Parameters** | ~2B | ~4B | ~3.8B | ~30.7B |",
    "| **Total Parameters** | ~5.1B | ~8B | ~25.2B | ~30.7B |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Context Window** | 128K tokens | 128K tokens | 256K tokens | 256K tokens |"
   ],
   "license_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **License** | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "### MoE Architecture \u2014 128 Experts, 8+1 Active",
    "The 26B model uses a **Mixture-of-Experts (MoE)** architecture with 128 total experts, activating 8 routed experts plus 1 shared expert per token (8+1 configuration)."
   ]
  },
  {
   "url": "https://aiworkflows.tools/blog/google-gemma-4-complete-guide-open-weight-agentic-ai-2026",
   "title": "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI (2026)",
   "published_at": "2026-04-05T08:46:19",
   "organization": "Google DeepMind",
   "model": "Gemma 4 31B Dense",
   "release_date": "2026-04-02",
   "parameters": "~30.7B Total Parameters",
   "context_window": "256K tokens",
   "license": "Apache 2.0",
   "significance": "Flagship dense model scoring 89.2% on AIME 2026 and 80.0% on LiveCodeBench.",
   "organization_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "model_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "release_date_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**."
   ],
   "parameters_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Total Parameters** | ~5.1B | ~8B | ~25.2B | ~30.7B |"
   ],
   "context_window_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **Context Window** | 128K tokens | 128K tokens | 256K tokens | 256K tokens |"
   ],
   "license_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "## Gemma 4 Model Family: Specs and Architecture",
    "| Feature | E2B | E4B | 26B (MoE) | 31B (Dense) |",
    "| **License** | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |"
   ],
   "significance_evidence": [
    "Google Gemma 4: The Complete Guide to Open-Weight Agentic AI in 2026",
    "**TL;DR:** Google DeepMind released Gemma 4 on April 2, 2026 \u2014 a family of four open-weight models (E2B, E4B, 26B MoE, 31B Dense) under a fully permissive **Apache 2.0 license**.",
    "The flagship 31B model scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench, making it the most capable open model available today."
   ]
  },
  {
   "url": "https://flightdeckadvertising.com/blog/kimi-k3-open-source-model-what-founders-need-to-know",
   "title": "Kimi K3 Just Became the Largest Open-Source AI Model Ever \u2014 What It Actually Means If You're Building on Top of a Model Right Now | Flightdeck Advertising",
   "published_at": null,
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight AI system ever released, with benchmark performance the company claims is competitive with Anthropic's Claude Fable 5 and ahead of Opus 4.8 and OpenAI's latest models on several tasks",
   "organization_evidence": [
    "On July 16, 2026, Chinese AI lab Moonshot released Kimi K3, a 2.8-trillion-parameter model the company says is the largest open-weight AI system ever released, with benchmark performance the company claims is competitive with Anthropic's Claude Fable 5 and ahead of Opus 4.8 and OpenAI's latest models on several tasks.",
    "# Kimi K3 Just Became the Largest Open-Source AI Model Ever"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On July 16, 2026, Chinese AI lab Moonshot released Kimi K3, a 2.8-trillion-parameter model the company says is the largest open-weight AI system ever released, with benchmark performance the company claims is competitive with Anthropic's Claude Fable 5 and ahead of Opus 4.8 and OpenAI's latest models on several tasks.",
    "# Kimi K3 Just Became the Largest Open-Source AI Model Ever"
   ],
   "parameters_evidence": [
    "On July 16, 2026, Chinese AI lab Moonshot released Kimi K3, a 2.8-trillion-parameter model the company says is the largest open-weight AI system ever released, with benchmark performance the company claims is competitive with Anthropic's Claude Fable 5 and ahead of Opus 4.8 and OpenAI's latest models on several tasks.",
    "# Kimi K3 Just Became the Largest Open-Source AI Model Ever"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On July 16, 2026, Chinese AI lab Moonshot released Kimi K3, a 2.8-trillion-parameter model the company says is the largest open-weight AI system ever released, with benchmark performance the company claims is competitive with Anthropic's Claude Fable 5 and ahead of Opus 4.8 and OpenAI's latest models on several tasks.",
    "# Kimi K3 Just Became the Largest Open-Source AI Model Ever"
   ]
  },
  {
   "url": "https://felloai.com/it/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026",
   "parameters": "30B dense",
   "context_window": null,
   "license": null,
   "significance": "Local and consumer-hardware coding, around $0.60 per million tokens",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/it/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": null,
   "license": null,
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/it/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/it/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": "1 million-token",
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": [
    "The current flagship **GLM-5.2** (June 2026) runs a **744B-parameter** mixture-of-experts design with a **1 million-token** context window at roughly **one-sixth** the price of GPT-5.5.",
    "**GLM = General Language Model**, a family of open-weight AI models from **Zhipu AI (now Z.ai)**, first introduced in **2021** at Tsinghua University."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/06/01/minimax-releases-minimax-m3",
   "title": "MiniMax Releases MiniMax M3 with MSA Architecture Supporting 1M-Token Context, Native Multimodality, and Agentic Coding",
   "published_at": "2026-06-01T20:40:05",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 is an open-weight model combining frontier-level coding performance, a 1M-token context window, and native multimodal input in a single architecture",
   "organization_evidence": [
    "**MiniMax officially released MiniMax** **M3 on June 1, 2026.",
    "MiniMax positions M3 as an open-weight model combining frontier-level coding performance, a 1M-token context window, and native multimodal input in a single architecture \u2014 the first to do so, per MiniMax."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**MiniMax officially released MiniMax** **M3 on June 1, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax positions M3 as an open-weight model combining frontier-level coding performance, a 1M-token context window, and native multimodal input in a single architecture \u2014 the first to do so, per MiniMax."
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-kimi",
   "title": "DeepSeek vs Kimi: 2026 Comparison for Developers",
   "published_at": "2026-04-25T14:07:53",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B (MoE)",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "SWE-Bench Verified ~79.0% (Flash), Terminal-Bench 2.0 56.9%",
   "organization_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context."
   ],
   "model_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |"
   ],
   "release_date_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Released | 2026-04-24 | 2026-04-24 | April 2026 |"
   ],
   "parameters_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Total parameters | 284B (MoE) | 1.6T (MoE) | 1T (MoE) |"
   ],
   "context_window_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens | 262,144 tokens |"
   ],
   "license_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Weights licence | MIT | MIT | Modified MIT |"
   ],
   "significance_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| SWE-Bench Verified | ~79.0% (Flash) | 80.6% | 80.2% |",
    "| Terminal-Bench 2.0 | 56.9% | 67.9% | 66.7% |"
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-kimi",
   "title": "DeepSeek vs Kimi: 2026 Comparison for Developers",
   "published_at": "2026-04-25T14:07:53",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T (MoE)",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "SWE-Bench Verified 80.6%, Terminal-Bench 2.0 67.9%",
   "organization_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context."
   ],
   "model_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |"
   ],
   "release_date_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Released | 2026-04-24 | 2026-04-24 | April 2026 |"
   ],
   "parameters_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Total parameters | 284B (MoE) | 1.6T (MoE) | 1T (MoE) |"
   ],
   "context_window_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "DeepSeek V4, released April 24, 2026, splits into two tiers built around a million-token context.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens | 262,144 tokens |"
   ],
   "license_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Weights licence | MIT | MIT | Modified MIT |"
   ],
   "significance_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| SWE-Bench Verified | ~79.0% (Flash) | 80.6% | 80.2% |",
    "| Terminal-Bench 2.0 | 56.9% | 67.9% | 66.7% |"
   ]
  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-kimi",
   "title": "DeepSeek vs Kimi: 2026 Comparison for Developers",
   "published_at": "2026-04-25T14:07:53",
   "organization": "Moonshot",
   "model": "Kimi K2.6",
   "release_date": "April 2026",
   "parameters": "1T (MoE)",
   "context_window": "262,144 tokens",
   "license": "Modified MIT",
   "significance": "SWE-Bench Verified 80.2%, Terminal-Bench 2.0 66.7%",
   "organization_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "Kimi K2.6, released a few days earlier, focuses on swarm-based agents and long-horizon coding inside a 256K window."
   ],
   "model_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "Kimi K2.6, released a few days earlier, focuses on swarm-based agents and long-horizon coding inside a 256K window.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |"
   ],
   "release_date_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "Kimi K2.6, released a few days earlier, focuses on swarm-based agents and long-horizon coding inside a 256K window.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Released | 2026-04-24 | 2026-04-24 | April 2026 |"
   ],
   "parameters_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Total parameters | 284B (MoE) | 1.6T (MoE) | 1T (MoE) |"
   ],
   "context_window_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "Kimi K2.6, released a few days earlier, focuses on swarm-based agents and long-horizon coding inside a 256K window.",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens | 262,144 tokens |"
   ],
   "license_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| Weights licence | MIT | MIT | Modified MIT |"
   ],
   "significance_evidence": [
    "# DeepSeek vs Kimi (2026): Which Open-Weight Model Wins?",
    "| Feature | DeepSeek V4-Flash | DeepSeek V4-Pro | Kimi K2.6 |",
    "| SWE-Bench Verified | ~79.0% (Flash) | 80.6% | 80.2% |",
    "| Terminal-Bench 2.0 | 56.9% | 67.9% | 66.7% |"
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/qwen3-6",
   "title": "Qwen3.6: Alibaba's Open-Weight Coding LLM | AI/TLDR",
   "published_at": null,
   "organization": "Alibaba Group",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B total / 3B active MoE",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-Experts model with 35B total and roughly 3B active parameters, sharing the hybrid attention architecture, vision input, and 262K context",
   "organization_evidence": [
    "# Qwen3.6",
    "Qwen3.6 is the April 2026 release from the Qwen team at Alibaba Group, building on Qwen3.5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.6",
    "The release leads with two open models: Qwen3.6-35B-A3B, a Mixture-of-Experts model with 35B total and roughly 3B active parameters (released 2026-04-16), and Qwen3.6-27B, a 27-billion-parameter dense model (released 2026-04-22) that the Qwen team frames as 'flagship-level coding in a 27B dense model.'"
   ],
   "parameters_evidence": [
    "# Qwen3.6",
    "The release leads with two open models: Qwen3.6-35B-A3B, a Mixture-of-Experts model with 35B total and roughly 3B active parameters (released 2026-04-16), and Qwen3.6-27B, a 27-billion-parameter dense model (released 2026-04-22) that the Qwen team frames as 'flagship-level coding in a 27B dense model.'"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6",
    "The release leads with two open models: Qwen3.6-35B-A3B, a Mixture-of-Experts model with 35B total and roughly 3B active parameters (released 2026-04-16), and Qwen3.6-27B, a 27-billion-parameter dense model (released 2026-04-22) that the Qwen team frames as 'flagship-level coding in a 27B dense model.'",
    "What is the difference between Qwen3.6-27B and Qwen3.6-35B-A3B?",
    "Qwen3.6-35B-A3B is a Mixture-of-Experts model with 35B total parameters but only ~3B active per token, trading a bit of peak accuracy for cheaper, faster inference."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/qwen3-6",
   "title": "Qwen3.6: Alibaba's Open-Weight Coding LLM | AI/TLDR",
   "published_at": null,
   "organization": "Alibaba Group",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": "27B dense",
   "context_window": null,
   "license": null,
   "significance": "reaches 77.2 on SWE-bench Verified and 59.3 on Terminal-Bench 2.0, edging past the much larger Qwen3.5-397B-A17B MoE from the previous generation on most coding benchmarks",
   "organization_evidence": [
    "# Qwen3.6",
    "Qwen3.6 is the April 2026 release from the Qwen team at Alibaba Group, building on Qwen3.5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.6",
    "The release leads with two open models: Qwen3.6-35B-A3B, a Mixture-of-Experts model with 35B total and roughly 3B active parameters (released 2026-04-16), and Qwen3.6-27B, a 27-billion-parameter dense model (released 2026-04-22) that the Qwen team frames as 'flagship-level coding in a 27B dense model.'"
   ],
   "parameters_evidence": [
    "# Qwen3.6",
    "The release leads with two open models: Qwen3.6-35B-A3B, a Mixture-of-Experts model with 35B total and roughly 3B active parameters (released 2026-04-16), and Qwen3.6-27B, a 27-billion-parameter dense model (released 2026-04-22) that the Qwen team frames as 'flagship-level coding in a 27B dense model.'"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6",
    "The headline story is parameter efficiency in agentic coding: the dense Qwen3.6-27B reaches 77.2 on SWE-bench Verified and 59.3 on Terminal-Bench 2.0, edging past the much larger Qwen3.5-397B-A17B MoE from the previous generation on most coding benchmarks."
   ]
  },
  {
   "url": "https://dev.to/x4nent/complete-guide-to-google-gemma-4-apache-20-open-model-benchmark-ple-architecture-to-ollama-35ad",
   "title": "Complete Guide to Google Gemma 4 \u2014 Apache 2.0 Open Model Benchmark: PLE Architecture to Ollama Local Deployment",
   "published_at": "2026-04-13T00:35:41",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B Dense model scores 89.2% on AIME 2026 math, 80.0% on LiveCodeBench v6 coding, and 84.3% on GPQA Diamond science \u2014 competing with 400B-class proprietary models on a parameter-efficiency basis.",
   "organization_evidence": [
    "In April 2026, Google DeepMind released **Gemma 4** \u2014 a family of open-weight models built on Gemini 3 research and distributed under the **Apache 2.0 license**.",
    "Complete Guide to Google Gemma 4 \u2014 Apache 2.0 Open Model Benchmark: PLE Architecture to Ollama Local Deployment"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "In April 2026, Google DeepMind released **Gemma 4** \u2014 a family of open-weight models built on Gemini 3 research and distributed under the **Apache 2.0 license**.",
    "Complete Guide to Google Gemma 4 \u2014 Apache 2.0 Open Model Benchmark: PLE Architecture to Ollama Local Deployment"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B Dense model scores **89.2%** on AIME 2026 math, **80.0%** on LiveCodeBench v6 coding, and **84.3%** on GPQA Diamond science \u2014 competing with 400B-class proprietary models on a parameter-efficiency basis.",
    "Complete Guide to Google Gemma 4 \u2014 Apache 2.0 Open Model Benchmark: PLE Architecture to Ollama Local Deployment"
   ]
  },
  {
   "url": "https://abhs.in/blog/kimi-k27-code-1-trillion-parameter-open-source-model-moonshot-ai-june-2026",
   "title": "Kimi K2.7-Code: Moonshot AI Releases 1T Parameter Open-Source Model, Claims to Beat Claude Opus on Tool Use",
   "published_at": "2026-06-14T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "1 trillion",
   "context_window": null,
   "license": null,
   "significance": "outperforms previous Kimi K2.6 by 21.8% on Kimi Code Bench v2 and claims tool-use performance above Claude Opus 4",
   "organization_evidence": [
    "# Kimi K2.7-Code: Moonshot AI Releases 1T Parameter Open-Source Model, Claims to Beat Claude Opus on Tool Use",
    "Chinese AI lab Moonshot AI released Kimi K2.7-Code on June 12 under a Modified MIT licence."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Chinese AI lab Moonshot AI released Kimi K2.7-Code on June 12, 2026 on Hugging Face under a Modified MIT licence."
   ],
   "parameters_evidence": [
    "# Kimi K2.7-Code: Moonshot AI Releases 1T Parameter Open-Source Model, Claims to Beat Claude Opus on Tool Use",
    "One trillion parameters, 32B active via MoE, 256K context window, and benchmark results that claim to exceed Claude Opus on tool-use tasks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark results show it outperforms the previous Kimi K2.6 by 21.8% on Kimi Code Bench v2 and claims tool-use performance above Claude Opus 4."
   ]
  },
  {
   "url": "https://iotdigitaltwinplm.com/glm-5-2-explained-architecture-benchmarks-2026",
   "title": "GLM-5.2 Explained: Architecture, Benchmarks, and Deployment (2026)",
   "published_at": "2026-07-16T22:47:03",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06",
   "parameters": "744 billion",
   "context_window": null,
   "license": null,
   "significance": "Beats GPT-5.5 on SWE-bench Pro (62.1 vs ~58.6) and provides a 1M context window with IndexShare and MTP.",
   "organization_evidence": [
    "# GLM-5.2 Explained: Architecture, Benchmarks, and Deployment (2026)",
    "It explains what is inside GLM-5.2, how Zhipu AI (operating globally as Z.ai) trained it, what its benchmark numbers actually mean, and what it costs to run \u2014 whether you rent the API or self-host the weights.",
    "Zhipu AI is a Beijing-based lab spun out of Tsinghua University, and the GLM (\u201cGeneral Language Model\u201d) family is its flagship line."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Explained: Architecture, Benchmarks, and Deployment (2026)",
    "In June 2026, a Chinese lab shipped an open-weight model that beat GPT-5.5 on a hard software-engineering benchmark and then handed the weights to anyone with a GPU cluster and an MIT license."
   ],
   "parameters_evidence": [
    "# GLM-5.2 Explained: Architecture, Benchmarks, and Deployment (2026)",
    "It is a 744-billion-parameter Mixture-of-Experts model that activates only about 40 billion parameters per token, reads a million tokens of context, and does so at roughly one-sixth the API cost of its closed frontier peers.",
    "The GLM-5.2 architecture is a sparse Mixture-of-Experts transformer with about 744 billion total parameters, of which roughly 40 billion are active for any single token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Explained: Architecture, Benchmarks, and Deployment (2026)",
    "In June 2026, a Chinese lab shipped an open-weight model that beat GPT-5.5 on a hard software-engineering benchmark and then handed the weights to anyone with a GPU cluster and an MIT license.",
    "On **SWE-bench Pro** \u2014 a hard, real-world software-engineering benchmark that measures resolving genuine GitHub issues \u2014 GLM-5.2 scores 62.1, edging past GPT-5.5\u2019s reported ~58.6."
   ]
  },
  {
   "url": "https://tensorlabs.io/articles/running-a-frontier-coder",
   "title": "Running a frontier coder on hardware you rent",
   "published_at": "2026-06-10T10:50:34",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "200-400B",
   "context_window": null,
   "license": null,
   "significance": "the first open-weight model to pair frontier coding with a 1M-token context and native multimodality",
   "organization_evidence": [
    "# Running a frontier coder on hardware you rent",
    "On June 1, 2026, MiniMax released MiniMax M3, the first open-weight model to pair frontier coding with a 1M-token context and native multimodality."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Running a frontier coder on hardware you rent",
    "On June 1, 2026, MiniMax released MiniMax M3, the first open-weight model to pair frontier coding with a 1M-token context and native multimodality."
   ],
   "parameters_evidence": [
    "## What MiniMax M3 is, and why the architecture matters",
    "M3 is a large open-weight model (community estimates put it in the 200-400B parameter range) built on a new attention mechanism MiniMax calls MSA, MiniMax Sparse Attention."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Running a frontier coder on hardware you rent",
    "On June 1, 2026, MiniMax released MiniMax M3, the first open-weight model to pair frontier coding with a 1M-token context and native multimodality."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/nvidias-new-550b-model-just-outpaced-every-us-open-b0332899",
   "title": "NVIDIA's New 550B Model Just Outpaced Every US Open-Source AI,Here's Why That Matters",
   "published_at": "2026-06-02T05:09:13",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Nemotron 550B scores 48 on the Artificial Analysis Intelligence Index, a widely used benchmark that measures AI reasoning and knowledge across multiple domains, representing a 9 to 15-point lead over every competitor in the open-source space.",
   "organization_evidence": [
    "# NVIDIA's New 550B Model Just Outpaced Every US Open-Source AI,Here's Why That Matters",
    "NVIDIA just released a 550-billion-parameter open-source AI model that outperforms every other publicly available American model by a significant margin, while running at speeds that shouldn't be possible for a model this large."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# NVIDIA's New 550B Model Just Outpaced Every US Open-Source AI,Here's Why That Matters",
    "The company unveiled Nemotron 3 Ultra during its Computex keynote in Taipei on June 1, 2026, marking a major shift in the open-source AI landscape and raising questions about how NVIDIA achieved such dramatic performance gains."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# NVIDIA's New 550B Model Just Outpaced Every US Open-Source AI,Here's Why That Matters",
    "Nemotron 550B scores 48 on the Artificial Analysis Intelligence Index, a widely used benchmark that measures AI reasoning and knowledge across multiple domains.",
    "That represents a 9 to 15-point lead over every competitor in the open-source space."
   ]
  },
  {
   "url": "https://cline.ghost.io/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T-parameter",
   "context_window": null,
   "license": null,
   "significance": "88.3 on Terminal-Bench 2.1, putting it very close to top closed models in the same category.",
   "organization_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 1. Kimi K3",
    "Kimi K3 was released by Moonshot AI in July 2026 as a 2.8T-parameter MoE model with a 1M token context window and native vision support."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 1. Kimi K3",
    "The full open-weight release was made on July 27, 2026, making it one of the largest open weight model releases so far."
   ],
   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 1. Kimi K3",
    "Kimi K3 was released by Moonshot AI in July 2026 as a 2.8T-parameter MoE model with a 1M token context window and native vision support."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 1. Kimi K3",
    "Moonshot reported 88.3 on Terminal-Bench 2.1, putting it very close to top closed models in the same category."
   ]
  },
  {
   "url": "https://cline.ghost.io/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "Scored 81.0 on Terminal-Bench 2.1, only a few points behind top closed frontier models like Claude Opus 4.8 and ahead of models like Gemini 3.1 Pro.",
   "organization_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "GLM-5.2 was released by Z.ai in June 2026 as a large open-weight MoE model built for coding, reasoning, tool use, and long-context workflows."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "GLM-5.2 was released by Z.ai in June 2026 as a large open-weight MoE model built for coding, reasoning, tool use, and long-context workflows."
   ],
   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "It has roughly 744B total parameters, around 40B active parameters, supports a 1M token context window and is released under an MIT license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 3. GLM-5.2",
    "In Z.ai\u2019s own release material, GLM-5.2 scored 81.0 on Terminal-Bench 2.1."
   ]
  },
  {
   "url": "https://cline.ghost.io/best-open-weight-models-that-matter-in-2026",
   "title": "Top 5 Open Weight Models That Matter to Developers in 2026",
   "published_at": "2026-08-04T12:55:01",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": "428B",
   "context_window": null,
   "license": null,
   "significance": "Scores 59.0% on SWE-bench Pro, slightly above GPT-5.5 at 58.6% and ahead of Gemini 3.1 Pro at 54.2% in the same comparison.",
   "organization_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
    "MiniMax M3 was released by MiniMax in June 2026 as a native multimodal open weight model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
    "MiniMax M3 was released by MiniMax in June 2026 as a native multimodal open weight model."
   ],
   "parameters_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
    "It has 428B total parameters, about 23B active parameters, and a 1M token context window."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Top 5 Open Weight Models That Matter to Developers in 2026",
    "### 5. MiniMax M3",
    "On benchmarks, MiniMax scores 59.0% on SWE-bench Pro, slightly above GPT-5.5 at 58.6% and ahead of Gemini 3.1 Pro at 54.2% in the same comparison."
   ]
  },
  {
   "url": "https://scriptbyai.com/qwen-timeline",
   "title": "Qwen Timeline: Model & Product Release History",
   "published_at": "2026-07-12T15:53:53",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Brought Qwen3.6's multimodal reasoning and agentic coding to a widely used local-deployment size with a dense architecture.",
   "organization_evidence": [
    "Qwen Timeline: Model & Product Release History",
    "Qwen is Alibaba\u2019s family of artificial intelligence models and products.",
    "**Qwen3.6-27B**, released on April 22, 2026, is the latest open-weight general Qwen model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen Model Release History",
    "| Release | What changed |",
    "| **Qwen3.6-27B** *April 22* | This dense open-weight model brought Qwen3.6\u2019s multimodal reasoning and agentic coding to a widely used local-deployment size. Its dense architecture also avoided the routing requirements of an MoE model. |",
    "**Qwen3.6-27B**, released on April 22, 2026, is the latest open-weight general Qwen model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen Model Release History",
    "| Release | What changed |",
    "| **Qwen3.6-27B** *April 22* | This dense open-weight model brought Qwen3.6\u2019s multimodal reasoning and agentic coding to a widely used local-deployment size. Its dense architecture also avoided the routing requirements of an MoE model. |"
   ]
  },
  {
   "url": "https://medium.com/@sai1004/one-model-to-rule-them-all-2a79cfcf1405",
   "title": "One Model to Rule Them All",
   "published_at": "2026-03-18T08:25:22",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-17",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-Experts architecture with 128 experts (activating 4 per token), a configurable reasoning effort parameter, and a 256K context window.",
   "organization_evidence": [
    "# One Model to Rule Them All",
    "On 17\u201303\u20132026 at NVIDIA GTC 2026, Mistral AI quietly dropped what might be the most consequential open-source model release of the year."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# One Model to Rule Them All",
    "On 17\u201303\u20132026 at NVIDIA GTC 2026, Mistral AI quietly dropped what might be the most consequential open-source model release of the year."
   ],
   "parameters_evidence": [
    "# One Model to Rule Them All",
    "Mistral just shipped Small 4 \u2014 a 119B-parameter monster that activates only 6B per token, kills the \u201cseparate models\u201d problem, and ships Apache 2.0. Here\u2019s what every AI engineer needs to know."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# One Model to Rule Them All",
    "The architecture is **Mixture-of-Experts (MoE)** \u2014 128 experts, with only 4 activating per token.",
    "It\u2019s not the 256K context window (though that\u2019s significant). It\u2019s the configurable reasoning effort \u2014 a single API parameter that lets you dial between speed and depth."
   ]
  },
  {
   "url": "https://dev.to/akaranjkar08/google-gemma-4-apache-20-open-models-that-run-on-your-laptop-2026-4mp5",
   "title": "Google Gemma 4: Apache 2.0 Open Models That Run on Your Laptop (2026)",
   "published_at": "2026-07-13T19:16:40",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 licensing and hardware efficiency that no competitor at this capability level matches.",
   "organization_evidence": [
    "# Google Gemma 4: Apache 2.0 Open Models That Run on Your Laptop (2026)",
    "Google DeepMind released Gemma 4 on April 2, 2026, and two things make it immediately significant: Apache 2.0 licensing and hardware efficiency that no competitor at this capability level matches."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4: Apache 2.0 Open Models That Run on Your Laptop (2026)",
    "Google DeepMind released Gemma 4 on April 2, 2026, and two things make it immediately significant: Apache 2.0 licensing and hardware efficiency that no competitor at this capability level matches."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4: Apache 2.0 Open Models That Run on Your Laptop (2026)",
    "Google DeepMind released Gemma 4 on April 2, 2026, and two things make it immediately significant: Apache 2.0 licensing and hardware efficiency that no competitor at this capability level matches."
   ]
  },
  {
   "url": "https://bitsminds.com/news/moonshot-kimi-k2-7-code-coding-model-2026",
   "title": "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
   "published_at": "2026-06-14T12:33:31",
   "organization": "Moonshot AI",
   "model": "Kimi K2.7-Code",
   "release_date": "2026-06-12",
   "parameters": "1-trillion-parameter",
   "context_window": "256K-token",
   "license": "modified MIT license",
   "significance": "cuts reasoning-token usage by about 30% versus K2.6",
   "organization_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "Moonshot AI has released Kimi K2.7-Code, an open-weight model built specifically for coding and agentic tasks, publishing the weights on [Hugging Face](https://huggingface.co) on June 12 under a modified MIT license."
   ],
   "model_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "Moonshot AI has released Kimi K2.7-Code, an open-weight model built specifically for coding and agentic tasks, publishing the weights on [Hugging Face](https://huggingface.co) on June 12 under a modified MIT license."
   ],
   "release_date_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "Moonshot AI has released Kimi K2.7-Code, an open-weight model built specifically for coding and agentic tasks, publishing the weights on [Hugging Face](https://huggingface.co) on June 12 under a modified MIT license."
   ],
   "parameters_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "The Beijing lab\u2019s fifth K2 release in a year is a coding-first, 1-trillion-parameter open-weight model under a modified MIT license \u2014 but independent testers say the headline benchmarks are Moonshot\u2019s own and don\u2019t all hold up."
   ],
   "context_window_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "The architecture is unchanged in headline terms: a Mixture-of-Experts design with 1 trillion total parameters and roughly 32 billion active per token, paired with a 256K-token context window."
   ],
   "license_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "The Beijing lab\u2019s fifth K2 release in a year is a coding-first, 1-trillion-parameter open-weight model under a modified MIT license \u2014 but independent testers say the headline benchmarks are Moonshot\u2019s own and don\u2019t all hold up."
   ],
   "significance_evidence": [
    "Moonshot AI Ships Kimi K2.7-Code, an Open-Weight Coding Model It Says Uses 30% Fewer Reasoning Tokens",
    "The Beijing company says K2.7-Code cuts reasoning-token usage by about 30% versus K2.6, attacking the \"overthinking\" that inflates latency and API bills when a model talks itself in circles before producing an answer."
   ]
  },
  {
   "url": "https://felloai.com/ko/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "744-billion-parameter Mixture-of-Experts design with a 1-million-token context window and MIT license",
   "organization_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** is the latest flagship large language model from **Zhipu AI**, a Beijing-based lab that has become one of China\u2019s most aggressive open-weight model publishers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "parameters_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run."
   ]
  },
  {
   "url": "https://datanorth.ai/news/nvidia-releases-alpamayo-2-super",
   "title": "NVIDIA releases Alpamayo 2 Super for autonomous driving",
   "published_at": "2026-08-06T12:31:47",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-05",
   "parameters": "34 billion",
   "context_window": null,
   "license": null,
   "significance": "Alpamayo 2 Super scores 79.2 on the LingoQA Lingo-Judge metric, first among nearly 40 models evaluated, and produces a planned trajectory, a causal explanation and a meta-action from a single pass over full-surround camera video.",
   "organization_evidence": [
    "NVIDIA releases Alpamayo 2 Super for autonomous driving",
    "NVIDIA released Alpamayo 2 Super, a 34-billion-parameter open driving foundation model, licensed for commercial use, that outputs a trajectory, a causal explanation and a meta-action in one pass."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA releases Alpamayo 2 Super for autonomous driving",
    "NVIDIA made Alpamayo 2 Super available for commercial use on 5 August 2026, releasing the weights of its 34-billion-parameter vision-language-action model for autonomous driving on Hugging Face under the OpenMDW-1.1 licence."
   ],
   "parameters_evidence": [
    "NVIDIA releases Alpamayo 2 Super for autonomous driving",
    "NVIDIA released Alpamayo 2 Super, a 34-billion-parameter open driving foundation model, licensed for commercial use, that outputs a trajectory, a causal explanation and a meta-action in one pass."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA releases Alpamayo 2 Super for autonomous driving",
    "Alpamayo 2 Super scores 79.2 on the LingoQA Lingo-Judge metric, first among nearly 40 models evaluated, and produces a planned trajectory, a causal explanation and a meta-action from a single pass over full-surround camera video."
   ]
  },
  {
   "url": "https://aichina.news/blog/unlock-web-vision-new-open-weights-vlm-on-modelers-cn-dy4fvw",
   "title": "Unlock Web Vision: New Open-Weights VLM on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:17:57",
   "organization": "allenai",
   "model": "MolmoWeb-Pretrained-8B",
   "release_date": "2026-07-21",
   "parameters": "8B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Specifically web-pretrained for UI screenshots, document layouts, or complex infographics, serving as a base for fine-tuning.",
   "organization_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "model_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "Today, they\u2019ve added a compelling new tool to their roster: `allenai/MolmoWeb-Pretrained-8B`."
   ],
   "release_date_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "Unlike general-purpose vision-language models (VLMs) trained on generic internet data, this 8-billion parameter model has been specifically web-pretrained."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "It is hosted on Modelers.cn, meaning it is readily accessible for developers working within the Huawei Ascend NPU environment, and it carries a fully permissive Apache 2.0 licence."
   ],
   "significance_evidence": [
    "Unlock Web Vision: New Open-Weights VLM on Modelers.cn",
    "For developers looking to build systems that can truly understand the chaotic visual data of the modern web\u2014be it UI screenshots, document layouts, or complex infographics\u2014this model offers a fresh, open-source foundation."
   ]
  },
  {
   "url": "https://vibecodedthis.com/blog/kimi-k3-open-weights-download-available-july-2026",
   "title": "Kimi K3's 2.8-Trillion-Parameter Weights Are Now Free to Download",
   "published_at": "2026-07-27T09:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "K3 ranked first on the Frontend Code Arena, a blind developer voting benchmark, ahead of Claude Fable 5. It also scores 81.2 on FrontierSWE and 88.3 on Terminal-Bench 2.1.",
   "organization_evidence": [
    "Kimi K3's 2.8-Trillion-Parameter Weights Are Now Free to Download",
    "Moonshot AI released the full open weights for Kimi K3 on July 26-27."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3's 2.8-Trillion-Parameter Weights Are Now Free to Download",
    "Moonshot AI released the full open weights for Kimi K3 on July 26-27."
   ],
   "parameters_evidence": [
    "Kimi K3's 2.8-Trillion-Parameter Weights Are Now Free to Download",
    "This is the follow-through on what Moonshot announced when they launched the model on July 16: a 2.8-trillion-parameter mixture-of-experts model that would go open-weight by July 27."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "K3 ranked first on the Frontend Code Arena, a blind developer voting benchmark, ahead of Claude Fable 5.",
    "It also scores 81.2 on FrontierSWE and 88.3 on Terminal-Bench 2.1.",
    "## Why this matters for open-source AI"
   ]
  },
  {
   "url": "https://ainewsdesk.app/gemma-4-vs-opus-4-6-gpt-5-4-pro-gemini-3-pro",
   "title": "Gemma 4 vs Opus 4.6, GPT-5.4 Pro, Gemini 3 Pro",
   "published_at": "2026-04-08T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Reaches #3 on the Arena open-source text leaderboard for the 31B Dense and #6 for the 26B MoE, outcompeting models 20x their size while running under Apache 2.0.",
   "organization_evidence": [
    "Google released Gemma 4 on April 2, 2026, and it is the first open-weights family that genuinely makes me reconsider what I run locally versus what I pay an API for.",
    "Comparison refreshed for May 2026 \u2014 Gemma 4 (open weights) and Claude Opus 4.7 are now the most relevant updates."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google released Gemma 4 on April 2, 2026, and it is the first open-weights family that genuinely makes me reconsider what I run locally versus what I pay an API for.",
    "Comparison refreshed for May 2026 \u2014 Gemma 4 (open weights) and Claude Opus 4.7 are now the most relevant updates."
   ],
   "parameters_evidence": [
    "Gemma 4 launched April 2, 2026 in four sizes: E2B, E4B, 26B Mixture-of-Experts (with 4B active parameters), and 31B Dense \u2014 all under [Apache 2.0](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), per Google\u2019s official announcement.",
    "Comparison refreshed for May 2026 \u2014 Gemma 4 (open weights) and Claude Opus 4.7 are now the most relevant updates."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B Dense currently ranks #3 on the [Arena AI open-source text leaderboard](https://arena.ai/leaderboard/text?license=open-source), and the 26B MoE ranks #6 \u2014 Google\u2019s blog says they outcompete models \u201c20x their size.\u201d",
    "Comparison refreshed for May 2026 \u2014 Gemma 4 (open weights) and Claude Opus 4.7 are now the most relevant updates."
   ]
  },
  {
   "url": "https://bregg.com/blog/kimi-k3-moonshot-open-weight-healthcare-ai-strategy-2026-07-17",
   "title": "Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest open-source AI model in the world",
   "organization_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "parameters_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ]
  },
  {
   "url": "https://shaam.blog/articles/glm-5-3-zhipu-z-ai-next-model-vision-roadmap-2026",
   "title": "GLM 5.3: What Zhipu's Next Open-Weight Model Will Likely Bring (and When)",
   "published_at": "2026-08-04T19:32:10",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "753B MoE; 1M token context; MIT weights",
   "organization_evidence": [
    "# GLM 5.3: What Zhipu's Next Open-Weight Model Will Likely Bring (and When)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM 5.3: What Zhipu's Next Open-Weight Model Will Likely Bring (and When)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "How did we get here: GLM 5.2 is the baseline",
    "| GLM-5.2 (confirmed) | GLM-5.3 (expected) |",
    "| ~753B (~40B active per token) | Same, or larger per leaks |",
    "| 1,000,000 tokens | 1M tokens or larger |",
    "| MIT (open weights) | Expected MIT (community assumption) |"
   ]
  },
  {
   "url": "https://finance.biggo.com/news/8tpTzp4BeeRcyTJI67t7",
   "title": "MiniMax Open-Sources 428B-Parameter Flagship Model M3, Revealing Native Multimodal Training Details for the First Time",
   "published_at": "2026-06-16T02:47:46",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-15",
   "parameters": "428 billion total parameters with 23 billion active parameters",
   "context_window": null,
   "license": null,
   "significance": "M3 is the world's first open-source model to employ multimodal mixed training from the very first training step and achieved the highest ranking among global open-source models on the Artificial Analysis composite intelligence index.",
   "organization_evidence": [
    "MiniMax Open-Sources 428B-Parameter Flagship Model M3, Revealing Native Multimodal Training Details for the First Time",
    "Chinese AI startup MiniMax officially open-sourced its native multimodal flagship model M3 on June 15, releasing model weights on GitHub and Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax Open-Sources 428B-Parameter Flagship Model M3, Revealing Native Multimodal Training Details for the First Time",
    "Chinese AI startup MiniMax officially open-sourced its native multimodal flagship model M3 on June 15, releasing model weights on GitHub and Hugging Face."
   ],
   "parameters_evidence": [
    "MiniMax Open-Sources 428B-Parameter Flagship Model M3, Revealing Native Multimodal Training Details for the First Time",
    "The model features 428 billion total parameters with 23 billion active parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Open-Sources 428B-Parameter Flagship Model M3, Revealing Native Multimodal Training Details for the First Time",
    "M3 is the world's first open-source model to employ multimodal mixed training from the very first training step.",
    "Since its launch, M3 has achieved the highest ranking among global open-source models on the Artificial Analysis composite intelligence index and other authoritative benchmarks, with particularly strong performance in finance and coding tasks."
   ]
  },
  {
   "url": "https://deepseekai.guide/guides/what-is-deepseek",
   "title": "What Is DeepSeek? V4 Models, Pricing & API Explained",
   "published_at": "2026-04-25T09:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) \u2014 both supporting a context length of one million tokens.",
   "organization_evidence": [
    "What Is DeepSeek? The Independent Guide for 2026",
    "DeepSeek is a Hangzhou-based AI lab that builds open-weight large language models, distributes them on Hugging Face, and runs a low-cost API that mirrors the OpenAI and Anthropic wire formats."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# What Is DeepSeek? The Independent Guide for 2026",
    "![What is DeepSeek card showing the four facts that frame the introductory guide as of April 2026: V4 is the current generation, the architecture is Mixture-of-Experts, the V4 weights ship under MIT for both code and weights, and the V4 Preview launched on April 24, 2026.](https://deepseekai.guide/wp-content/uploads/2026/04/what-is-deepseek-card.png)",
    "Its current flagship, the DeepSeek V4 Preview, was released on April 24, 2026, and ships as two Mixture-of-Experts models with a one-million-token context window."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## The DeepSeek V4 family \u2014 the current generation",
    "DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) \u2014 both supporting a context length of one million tokens."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "February 2026",
   "parameters": "744B total / 40B active (MoE)",
   "context_window": "128K tokens",
   "license": "MIT \u2705 (most permissive available)",
   "significance": "#1 among open models (ELO 1451)",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **Developer** | Zhipu AI (Z.ai) |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **Release** | February 2026 |",
    "| Spec | Value |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **Parameters** | 744B total / 40B active (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **Context Window** | 128K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **License** | MIT \u2705 (most permissive available) |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### \ud83e\udd47 1. GLM-5 \u2014 Best Overall Open-Weight Model",
    "| **Arena AI Rank** | #1 among open models (ELO 1451) |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Moonshot AI",
   "model": "Kimi K2.5",
   "release_date": "January 27, 2026",
   "parameters": "~1 trillion (MoE)",
   "context_window": "256K tokens",
   "license": "MIT \u2705",
   "significance": "BrowseComp 78.4% (best of any model)",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Developer** | Moonshot AI |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Release** | January 27, 2026 |",
    "| Spec | Value |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Parameters** | ~1 trillion (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Context Window** | 256K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **License** | MIT \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **BrowseComp** | 78.4% (best of any model) |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Google DeepMind",
   "model": "Gemma 4 31B",
   "release_date": "April 2, 2026",
   "parameters": "E2B / E4B / 26B MoE (4B active) / 31B Dense",
   "context_window": "128K (E2B/E4B) / 256K (26B/31B)",
   "license": "Apache 2.0 \u2705 (first time for Gemma)",
   "significance": "#3 open model globally (ELO ~1452)",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Developer** | Google DeepMind |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Release** | April 2, 2026 |",
    "| Spec | Value |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Sizes** | E2B / E4B / 26B MoE (4B active) / 31B Dense |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Context Window** | 128K (E2B/E4B) / 256K (26B/31B) |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **License** | Apache 2.0 \u2705 (first time for Gemma) |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "| **Arena AI Rank** | #3 open model globally (ELO ~1452) |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "DeepSeek",
   "model": "DeepSeek V3.2",
   "release_date": null,
   "parameters": "~685B total / 37B active (MoE)",
   "context_window": "128K tokens",
   "license": "MIT \u2705",
   "significance": "Gold-medal results on 2025 IMO and IOI",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost",
    "| **Developer** | DeepSeek |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost",
    "| **Parameters** | ~685B total / 37B active (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost",
    "| **Context Window** | 128K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost",
    "| **License** | MIT \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 4. DeepSeek V3.2 \u2014 Best Value: 90% of GPT-5.4 at 1/50th the Cost",
    "| **Notable** | Gold-medal results on 2025 IMO and IOI |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "April 5, 2025",
   "parameters": "109B / 17B (16 experts)",
   "context_window": "10M tokens",
   "license": "Llama 4 Community License (700M MAU clause)",
   "significance": "10M tokens context window \u2014 10\u00d7 longer than any competitor",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "Six major labs now ship frontier-competitive open-weight models: **Google** (Gemma 4), **Meta** (Llama 4), **Alibaba** (Qwen 3.5/3.6), **Zhipu AI** (GLM-5), **Moonshot AI** (Kimi K2.5), and even **OpenAI** (gpt-oss-120b) \u2014 which crossed a historic threshold by releasing open weights for the first time."
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Release** | April 5, 2025 (still current best) |",
    "| Spec | Scout | Maverick |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Parameters (total/active)** | 109B / 17B (16 experts) | 400B / 17B (128 experts) |",
    "| Spec | Scout | Maverick |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Context Window** | **10M tokens \u2b50** | 1M tokens |",
    "| Spec | Scout | Maverick |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **License** | Llama 4 Community License (700M MAU clause) |",
    "| Spec | Scout | Maverick |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "Llama 4 Scout has one defining competitive advantage that no other model in the world can match: a **10 million token context window** \u2014 10\u00d7 longer than any competitor."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Meta",
   "model": "Llama 4 Maverick",
   "release_date": "April 5, 2025",
   "parameters": "400B / 17B (128 experts)",
   "context_window": "1M tokens",
   "license": "Llama 4 Community License (700M MAU clause)",
   "significance": "offers the best quality-per-active-parameter ratio in the family",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "Six major labs now ship frontier-competitive open-weight models: **Google** (Gemma 4), **Meta** (Llama 4), **Alibaba** (Qwen 3.5/3.6), **Zhipu AI** (GLM-5), **Moonshot AI** (Kimi K2.5), and even **OpenAI** (gpt-oss-120b) \u2014 which crossed a historic threshold by releasing open weights for the first time."
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Release** | April 5, 2025 (still current best) |",
    "| Spec | Scout | Maverick |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Parameters (total/active)** | 109B / 17B (16 experts) | 400B / 17B (128 experts) |",
    "| Spec | Scout | Maverick |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **Context Window** | **10M tokens \u2b50** | 1M tokens |",
    "| Spec | Scout | Maverick |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "| **License** | Llama 4 Community License (700M MAU clause) |",
    "| Spec | Scout | Maverick |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 5. Llama 4 Maverick + Scout \u2014 Best for Ultra-Long Context",
    "Maverick, with 1M context and 128 experts, offers the best quality-per-active-parameter ratio in the family."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Alibaba",
   "model": "Qwen 3.6 Plus",
   "release_date": "March 31, 2026",
   "parameters": null,
   "context_window": "1M tokens",
   "license": "Apache 2.0 \u2705",
   "significance": "runs at approximately **3\u00d7 the inference speed of Claude Opus 4.6** in community benchmarks",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages",
    "| **Developer** | Alibaba (Qwen Team) |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages",
    "| **Qwen 3.6 Plus** | Released March 31, 2026 \u2014 free preview |",
    "| Spec | Value |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages",
    "| **Context Window** | Qwen 3.6 Plus: 1M tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages",
    "| **License** | Apache 2.0 \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 6. Qwen 3.5 / 3.6 Plus \u2014 Best for Coding and CJK Languages",
    "Qwen 3.6 Plus, released March 31, 2026, adds a **1M token context window** and runs at approximately **3\u00d7 the inference speed of Claude Opus 4.6** in community benchmarks."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "March 2026",
   "parameters": "119B total / 6.5B active (MoE)",
   "context_window": "256K tokens",
   "license": "Apache 2.0 \u2705",
   "significance": "unifies all three into a single 119B MoE model with **adjustable reasoning effort at inference time**",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "| **Developer** | Mistral AI (France) |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "| **Release** | March 2026 |",
    "| Spec | Value |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "| **Parameters** | 119B total / 6.5B active (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "| **Context Window** | 256K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "| **License** | Apache 2.0 \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 7. Mistral Small 4 \u2014 Best Single-Model Architecture",
    "Mistral Small 4 takes a unique architectural approach: instead of shipping separate models for instruction following, reasoning, and coding, it unifies all three into a single 119B MoE model with **adjustable reasoning effort at inference time**."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "OpenAI",
   "model": "gpt-oss-120b",
   "release_date": "Early 2026",
   "parameters": "117B total / 5.1B active (MoE)",
   "context_window": "128K tokens",
   "license": "Apache 2.0 \u2705 (historic first for OpenAI)",
   "significance": "it is the first time OpenAI has ever released model weights publicly \u2014 under Apache 2.0, no less",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "| **Developer** | OpenAI |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model"
   ],
   "release_date_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "| **Release** | Early 2026 |",
    "| Spec | Value |"
   ],
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "| **Parameters** | 117B total / 5.1B active (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "| **Context Window** | 128K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "| **License** | Apache 2.0 \u2705 (historic first for OpenAI) |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 8. gpt-oss-120b \u2014 OpenAI\u2019s First Open-Weight Model",
    "gpt-oss-120b is historically significant: it is the first time OpenAI has ever released model weights publicly \u2014 under Apache 2.0, no less."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "MiniMax AI",
   "model": "MiniMax M2.5",
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": "MIT \u2705",
   "significance": "Top-4 globally on key evals",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 9. MiniMax M2.5 \u2014 Best for Creative and Multimodal Tasks",
    "| **Developer** | MiniMax AI |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 9. MiniMax M2.5 \u2014 Best for Creative and Multimodal Tasks"
   ],
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 9. MiniMax M2.5 \u2014 Best for Creative and Multimodal Tasks",
    "| **License** | MIT \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 9. MiniMax M2.5 \u2014 Best for Creative and Multimodal Tasks",
    "| **Notable** | Top-4 globally on key evals |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Xiaomi",
   "model": "MiMo-V2-Pro",
   "release_date": null,
   "parameters": "1T+ total / 42B active (MoE)",
   "context_window": null,
   "license": null,
   "significance": "was \u2018Hunter Alpha\u2019 \u2014 most mysterious AI model release of 2026",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 10. Xiaomi MiMo-V2-Pro \u2014 Best Budget/Volume Option",
    "| **Developer** | Xiaomi |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 10. Xiaomi MiMo-V2-Pro \u2014 Best Budget/Volume Option"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 10. Xiaomi MiMo-V2-Pro \u2014 Best Budget/Volume Option",
    "| **Parameters** | 1T+ total / 42B active (MoE) |",
    "| Spec | Value |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 10. Xiaomi MiMo-V2-Pro \u2014 Best Budget/Volume Option",
    "| **Notable** | Was \u201cHunter Alpha\u201d \u2014 most mysterious AI model release of 2026 |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "NVIDIA",
   "model": "Nemotron-3 Super 120B",
   "release_date": null,
   "parameters": "120B",
   "context_window": null,
   "license": "Apache 2.0 \u2705",
   "significance": "achieves performance comparable to Llama 4 Maverick at approximately half the parameter weight",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 11. NVIDIA Nemotron-3 Super 120B \u2014 Best for Enterprise/Edge",
    "| **Developer** | NVIDIA |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 11. NVIDIA Nemotron-3 Super 120B \u2014 Best for Enterprise/Edge"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 11. NVIDIA Nemotron-3 Super 120B \u2014 Best for Enterprise/Edge",
    "| **Parameters** | 120B |",
    "| Spec | Value |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 11. NVIDIA Nemotron-3 Super 120B \u2014 Best for Enterprise/Edge",
    "| **License** | Apache 2.0 \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 11. NVIDIA Nemotron-3 Super 120B \u2014 Best for Enterprise/Edge",
    "Apache 2.0 licensed, achieves performance comparable to Llama 4 Maverick at approximately half the parameter weight, and benefits from NVIDIA\u2019s native CUDA and TensorRT optimization."
   ]
  },
  {
   "url": "https://primeaicenter.com/best-open-source-ai-models",
   "title": "12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware - PrimeAIcenter",
   "published_at": "2026-04-05T14:24:23",
   "organization": "Microsoft Research",
   "model": "Phi-4",
   "release_date": null,
   "parameters": "14B dense",
   "context_window": "16K tokens",
   "license": "MIT \u2705",
   "significance": "Outperforms models 5\u00d7 its size on reasoning",
   "organization_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI",
    "| **Developer** | Microsoft Research |",
    "| Spec | Value |"
   ],
   "model_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI",
    "| **Parameters** | 14B dense |",
    "| Spec | Value |"
   ],
   "context_window_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI",
    "| **Context Window** | 16K tokens |",
    "| Spec | Value |"
   ],
   "license_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI",
    "| **License** | MIT \u2705 |",
    "| Spec | Value |"
   ],
   "significance_evidence": [
    "# 12 Best Open Source AI Models 2026: Ranked \u2014 Download Links, Benchmarks & Which to Run on Your Hardware",
    "### 12. Phi-4 \u2014 Best for Constrained Hardware and Edge AI",
    "| **Specialty** | Outperforms models 5\u00d7 its size on reasoning |",
    "| Spec | Value |"
   ]
  },
  {
   "url": "https://ddrinnova.com/blog/mistral-medium-35-open-weight-unified-model-2026",
   "title": "Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
   "published_at": "2026-05-02T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-bench Verified",
   "organization_evidence": [
    "# Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5: One Open-Weight 128B Model Replaces Reasoning, Coding, and Chat",
    "On April 29, 2026, Mistral AI released **Mistral Medium 3.5** \u2014 a 128 billion-parameter dense model that does something strategically significant: it retires two of the company\u2019s existing specialist models and replaces them with a single set of open weights."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmark Performance: Where It Lands",
    "Mistral Medium 3.5 scores **77.6% on SWE-bench Verified**, the industry\u2019s most cited real-world coding benchmark."
   ]
  },
  {
   "url": "https://medium.com/@rowanblackwoon/gemma-4-12b-the-mid-sized-multimodal-model-id-actually-run-on-my-laptop-8be2898e8b9d",
   "title": "Gemma 4 12B: The Mid-Sized Multimodal Model I\u2019d Actually Run on My Laptop",
   "published_at": "2026-07-21T03:38:14",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95B dense",
   "context_window": null,
   "license": null,
   "significance": "the first mid-sized model with native audio input, and it does that without a separate vision or audio encoder",
   "organization_evidence": [
    "# Gemma 4 12B: The Mid-Sized Multimodal Model I\u2019d Actually Run on My Laptop",
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4 12B: The Mid-Sized Multimodal Model I\u2019d Actually Run on My Laptop",
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "parameters_evidence": [
    "# Gemma 4 12B: The Mid-Sized Multimodal Model I\u2019d Actually Run on My Laptop",
    "- Parameters: 11.95B dense"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4 12B: The Mid-Sized Multimodal Model I\u2019d Actually Run on My Laptop",
    "But the part that caught my attention is this: it is the first mid-sized model with native audio input, and it does that without a separate vision or audio encoder."
   ]
  },
  {
   "url": "https://kie.ai/de/blog/what-is-kimi-k3",
   "title": "What Is Kimi K3? Moonshot\u2019s 2.5T-Parameter, 1M-Context Flagship",
   "published_at": "2026-07-15T05:01:25",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A ~2.8T MoE with a 1M-token context window, aimed at long-horizon coding and agent workloads.",
   "organization_evidence": [
    "What Is Kimi K3? Moonshot's 2.8T, 1M-Context Flagship",
    "Kimi K3 at a Glance",
    "| Developer | Moonshot AI |"
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   "model_evidence": null,
   "release_date_evidence": [
    "Published: July 14, 2026",
    "TLDRKimi K3 is Moonshot AI's next-gen model \u2014 a ~2.8T MoE with a 1M-token context window, launched July 16, 2026 on Kimi Code and the Kimi app.",
    "Kimi K3 is Moonshot AI's next-generation large language model, launched on July 16, 2026 after a leaked promotion page on Moonshot's own Kimi Open Platform tipped the release a day early."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "TLDRKimi K3 is Moonshot AI's next-gen model \u2014 a ~2.8T MoE with a 1M-token context window, launched July 16, 2026 on Kimi Code and the Kimi app.",
    "It is a new-architecture Mixture-of-Experts model with roughly 2.8 trillion total parameters and a 1-million-token context window, aimed at long-horizon coding and agent workloads."
   ]
  },
  {
   "url": "https://groundy.com/articles/glm-5-2s-753b-moe-costs-more-to-self-host-than-the-mit-license-suggests",
   "title": "GLM-5.2's 753B MoE Costs More to Self-Host Than the MIT License Suggests",
   "published_at": "2026-06-19T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "753B",
   "context_window": null,
   "license": null,
   "significance": "62.1% on SWE-bench Pro1 and 81.0 on Terminal-Bench 2.11 put this model in serious contention for agentic coding tasks.",
   "organization_evidence": [
    "# GLM-5.2's 753B MoE Costs More to Self-Host Than the MIT License Suggests",
    "Zhipu released GLM-5.2 on June 13, 2026, under a MIT license with weights live on HuggingFace.2 The FP8 quantized variant has already logged roughly 93,900 downloads.3 Both numbers are real."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2's 753B MoE Costs More to Self-Host Than the MIT License Suggests",
    "Zhipu released GLM-5.2 on June 13, 2026, under a MIT license with weights live on HuggingFace.2 The FP8 quantized variant has already logged roughly 93,900 downloads.3 Both numbers are real."
   ],
   "parameters_evidence": [
    "# GLM-5.2's 753B MoE Costs More to Self-Host Than the MIT License Suggests",
    "GLM-5.2 ships 753B parameters under MIT with strong coding benchmarks, but the full MoE weight load makes self-hosting far heavier than the license terms imply."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2's 753B MoE Costs More to Self-Host Than the MIT License Suggests",
    "Zhipu released GLM-5.2 on June 13, 2026, under a MIT license with weights live on HuggingFace.2 The FP8 quantized variant has already logged roughly 93,900 downloads.3 Both numbers are real. So is the benchmark story: 62.1% on SWE-bench Pro1 and 81.0 on Terminal-Bench 2.11 put this model in serious contention for agentic coding tasks."
   ]
  },
  {
   "url": "https://heybeagle.com/blog/minimax-m3-ships-a-million-token-context-in-open-weights",
   "title": "MiniMax M3 Ships a Million-Token Context in Open Weights",
   "published_at": "2026-06-21T04:02:51",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "first open-weight model to combine a 1M-token context window, native multimodality, and frontier-adjacent coding benchmarks",
   "organization_evidence": [
    "MiniMax M3 Ships a Million-Token Context in Open Weights",
    "MiniMax, the Shanghai-based AI lab, released M3 on June 1, 2026."
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   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Ships a Million-Token Context in Open Weights",
    "MiniMax, the Shanghai-based AI lab, released M3 on June 1, 2026."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Ships a Million-Token Context in Open Weights",
    "MiniMax's M3 is the first open-weight model to combine a 1M-token context window, native multimodality, and frontier-adjacent coding benchmarks."
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  },
  {
   "url": "https://texxr.com/1162227/ai2-launches-open-coding-agents-sera",
   "title": "Ai2 launches Open Coding Agents, starting with SERA, an open-source family that includes 32B and 8B parameter models designed to adapt to private codebases",
   "published_at": "2026-01-27T00:00:00",
   "organization": "Ai2",
   "model": null,
   "release_date": null,
   "parameters": "32B and 8B",
   "context_window": null,
   "license": null,
   "significance": "State-of-the-art agent for its size, achieves state-of-the-art SWE-Bench Verified performance for open systems, and matches Devstral 2 at a fraction of the cost.",
   "organization_evidence": [
    "Ai2 launches Open Coding Agents, starting with SERA, an open-source family that includes 32B and 8B parameter models designed to adapt to private codebases",
    "Introducing Ai2 Open Coding Agents\u2014starting with SERA, our first-ever coding models."
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Ai2 launches Open Coding Agents, starting with SERA, an open-source family that includes 32B and 8B parameter models designed to adapt to private codebases",
    "Introducing Ai2 Open Coding Agents\u2014starting with SERA, our first-ever coding models."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Ai2 launches Open Coding Agents, starting with SERA, an open-source family that includes 32B and 8B parameter models designed to adapt to private codebases",
    "SERA is a SoTA agent for its size, super simple, and 26x more efficient than RL."
   ]
  },
  {
   "url": "https://remoteopenclaw.com/blog/best-qwen-models-2026",
   "title": "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
   "published_at": "2018-10-20T01:46:40",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation",
   "organization_evidence": [
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse",
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.5 (Feb 2026) | MoE | 397B | 17B | 262K | Apache 2.0 |",
    "The Qwen 3 Model Family",
    "Qwen3 includes six dense models and two Mixture-of-Experts models, plus Qwen3.5 and Qwen3-Coder as major extensions released in 2026.",
    "**Qwen3.5**, released February 16-17, 2026, is the latest generation."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen3.5, released February 2026, extended the family to 397 billion parameters with 201 language support and throughput up to 19x faster than the previous generation.",
    "Best Qwen Models in 2026 \u2014 Alibaba's Open-Source AI Powerhouse"
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/mistral",
   "title": "Mistral AI",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First merged flagship model (unites Magistral reasoning + Devstral 2 coding) 77.6% on SWE-Bench Verified Reasoning mode (toggleable between instant reply and reasoning) Multimodal (text + image)",
   "organization_evidence": [
    "LLM Mistral AI France",
    "# Mistral AI",
    "Mistral Medium 3.5 Recommended | 29 April 2026 | | First merged flagship model (unites Magistral reasoning + Devstral 2 coding) 128B dense architecture 256k context 77.6% on SWE-Bench Verified Reasoning mode (toggleable between instant reply and reasoning) Multimodal (text + image) Open weights (modified MIT licence) | Higher cost than Small 4 | Current |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Overview of available model variants",
    "Mistral Medium 3.5 Recommended | 29 April 2026 | | First merged flagship model (unites Magistral reasoning + Devstral 2 coding) 128B dense architecture 256k context 77.6% on SWE-Bench Verified Reasoning mode (toggleable between instant reply and reasoning) Multimodal (text + image) Open weights (modified MIT licence) | Higher cost than Small 4 | Current |",
    "On **29 April 2026**, Mistral released **Medium 3.5**, its first merged flagship model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Overview of available model variants",
    "Mistral Medium 3.5 Recommended | 29 April 2026 | | First merged flagship model (unites Magistral reasoning + Devstral 2 coding) 128B dense architecture 256k context 77.6% on SWE-Bench Verified Reasoning mode (toggleable between instant reply and reasoning) Multimodal (text + image) Open weights (modified MIT licence) | Higher cost than Small 4 | Current |",
    "On **29 April 2026**, Mistral released **Medium 3.5**, its first merged flagship model."
   ]
  },
  {
   "url": "https://innfactory.ai/en/ai-models/mistral",
   "title": "Mistral AI",
   "published_at": "2026-06-09T00:00:00",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03-16",
   "parameters": "119B MoE (6B active)",
   "context_window": "256k context",
   "license": "Apache 2.0",
   "significance": "119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter",
   "organization_evidence": [
    "LLM Mistral AI France",
    "# Mistral AI",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |"
   ],
   "model_evidence": [
    "LLM Mistral AI France",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |",
    "Mistral Small 4 - The New Highlight"
   ],
   "release_date_evidence": [
    "Overview of available model variants",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |",
    "Released in March 2026, **Mistral Small 4** sets new standards for efficient AI models."
   ],
   "parameters_evidence": [
    "Overview of available model variants",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |",
    "Released in March 2026, **Mistral Small 4** sets new standards for efficient AI models."
   ],
   "context_window_evidence": [
    "Overview of available model variants",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |",
    "Mistral Small 4 - The New Highlight"
   ],
   "license_evidence": [
    "LLM Mistral AI France",
    "License Apache 2.0 (Small 4), Mistral Research License (Large 3)",
    "License & Hosting",
    "**Open Source**: Mistral Small 4 and other models available under Apache 2.0 for self-hosting"
   ],
   "significance_evidence": [
    "Overview of available model variants",
    "Mistral Small 4 Recommended | 16 March 2026 | | 119B MoE (6B active) - extremely efficient 256k context Multimodal (vision) reasoning_effort parameter Open source | \u2014 | Current |",
    "Mistral Small 4 - The New Highlight"
   ]
  },
  {
   "url": "https://ai-primer.com/engineer/stories/gemma-4-open-model-release",
   "title": "Gemma 4 ships 31B Dense and 26B MoE open models under Apache 2.0",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked third among open models with a 1452 score on Arena, called the top-ranked US open model, and scored 85.7% on GPQA Diamond.",
   "organization_evidence": [
    "Gemma 4 ships 31B Dense and 26B MoE open models under Apache 2.0",
    "Google DeepMind released Gemma 4 in four open models with up to 256K context, multimodal inputs, and native tool-calling for local agent workflows."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 ships 31B Dense and 26B MoE open models under Apache 2.0",
    "Arena and GPQA numbers\n\nGoogle's best headline is parameter efficiency. [Arena](https://x.com/arena/status/2039739429930299574) ranked Gemma 4 31B third among open models with a 1452 score, and [Arena's follow-up](https://x.com/arena/status/2039782449648214247) called it the top-ranked US open model while noting it is far smaller than GLM-5 and Kimi-K2.5."
   ]
  },
  {
   "url": "https://pick-right.com/news/moonshot-kimi-k3-largest-open-weight-model-2026-07-16",
   "title": "Moonshot's Kimi K3 is the largest open-weight model ever \u2014 and it just took #1 on a coding benchmark from Claude Fable 5",
   "published_at": "2026-07-20T13:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "#1 on Arena's Frontend Code leaderboard at 1,679 Elo, ahead of Claude Fable 5, winning six of seven frontend domains",
   "organization_evidence": [
    "Moonshot's Kimi K3 is the largest open-weight model ever \u2014 and it just took #1 on a coding benchmark from Claude Fable 5",
    "Beijing\u2019s **Moonshot AI** released **Kimi K3** on **July 16** \u2014 a **2.8-trillion-parameter open-weight model**, the **largest ever released** (the first open \u201c3T-class\u201d system)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Beijing\u2019s **Moonshot AI** released **Kimi K3** on **July 16** \u2014 a **2.8-trillion-parameter open-weight model**, the **largest ever released** (the first open \u201c3T-class\u201d system).",
    "Moonshot's Kimi K3 is the largest open-weight model ever \u2014 and it just took #1 on a coding benchmark from Claude Fable 5"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It debuted **#1 on Arena\u2019s Frontend Code leaderboard at 1,679 Elo, ahead of [Claude Fable 5](/tools/claude/)**, winning **six of seven** frontend domains \u2014 a **17-place jump** from Kimi K2.6\u2019s #18.",
    "Moonshot's Kimi K3 is the largest open-weight model ever \u2014 and it just took #1 on a coding benchmark from Claude Fable 5"
   ]
  },
  {
   "url": "https://vertu.com/ai-tools/zhipu-glm-5-2-open-source-2026",
   "title": "Zhipu GLM 5.2 Open Source: 744B MoE, 1M Context, MIT (2026)",
   "published_at": "2026-06-30T16:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 is positioned as a top open-weight coding model.",
   "organization_evidence": [
    "**What is Zhipu GLM 5.2?** A frontier-tier open-weight MoE LLM from Zhipu AI, released June 13, 2026 (API) and June 16, 2026 (weights). 744B total parameters, 12\u201324B active per token, 1M context window, MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**What is Zhipu GLM 5.2?** A frontier-tier open-weight MoE LLM from Zhipu AI, released June 13, 2026 (API) and June 16, 2026 (weights). 744B total parameters, 12\u201324B active per token, 1M context window, MIT license."
   ],
   "parameters_evidence": [
    "**What is Zhipu GLM 5.2?** A frontier-tier open-weight MoE LLM from Zhipu AI, released June 13, 2026 (API) and June 16, 2026 (weights). 744B total parameters, 12\u201324B active per token, 1M context window, MIT license."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM 5.2 is positioned as a top open-weight coding model."
   ]
  },
  {
   "url": "https://swfte.com/pt/ai/models/minimax-m3",
   "title": "MiniMax M3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-07-28T00:36:27",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context. Built on MiniMax Sparse Attention (MSA): ~1/20th the per-token compute at 1M context, >9x faster prefill, >15x faster decode. SWE-bench Pro 59.0% surpasses GPT-5.5 and Gemini 3.1 Pro and approaches Opus 4.7; Terminal-Bench 2.1 66.0%, OSWorld-Verified 70.06%.",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is a open-source AI model by MiniMax, released on June 1, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax's June 1 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context.",
    "MiniMax M3 is a open-source AI model by MiniMax, released on June 1, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax's June 1 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context. Built on MiniMax Sparse Attention (MSA): ~1/20th the per-token compute at 1M context, >9x faster prefill, >15x faster decode. SWE-bench Pro 59.0% surpasses GPT-5.5 and Gemini 3.1 Pro and approaches Opus 4.7; Terminal-Bench 2.1 66.0%, OSWorld-Verified 70.06%."
   ]
  },
  {
   "url": "https://medium.com/@marc.bara.iniesta/nvidia-gets-serious-about-open-weights-as-china-sets-the-pace-54f3c4ad4808",
   "title": "Nvidia Gets Serious About Open Weights as China Sets the Pace",
   "published_at": "2026-04-29T09:25:55",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It is an open multimodal model with 30 billion parameters and roughly 3 billion active per inference. It handles audio, video, image and text in a single reasoning loop, which means a single model can watch a video, listen to its audio, read on-screen text and answer questions about all three together.",
   "organization_evidence": [
    "# Nvidia Gets Serious About Open Weights as China Sets the Pace",
    "Nvidia released [Nemotron 3 Nano Omni](https://blogs.nvidia.com/blog/nemotron-3-nano-omni-multimodal-ai-agents/) on April 28."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Nvidia Gets Serious About Open Weights as China Sets the Pace",
    "Nvidia released [Nemotron 3 Nano Omni](https://blogs.nvidia.com/blog/nemotron-3-nano-omni-multimodal-ai-agents/) on April 28."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Nvidia Gets Serious About Open Weights as China Sets the Pace",
    "It is an open multimodal model with 30 billion parameters and roughly 3 billion active per inference.",
    "It handles audio, video, image and text in a single reasoning loop, which means a single model can watch a video, listen to its audio, read on-screen text and answer questions about all three together."
   ]
  },
  {
   "url": "https://qwen3lm.com/qwen3.5",
   "title": "Qwen 3.5: Open Weight Multimodal AI Model",
   "published_at": null,
   "organization": "Alibaba Group",
   "model": null,
   "release_date": "2026",
   "parameters": "0.8B, 4B, 9B, 32B, 397B-A17B",
   "context_window": null,
   "license": null,
   "significance": "Native multimodal early fusion, hybrid attention & sparse mixture-of-experts, 262K to 1M token context window, advanced agentic & tool-use capabilities, multilingual support across 201 languages.",
   "organization_evidence": [
    "## What is Qwen 3.5?",
    "Qwen 3.5 represents the latest evolutionary leap in Alibaba Group's open-weight large language model series, officially released in early 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What is Qwen 3.5?",
    "Qwen 3.5 represents the latest evolutionary leap in Alibaba Group's open-weight large language model series, officially released in early 2026."
   ],
   "parameters_evidence": [
    "## What is Qwen 3.5?",
    "The model family spans multiple scales to serve diverse deployment environments.",
    "At the lightweight end, the `0.8B` and `4B` variants are engineered for edge devices, smartphones, and IoT controllers.",
    "Mid-tier models like `9B` and `32B` strike an optimal balance for local workstation inference and rapid prototyping.",
    "The flagship `397B-A17B` MoE variant activates only ~17 billion parameters per forward pass, delivering reasoning capabilities comparable to trillion-parameter dense models while consuming a fraction of the GPU memory and compute."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What is Qwen 3.5?",
    "Building upon the groundbreaking Qwen3 architecture, Qwen 3.5 introduces a fundamentally redesigned training pipeline, native multimodal fusion from day one, and a highly optimized Mixture-of-Experts (MoE) routing mechanism that dramatically reduces inference costs while preserving\u2014or in many domains exceeding\u2014the reasoning capabilities of dense frontier models.",
    "Released under the permissive **Apache 2.0 license** for all open-weight variants, Qwen 3.5 has quickly become a cornerstone of the global open AI ecosystem."
   ]
  },
  {
   "url": "https://news.geobrowser.io/story/fa687bfec40a44a282f7cbbfe5593df2",
   "title": "Mistral AI Releases Shieldstral, 3B-Parameter Open-Source Safety Classifier for AI Content Moderation \u2014 Geo News",
   "published_at": "2026-08-04T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "3-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Achieved an 84.9% average F1 score on text safety benchmarks, matching models seven times its size, and scored 83.8% on multimodal safety benchmarks.",
   "organization_evidence": [
    "Mistral AI Releases Shieldstral, 3B-Parameter Open-Source Safety Classifier for AI Content Moderation",
    "On August 4, 2026, Mistral AI released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On August 4, 2026, Mistral AI released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier."
   ],
   "parameters_evidence": [
    "Mistral AI Releases Shieldstral, 3B-Parameter Open-Source Safety Classifier for AI Content Moderation",
    "On August 4, 2026, Mistral AI released Shieldstral, a 3-billion-parameter open-weights multimodal safety classifier."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral AI Releases Shieldstral, 3B-Parameter Open-Source Safety Classifier for AI Content Moderation",
    "Trained on 54.1 million samples, the model achieved an 84.9% average F1 score on text safety benchmarks, matching models seven times its size, and scored 83.8% on multimodal safety benchmarks."
   ]
  },
  {
   "url": "https://precisionaiacademy.com/blog/news-2026-04-12-2",
   "title": "Google Gemma 4: A 31B Open Model That Punches 20x Above Its Weight",
   "published_at": "2026-04-12T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "Effective 2B (E2B), Effective 4B (E4B), 26B Mixture of Experts, and 31B Dense",
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 license, natively multimodal across all sizes, 256K context windows, and 31B ranks #3 open model on the Arena leaderboard.",
   "organization_evidence": [
    "Google just released the most permissive, most capable open AI model family it has ever built.",
    "[Gemma 4](/ai-tools-database/gemma-4) shipped on April 2, 2026 under the Apache 2.0 license \u2014 four variants, natively multimodal across every size, with the smaller ones engineered to run completely offline on edge hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google just released the most permissive, most capable open AI model family it has ever built.",
    "[Gemma 4](/ai-tools-database/gemma-4) shipped on April 2, 2026 under the Apache 2.0 license \u2014 four variants, natively multimodal across every size, with the smaller ones engineered to run completely offline on edge hardware."
   ],
   "parameters_evidence": [
    "Google just released the most permissive, most capable open AI model family it has ever built.",
    "[Gemma 4](/ai-tools-database/gemma-4) shipped on April 2, 2026 under the Apache 2.0 license \u2014 four variants, natively multimodal across every size, with the smaller ones engineered to run completely offline on edge hardware.",
    "- **Four sizes:** Effective 2B (E2B), Effective 4B (E4B), 26B Mixture of Experts, and 31B Dense."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google just released the most permissive, most capable open AI model family it has ever built.",
    "[Gemma 4](/ai-tools-database/gemma-4) shipped on April 2, 2026 under the Apache 2.0 license \u2014 four variants, natively multimodal across every size, with the smaller ones engineered to run completely offline on edge hardware.",
    "- **Apache 2.0** license \u2014 the gold standard for commercial use. No restrictions, no rug-pulls.",
    "- **Natively multimodal** across all sizes: text, images, video, OCR. Smaller variants add audio.",
    "- **256K context windows** \u2014 enough for an entire medium codebase or a day of meeting transcripts.",
    "- **31B ranks #3 open model** on the Arena leaderboard. 26B ranks #6."
   ]
  },
  {
   "url": "https://korben.info/en/moonshot-kimi-k3-largest-ai-model-free-download.html",
   "title": "Moonshot releases Kimi K3, the largest AI model ever made available for free download",
   "published_at": "2026-07-28T11:55:40",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "With 2.8 trillion parameters, it instantly becomes the largest model ever made available for free download. On current leaderboards, K3 slots in just behind the best closed models from OpenAI and Anthropic, and even outright beats them on coding benchmarks.",
   "organization_evidence": [
    "Moonshot releases Kimi K3, the largest AI model ever made available for free download",
    "Chinese startup Moonshot AI - you may know it for its Kimi assistant, and it counts Alibaba among its backers - yesterday published the full weights of Kimi K3 on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot releases Kimi K3, the largest AI model ever made available for free download",
    "Chinese startup Moonshot AI - you may know it for its Kimi assistant, and it counts Alibaba among its backers - yesterday published the full weights of Kimi K3 on Hugging Face.",
    "Source: [Simon Willison](https://simonwillison.net/2026/Jul/27/kimi-k3/)"
   ],
   "parameters_evidence": [
    "Moonshot releases Kimi K3, the largest AI model ever made available for free download",
    "With 2.8 trillion parameters, it instantly becomes the largest model ever made available for free download."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot releases Kimi K3, the largest AI model ever made available for free download",
    "With 2.8 trillion parameters, it instantly becomes the largest model ever made available for free download.",
    "On current leaderboards, K3 slots in just behind the best closed models from OpenAI and Anthropic, and even outright beats them on coding benchmarks - which is a pretty remarkable result for a model anyone can grab for free."
   ]
  },
  {
   "url": "https://aimodelsnavi.com/models/glm-5-1",
   "title": "GLM 5.1 | AI Models Navi",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "Best open-weight model for agentic coding \u2014 leads SWE-Bench Pro (58.4%) and CyberGym (68.7%) globally",
   "organization_evidence": [
    "Zhipu AI\u958b\u767a\u306e\u9ad8\u6027\u80fd\u57fa\u76e4\u30e2\u30c7\u30eb\u3002\u4e2d\u56fd\u8a9e\u5bfe\u5fdc\u306b\u512a\u308c\u3001\u591a\u69d8\u306a\u30bf\u30b9\u30af\u306b\u5bfe\u5fdc\u3002",
    "[GLM-5](/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "\u30ea\u30ea\u30fc\u30b9\u65e5\n\n2026-03-27",
    "[GLM-5](/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "parameters_evidence": [
    "\u30d1\u30e9\u30e1\u30fc\u30bf\n\n754",
    "Parameters (Active/Total)\n\n40B / 744B",
    "[GLM-5](/models/glm-5).1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### \u5f37\u307f",
    "- \u30fbBest open-weight model for agentic coding \u2014 leads SWE-Bench Pro (58.4%) and CyberGym (68.7%) globally"
   ]
  },
  {
   "url": "https://namoclaw.ai/models/minimax/minimax-m3-20260531",
   "title": "Multimodal AI with 1M Token Context Window \u00b7 NamoClaw",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Positioned as the first open-weight model to combine three frontier capabilities in one architecture: strong coding/agentic performance, a 1-million-token context window, and native multimodality (text, image, and video understanding).",
   "organization_evidence": [
    "## Overview",
    "MiniMax-M3 is the Shanghai-based lab's flagship model, officially released on June 1, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Overview",
    "It's positioned as the first open-weight model to combine three frontier capabilities in one architecture: strong coding/agentic performance, a 1-million-token context window, and native multimodality (text, image, and video understanding)."
   ]
  },
  {
   "url": "https://digitalapplied.com/blog/google-diffusiongemma-open-weight-text-diffusion-model-guide",
   "title": "Google DiffusionGemma: First Open-Weight Text Diffusion",
   "published_at": "2026-06-12T23:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Google DeepMind\u2019s first open-weight text diffusion model",
   "organization_evidence": [
    "Google DiffusionGemma is Google DeepMind\u2019s first open-weight text diffusion model, released on June 10, 2026 under an Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DiffusionGemma is Google DeepMind\u2019s first open-weight text diffusion model, released on June 10, 2026 under an Apache 2.0 license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google DiffusionGemma is Google DeepMind\u2019s first open-weight text diffusion model, released on June 10, 2026 under an Apache 2.0 license."
   ]
  },
  {
   "url": "https://aichina.news/blog/radical-transparency-on-ascend-why-developers-should-look-at-the-olmo-r6svj4",
   "title": "Radical Transparency on Ascend: Why Developers Should Look at the OLMo-2-0425-1B Release \u2014 AICHINA.news Blog",
   "published_at": "2026-04-16T14:11:54",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "1B",
   "context_window": null,
   "license": null,
   "significance": "Provides full access to training data, code, and weights, specifically optimized for Huawei Ascend NPU hardware.",
   "organization_evidence": [
    "Radical Transparency on Ascend: Why Developers Should Look at the OLMo-2-0425-1B Release",
    "In a landscape where 'open source' often refers only to model weights, the Allen Institute for AI (AI2) is pushing the boundaries of transparency with OLMo-2-0425-1B."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Radical Transparency on Ascend: Why Developers Should Look at the OLMo-2-0425-1B Release",
    "models/allenai April 16, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Radical Transparency on Ascend: Why Developers Should Look at the OLMo-2-0425-1B Release",
    "Now available on Modelers.cn\u2014the central hub for the Huawei Ascend AI ecosystem\u2014this 1-billion parameter model is designed for developers who demand to know exactly what goes into their AI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Radical Transparency on Ascend: Why Developers Should Look at the OLMo-2-0425-1B Release",
    "- **Truly open-source:** Provides full access to training data, code, and weights, allowing for total reproducibility."
   ]
  },
  {
   "url": "https://7minai.com/news/inkling-open-weights-975b",
   "title": "Thinking Machines ships Inkling, a 975B open-weights MoE \u2014 what it means if you want a self-hostable frontier model",
   "published_at": "2026-07-16T00:00:00",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975B-parameter",
   "context_window": null,
   "license": null,
   "significance": "SWE-Bench Verified: 77.6% \u2014 near GLM 5.2 (80.0%) and DeepSeek V4 Pro (80.6%), below Claude Fable 5 (95.0%). Terminal-Bench 2.1: 63.8% \u2014 behind GLM 5.2 (82.7%) and GPT-5.6 Sol (89.5%). GPQA Diamond: 87.2%; AIME 2026: 97.1%.",
   "organization_evidence": [
    "Thinking Machines ships Inkling, a 975B open-weights MoE \u2014 what it means if you want a self-hostable frontier model",
    "Mira Murati's Thinking Machines Lab released Inkling on July 15, 2026: a 975B-parameter, 41B-active multimodal Mixture-of-Experts with open weights, a 1M-token context, and a fine-tuning path on Tinker."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Thinking Machines ships Inkling, a 975B open-weights MoE \u2014 what it means if you want a self-hostable frontier model",
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   "parameters_evidence": [
    "Thinking Machines ships Inkling, a 975B open-weights MoE \u2014 what it means if you want a self-hostable frontier model",
    "Mira Murati's Thinking Machines Lab released Inkling on July 15, 2026: a 975B-parameter, 41B-active multimodal Mixture-of-Experts with open weights, a 1M-token context, and a fine-tuning path on Tinker."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Thinking Machines ships Inkling, a 975B open-weights MoE \u2014 what it means if you want a self-hostable frontier model",
    "On coding, the numbers are competitive with the open-weights pack but short of the closed frontier:",
    "- **SWE-Bench Verified: 77.6%** \u2014 near GLM 5.2 (80.0%) and DeepSeek V4 Pro (80.6%), below Claude Fable 5 (95.0%). (Source: Thinking Machines Lab)",
    "- **Terminal-Bench 2.1: 63.8%** \u2014 behind GLM 5.2 (82.7%) and GPT-5.6 Sol (89.5%). (Source: Thinking Machines Lab)",
    "- **GPQA Diamond: 87.2%**; **AIME 2026: 97.1%**. (Source: Thinking Machines Lab)"
   ]
  },
  {
   "url": "https://acingai.com/articles/open-source-llm-power-shift",
   "title": "The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
   "published_at": "2026-04-13T20:46:30",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03",
   "parameters": "119B MoE activating just 6B per token",
   "context_window": null,
   "license": "permissive NVIDIA Open Model License",
   "significance": "A 119B MoE combining instruction following, reasoning, vision, and coding in a single model, punching well above its active parameter count.",
   "organization_evidence": [
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    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
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    "Mistral ships its biggest release yet with Mistral Large 3 (675B) in December 2025, followed by Mistral Small 4 in March 2026."
   ],
   "model_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "Late 2025-Early 2026: Qwen 3.5 and the maturation.",
    "Mistral ships its biggest release yet with Mistral Large 3 (675B) in December 2025, followed by Mistral Small 4 in March 2026."
   ],
   "release_date_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "Late 2025-Early 2026: Qwen 3.5 and the maturation.",
    "Mistral ships its biggest release yet with Mistral Large 3 (675B) in December 2025, followed by Mistral Small 4 in March 2026."
   ],
   "parameters_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "Mistral: Europe's Standard-Bearer",
    "**Mistral Small 4 (March 2026):** A 119B MoE activating just 6B per token, combining instruction following, reasoning, vision, and coding in a single model."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
    "All models support up to 1M token context and are optimized for NVIDIA's TensorRT-LLM inference stack, meaning they run best on the GPUs that already dominate the market.",
    "NVIDIA releases them under the Nemotron Open Model License, which is commercially permissive but requires attribution."
   ],
   "significance_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "Mistral: Europe's Standard-Bearer",
    "**Mistral Small 4 (March 2026):** A 119B MoE activating just 6B per token, combining instruction following, reasoning, vision, and coding in a single model. Punches well above its active parameter count."
   ]
  },
  {
   "url": "https://acingai.com/articles/open-source-llm-power-shift",
   "title": "The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
   "published_at": "2026-04-13T20:46:30",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Super",
   "release_date": "2026",
   "parameters": "120B total, 12B active per token",
   "context_window": "up to 1M token context",
   "license": "permissive NVIDIA Open Model License",
   "significance": "A high-throughput reasoning model aimed at multi-agent workflows, delivering up to 7.5x faster inference than Qwen3.5-122B, holding up remarkably well at 1M token contexts.",
   "organization_evidence": [
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   ],
   "model_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
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   ],
   "release_date_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
    "**Nemotron 3 Super** (120B total, 12B active per token) \u2014 released early 2026. A high-throughput reasoning model aimed at multi-agent workflows."
   ],
   "parameters_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
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    "**Nemotron 3 Super** (120B total, 12B active per token) \u2014 released early 2026. A high-throughput reasoning model aimed at multi-agent workflows."
   ],
   "context_window_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
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   ],
   "license_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
    "NVIDIA releases them under the Nemotron Open Model License, which is commercially permissive but requires attribution."
   ],
   "significance_evidence": [
    "AI Research & Industry",
    "# The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything",
    "NVIDIA Nemotron: The American Entry",
    "**Nemotron 3 Super** (120B total, 12B active per token) \u2014 released early 2026. A high-throughput reasoning model aimed at multi-agent workflows.",
    "Where Nemotron does shine is throughput and long-context handling; Super delivers up to 7.5x faster inference than Qwen3.5-122B, and holds up remarkably well at 1M token contexts."
   ]
  },
  {
   "url": "https://pondero.ai/news/2026-07-08-leanstral-1-5",
   "title": "Mistral releases Leanstral 1.5, an open formal-verification model that solved 587 of 672 Putnam math problems",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": "119-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "saturated miniF2F, solved 587 of 672 PutnamBench problems, and found five previously unknown bugs in production code across 57 repositories",
   "organization_evidence": [
    "## Mistral releases Leanstral 1.5, an open formal-verification model that solved 587 of 672 Putnam math problems",
    "Mistral AI released Leanstral 1.5 on July 2, a 119-billion-parameter open model for Lean 4 formal verification that saturated miniF2F, solved 587 of 672 PutnamBench problems, and found five previously unknown bugs in production code across 57 repositories."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral AI released Leanstral 1.5 on July 2, a 119-billion-parameter open model for Lean 4 formal verification that saturated miniF2F, solved 587 of 672 PutnamBench problems, and found five previously unknown bugs in production code across 57 repositories.",
    "- [Leanstral 1.5: Proof Abundance for All](https://mistral.ai/news/leanstral-1-5/?utm_source=pondero.ai&utm_medium=referral&utm_campaign=2026-07-08-leanstral-1-5) - Mistral AI, July 2, 2026",
    "## Mistral releases Leanstral 1.5, an open formal-verification model that solved 587 of 672 Putnam math problems"
   ],
   "parameters_evidence": [
    "Mistral AI released Leanstral 1.5 on July 2, a 119-billion-parameter open model for Lean 4 formal verification that saturated miniF2F, solved 587 of 672 PutnamBench problems, and found five previously unknown bugs in production code across 57 repositories.",
    "## Mistral releases Leanstral 1.5, an open formal-verification model that solved 587 of 672 Putnam math problems"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral AI released Leanstral 1.5 on July 2, a 119-billion-parameter open model for Lean 4 formal verification that saturated miniF2F, solved 587 of 672 PutnamBench problems, and found five previously unknown bugs in production code across 57 repositories.",
    "## Mistral releases Leanstral 1.5, an open formal-verification model that solved 587 of 672 Putnam math problems"
   ]
  },
  {
   "url": "https://gigazine.net/gsc_news/en/20260403-google-released-gemma-4",
   "title": "Google announces open AI model 'Gemma 4' and changes its license to Apache 2.0.",
   "published_at": "2026-04-03T02:18:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Based on similar technologies to Gemini 3, this model is specifically designed for advanced inference and autonomous agent capabilities.",
   "organization_evidence": [
    "# Google announces open AI model 'Gemma 4' and changes its license to Apache 2.0.",
    "Google released **Gemma 4** , a suite of open models, on April 2, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google announces open AI model 'Gemma 4' and changes its license to Apache 2.0.",
    "Google released **Gemma 4** , a suite of open models, on April 2, 2026."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google announces open AI model 'Gemma 4' and changes its license to Apache 2.0.",
    "Google released **Gemma 4** , a suite of open models, on April 2, 2026. Based on similar technologies to Gemini 3, this model is specifically designed for advanced inference and autonomous agent capabilities."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/moonshot-ai-just-open-sourced-its-most-powerful-mo-719519e5",
   "title": "Moonshot AI Just Open-Sourced Its Most Powerful Model,Here's Why That Matters",
   "published_at": "2026-07-28T05:37:34",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Kimi K3 is a 2.8-trillion-parameter Mixture-of-Experts (MoE) model that outperforms Anthropic's Claude Fable 5 on several coding-specific tests, including Terminal-Bench 2.1, SWE-Bench, and SWE-Marathon.",
   "organization_evidence": [
    "# Moonshot AI Just Open-Sourced Its Most Powerful Model,Here's Why That Matters",
    "Moonshot AI has released the complete weights and infrastructure for Kimi K3, its most capable AI model to date, making it freely available for developers to download, customize, and run on their own systems."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI Just Open-Sourced Its Most Powerful Model,Here's Why That Matters",
    "According to Moonshot's technical benchmarks, Kimi K3 outperforms Anthropic's Claude Fable 5 on several coding-specific tests, including Terminal-Bench 2.1, SWE-Bench, and SWE-Marathon."
   ]
  },
  {
   "url": "https://felloai.com/ja/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": null,
   "license": null,
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/ja/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://felloai.com/ja/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": null,
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "parameters_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes."
   ]
  },
  {
   "url": "https://llm-stats.com/models/minimax-m3",
   "title": "MiniMax M3 Benchmarks, Pricing & Context Window",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 is the first open-weight model to combine three frontier capabilities: top-tier coding and agentic performance, a 1M-token context window, and native multimodality.",
   "organization_evidence": [
    "MiniMax M3 is a language model from MiniMax, released in June 2026, with multimodal input.",
    "MiniMax M3: Benchmarks, Pricing & Context Window",
    "MiniMax M3 was released on June 1, 2026 by MiniMax."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released on Jun 1, 2026",
    "MiniMax M3: Benchmarks, Pricing & Context Window",
    "MiniMax M3 was released on June 1, 2026 by MiniMax."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 is the first open-weight model to combine three frontier capabilities: top-tier coding and agentic performance, a 1M-token context window, and native multimodality.",
    "MiniMax M3: Benchmarks, Pricing & Context Window"
   ]
  },
  {
   "url": "https://insideai.news/news/generative-ai/thinking-machines-launches-inkling-an-open-weight-ai-model-from-the-us/4503",
   "title": "Thinking Machines Launches Inkling: An Open-Weight AI Model from the US",
   "published_at": "2026-07-16T15:55:10",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "975 billion",
   "context_window": null,
   "license": null,
   "significance": "975-billion-parameter large language model that lets users dial thinking effort up or down, supports one million tokens context window, and is available on HuggingFace under an open-source license.",
   "organization_evidence": [
    "Thinking Machines Launches Inkling: An Open-Weight AI Model from the US",
    "Thinking Machines Lab, founded by ex-OpenAI CTO Mira Murati, has released Inkling, its first open-weight AI model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Thinking Machines Launches Inkling: An Open-Weight AI Model from the US",
    "By Shamil Khan Published on: July 16, 2026"
   ],
   "parameters_evidence": [
    "Thinking Machines Launches Inkling: An Open-Weight AI Model from the US",
    "With 975 billion parameters and a unique controllable thinking feature, it targets enterprises wanting to fine-tune their own AI rather than rely on closed, one-size-fits-all systems."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Thinking Machines Launches Inkling: An Open-Weight AI Model from the US",
    "Called **Inkling**, it is an open-weight, 975-billion-parameter large language model that lets users dial thinking effort up or down."
   ]
  },
  {
   "url": "https://ragyfied.com/articles/meta-llama-5-released",
   "title": "Meta Releases Llama 5: 600B Parameters, 5M Token Context, and Recursive Self-Improvement",
   "published_at": "2026-04-11T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "600B+",
   "context_window": null,
   "license": null,
   "significance": "Matches or exceeds OpenAI's GPT-5 and Google's Gemini 2.0 on major benchmarks; introduces recursive self-improvement",
   "organization_evidence": [
    "Meta Releases Llama 5: 600B Parameters, 5M Token Context, and Recursive Self-Improvement",
    "Meta released Llama 5 on April 8, 2026, and it's the most direct challenge to closed-source frontier models that the open-source AI ecosystem has produced."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta Releases Llama 5: 600B Parameters, 5M Token Context, and Recursive Self-Improvement",
    "Meta released Llama 5 on April 8, 2026, and it's the most direct challenge to closed-source frontier models that the open-source AI ecosystem has produced."
   ],
   "parameters_evidence": [
    "Meta Releases Llama 5: 600B Parameters, 5M Token Context, and Recursive Self-Improvement",
    "At 600 billion parameters with a 5 million token context window, it matches or exceeds OpenAI's GPT-5 and Google's Gemini 2.0 on major benchmarks \u2014 and it's fully open-weight.",
    "| **Parameters** | 600B+ |",
    "| Spec | Llama 5 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Releases Llama 5: 600B Parameters, 5M Token Context, and Recursive Self-Improvement",
    "Meta's Llama 5 matches frontier closed-source models on benchmarks, ships with a 5 million token context window, and introduces recursive self-improvement \u2014 a first for an open-weight model.",
    "At 600 billion parameters with a 5 million token context window, it matches or exceeds OpenAI's GPT-5 and Google's Gemini 2.0 on major benchmarks \u2014 and it's fully open-weight.",
    "| **Benchmark vs GPT-5** | Matches or exceeds |",
    "| **Benchmark vs Gemini 2.0** | Matches or exceeds |",
    "| **Key capability** | Recursive Self-Improvement |",
    "| Spec | Llama 5 |"
   ]
  },
  {
   "url": "https://loopjar.ai/blog/gemma-4-local-llm-complete-guide",
   "title": "Gemma 4: Google's Open-Weight Powerhouse and How to Run It Locally",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3B to 31B",
   "context_window": null,
   "license": null,
   "significance": "A 31B dense model that ranks #3 among all open models on the Arena leaderboard, beating competitors with over 400 billion parameters, and native multimodality across all sizes.",
   "organization_evidence": [
    "# Gemma 4: Google's Open-Weight Powerhouse and How to Run It Locally",
    "On April 2, 2026, Google DeepMind quietly dropped the most consequential open-weight model release of the year."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI & Development",
    "# Gemma 4: Google's Open-Weight Powerhouse and How to Run It Locally",
    "On April 2, 2026, Google DeepMind quietly dropped the most consequential open-weight model release of the year."
   ],
   "parameters_evidence": [
    "AI & Development",
    "# Gemma 4: Google's Open-Weight Powerhouse and How to Run It Locally",
    "Meet **Gemma 4** \u2014 a four-model family ranging from a 2.3B model that runs on your phone to a 31B dense model that ranks #3 among all open models on the Arena leaderboard, beating competitors with over 400 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI & Development",
    "# Gemma 4: Google's Open-Weight Powerhouse and How to Run It Locally",
    "Google's Gemma 4 just dropped \u2014 Apache 2.0, natively multimodal, a 31B model beating 400B+ rivals, and running on a laptop.",
    "Meet **Gemma 4** \u2014 a four-model family ranging from a 2.3B model that runs on your phone to a 31B dense model that ranks #3 among all open models on the Arena leaderboard, beating competitors with over 400 billion parameters."
   ]
  },
  {
   "url": "https://codersera.com/blog/kimi-k3-complete-guide-2026/amp",
   "title": "Kimi K3: Specs, Pricing & Release (2026)",
   "published_at": "2026-07-17T18:05:59",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "At 2.8 trillion parameters it is the largest open-weight model released so far, and on independent testing it lands fourth among all frontier models \u2014 trailing only Claude Fable 5 and GPT-5.6 Sol, and edging past Claude Opus 4.8.",
   "organization_evidence": [
    "# Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
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   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
    "At 2.8 trillion parameters it is the largest open-weight model released so far, and on independent testing it lands fourth among all frontier models \u2014 trailing only Claude Fable 5 and GPT-5.6 Sol, and edging past Claude Opus 4.8."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/olmo-3-hybrid-tokenizer-think-dev",
   "title": "allenai/olmo-3-hybrid-tokenizer-think-dev \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": null,
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   "license": null,
   "significance": "OLMo-3 Hybrid Tokenizer Think-Dev is an experimental language model from Allen AI that combines a 'hybrid' tokenizer (likely mixing multiple tokenisation schemes) with a chain-of-thought ('think') training regime aimed at developer/programming tasks.",
   "organization_evidence": [
    "OLMo-3 Hybrid Tokenizer Think-Dev is an experimental language model from Allen AI that combines a 'hybrid' tokenizer (likely mixing multiple tokenisation schemes) with a chain-of-thought ('think') training regime aimed at developer/programming tasks.",
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   "model_evidence": null,
   "release_date_evidence": [
    "**Created:** 2026-04-12 | **Updated:** 2026-04-13",
    "Modelers",
    "allenai / olmo-3-hybrid-tokenizer-think-dev"
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   "license_evidence": null,
   "significance_evidence": [
    "OLMo-3 Hybrid Tokenizer Think-Dev is an experimental language model from Allen AI that combines a 'hybrid' tokenizer (likely mixing multiple tokenisation schemes) with a chain-of-thought ('think') training regime aimed at developer/programming tasks.",
    "Modelers",
    "allenai / olmo-3-hybrid-tokenizer-think-dev"
   ]
  },
  {
   "url": "https://techpillow.co/blog/deepseek-v4-flash-0731-agentic-coding-open-weight",
   "title": "DeepSeek V4 Flash 0731: Open-Weight MoE Beats Bigger Model",
   "published_at": "2026-08-05T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "284-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Outperforms the larger DeepSeek-V4-Pro (Preview) on every agentic benchmark the company has published.",
   "organization_evidence": [
    "DeepSeek V4 Flash 0731: How Better Training Beat a Bigger Model",
    "On 31 July 2026, DeepSeek released DeepSeek-V4-Flash-0731, publishing the weights on Hugging Face under an MIT licence and moving the official V4-Flash API into public beta."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Flash 0731: How Better Training Beat a Bigger Model",
    "On 31 July 2026, DeepSeek released DeepSeek-V4-Flash-0731, publishing the weights on Hugging Face under an MIT licence and moving the official V4-Flash API into public beta."
   ],
   "parameters_evidence": [
    "DeepSeek V4 Flash 0731: How Better Training Beat a Bigger Model",
    "The model is a 284-billion-parameter sparse Mixture-of-Experts architecture that activates 13 billion parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark Results: Outperforming V4-Pro",
    "DeepSeek reports that V4 Flash 0731 surpasses the larger DeepSeek-V4-Pro (Preview) on every agentic benchmark the company has published."
   ]
  },
  {
   "url": "https://awesomeagents.ai/news/alibaba-qwen36-max-closed-weights",
   "title": "Alibaba's Qwen3.6-Max Ships Closed - Tops Six Coding Evals",
   "published_at": "2026-04-21T12:03:57",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Fully open-source on Hugging Face under Apache 2.0, running on consumer hardware with 24GB of RAM.",
   "organization_evidence": [
    "Qwen has been one of the most aggressively open families in the frontier model space.",
    "[Qwen3.6-35B-A3B, released April 16](/news/alibaba-qwen3-6-plus-enterprise-agentic-ai/), is still fully open-source on Hugging Face under Apache 2.0 and runs on consumer hardware with 24GB of RAM."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[Qwen3.6-35B-A3B, released April 16](/news/alibaba-qwen3-6-plus-enterprise-agentic-ai/), is still fully open-source on Hugging Face under Apache 2.0 and runs on consumer hardware with 24GB of RAM."
   ],
   "parameters_evidence": [
    "[Qwen3.6-35B-A3B, released April 16](/news/alibaba-qwen3-6-plus-enterprise-agentic-ai/), is still fully open-source on Hugging Face under Apache 2.0 and runs on consumer hardware with 24GB of RAM."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "[Qwen3.6-35B-A3B, released April 16](/news/alibaba-qwen3-6-plus-enterprise-agentic-ai/), is still fully open-source on Hugging Face under Apache 2.0 and runs on consumer hardware with 24GB of RAM."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/google-gemma-4-31b-beats-400b-rivals-at-zero-license-cost",
   "title": "Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
   "published_at": "2026-05-30T17:03:00",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Scores 89.2% on AIME 2026, beating Llama 4 across math, code, and agentic tasks; #3 on Arena AI text leaderboard",
   "organization_evidence": [
    "Model Release",
    "# Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
    "Google Gemma 4 31B scores 89.2% on AIME 2026 and beats Llama 4 across math, code, and agentic tasks, all under a free Apache 2.0 open license."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Model Release",
    "# Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
    "Google Gemma 4 31B scores 89.2% on AIME 2026 and beats Llama 4 across math, code, and agentic tasks, all under a free Apache 2.0 open license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
    "Google Gemma 4 31B scores 89.2% on AIME 2026 and beats Llama 4 across math, code, and agentic tasks, all under a free Apache 2.0 open license."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/google-gemma-4-31b-beats-400b-rivals-at-zero-license-cost",
   "title": "Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
   "published_at": "2026-05-30T17:03:00",
   "organization": "Google",
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   "release_date": "2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A family of four open-weight models built on the same research stack as Gemini 3, spanning phone-grade to workstation-grade sizes.",
   "organization_evidence": [
    "Model Release",
    "## What Actually Happened",
    "Google DeepMind released **Gemma 4**, a family of four open-weight models built on the same research stack as Gemini 3 and shipped under the permissive **Apache 2.0** license."
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   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# Google Gemma 4 31B Beats 400B Rivals at Zero License Cost",
    "Google Gemma 4 31B scores 89.2% on AIME 2026 and beats Llama 4 across math, code, and agentic tasks, all under a free Apache 2.0 open license."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Model Release",
    "## What Actually Happened",
    "Google DeepMind released **Gemma 4**, a family of four open-weight models built on the same research stack as Gemini 3 and shipped under the permissive **Apache 2.0** license."
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  },
  {
   "url": "https://wpnews.pro/news/what-is-kimi-k3-a-complete-developer-guide-for-2026",
   "title": "What Is Kimi K3? A Complete Developer Guide for 2026",
   "published_at": "2026-08-13T18:39:52",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-27",
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   "significance": "largest open-weight model as of August 2026, with benchmarks matching proprietary models like GPT-5.6 Sol and Claude Fable 5",
   "organization_evidence": [
    "What Is Kimi K3? A Complete Developer Guide for 2026",
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   "release_date_evidence": [
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   "parameters_evidence": [
    "What Is Kimi K3? A Complete Developer Guide for 2026",
    "Moonshot AI released Kimi K3, a 2.8-trillion-parameter open-weight model, on July 27, 2026, making it the largest open-weight model as of August 2026."
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   "context_window_evidence": null,
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    "What Is Kimi K3? A Complete Developer Guide for 2026",
    "Moonshot AI released Kimi K3, a 2.8-trillion-parameter open-weight model, on July 27, 2026, making it the largest open-weight model as of August 2026."
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  },
  {
   "url": "https://bota.chat/z-ai/glm-5-2",
   "title": "GLM-5.2: 62.1% SWE-bench, 1M Context, MIT Open Model",
   "published_at": "2026-07-18T19:33:43",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": "753B",
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   "significance": "Scores 62.1% on SWE-bench Pro, 74.4% on FrontierSWE; 99.2% on AIME 2026 math",
   "organization_evidence": [
    "# GLM-5.2: Z.ai\u2019s Open-Weight Coding Model with 1M-Token Context Explained",
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   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2: Z.ai\u2019s Open-Weight Coding Model with 1M-Token Context Explained",
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   "parameters_evidence": [
    "# GLM-5.2: Z.ai\u2019s Open-Weight Coding Model with 1M-Token Context Explained",
    "It is built on a 753-billion-parameter Mixture-of-Experts architecture, scores 62.1% on SWE-bench Pro, and undercuts GPT-5.5 by roughly one-sixth the API cost."
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   "context_window_evidence": null,
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    "# GLM-5.2: Z.ai\u2019s Open-Weight Coding Model with 1M-Token Context Explained",
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  },
  {
   "url": "https://digitalapplied.com/blog/open-weight-model-wave-july-2026-momentum-tracker",
   "title": "July 2026 Open-Weight Wave: K3, Inkling and the New Gap",
   "published_at": "2026-07-16T23:00:00",
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   "release_date_evidence": [
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   "context_window_evidence": null,
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  },
  {
   "url": "https://aiproductivity.ai/news/mistral-medium-3-5-128b-open-weight-hugging-face",
   "title": "Mistral Releases 128-Billion Parameter Open-Weight Model on Hugging Face",
   "published_at": "2026-04-29T15:14:40",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
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   "significance": "128 billion parameters open-weight model",
   "organization_evidence": [
    "Open Source Notable",
    "# Mistral Releases 128-Billion Parameter Open-Weight Model on Hugging Face",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source Notable",
    "# Mistral Releases 128-Billion Parameter Open-Weight Model on Hugging Face",
    "April 29, 20261 min read"
   ],
   "parameters_evidence": [
    "Open Source Notable",
    "# Mistral Releases 128-Billion Parameter Open-Weight Model on Hugging Face",
    "128 billion parameters. That's what Mistral just made freely available with the [release of Mistral-Medium-3.5-128B on Hugging Face](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B), an open-weight model anyone can download and run."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Open Source Notable",
    "# Mistral Releases 128-Billion Parameter Open-Weight Model on Hugging Face",
    "128 billion parameters. That's what Mistral just made freely available with the [release of Mistral-Medium-3.5-128B on Hugging Face](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B), an open-weight model anyone can download and run."
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  },
  {
   "url": "https://cloudinsight.cc/blog/gemma-4-guide",
   "title": "Gemma 4 Complete Guide: The Most Powerful Open Source Model of 2026",
   "published_at": "2026-04-06T00:00:00",
   "organization": "Google DeepMind",
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   "release_date": "2026-04-02",
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   "significance": "AIME math reasoning jumping from 20.8% with Gemma 3 to 89.2%? That's not incremental improvement \u2014 that's a generational leap.",
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    "Gemma 4 Complete Guide: The Most Powerful Open Source Model of 2026",
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   "release_date_evidence": [
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    "Gemma 4 Complete Guide: The Most Powerful Open Source Model of 2026",
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  },
  {
   "url": "https://codersera.com/blog/kimi-k3-complete-guide-2026",
   "title": "Kimi K3: Specs, Pricing & Release (2026)",
   "published_at": "2026-07-17T18:05:59",
   "organization": "Moonshot AI",
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   "significance": "At 2.8 trillion parameters it is the largest open-weight model released so far, and on independent testing it lands fourth among all frontier models \u2014 trailing only Claude Fable 5 and GPT-5.6 Sol, and edging past Claude Opus 4.8.",
   "organization_evidence": [
    "Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
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   "release_date_evidence": [
    "Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
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    "| Released | July 16, 2026 |",
    "Kimi K3 key specifications"
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   "parameters_evidence": [
    "Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
    "Kimi K3 is Moonshot AI\u2019s 2.8-trillion-parameter open-weight model, released July 2026.",
    "| Total parameters | 2.8 trillion |",
    "Kimi K3 key specifications"
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   "significance_evidence": [
    "Kimi K3: Moonshot AI\u2019s 2.8T Open-Weight Model \u2014 Release, Specs & Pricing (2026)",
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  },
  {
   "url": "https://blockchain-council.org/ai/what-is-glm-5-2",
   "title": "What Is GLM 5.2?",
   "published_at": "2026-06-29T18:00:01",
   "organization": "Zhipu AI",
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   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter",
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   "license": null,
   "significance": "Scored 62.1 on SWE-bench Pro decisively beating GPT-5.5's score of 58.6 and its own predecessor GLM-5.1's 58.4.",
   "organization_evidence": [
    "What Is GLM 5.2?",
    "On June 13, 2026, Z.ai the international brand of Chinese AI lab Zhipu AI released **GLM 5.2**, the latest flagship in its General Language Model series."
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   "model_evidence": null,
   "release_date_evidence": [
    "What Is GLM 5.2?",
    "On June 13, 2026, Z.ai the international brand of Chinese AI lab Zhipu AI released **GLM 5.2**, the latest flagship in its General Language Model series."
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   "parameters_evidence": [
    "What Is GLM 5.2?",
    "It is the third major release in the GLM-5 model family, following GLM-5 and GLM-5.1, and is built on the same 744-billion-parameter Mixture-of-Experts architecture as its predecessors with meaningful architectural improvements that substantially advance its performance on long-horizon coding and agentic tasks."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "What Is GLM 5.2?",
    "Within days of release, benchmark data confirmed what developers had observed in practice."
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  },
  {
   "url": "https://promptslove.com/blog/minimax-m3-review",
   "title": "MiniMax M3 Review: I Built 5 Apps in One Go and Here Is What Happened",
   "published_at": "2026-06-02T16:13:19",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 launched June 1, 2026, and it is the first open-weight model combining frontier-level coding, a 1-million-token context window, and native multimodality in a single package.",
   "organization_evidence": [
    "MiniMax M3 Review: I Built 5 Apps in One Go and Here Is What Happened"
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   "release_date_evidence": [
    "MiniMax M3 Review: I Built 5 Apps in One Go and Here Is What Happened"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Review: I Built 5 Apps in One Go and Here Is What Happened",
    "## Key Takeaways",
    "MiniMax M3 launched June 1, 2026, and it is the first open-weight model combining frontier-level coding, a 1-million-token context window, and native multimodality in a single package."
   ]
  },
  {
   "url": "https://aigetfree.com/deepseek-v4-pro-review",
   "title": "DeepSeek V4 Pro Review 2026 \u2014 The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks - AIGetFree",
   "published_at": "2026-07-22T08:40:07",
   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
   "parameters": "1.6 trillion",
   "context_window": null,
   "license": null,
   "significance": "Ties or beats GPT-5.6, Claude Opus 4.8, and Gemini 3.1 Pro on coding benchmarks; scores 80.6% on SWE-bench Verified, 93.5% on LiveCodeBench, and 3,206 on Codeforces.",
   "organization_evidence": [
    "DeepSeek V4 Pro Review 2026 \u2014 The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks",
    "On April 24, 2026, DeepSeek released V4 Pro \u2014 a 1.6T-parameter open-weight model that ties or beats GPT-5.6, Claude Opus 4.8, and Gemini 3.1 Pro on coding benchmarks, at roughly one-thirtieth the price."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Pro Review 2026 \u2014 The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks",
    "On April 24, 2026, DeepSeek released V4 Pro \u2014 a 1.6T-parameter open-weight model that ties or beats GPT-5.6, Claude Opus 4.8, and Gemini 3.1 Pro on coding benchmarks, at roughly one-thirtieth the price."
   ],
   "parameters_evidence": [
    "DeepSeek V4 Pro Review 2026 \u2014 The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks",
    "On April 24, 2026, DeepSeek released V4 Pro \u2014 a 1.6T-parameter open-weight model that ties or beats GPT-5.6, Claude Opus 4.8, and Gemini 3.1 Pro on coding benchmarks, at roughly one-thirtieth the price.",
    "It's a **1.6 trillion parameter Mixture-of-Experts model** that activates only 49 billion parameters per token \u2014 the knowledge capacity of a giant model at the inference cost of a much smaller one."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "DeepSeek V4 Pro Review 2026 \u2014 The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks",
    "On April 24, 2026, DeepSeek released V4 Pro \u2014 a 1.6T-parameter open-weight model that ties or beats GPT-5.6, Claude Opus 4.8, and Gemini 3.1 Pro on coding benchmarks, at roughly one-thirtieth the price."
   ]
  },
  {
   "url": "https://nextsoftcorp.com/en/blog/meta-llama-4-open-source-ai-2026-en",
   "title": "Meta Launches Llama 4: The Open Source Model That Shifts the AI Power Balance",
   "published_at": "2026-05-08T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-05",
   "parameters": "400-billion-parameter",
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   "license": null,
   "significance": "Outperformed GPT-5.4 and Claude Opus 4.6 on eight of twelve Stanford HELM benchmarks.",
   "organization_evidence": [
    "Meta published **Llama 4** \u2014 a 400-billion-parameter model available under a license that allows commercial use without scale restrictions."
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   "model_evidence": null,
   "release_date_evidence": [
    "The premise that the best AI models could only be proprietary shattered in May 2026."
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   "parameters_evidence": [
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   "context_window_evidence": null,
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   "significance_evidence": [
    "The numbers spoke for themselves: Llama 4 outperformed GPT-5.4 and Claude Opus 4.6 on eight of twelve Stanford HELM benchmarks."
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  },
  {
   "url": "https://dev.to/damogallagher/mistral-medium-35-is-an-open-weights-coding-and-agent-model-with-a-256k-context-window-4knh",
   "title": "Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
   "published_at": "2026-06-20T03:22:02",
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Reported 77.6% on SWE-bench Verified and 91.4 on \u0307\u0003-Telecom, with support for self-hosting on as few as four GPUs and a vision encoder trained from scratch.",
   "organization_evidence": [
    "# Mistral Medium 3.5 is an open-weights coding and agent model with a 256k context window",
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What Mistral announced",
    "Mistral Medium 3.5",
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    "Mistral says Medium 3.5 combines instruction following, reasoning, and coding in one model, with:",
    "- Reported **77.6% on SWE-bench Verified**",
    "- Reported **91.4 on \u03c4\u00b3-Telecom**, a tool/agent benchmark",
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    "A vision encoder trained from scratch for variable image sizes and aspect ratios"
   ]
  },
  {
   "url": "https://nativeai.agency/hub/tools/gemma",
   "title": "Open Weight LLMs from E2B to 31B (2026 Guide)",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04",
   "parameters": "E2B, E4B, 26B, and 31B",
   "context_window": null,
   "license": null,
   "significance": "Arena AI rating of 1452, ranking as the #3 open model in the world on the Arena AI text leaderboard",
   "organization_evidence": [
    "# Google Gemma",
    "Google DeepMind's open weight LLM family built from the same research as Gemini, now in its fourth generation with sizes from E2B to 31B parameters.",
    "Gemma is a family of open weight large language models by Google DeepMind."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma",
    "Open Source \u00b7 Updated 1 Jun 2026",
    "The current generation is Gemma 4 (released April 2026), built from Gemini 3 research, purpose built for advanced reasoning and agentic workflows."
   ],
   "parameters_evidence": [
    "# Google Gemma",
    "Google DeepMind's open weight LLM family built from the same research as Gemini, now in its fourth generation with sizes from E2B to 31B parameters.",
    "Gemma 4 is available in four sizes: E2B, E4B, 26B (Mixture of Experts with 4B active parameters), and 31B (Dense)."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma",
    "The Gemma 4 31B model achieved an Arena AI rating of 1452, ranking as the #3 open model in the world on the Arena AI text leaderboard."
   ]
  },
  {
   "url": "https://superframeworks.com/articles/kimi-k3-open-source-frontier-gap",
   "title": "Kimi K3 Just Dropped \u2014 And Open Source Is Now 3 Points From the Frontier",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Scores 57 on the Artificial Analysis Intelligence Index (three points behind Claude Fable 5, two behind GPT-5.6 Sol), beats last-gen flagships like Claude Opus 4.8 on most of Moonshot's benchmarks, places top-three across six coding benchmarks, and leads SWE Marathon and Program Bench outright.",
   "organization_evidence": [
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    "Moonshot AI's Kimi K3 launched July 16 with 2.8 trillion parameters and a 1M-token context window \u2014 the largest open-weights model ever, with weights promised by July 27"
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   "model_evidence": null,
   "release_date_evidence": [
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    "On July 16, Moonshot AI released **Kimi K3** \u2014 a 2.8-trillion-parameter model that lands within **three points of Claude Fable 5**, the strongest model on the planet, on the Artificial Analysis Intelligence Index."
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   "parameters_evidence": [
    "Kimi K3 Just Dropped \u2014 And Open Source Is Now 3 Points From the Frontier",
    "Moonshot AI's Kimi K3 is a 2.8-trillion-parameter open model scoring within 3 points of Claude Fable 5.",
    "Moonshot AI's Kimi K3 launched July 16 with 2.8 trillion parameters and a 1M-token context window \u2014 the largest open-weights model ever, with weights promised by July 27"
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    "Moonshot AI's Kimi K3 is a 2.8-trillion-parameter open model scoring within 3 points of Claude Fable 5.",
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    "K3 scores **57** on the Artificial Analysis Intelligence Index, behind only Claude Fable 5 (60) and GPT-5.6 Sol (59)."
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  {
   "url": "https://unbiasedheadlines.com/article/chinas-zhipu-glm-52-closes-to-within-1-point-of-anthropics-best-model-at-one-fifth-the-cost",
   "title": "China's Zhipu GLM 5.2 Closes to Within 1 Point of Anthropic's Best Model, at One-Fifth the Cost",
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    "Chinese AI startup Zhipu released GLM 5.2 last week, an open-source model that now sits within a single percentage point of Anthropic's Opus 4.8 on a leading agentic benchmark, according to CNBC."
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    "China's Zhipu GLM 5.2 Closes to Within 1 Point of Anthropic's Best Model, at One-Fifth the Cost",
    "Chinese AI startup Zhipu released GLM 5.2 last week, an open-source model that now sits within a single percentage point of Anthropic's Opus 4.8 on a leading agentic benchmark, according to CNBC."
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  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
   "published_at": "2026-05-20T17:50:12",
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  },
  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
   "published_at": "2026-05-20T17:50:12",
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    "The [27B](https://aimlapi.com/models/qwen-3-6-27b) is the variant most likely to become a workhorse across mid-sized deployments \u2014 large enough to handle genuinely complex language tasks, small enough to fit comfortably on a single 80GB A100 in BF16 or two consumer 24GB GPUs with quantization."
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  },
  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
   "published_at": "2026-05-20T17:50:12",
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   "context_window_evidence": null,
   "license_evidence": [
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  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
   "published_at": "2026-05-20T17:50:12",
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  },
  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
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   "context_window_evidence": [
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    "[Qwen3.6-Plus](https://aimlapi.com/models/qwen3-6-plus) occupies the sweet spot that most production teams will actually deploy: meaningfully more capable than Flash, meaningfully cheaper to run than the 72B or 235B configurations."
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  },
  {
   "url": "https://aimlapi.com/blog/qwen-3-6-series-alibabas-open-source-llm-revolution-in-2026",
   "title": "Qwen 3.6 Series: Alibaba's Open-Source LLM Revolution in 2026",
   "published_at": "2026-05-20T17:50:12",
   "organization": "Alibaba",
   "model": "Qwen3.6-Flash",
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    "Qwen3.6-Flash",
    "Built for speed without sacrificing coherence, [Qwen3.6-Flash](https://aimlapi.com/models/qwen3-6-flash) is the latency-optimized member of the family."
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   "license_evidence": [
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    "Qwen3.6-Flash",
    "Built for speed without sacrificing coherence, [Qwen3.6-Flash](https://aimlapi.com/models/qwen3-6-flash) is the latency-optimized member of the family."
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  },
  {
   "url": "https://deepseekai.guide/comparisons/deepseek-vs-mistral",
   "title": "DeepSeek vs Mistral: 2026 Pricing, Benchmarks, Verdict",
   "published_at": "2026-04-25T14:35:37",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DeepSeek V4-Flash achieves a 1,000,000-token context window with low cost, and DeepSeek V4-Pro achieves 80.6% on SWE-Bench Verified.",
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   "release_date_evidence": [
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    "| Released | 2026-04-24 | 2026-04-24 | 2025-05 | 2025-12 |"
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   "context_window_evidence": null,
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    "DeepSeek vs Mistral in 2026: Which AI Should You Actually Use?",
    "DeepSeek\u2019s announcement put V4-Pro at **80.6% on SWE-Bench Verified**, and the company also reported V4-Pro outperforming Claude on Terminal-Bench."
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  },
  {
   "url": "https://dapta.ai/blog-posts/ai-news-week-14-google-gemma-4",
   "title": "Google Launches Gemma 4, the Most Capable Open Model Yet",
   "published_at": "2026-04-08T06:35:46",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
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   "significance": "rank third on the Arena AI open model leaderboard, dramatic performance improvements over Gemma 3",
   "organization_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026, and it represents the most significant update to the company\u2019s open-weight AI model family to date."
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   "release_date_evidence": [
    "Google DeepMind released Gemma 4 on April 2, 2026, and it represents the most significant update to the company\u2019s open-weight AI model family to date."
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  {
   "url": "https://singularity.kiwi/kimi-k3-2-8-trillion-parameters-open-frontier-2026",
   "title": "Moonshot's Kimi K3 Cracks 2.8 Trillion Parameters \u2014 and the Open-Weight Gap Just Vanished",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-27",
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   "significance": "The world's first open 3T-class model, offering a 2.5x scaling efficiency improvement and competitive or first-place positioning on long-horizon coding and browsing tasks.",
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   "release_date_evidence": [
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   "context_window_evidence": null,
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  {
   "url": "https://remoteopenclaw.com/blog/glm-5-openclaw-setup-guide",
   "title": "GLM-5 on OpenClaw: Setup Guide, Benchmarks, and When to Use It",
   "published_at": "2026-05-07T02:12:38",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
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   "significance": "77.8% SWE-bench Verified, 92.7% AIME 2024, 91.2% HumanEval, 88.4% MMLU",
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    "| **Developer** | Zhipu AI (Z.ai) |"
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   "release_date_evidence": [
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    "| **SWE-bench Verified** | 77.8% | Top-tier for open models; competitive with Claude Sonnet 4 |",
    "| **AIME 2024** | 92.7% | Near-perfect on competition-level math |",
    "| **HumanEval** | 91.2% | Strong code generation from natural language |",
    "| **MMLU** | 88.4% | Broad knowledge coverage across 57 subjects |"
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  {
   "url": "https://business20channel.tv/diffusiongemma-nvidia-rtx-accelerates-googles-parallel-text-ai-model-11-june-2026",
   "title": "DiffusionGemma: NVIDIA RTX Accelerates Google's Parallel Text AI Model",
   "published_at": "2026-06-11T11:01:06",
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   "release_date": "2026-06-10",
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   "license": null,
   "significance": "Generates text in 256-token parallel blocks rather than one token at a time through diffusion, delivering 4x throughput compared to autoregressive equivalents in single-user workloads.",
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   "release_date_evidence": [
    "LONDON, June 11, 2026 \u2014 [Google DeepMind released DiffusionGemma](https://blogs.nvidia.com/blog/rtx-ai-garage-local-gemma-diffusion/) on June 10, a new open-weight language model that generates text through diffusion rather than sequential token prediction."
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   "significance_evidence": [
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    "LONDON, June 11, 2026 \u2014 [Google DeepMind released DiffusionGemma](https://blogs.nvidia.com/blog/rtx-ai-garage-local-gemma-diffusion/) on June 10, a new open-weight language model that generates text through diffusion rather than sequential token prediction."
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  {
   "url": "https://aichina.news/blog/radical-transparency-on-ascend-why-you-should-try-olmo-7b-on-modelers-fq5fh2",
   "title": "Radical Transparency on Ascend: Why You Should Try OLMo-7B on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-04-15T14:41:44",
   "organization": "Allen Institute for AI",
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   "release_date": "2026-04-15",
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   "significance": "A complete framework providing radical transparency including training data, code, and intermediate logs, optimized for Ascend hardware.",
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   "release_date_evidence": [
    "Radical Transparency on Ascend: Why You Should Try OLMo-7B on Modelers.cn",
    "models/allenai April 15, 2026 2 min read Auto-generated from Modelers"
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    "Radical Transparency on Ascend: Why You Should Try OLMo-7B on Modelers.cn",
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  },
  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B - 31B (dense/MoE)",
   "context_window": null,
   "license": null,
   "significance": "third on the Arena AI text leaderboard",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Gemma 4](https://deepmind.google/models/gemma/gemma-4/) | Google DeepMind | 2B - 31B (dense/MoE) | Apache 2.0 | On-device and edge deployment |"
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   "release_date_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Gemma 4: Google\u2019s Edge AI Powerhouse",
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   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Gemma 4](https://deepmind.google/models/gemma/gemma-4/) | Google DeepMind | 2B - 31B (dense/MoE) | Apache 2.0 | On-device and edge deployment |"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Gemma 4: Google\u2019s Edge AI Powerhouse",
    "**Model variants:** E2B (smartphones via Android AICore), E4B (edge devices), 26B MoE, 31B Dense flagship (third on the Arena AI text leaderboard)."
   ]
  },
  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Alibaba Cloud",
   "model": null,
   "release_date": "2026-02-01",
   "parameters": "0.8B - 397B MoE",
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   "license": null,
   "significance": "broadest in any open 2026 model",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Qwen 3.5](https://github.com/QwenLM/Qwen3.5) | Alibaba Cloud | 0.8B - 397B MoE | Apache 2.0 | Multilingual and multimodal tasks |"
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   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Qwen 3.5: The Multilingual Efficiency Leader",
    "Alibaba released Qwen 3.5 in phases across late February and early March 2026."
   ],
   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Qwen 3.5](https://github.com/QwenLM/Qwen3.5) | Alibaba Cloud | 0.8B - 397B MoE | Apache 2.0 | Multilingual and multimodal tasks |"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Qwen 3.5: The Multilingual Efficiency Leader",
    "**Key innovations:** Gated Delta Networks (high-throughput attention with minimal latency); Sparse MoE that activates only needed parameters; 201-language support (broadest in any open 2026 model); near-100% multimodal training efficiency."
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  },
  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": "109B - 400B MoE (17B active)",
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   "license": null,
   "significance": "10 million token context window unmatched by any other open model",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Llama 4](https://www.llama.com/models/llama-4/) | Meta | 109B - 400B MoE (17B active) | Llama Community | Long-context and multimodal workloads |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [Llama 4](https://www.llama.com/models/llama-4/) | Meta | 109B - 400B MoE (17B active) | Llama Community | Long-context and multimodal workloads |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Llama 4: Meta\u2019s Context Window Champion",
    "Llama 4 is Meta\u2019s first natively multimodal MoE open family, with the Scout variant offering a 10 million token context window unmatched by any other open model."
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  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "DeepSeek AI",
   "model": null,
   "release_date": null,
   "parameters": "685B MoE (37B active)",
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   "license": null,
   "significance": "The high-compute variant, DeepSeek-V3.2-Speciale, outperforms GPT-5 on several reasoning benchmarks and matches Gemini 3.0 Pro, making V3.2 the open-source model most likely to be considered a drop-in replacement for proprietary APIs.",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [DeepSeek V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) | DeepSeek AI | 685B MoE (37B active) | MIT | Reasoning and agentic applications |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [DeepSeek V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) | DeepSeek AI | 685B MoE (37B active) | MIT | Reasoning and agentic applications |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## DeepSeek V3.2: The Reasoning Benchmark King",
    "The high-compute variant, DeepSeek-V3.2-Speciale, outperforms GPT-5 on several reasoning benchmarks and matches Gemini 3.0 Pro, making V3.2 the open-source model most likely to be considered a drop-in replacement for proprietary APIs."
   ]
  },
  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-13",
   "parameters": "744B MoE (40B active)",
   "context_window": null,
   "license": null,
   "significance": "scoring 77.8% on SWE-bench Verified while trained entirely on Huawei Ascend chips without a single NVIDIA GPU",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [GLM-5](https://huggingface.co/zai-org/GLM-5) | Zhipu AI | 744B MoE (40B active) | MIT | Coding and systems engineering |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## GLM-5: The Coding Specialist",
    "Zhipu AI released GLM-5 on February 13, 2026 - a 744B parameter open model under MIT license."
   ],
   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [GLM-5](https://huggingface.co/zai-org/GLM-5) | Zhipu AI | 744B MoE (40B active) | MIT | Coding and systems engineering |"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## GLM-5: The Coding Specialist",
    "GLM-5 is the open-source coding leader of 2026, scoring 77.8% on SWE-bench Verified while trained entirely on Huawei Ascend chips without a single NVIDIA GPU."
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  },
  {
   "url": "https://aiproductivity.ai/blog/open-source-ai-models-comparison-2026",
   "title": "Best Open-Source AI Models 2026: 6 Free Picks Compared",
   "published_at": "2026-04-03T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": "229B MoE",
   "context_window": null,
   "license": null,
   "significance": "MiniMax M2.7 is the first 229B-parameter MoE model to participate in its own reinforcement learning, opening a new path for self-improving open-source agents.",
   "organization_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) | MiniMax | 229B MoE | Custom (non-commercial) | Self-improving agent workflows |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## MiniMax M2.7: The Self-Evolving Wildcard",
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   "parameters_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## Quick Comparison: Open Source AI Models at a Glance",
    "| Model | Developer | Parameters | License | Best For |",
    "| [MiniMax M2.7](https://www.minimax.io/news/minimax-m27-en) | MiniMax | 229B MoE | Custom (non-commercial) | Self-improving agent workflows |"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Best Open-Source AI Models 2026: 6 Free Picks Compared",
    "## MiniMax M2.7: The Self-Evolving Wildcard",
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  },
  {
   "url": "https://aipedia.wiki/news/2026-04-16-qwen-3-6-35b-a3b-release",
   "title": "Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
   "published_at": "2026-04-16T00:25:51",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
   "release_date": "2026-04-16",
   "parameters": "35B total parameters, only 3B activated per token",
   "context_window": "262,144 native, 1,010,000 extensible via YaRN",
   "license": "Apache 2.0",
   "significance": "Aggregate benchmarks trail Claude Opus 4.7 by roughly 18 points (77 vs 94) but close significantly on knowledge tasks; the gap widens on agentic + MCP tool use where Opus still leads.",
   "organization_evidence": [
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   "model_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
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   "release_date_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
    "Alibaba's Qwen team released Qwen3.6-35B-A3B on April 16, 2026 under Apache 2.0."
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   "parameters_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
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    "## What\u2019s actually in it",
    "- Total parameters: 35B"
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   "context_window_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
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    "## What\u2019s actually in it",
    "- Context: 262,144 native, 1,010,000 extensible via YaRN"
   ],
   "license_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
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   "significance_evidence": [
    "NewsArticleAI Industry News",
    "# Alibaba Open-Sources Qwen3.6-35B-A3B, A Sparse MoE With Only 3B Active Params",
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  },
  {
   "url": "https://tpsreport.news/news/mistral-shieldstral-3b-safety-classifier",
   "title": "Mistral Releases Shieldstral, a 3B Open-Weights Safety Classifier That Matches Models 7x Its Size",
   "published_at": "2026-08-04T14:06:36",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "3B",
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   "significance": "matches or outperforms guard models up to 7x its size on text safety and multimodal benchmarks",
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   "release_date_evidence": [
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    "model release",
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    "Mistral released Shieldstral on August 4, 2026, a 3-billion-parameter open-weights safety classifier for text and image content moderation."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "model release",
    "Mistral Releases Shieldstral, a 3B Open-Weights Safety Classifier That Matches Models 7x Its Size",
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  },
  {
   "url": "https://wowhow.cloud/blogs/google-gemma-4-apache-2-open-weight-model-developer-guide-2026",
   "title": "Google Gemma 4: Apache 2.0 Open Models That Run on Your Laptop (2026)",
   "published_at": "2026-04-04T14:30:00",
   "organization": "Google DeepMind",
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   "release_date": "2026-04-02",
   "parameters": "2.3B effective, 4.5B effective, 26B total (3.8B active per token), 31B",
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   "license": null,
   "significance": "Gemma 4\u2019s 31B Dense model ranks third globally among open-weight models despite being a fraction of the size of Llama 4 Maverick (400B total parameters).",
   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
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   "parameters_evidence": [
    "Google Gemma 4 launches April 2026 under Apache 2.0 4 model sizes from smartphone to 31B Dense, benchmarks beating Llama 4 Maverick on coding and math.",
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  {
   "url": "https://aidb.digital/blog/2026-07-27-moonshot-ai-releases-kimi-k3-open-weights-a-2-8-trillion-parameter-mo",
   "title": "Moonshot AI Releases Kimi K3 Open Weights: A 2.8-Trillion-Parameter Model That Challenges US Frontier Systems \u00b7 AIDB",
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  {
   "url": "https://agenticsearch.cloud/posts/glm-5-3-model-review",
   "title": "GLM-5.3 Model Review: Built to Code, Cyber-Ready (2026)",
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   "parameters": "743B-parameter",
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  {
   "url": "https://theaidatabase.com/m/minimax-minimax-m2-7.html",
   "title": "AIDb, the IMDb for AI Models",
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   "significance": "Vals Index 59.58 (Mar 2026).",
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    "Native multimodal (voice in/out, image, video) M-series update; \"self-evolution\" agentic training. Vals Index 59.58 (Mar 2026). Sources..."
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  },
  {
   "url": "https://bitcoinethereumnews.com/tech/nvidia-releases-its-best-open-ai-model-yet-but-still-lags-behind-china",
   "title": "Nvidia Releases Its Best Open AI Model Yet\u2014But Still Lags Behind China",
   "published_at": "2026-06-02T19:05:08",
   "organization": "Nvidia",
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   "release_date": "2026-06-04",
   "parameters": "550 billion",
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   "license": null,
   "significance": "top U.S. open-weight model",
   "organization_evidence": [
    "Nvidia Releases Its Best Open AI Model Yet\u2014But Still Lags Behind China",
    "NVIDIA unveiled Nemotron 3 Ultra at Computex on June 1, a 550-billion-parameter open-weight model."
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   "release_date_evidence": [
    "Nvidia Releases Its Best Open AI Model Yet\u2014But Still Lags Behind China",
    "Nemotron 3 Ultra ships June 4."
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    "Nvidia Releases Its Best Open AI Model Yet\u2014But Still Lags Behind China",
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   "significance_evidence": [
    "Nvidia Releases Its Best Open AI Model Yet\u2014But Still Lags Behind China",
    "That makes it the top U.S. open-weight model by a comfortable margin."
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  {
   "url": "https://sdd.sh/2026/04/deepseek-v4-open-weight-frontier-huawei-ascend",
   "title": "DeepSeek V4: Near-Frontier Performance, Open Weights, and the First Major Model Built for Huawei Chips",
   "published_at": "2026-04-28T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion parameters total with 49 billion active at inference time",
   "context_window": null,
   "license": null,
   "significance": "Beats all rival open-weight models on math and coding benchmarks and trails only Google\u2019s Gemini 3.1 Pro \u2014 a closed model \u2014 on world knowledge tasks.",
   "organization_evidence": [
    "DeepSeek V4: Near-Frontier Performance, Open Weights, and the First Major Model Built for Huawei Chips",
    "Released on April 24, 2026, DeepSeek V4 is the company\u2019s new flagship series: a 1.6 trillion-parameter Pro model and a 284 billion-parameter Flash variant, both Mixture-of-Experts (MoE) architectures, both MIT-licensed, and both available via the DeepSeek API on day one."
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   "release_date_evidence": [
    "DeepSeek V4: Near-Frontier Performance, Open Weights, and the First Major Model Built for Huawei Chips",
    "Released on April 24, 2026, DeepSeek V4 is the company\u2019s new flagship series: a 1.6 trillion-parameter Pro model and a 284 billion-parameter Flash variant, both Mixture-of-Experts (MoE) architectures, both MIT-licensed, and both available via the DeepSeek API on day one."
   ],
   "parameters_evidence": [
    "DeepSeek V4: Near-Frontier Performance, Open Weights, and the First Major Model Built for Huawei Chips",
    "## The Models#",
    "**DeepSeek-V4-Pro** packs 1.6 trillion total parameters with 49 billion active at inference time \u2014 a hallmark of the MoE efficiency architecture DeepSeek has mastered."
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   "context_window_evidence": null,
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    "DeepSeek V4: Near-Frontier Performance, Open Weights, and the First Major Model Built for Huawei Chips",
    "## The Models#",
    "Performance: V4-Pro beats all rival open-weight models on math and coding benchmarks and trails only Google\u2019s Gemini 3.1 Pro \u2014 a closed model \u2014 on world knowledge tasks."
   ]
  },
  {
   "url": "https://note.com/humble_bobcat51/n/nb24a63bee5f1?hl=en",
   "title": "Qwen 3.6-27B is here! It beats the Qwen 3.6-35B-A3B in overall performance, and the real deal for 24GB+ VRAM is finally here! It's even being researched in 2-bit, coming in at just 9.38GB!",
   "published_at": "2026-04-22T16:38:47",
   "organization": "Qwen team",
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   "release_date": "2026-04-22",
   "parameters": null,
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   "license": null,
   "significance": "Despite being a mid-sized model with 27B parameters, its main feature is its extremely high agentic coding capability, which even surpasses their own ultra-large model (397B MoE) in coding performance.",
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    "Qwen 3.6-27B is here! It beats the Qwen 3.6-35B-A3B in overall performance, and the real deal for 24GB+ VRAM is finally here! It's even being researched in 2-bit, coming in at just 9.38GB!",
    "On April 22, 2026, the Qwen team released their latest dense LLM, 'Qwen3.6-27B'."
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    "On April 22, 2026, the Qwen team released their latest dense LLM, 'Qwen3.6-27B'."
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    "Qwen 3.6-27B is here! It beats the Qwen 3.6-35B-A3B in overall performance, and the real deal for 24GB+ VRAM is finally here! It's even being researched in 2-bit, coming in at just 9.38GB!",
    "On April 22, 2026, the Qwen team released their latest dense LLM, 'Qwen3.6-27B'. Despite being a mid-sized model with 27B parameters, its main feature is its extremely high agentic coding capability, which even surpasses their own ultra-large model (397B MoE) in coding performance."
   ]
  },
  {
   "url": "https://bitsminds.com/news/meta-llama-5-open-source-600b-2026",
   "title": "Meta Releases Llama 5: Open-Source 600B Model Claims Frontier Dominance",
   "published_at": "2026-04-12T11:17:54",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
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   "license": null,
   "significance": "Outperforms GPT-5 and Gemini on key benchmarks; introduces 'Recursive Self-Improvement' capability and heavy focus on 'System 2' reasoning.",
   "organization_evidence": [
    "# Meta Releases Llama 5: Open-Source 600B Model Claims Frontier Dominance",
    "Meta CEO Mark Zuckerberg made a landmark announcement on April 8, 2026, unveiling Llama 5 \u2014 the company's most ambitious open-source AI release to date."
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   "release_date_evidence": [
    "# Meta Releases Llama 5: Open-Source 600B Model Claims Frontier Dominance",
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   "significance_evidence": [
    "# Meta Releases Llama 5: Open-Source 600B Model Claims Frontier Dominance",
    "Meta CEO Mark Zuckerberg unveiled Llama 5, a 600-billion-parameter open-weight model trained on 500,000 Blackwell GPUs that Meta says outperforms GPT-5 and Gemini on key benchmarks."
   ]
  },
  {
   "url": "https://opensourceforu.com/2026/07/mistral-releases-apache-2-0-open-source-leanstral-1-5",
   "title": "Mistral Releases Apache 2.0 Open Source Leanstral 1.5 - Open Source For You",
   "published_at": "2026-07-10T08:33:11",
   "organization": "Mistral AI",
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   "significance": "According to Mistral, Leanstral achieved 100% on both the validation and test sets of the miniF2F benchmark and solved 587 of 672 problems on PutnamBench, which evaluates logical reasoning and long proof chains.",
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    "Mistral AI has released Leanstral 1.5 as an open-source AI model under the Apache 2.0 licence, expanding access to advanced formal verification for mathematical proofs and software correctness."
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    "According to Mistral, Leanstral achieved 100% on both the validation and test sets of the miniF2F benchmark and solved 587 of 672 problems on PutnamBench, which evaluates logical reasoning and long proof chains."
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  {
   "url": "https://agentupdate.ai/news/google-gemma-4-apache-2-0-open-model",
   "title": "Google's Gemma 4 Model Family Now Available Under Apache 2.0 License, Boosting Agentic AI Capabilities",
   "published_at": "2026-04-02T18:06:37",
   "organization": "Google",
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   "release_date": "2026-04-03",
   "parameters": "Effective 2B (E2B), Effective 4B (E4B), 26B Mixture-of-Experts (MoE), and 31B Dense model",
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   "significance": "Holds the 3rd position among all open models worldwide on the Arena AI Text Leaderboard (31B Dense), ranks 6th (26B MoE), outperforms models 20 times its size, delivers significant improvements in multi-step reasoning and mathematical tasks, and natively supports function calling, structured JSON output, and system instructions for agentic workflows.",
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   "release_date_evidence": [
    "\ud83d\udcc5 April 3, 2026",
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    "Gemma 4 comes in four sizes, catering to everything from edge devices to high-end workstations: Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts (MoE) model, and a 31B Dense model.",
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    "The 31B model currently holds the 3rd position among all open models worldwide on the Arena AI Text Leaderboard, while the 26B MoE model ranks 6th.",
    "Google states that Gemma 4 outperforms models 20 times its size.",
    "According to Google, all Gemma 4 models deliver significant improvements in multi-step reasoning and mathematical tasks.",
    "Google's Gemma 4 Model Family Now Available Under Apache 2.0 License, Boosting Agentic AI Capabilities"
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  },
  {
   "url": "https://swfte.com/ko/ai/models/minimax-m3",
   "title": "MiniMax M3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-08-04T22:03:15",
   "organization": "MiniMax",
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   "release_date": "2026-06-01",
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   "significance": "the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is a open-source AI model by MiniMax, released on June 1, 2026."
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   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax's 1 Jun 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context."
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   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax's 1 Jun 2026 open-weight flagship: the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context."
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  },
  {
   "url": "https://kblip.com/social/qwen-kicks-off-oss-gathering-in-shanghai-with-qwen-3-8-VzYkrGR",
   "title": "Qwen kicks off OSS gathering in Shanghai with Qwen 3.8 release",
   "published_at": "2026-07-19T11:02:22",
   "organization": "Qwen",
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   "release_date": "2026-07-19",
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   "organization_evidence": [
    "Social\u00b7Community",
    "# Qwen kicks off OSS gathering in Shanghai with Qwen 3.8 release",
    "On July 19, 2026, Qwen released version 3.8 of its open-source model during an OSS gathering in Shanghai, as announced via a tweet by RenLeanna."
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   "release_date_evidence": [
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    "# Qwen kicks off OSS gathering in Shanghai with Qwen 3.8 release",
    "On July 19, 2026, Qwen released version 3.8 of its open-source model during an OSS gathering in Shanghai, as announced via a tweet by RenLeanna."
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  },
  {
   "url": "https://startupfortune.com/mistral-is-trying-to-make-open-weight-ai-feel-enterprise-grade-again",
   "title": "Mistral is trying to make open-weight AI feel enterprise-grade again",
   "published_at": "2026-04-29T17:43:02",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "A dense open-weight flagship built to combine reasoning, coding, and agentic workflows in one self-hostable package for enterprise buyers.",
   "organization_evidence": [
    "Mistral Medium 3.5 is a 128B open-weight model built to combine reasoning, coding and agentic workflows in one self-hostable package, which is exactly the kind of release that could make open AI feel serious again for enterprise buyers.",
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   "release_date_evidence": [
    "Mistral Medium 3.5 is a 128B open-weight model built to combine reasoning, coding and agentic workflows in one self-hostable package, which is exactly the kind of release that could make open AI feel serious again for enterprise buyers.",
    "That matters because the conversation around open models has shifted.",
    "Medium 3.5 also fits neatly into Mistral\u2019s 2026 product cadence."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5 is a 128B open-weight model built to combine reasoning, coding and agentic workflows in one self-hostable package, which is exactly the kind of release that could make open AI feel serious again for enterprise buyers."
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    "Mistral Medium 3.5 is a 128B open-weight model built to combine reasoning, coding and agentic workflows in one self-hostable package, which is exactly the kind of release that could make open AI feel serious again for enterprise buyers."
   ]
  },
  {
   "url": "https://ai.google.dev/gemma",
   "title": "Gemma \u2014 Google DeepMind",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-06",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "A unified, encoder-free multimodal model",
   "organization_evidence": [
    "Large Language Models (LLMs), such as Gemma, may sometimes provide inaccurate or offensive content that doesn\u2019t represent Google\u2019s views.",
    "# Gemma"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "June 2026",
    "# Gemma",
    "Slide 1 of 15"
   ],
   "parameters_evidence": [
    "### Introducing Gemma 4 12B",
    "# Gemma",
    "Slide 1 of 15"
   ],
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   "significance_evidence": [
    "### Introducing Gemma 4 12B",
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    "# Gemma",
    "Slide 1 of 15"
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  },
  {
   "url": "https://freeainews.com/open-source/kimi-k2-6-open-source-2026",
   "title": "Kimi K2.6: The Open-Source 1T-Parameter Model Topping Coding Benchmarks (2026)",
   "published_at": "2026-06-01T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1T total parameters, 32B active per token",
   "context_window": null,
   "license": null,
   "significance": "58.6% on SWE-Bench Pro, 80.2% on SWE-Bench Verified, 54.0% on HLE with Tools, 75.0% on OSWorld",
   "organization_evidence": [
    "When Moonshot AI quietly dropped Kimi K2.6 on April 20, 2026, the [reaction on Hacker News](https://news.ycombinator.com/item?id=47993235) was swift: an open-source model had just matched or beaten the top closed-source models at the benchmark that developers actually care about.",
    "Kimi K2.6 is the fourth generation of Moonshot AI's K-series open-weight models."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "When Moonshot AI quietly dropped Kimi K2.6 on April 20, 2026, the [reaction on Hacker News](https://news.ycombinator.com/item?id=47993235) was swift: an open-source model had just matched or beaten the top closed-source models at the benchmark that developers actually care about."
   ],
   "parameters_evidence": [
    "**Architecture at a glance:** 1T total parameters, 32B active per token \u00b7 MoE (Mixture-of-Experts) \u00b7 256K context window \u00b7 Thinking mode + Instant mode \u00b7 Modified MIT License \u00b7 Released April 20, 2026"
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   "significance_evidence": [
    "| Benchmark | Kimi K2.6 | GPT-5.4 | Claude Opus 4.6 |",
    "| SWE-Bench Pro | 58.6% | 57.7% | Below K2.6 |",
    "| SWE-Bench Verified | 80.2% | -- | -- |",
    "| HLE with Tools | 54.0% | Below K2.6 | Below K2.6 |",
    "| OSWorld (Desktop GUI) | 75.0% | -- | -- |",
    "Moonshot published full benchmark results alongside the model release, with results for K2.5, Claude Opus 4.6 (max effort), GPT-5.4 (xhigh reasoning), and Gemini 3.1 Pro (high thinking) all evaluated under comparable conditions."
   ]
  },
  {
   "url": "https://dev.to/jamilxt/glm-52-zhipus-open-weight-frontier-model-with-1m-context-1i6",
   "title": "GLM 5.2: Zhipu's Open-Weight Frontier Model With 1M Context",
   "published_at": "2026-06-17T14:10:50",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM 5.2 is the highest-ranked open-source model across all three benchmarks. For tasks under 200K tokens, the gap with closed frontier models is barely noticeable.",
   "organization_evidence": [
    "GLM 5.2: Zhipu's Open-Weight Frontier Model With 1M Context",
    "On June 13, 2026, Zhipu AI (operating as Z.ai) released GLM 5.2 \u2014 and did something unusual for a flagship model launch."
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   "release_date_evidence": [
    "GLM 5.2: Zhipu's Open-Weight Frontier Model With 1M Context",
    "On June 13, 2026, Zhipu AI (operating as Z.ai) released GLM 5.2 \u2014 and did something unusual for a flagship model launch."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What It Actually Does Well",
    "The takeaway: GLM 5.2 is the highest-ranked **open-source** model across all three benchmarks."
   ]
  },
  {
   "url": "https://awesomeagents.ai/models/minimax-m2-7",
   "title": "MiniMax M2.7",
   "published_at": "2026-05-08T16:02:03",
   "organization": "MiniMax",
   "model": "MiniMax M2.7",
   "release_date": "2026-04-12",
   "parameters": "230B total / 10B active (Mixture-of-Experts)",
   "context_window": "200K tokens",
   "license": "Modified-MIT (non-commercial without written authorization)",
   "significance": "Scoring 56.22% on SWE-Pro and 78% on SWE-bench Verified, with a self-evolving architecture that ran 100+ autonomous optimization cycles.",
   "organization_evidence": [
    "MiniMax M2.7 is the latest in Shanghai-based MiniMax's M2 family - a Mixture-of-Experts model with 230 billion total parameters and 10 billion active per token.",
    "| Provider | MiniMax (Shanghai, China) |",
    "Key Specifications"
   ],
   "model_evidence": [
    "MiniMax M2.7 is a 230B MoE coding agent that handles 30-50% of MiniMax's own RL research workflow, scoring 56.22% on SWE-Pro and 78% on SWE-bench Verified at $0.30/M input tokens.",
    "MiniMax M2.7 is the latest in Shanghai-based MiniMax's M2 family - a Mixture-of-Experts model with 230 billion total parameters and 10 billion active per token."
   ],
   "release_date_evidence": [
    "Announced on March 18, 2026, with open weights released April 12, 2026, M2.7 picks up where [MiniMax M2.5](/models/minimax-m2-5/) left off on coding benchmarks while adding a headline new capability: self-evolution.",
    "| Release Date | March 18, 2026 (weights: April 12, 2026) |",
    "Key Specifications"
   ],
   "parameters_evidence": [
    "MiniMax M2.7 is the latest in Shanghai-based MiniMax's M2 family - a Mixture-of-Experts model with 230 billion total parameters and 10 billion active per token.",
    "| Parameters | 230B total / 10B active (Mixture-of-Experts) |",
    "Key Specifications"
   ],
   "context_window_evidence": [
    "MiniMax M2.7 is a 230B MoE coding agent that handles 30-50% of MiniMax's own RL research workflow, scoring 56.22% on SWE-Pro and 78% on SWE-bench Verified at $0.30/M input tokens.",
    "| Context Window | 200K tokens |",
    "Key Specifications"
   ],
   "license_evidence": [
    "What complicates the story is the license. MiniMax tagged M2.7 a \"Modified-MIT\" release when the weights landed on HuggingFace, but the actual terms require written authorization from MiniMax for any commercial deployment.",
    "| License | Modified-MIT (non-commercial without written authorization) |",
    "Key Specifications"
   ],
   "significance_evidence": [
    "MiniMax M2.7 is a 230B MoE coding agent that handles 30-50% of MiniMax's own RL research workflow, scoring 56.22% on SWE-Pro and 78% on SWE-bench Verified at $0.30/M input tokens.",
    "Announced on March 18, 2026, with open weights released April 12, 2026, M2.7 picks up where [MiniMax M2.5](/models/minimax-m2-5/) left off on coding benchmarks while adding a headline new capability: self-evolution."
   ]
  },
  {
   "url": "https://economictimes.indiatimes.com/tech/technology/ces-2026-all-you-need-to-know-about-nvidias-major-announcements/printarticle/126369415.cms",
   "title": "CES 2026: All you need to know about Nvidia's major announcements",
   "published_at": null,
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-01-06",
   "parameters": "10-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "brings human-like thinking to AV decision-making",
   "organization_evidence": [
    "Synopsis",
    "Nvidia unveiled Alpamayo at CES 2026, an open-source AI model that adds reasoning to autonomous vehicles and robotics."
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   "model_evidence": null,
   "release_date_evidence": [
    "Synopsis",
    "Nvidia unveiled Alpamayo at CES 2026, an open-source AI model that adds reasoning to autonomous vehicles and robotics.",
    "( Originally published on Jan 06, 2026 )"
   ],
   "parameters_evidence": [
    "The flagship model: Alpamayo 1",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The flagship model: Alpamayo 1",
    "\u201cThe Alpamayo family introduces chain-of-thought, reasoning-based vision language action (VLA) models that bring human-like thinking to AV decision-making,\u201d the company said in a blog."
   ]
  },
  {
   "url": "https://aimidday.com/qwen-open-sources-27b-model-that-runs-on-home-gpus",
   "title": "Qwen open-sources 27B model that runs on home GPUs",
   "published_at": "2026-08-14T16:41:20",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "27-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "beats its own Qwen3.7-Plus on coding and office work, introduces reasoning_effort to let developers dial a model's thinking depth up or down per task",
   "organization_evidence": [
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    "Two open-weights signals landed within hours of each other: Alibaba pushed the most practical member of its Qwen3.8 family out to the open-source community, and Meta's most expensive hire of the AI talent war walked out the door."
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Qwen open-sources 27B model that runs on home GPUs",
    "Qwen has open-sourced Qwen3.8-27B, a 27-billion-parameter dense multimodal model under Apache 2.0 that is small enough to run on consumer GPUs \u2014 free to download, deploy, and use commercially."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen open-sources 27B model that runs on home GPUs",
    "The model, announced Friday, carries 262K tokens of native context (extendable to 1M with YaRN), and Qwen says it beats its own Qwen3.7-Plus on coding and office work."
   ]
  },
  {
   "url": "https://wikipedia.org/wiki/Gemma_%28language_model%29",
   "title": "Gemma (language model)",
   "published_at": "2026-04-17T13:28:28",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place.",
   "organization_evidence": [
    "Gemma is a series of source-available large language models developed by Google DeepMind.",
    "Technical specifications of Gemma models",
    "Gemma 4"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 2, 2026, Google released Gemma 4 under the free and open-source Apache 2.0 license.",
    "Technical specifications of Gemma models",
    "Gemma 4"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place.",
    "Technical specifications of Gemma models",
    "Gemma 4"
   ]
  },
  {
   "url": "https://thundercompute.com/blog/kimi-k3",
   "title": "Kimi K3: Release Date, Open-Source Weights, and How to Run It",
   "published_at": "2026-08-05T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8T total parameters",
   "context_window": "1M tokens",
   "license": "Kimi K3 License",
   "significance": "largest open-weight model released to date; first open model in the 3T-parameter class; frontier-level benchmark performance",
   "organization_evidence": [
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "model_evidence": [
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "release_date_evidence": [
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "parameters_evidence": [
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "context_window_evidence": [
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "license_evidence": [
    "Kimi K3 is open-weight, not fully open source. The trained weights are freely downloadable under the Kimi K3 License, which is MIT-like for most uses.",
    "Kimi K3 is an open-weight multimodal model from Moonshot AI with 2.8T total parameters and 104B active parameters per forward pass.",
    "It supports text, images, and video natively, ships with a 1M-token context window, and is the largest open-weight model released to date.",
    "Weights were published July 27, 2026."
   ],
   "significance_evidence": [
    "Kimi K3 is the largest open-weight model released to date.",
    "Moonshot AI announced it on July 16, 2026, and published the full weights on July 27, making it the first open model in the 3T-parameter class.",
    "Kimi K3 performs at frontier level while trailing the top proprietary models."
   ]
  },
  {
   "url": "https://felloai.com/pt/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Local and consumer-hardware coding, around $0.60 per million tokens",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ]
  },
  {
   "url": "https://felloai.com/pt/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": "False",
   "license": "MIT",
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "model_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "release_date_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "parameters_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "It still shipped under the permissive MIT license."
   ],
   "significance_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/pt/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": "False",
   "license": "open weights",
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "model_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "release_date_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "parameters_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "**GLM-5.1** was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around **94% of Claude Opus** while keeping the open weights."
   ],
   "significance_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/pt/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": "1 million tokens",
   "license": "False",
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5's cost",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "model_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "release_date_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "parameters_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "context_window_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "license_evidence": null,
   "significance_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ]
  },
  {
   "url": "https://24-ai.news/en/news/2026-07-24/amd-instella-moe-open-model",
   "title": "AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
   "published_at": "2026-07-24T00:00:00",
   "organization": "AMD",
   "model": "Instella-MoE-16B-A3B",
   "release_date": "2026-07-24",
   "parameters": "16 billion total and 2.8 billion active parameters per token",
   "context_window": "64,000 tokens",
   "license": "Research RAIL license",
   "significance": "Averages 76.7 across standard benchmarks, 86.5 on WinoGrande, 65.7 on HumanEval+, 79.4 on RULER at 64K context; Think checkpoint scores 73.40 on AIME25.",
   "organization_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "Instella-MoE is AMD's fully open language model released on July 24, 2026, with a mixture-of-experts architecture."
   ],
   "model_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "On July 24, 2026, AMD released Instella-[MoE](/en/glossary/mixture-of-experts/)-16B-A3B, a fully open language model with a mixture-of-experts (MoE) architecture."
   ],
   "release_date_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "Instella-MoE is AMD's fully open language model released on July 24, 2026, with a mixture-of-experts architecture."
   ],
   "parameters_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "It has 16 billion total and 2.8 billion active parameters per token, trained on 7.1 trillion tokens on Instinct GPUs."
   ],
   "context_window_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "On the RULER long-context test it scores 79.4 at 64,000 tokens."
   ],
   "license_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "Both models are available under the Research RAIL license on Hugging Face and GitHub."
   ],
   "significance_evidence": [
    "# AMD: Instella-MoE, a fully open MoE model with 16 billion parameters trained on Instinct GPUs",
    "The base model averages 76.7 across benchmarks, and the Think checkpoint scores 73.40 on AIME25."
   ]
  },
  {
   "url": "https://launchberg.com/meta-llama-4-scout-maverick",
   "title": "Meta Llama 4: Two MoE Models, 10M Token Context | Launchberg",
   "published_at": "2026-04-06T12:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "109 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Meta's first MoE release, natively multimodal, 10 million token context window",
   "organization_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026."
   ],
   "parameters_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Llama 4 Scout runs 109B total parameters across 16 experts with a 10 million token context window.",
    "Llama 4 Scout has 109 billion total parameters split across 16 experts, with only 17 billion active at inference."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Both are open-weight, natively multimodal, and Meta's first MoE release.",
    "Ten million tokens of context in an open-weight model."
   ]
  },
  {
   "url": "https://launchberg.com/meta-llama-4-scout-maverick",
   "title": "Meta Llama 4: Two MoE Models, 10M Token Context | Launchberg",
   "published_at": "2026-04-06T12:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "400 billion parameters",
   "context_window": null,
   "license": null,
   "significance": "First mixture-of-experts model in the Llama family, natively multimodal, 1 million token context window with the instruct-tuned variant",
   "organization_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026."
   ],
   "parameters_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Maverick scales to 400B across 128 experts.",
    "Maverick takes the same 17 billion active parameters but distributes them across 128 experts, totaling 400 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta Llama 4: Two MoE Models, 10M Token Context",
    "Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026.",
    "They\u2019re the first mixture-of-experts models in the Llama family, the first natively multimodal Llama models, and they were trained on 40 trillion tokens of data.",
    "It supports a 1 million token context window with the instruct-tuned variant."
   ]
  },
  {
   "url": "https://mgsoftware.nl/en/blog/google-gemma-4-most-capable-open-ai-model-you-can-run-yourself",
   "title": "Google Gemma 4: The Most Capable Open AI Model You Can Run Yourself",
   "published_at": "2026-04-03T10:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2.3 billion",
   "context_window": null,
   "license": null,
   "significance": "outperforms its 27 billion parameter predecessor",
   "organization_evidence": [
    "Google Gemma 4: The Most Capable Open AI Model You Can Run Yourself",
    "Google DeepMind released Gemma 4 on April 2, four open-source models under Apache 2.0 that range from Raspberry Pi to datacenter scale."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: The Most Capable Open AI Model You Can Run Yourself",
    "Google DeepMind released Gemma 4 on April 2, four open-source models under Apache 2.0 that range from Raspberry Pi to datacenter scale."
   ],
   "parameters_evidence": [
    "Google Gemma 4: The Most Capable Open AI Model You Can Run Yourself",
    "A 2.3 billion parameter model that outperforms its 27 billion parameter predecessor."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: The Most Capable Open AI Model You Can Run Yourself",
    "A 2.3 billion parameter model that outperforms its 27 billion parameter predecessor."
   ]
  },
  {
   "url": "https://aistackguide.blog/moonshot-ai-kimi-k2",
   "title": "Moonshot AI Kimi K2 Explained: The Open-Source Model Series That Changed AI in 2026 -",
   "published_at": "2026-08-04T05:57:10",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion",
   "context_window": null,
   "license": null,
   "significance": "Dominated in agentic and coding tasks, achieving 58.6% on SWE-Bench Pro, 83.0% accuracy on DeepSearchQA, and scaling Agent Swarm to 300 sub-agents.",
   "organization_evidence": [
    "[Moonshot AI\u2019s Kimi K2](https://www.kimi.com/activities/invite/share?scenario=invite&from=share_poster&invitation_code=CEWHQL) series redefined open-source AI in 2026.",
    "Moonshot AI is a Beijing-based artificial intelligence lab founded in 2023 by Yang Zhilin, a former Meta AI and Google Brain researcher."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Kimi K2 Series: From K2 to K2.6",
    "Kimi K2.6 arrived on April 20, 2026, and represented the pinnacle of the K2 line."
   ],
   "parameters_evidence": [
    "What Is Moonshot AI?",
    "The Kimi K2 Series: From K2 to K2.6",
    "| **Total Parameters** | 1 trillion |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark Performance: How Kimi K2.6 Stacked Up",
    "K2.6\u2019s benchmark story was nuanced: it dominated in agentic and coding tasks while trailing slightly on pure reasoning without tools.",
    "- **SWE-Bench Pro:** 58.6%\u2014ahead of GPT-5.4 (57.7%), Claude Opus 4.6 (53.4%), and Gemini 3.1 Pro (54.2%)",
    "- **DeepSearchQA:** 83.0% accuracy and 92.5 F1\u2014substantially ahead of GPT-5.4 (63.7% / 78.6 F1)"
   ]
  },
  {
   "url": "https://aistackguide.blog/moonshot-ai-kimi-k2",
   "title": "Moonshot AI Kimi K2 Explained: The Open-Source Model Series That Changed AI in 2026 -",
   "published_at": "2026-08-04T05:57:10",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1 million",
   "license": "Kimi K3 License",
   "significance": "The largest open-weight model ever released, ranking #5 on the BenchLM Intelligence Index and #1 in frontend coding on Arena WebDev.",
   "organization_evidence": [
    "[Moonshot AI\u2019s Kimi K2](https://www.kimi.com/activities/invite/share?scenario=invite&from=share_poster&invitation_code=CEWHQL) series redefined open-source AI in 2026.",
    "On July 16, 2026, Moonshot launched **Kimi K3**\u2014the successor that made the K2 series obsolete."
   ],
   "model_evidence": [
    "What Replaced Kimi K2? Introducing Kimi K3",
    "On July 16, 2026, Moonshot launched **Kimi K3**\u2014the successor that made the K2 series obsolete."
   ],
   "release_date_evidence": [
    "What Replaced Kimi K2? Introducing Kimi K3",
    "The full K3 weights were released on July 27, 2026, making it the largest open-weight model ever released."
   ],
   "parameters_evidence": [
    "What Replaced Kimi K2? Introducing Kimi K3",
    "K3 is a 2.8-trillion-parameter MoE model with a 1-million-token context window, native vision, and new architectural innovations including Kimi Delta Attention (KDA) and Attention Residuals (AttnRes)."
   ],
   "context_window_evidence": [
    "What Replaced Kimi K2? Introducing Kimi K3",
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    "| **Context Window** | 256K | 1M tokens |"
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    "What Replaced Kimi K2? Introducing Kimi K3",
    "| **License** | Modified MIT | Kimi K3 License |"
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   ]
  },
  {
   "url": "https://dev.to/ail_akram_dcc5063c428734b/glm-52-the-open-weight-model-challenging-gpt-55-and-claude-opus-at-a-fraction-of-the-cost-k3n",
   "title": "GLM-5.2: The Open-Weight Model Challenging GPT-5.5 and Claude Opus at a Fraction of the Cost",
   "published_at": "2026-06-24T08:33:35",
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   "release_date": "2026-06-13",
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   "significance": "Highest Intelligence Index score (51) of any open-weights model to date, first open-weight model to cross 80% on Terminal-Bench 2.1 (81.0), and ranked #1 worldwide for frontend coding on the Code Arena: Frontend leaderboard.",
   "organization_evidence": [
    "# GLM-5.2: The Open-Weight Model Challenging GPT-5.5 and Claude Opus at a Fraction of the Cost",
    "GLM-5.2, released on June 13, 2026 by Z.ai, the commercial brand of Tsinghua University-spawned Zhipu AI, marks a significant inflection point in the open-weight large language model landscape."
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   "release_date_evidence": [
    "# GLM-5.2: The Open-Weight Model Challenging GPT-5.5 and Claude Opus at a Fraction of the Cost",
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   "significance_evidence": [
    "# GLM-5.2: The Open-Weight Model Challenging GPT-5.5 and Claude Opus at a Fraction of the Cost",
    "On the Artificial Analysis Intelligence Index v4.1, GLM-5.2 scores 51, the highest of any open-weights model to date.",
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    "Notably, GLM-5.2 has achieved the number one ranking worldwide for frontend coding on the Code Arena: Frontend leaderboard, beating all models including Claude Opus 4.8."
   ]
  },
  {
   "url": "https://aichina.news/blog/huawei-ascend-meets-open-source-accessing-the-olmo-3-7b-rl-zero-mix-zhz3vo",
   "title": "Huawei Ascend Meets Open Source: Accessing the Olmo-3-7B-RL-Zero-Mix on Modelers.cn \u2014 AICHINA.news Blog",
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   "organization": "Allen Institute for AI",
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   "release_date": "2026-07-21",
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   "release_date_evidence": [
    "\u26053 Modelers Product Launch",
    "# Huawei Ascend Meets Open Source: Accessing the Olmo-3-7B-RL-Zero-Mix on Modelers.cn",
    "models/allenai July 21, 2026 1 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
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  {
   "url": "https://instadatanews.com/articles/2026-08-04-deepseek-v4-flash-open-weights-agents.html",
   "title": "DeepSeek V4 Flash Goes Open Weights and Beats Its Own Flagship on Agents",
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   ]
  },
  {
   "url": "https://aicost.tools/llm-cost/mistral",
   "title": "Mistral API Pricing \u2014 Model Cost & EU Access \u00b7 AI//COST",
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   "parameters": null,
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   "license": null,
   "significance": "cheap open multimodal tier currently listed at $0.10/$0.30, 256K context",
   "organization_evidence": [
    "EU FRONTIER LABFOUNDED 2023PARIS \u00b7 FRANCEOPEN WEIGHTSLE CHAT MAKERS",
    "Major releases, funding, and EU-infrastructure moves. Sourced from official Mistral announcements + press coverage, verified at publication."
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    "Major releases, funding, and EU-infrastructure moves. Sourced from official Mistral announcements + press coverage, verified at publication.",
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   ]
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  {
   "url": "https://infoq.com/news/2026/04/google-gemm4",
   "title": "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
   "published_at": "2026-04-16T17:05:00",
   "organization": "Google",
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   "release_date": "2026-04-16",
   "parameters": "2B and 4B edge variants, a 26B Mixture-of-Experts model, and a 31B dense model",
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  {
   "url": "https://crowdfundinsider.com/2026/07/294116-moonshot-ai-wraps-up-kimi-k3-rollout-with-full-model-weight-release",
   "title": "Moonshot AI Wraps Up Kimi K3 Rollout With Full Model Weight Release | Crowdfund Insider",
   "published_at": "2026-07-28T22:32:21",
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   "significance": "Positions as the first open-weight system in the three-trillion-parameter class, delivering an estimated 2.5-fold gain in scaling efficiency relative to the earlier Kimi K2 generation, and ranks near the frontier on aggregate intelligence indexes.",
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   "release_date_evidence": [
    "The weights became downloadable on July 27, [2026](https://www.crowdfundinsider.com/tag/outlook-2026), eleven days after the initial hosted service debut on July 16, fulfilling the timeline the Beijing-based firm announced at launch."
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  {
   "url": "https://aimoneytools.net/blog/glm-52-open-source-ai-model-guide-2026",
   "title": "GLM-5.2: The Free Open-Source AI Model That Just Beat GPT-5.5 on Coding",
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   "release_date": "2026-06-13",
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   "significance": "Scored 74.4% on FrontierSWE, edging out GPT-5.5 (72.6%) and becoming the first open-source model to crack the top 3 on this benchmark.",
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    "# GLM-5.2: The Free Open-Source AI Model That Just Beat GPT-5.5 on Coding",
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   "release_date_evidence": [
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   ]
  },
  {
   "url": "https://aiautomationglobal.com/blog/minimax-m3-msa-sparse-attention-1m-context-agentic-2026",
   "title": "MiniMax M3 MSA: 1M Context, Agentic Coding, Open Weights",
   "published_at": "2026-06-10T10:38:16",
   "organization": "MiniMax",
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   "release_date": "2026-06-01",
   "parameters": "229.9B",
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   "license": null,
   "significance": "Scoring 59.0% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro and closing in on Claude Opus.",
   "organization_evidence": [
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   "release_date_evidence": [
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   "parameters_evidence": [
    "**Key Insight:** M3 activates only 9.8B of its 229.9B parameters per token across 256 fine-grained experts \u2014 frontier-level capability with inference economics closer to a 10B dense model."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "The model introduces **MiniMax Sparse Attention (MSA)** \u2014 a new attention mechanism that delivers more than 15\u00d7 faster decoding at million-token contexts while scoring 59.0% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro and closing in on Claude Opus."
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  {
   "url": "https://agora-intelligence.com/en/blog/nova-inkling-open-weights-moe-2026",
   "title": "Thinking Machines Lab Releases Inkling: 975B Open-Weights MoE With Native Audio, Vision, and Apache 2.0 License",
   "published_at": "2026-07-16T05:39:14",
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   "release_date": "2026-07-15",
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   "significance": "Inkling is designed to be a broad, balanced foundation model built for customization, with multimodal breadth as its primary differentiator rather than benchmark supremacy on any single leaderboard.",
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   "release_date_evidence": [
    "**On July 15, 2026, [Thinking Machines Lab](https://thinkingmachines.ai) released Inkling under Apache 2.0, a [975B-parameter Mixture-of-Experts model with 41B active parameters](https://thinkingmachines.ai/news/introducing-inkling/)available in full on Hugging Face and deployable today across Databricks, Baseten, Modal, Fireworks, and TogetherAI.**"
   ],
   "parameters_evidence": [
    "**On July 15, 2026, [Thinking Machines Lab](https://thinkingmachines.ai) released Inkling under Apache 2.0, a [975B-parameter Mixture-of-Experts model with 41B active parameters](https://thinkingmachines.ai/news/introducing-inkling/)available in full on Hugging Face and deployable today across Databricks, Baseten, Modal, Fireworks, and TogetherAI.**"
   ],
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    "Thinking Machines Lab enters this race with a deliberate counter-positioning: Inkling is designed to be a broad, balanced foundation model built for customization, with multimodal breadth as its primary differentiator rather than benchmark supremacy on any single leaderboard."
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  },
  {
   "url": "https://techsifted.com/posts/mistral-medium-3-5-review-2026",
   "title": "Mistral Medium 3.5 Review: A 128B Open-Weight Model With a Coding Agent That Opens PRs For You",
   "published_at": "2026-05-03T00:00:00",
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   "release_date": "2026-04-29",
   "parameters": "128B",
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   "license": null,
   "significance": "scores 77.6% on SWE-Bench Verified",
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   "model_evidence": null,
   "release_date_evidence": [
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  },
  {
   "url": "https://decrypt.co/363178/google-gemma-4-open-source-ai?amp=1",
   "title": "Google Jumps Back Into the Open Source AI Race With Gemma 4",
   "published_at": "2026-04-02T18:18:58",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B Dense currently ranks third among all open models on Arena AI's text leaderboard. The 26B MoE sits sixth. Google claims both outcompete models 20 times their size",
   "organization_evidence": [
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   ]
  },
  {
   "url": "https://kensink.com/models/kimi/k3",
   "title": "The First Open 3T-Class Model, Benchmarks and Pricing \u00b7 Kensink Labs",
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  {
   "url": "https://colendiai.com/intelligence-papers/glm-45-to-glm-52",
   "title": "GLM-4.5 to GLM-5.2 (Zhipu AI / Z.ai open-weight MoE series)",
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   "significance": "sparse attention and a million-token context, targets reasoning and agentic software engineering",
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    "The series targets reasoning and agentic software engineering and is also served through the z.ai API, but the checkpoints are genuinely public and self-hostable."
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  },
  {
   "url": "https://kingy.ai/ai-launch-radar/ai-launch-tracker-minimax-m3-specs-benchmarks-a-chinese-open-weight-model-takes-aim-at-the-frontier",
   "title": "AI Launch Tracker \u2013 MiniMax M3 Specs & Benchmarks: A Chinese Open-Weight Model Takes Aim at the Frontier",
   "published_at": "2026-06-01T22:08:47",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-05-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open model to fuse frontier-level coding and agentic performance, a one-million-token context window, and native multimodality with computer use.",
   "organization_evidence": [
    "Shanghai-based [AI](https://Kingy.ai) startup MiniMax pushed its most ambitious model yet into the world.",
    "MiniMax M3"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On the evening of May 31, 2026 (Eastern time), Shanghai-based [AI](https://Kingy.ai) startup MiniMax pushed its most ambitious model yet into the world.",
    "MiniMax M3"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "[MiniMax claims M3](https://www.minimax.io/blog/minimax-m3) is the first open model to fuse three capabilities that, until now, only the closed frontier labs could offer simultaneously: frontier-level coding and agentic performance, a one-million-token context window, and native multimodality, complete with the ability to operate a desktop computer.",
    "MiniMax M3"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/nvidia-nemotron-3-nano-omni-multimodal-9x-throughput-open-model-2026",
   "title": "NVIDIA's Open Model Delivers 9x the Throughput of Every Competitor, For Free",
   "published_at": "2026-05-04T08:05:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30B total / 3B active",
   "context_window": null,
   "license": null,
   "significance": "9x higher output throughput than Qwen3-Omni at iso-interactivity, reaching 5,000 output tokens per second on a single NVIDIA B200",
   "organization_evidence": [
    "Model Release",
    "# NVIDIA's Open Model Delivers 9x the Throughput of Every Competitor, For Free",
    "NVIDIA launched Nemotron 3 Nano Omni on April 28, 2026, as the first publicly available member of the Nemotron 3 family."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "# NVIDIA's Open Model Delivers 9x the Throughput of Every Competitor, For Free",
    "Yet NVIDIA's Nemotron 3 Nano Omni arrived on April 28, 2026 with exactly those claims, and the benchmarks support every one of them."
   ],
   "parameters_evidence": [
    "Model Release",
    "# NVIDIA's Open Model Delivers 9x the Throughput of Every Competitor, For Free",
    "- Nemotron 3 Nano Omni features 30B total / 3B active parameters via hybrid MoE, running on 25GB RAM, within reach of a single NVIDIA RTX 4090 for local deployment"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# NVIDIA's Open Model Delivers 9x the Throughput of Every Competitor, For Free",
    "- Delivers 9x higher output throughput than Qwen3-Omni at iso-interactivity, reaching 5,000 output tokens per second on a single NVIDIA B200"
   ]
  },
  {
   "url": "https://fazm.ai/t/open-source-ai-model-release-may-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2025-12-01T06:41:17",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "128B dense multimodal model",
   "organization_evidence": [
    "Mistral Medium 3.5 | Mistral | Apr 29, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5 | Mistral | Apr 29, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5 arrived April 29, a 128B dense multimodal model with a 256K context under a modified-MIT open-weights license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5 arrived April 29, a 128B dense multimodal model with a 256K context under a modified-MIT open-weights license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ]
  },
  {
   "url": "https://fazm.ai/t/open-source-ai-model-release-may-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2025-12-01T06:41:17",
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": "1M-token",
   "license": "MIT",
   "significance": "Pro and Flash",
   "organization_evidence": [
    "DeepSeek V4 (Pro / Flash) | DeepSeek | Apr 24, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "model_evidence": [
    "DeepSeek V4 (Pro / Flash) | DeepSeek | Apr 24, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "release_date_evidence": [
    "DeepSeek V4 (Pro / Flash) | DeepSeek | Apr 24, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "DeepSeek V4 (Pro and Flash) landed on April 24 under an MIT license with a 1M-token context.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "license_evidence": [
    "DeepSeek V4 (Pro / Flash) | DeepSeek | Apr 24, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "significance_evidence": [
    "DeepSeek V4 (Pro and Flash) landed on April 24 under an MIT license with a 1M-token context.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ]
  },
  {
   "url": "https://fazm.ai/t/open-source-ai-model-release-may-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2025-12-01T06:41:17",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04-20",
   "parameters": "1-trillion-parameter",
   "context_window": "262,144-token",
   "license": "Modified MIT",
   "significance": "Mixture-of-Experts model (about 32B active per token)",
   "organization_evidence": [
    "Kimi K2.6 | Moonshot AI | Apr 20, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "model_evidence": [
    "Kimi K2.6 | Moonshot AI | Apr 20, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "release_date_evidence": [
    "Kimi K2.6 | Moonshot AI | Apr 20, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "parameters_evidence": [
    "Moonshot AI's Kimi K2.6 shipped April 20, a 1-trillion-parameter Mixture-of-Experts model (about 32B active per token) with a 262,144-token context under a Modified MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "context_window_evidence": [
    "Moonshot AI's Kimi K2.6 shipped April 20, a 1-trillion-parameter Mixture-of-Experts model (about 32B active per token) with a 262,144-token context under a Modified MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "license_evidence": [
    "Kimi K2.6 | Moonshot AI | Apr 20, 2026 | Yes &check; | Modified MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "significance_evidence": [
    "Moonshot AI's Kimi K2.6 shipped April 20, a 1-trillion-parameter Mixture-of-Experts model (about 32B active per token) with a 262,144-token context under a Modified MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ]
  },
  {
   "url": "https://fazm.ai/t/open-source-ai-model-release-may-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2025-12-01T06:41:17",
   "organization": "Z.ai",
   "model": "GLM-5.1",
   "release_date": "2026-04-07",
   "parameters": "744B",
   "context_window": "200K",
   "license": "MIT",
   "significance": "MoE (~40B active)",
   "organization_evidence": [
    "GLM-5.1 | Z.ai | Apr 7, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "model_evidence": [
    "GLM-5.1 | Z.ai | Apr 7, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "release_date_evidence": [
    "GLM-5.1 | Z.ai | Apr 7, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "parameters_evidence": [
    "Z.ai's GLM-5.1 (April 7) is a 744B MoE (~40B active, 200K context) under a standard MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "context_window_evidence": [
    "Z.ai's GLM-5.1 (April 7) is a 744B MoE (~40B active, 200K context) under a standard MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ],
   "license_evidence": [
    "GLM-5.1 | Z.ai | Apr 7, 2026 | Yes &check; | MIT",
    "Open-weight release ledger, verified May 26, 2026",
    "## The ledger: open vs closed, May and April 2026",
    "One column does all the work here, the \"open weights\" one."
   ],
   "significance_evidence": [
    "Z.ai's GLM-5.1 (April 7) is a 744B MoE (~40B active, 200K context) under a standard MIT license.",
    "Open-weight release ledger, verified May 26, 2026",
    "So what are the latest downloadable open-weight models?",
    "The April 2026 window is where the open weights are."
   ]
  },
  {
   "url": "https://7minai.com/news/gemma-4-12b",
   "title": "Gemma 4 12B: Google's encoder-free open model runs text, image, and audio on a 16GB laptop",
   "published_at": "2026-06-04T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "12 billion",
   "context_window": null,
   "license": null,
   "significance": "GPQA Diamond 78.8, MMLU Pro 77.2, LiveCodeBench 72, DocVQA 94.9, InfoVQA 88.4, MMMU Pro 69.1, and BBEH 53",
   "organization_evidence": [
    "Gemma 4 12B: Google's encoder-free open model runs text, image, and audio on a 16GB laptop",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 an Apache 2.0 multimodal model that drops its vision and audio encoders, handles text, images, and native audio, and fits on a 16GB consumer laptop."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 12B: Google's encoder-free open model runs text, image, and audio on a 16GB laptop",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 an Apache 2.0 multimodal model that drops its vision and audio encoders, handles text, images, and native audio, and fits on a 16GB consumer laptop."
   ],
   "parameters_evidence": [
    "Gemma 4 12B: Google's encoder-free open model runs text, image, and audio on a 16GB laptop",
    "- The model has **12 billion parameters**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 12B: Google's encoder-free open model runs text, image, and audio on a 16GB laptop",
    "The published Gemma 4 12B scores: **GPQA Diamond 78.8**, **MMLU Pro 77.2**, **LiveCodeBench 72**, **DocVQA 94.9**, **InfoVQA 88.4**, **MMMU Pro 69.1**, and **BBEH 53**."
   ]
  },
  {
   "url": "https://aiworld.eu/story/kimi-k3---open-source-is-only-one-release-cycle-behind-the-frontier",
   "title": "Kimi K3 - Open Source is only one release cycle behind the Frontier",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "world's first open 3T-class model",
   "organization_evidence": [
    "# Kimi K3 - Open Source is only one release cycle behind the Frontier",
    "Moonshot AI has released [Kimi K3](https://www.kimi.com/blog/kimi-k3), a 2.8 trillion parameter model that the company calls the \"world's first open 3T-class model\"."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 - Open Source is only one release cycle behind the Frontier",
    "It's available now through the Kimi app, Kimi Code, and the API, with full weights promised by July 27."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 - Open Source is only one release cycle behind the Frontier",
    "Moonshot AI has released [Kimi K3](https://www.kimi.com/blog/kimi-k3), a 2.8 trillion parameter model that the company calls the \"world's first open 3T-class model\"."
   ]
  },
  {
   "url": "https://datanorth.ai/news/zhipu-ai-releases-glm-5-2",
   "title": "Zhipu AI releases GLM-5.2",
   "published_at": "2026-06-18T09:37:58",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744 billion total parameters and roughly 40 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "strongest open-weight model on standard coding benchmarks, scoring 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro, and ranking second on Code Arena Frontend",
   "organization_evidence": [
    "# Zhipu AI releases GLM-5.2",
    "Zhipu AI, the Chinese lab also known as Z.ai, has released GLM-5.2, an open-weight large language model with a 1 million token context window and a permissive MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI releases GLM-5.2",
    "18-06-2026",
    "The model first launched on June 13, 2026 through the GLM Coding Plan, with open weights and a standalone API rolling out across providers in the days that followed."
   ],
   "parameters_evidence": [
    "GLM-5.2 is a Mixture-of-Experts model with around 744 billion total parameters and roughly 40 billion active per token, and on independent tests it matches or beats OpenAI\u2019s GPT-5.5 on several long-horizon coding benchmarks at about one sixth of the cost.",
    "# Zhipu AI releases GLM-5.2"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI releases GLM-5.2",
    "## GLM-5.2 benchmarks and technical specifications",
    "On those tests GLM-5.2 is the strongest open-weight model on standard coding benchmarks, scoring 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro, and ranking second on Code Arena Frontend."
   ]
  },
  {
   "url": "https://cornerforai.com/tools/verify-nvidia-releases-cosmos-3-and-nemotron-3-ultra-as-open-weight-ai-models-for-physical-world-and-language-tasks",
   "title": "Fact-Check: NVIDIA Releases Cosmos 3 and Nemotron 3 Ultra...",
   "published_at": "2026-06-08T03:07:05",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026",
   "parameters": "550-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest and most capable model in the Nemotron series, and the most intelligent open-weights model produced in the United States",
   "organization_evidence": [
    "**Article Title:** NVIDIA Releases Cosmos 3 and Nemotron 3 Ultra as Open-Weight AI Models for Physical World and Language Tasks",
    "**Source:** NVIDIA Newsroom",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Article Title:** NVIDIA Releases Cosmos 3 and Nemotron 3 Ultra as Open-Weight AI Models for Physical World and Language Tasks",
    "**Source:** NVIDIA Newsroom",
    "What is NVIDIA Nemotron 3 Ultra?",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "parameters_evidence": [
    "**Article Title:** NVIDIA Releases Cosmos 3 and Nemotron 3 Ultra as Open-Weight AI Models for Physical World and Language Tasks",
    "**Source:** NVIDIA Newsroom",
    "What is NVIDIA Nemotron 3 Ultra?",
    "**NVIDIA Nemotron 3 Ultra** is a **550-billion-parameter** large language model (LLM) announced by **Jensen Huang**, NVIDIA's chief executive officer, during his keynote at **Computex 2026** in Taipei."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**Article Title:** NVIDIA Releases Cosmos 3 and Nemotron 3 Ultra as Open-Weight AI Models for Physical World and Language Tasks",
    "**Source:** NVIDIA Newsroom",
    "What is NVIDIA Nemotron 3 Ultra?",
    "NVIDIA describes Nemotron 3 Ultra as both the largest and most capable model in the **Nemotron** series, and the most intelligent open-weights model produced in the United States."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/MolmoPoint-8B",
   "title": "allenai/MolmoPoint-8B \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "8B",
   "context_window": null,
   "license": null,
   "significance": "vision-language model fine-tuned specifically for pointing and visual grounding",
   "organization_evidence": [
    "Modelers",
    "# allenai / MolmoPoint-8B",
    "MolmoPoint-8B is an 8-billion parameter vision-language model from Allen AI that specialises in spatial understanding \u2014 pointing to and grounding objects in images."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Translated model card",
    "# allenai/MolmoPoint-8B",
    "Created: 2026-04-12 | Updated: 2026-04-12"
   ],
   "parameters_evidence": [
    "Modelers",
    "# allenai / MolmoPoint-8B",
    "Parameters",
    "8.0B"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What this model does",
    "MolmoPoint-8B is part of the Molmo (Multimodal Open Language Model) family from the Allen Institute for AI (Ai2).",
    "It is a vision-language model (VLM) fine-tuned specifically for **pointing and visual grounding** \u2014 the task of identifying and indicating precise pixel locations of objects referenced in natural-language queries."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/deepseek-v4",
   "title": "DeepSeek V4 Review 8.7 out of 10: 1.6T MoE, $0.14 per M Tokens",
   "published_at": "2026-04-29T07:36:30",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters (49 billion activated)",
   "context_window": null,
   "license": null,
   "significance": "V4-Pro hits 80.6 percent on SWE-bench Verified, within 0.2 points of Claude Opus 4.6.",
   "organization_evidence": [
    "DeepSeek V4 is the Chinese open-source flagship large language model released April 24, 2026.",
    "DeepSeek V4 is the fourth-generation flagship large language model from DeepSeek, the Hangzhou-based AI lab that became globally famous in early 2025 with the open-weights release of DeepSeek V3 and the reasoning-specialist DeepSeek R1."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 is the Chinese open-source flagship large language model released April 24, 2026."
   ],
   "parameters_evidence": [
    "The series ships in two Mixture-of-Experts variants: V4-Pro at 1.6 trillion total parameters (49 billion activated) and V4-Flash at 284 billion total (13 billion activated), both with a 1 million token context window and MIT-licensed weights on Hugging Face.",
    "DeepSeek V4 is the Chinese open-source flagship large language model released April 24, 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "V4-Pro hits 80.6 percent on SWE-bench Verified, within 0.2 points of Claude Opus 4.6.",
    "DeepSeek V4 is the Chinese open-source flagship large language model released April 24, 2026."
   ]
  },
  {
   "url": "https://singularitybyte.com/models/qwen-3-6.html",
   "title": "Qwen 3.6",
   "published_at": "2026-06-29T09:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "beats previous generation's 397B Mixture-of-Experts flagship on coding",
   "organization_evidence": [
    "Qwen 3.6",
    "Alibaba's Qwen 3.6 ships an open dense 27B that beats the old 397B flagship on coding and fits a 24GB GPU, under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "parameters_evidence": [
    "Qwen 3.6",
    "Alibaba's Qwen 3.6 ships an open dense 27B that beats the old 397B flagship on coding and fits a 24GB GPU, under Apache 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen 3.6",
    "Alibaba's Qwen 3.6 is two stories in one.",
    "The good one for open-source builders: a dense 27-billion-parameter model that beats the previous generation's 397B Mixture-of-Experts flagship on coding, and runs on a single 24GB GPU, under Apache 2.0."
   ]
  },
  {
   "url": "https://singularitybyte.com/models/qwen-3-6.html",
   "title": "Qwen 3.6",
   "published_at": "2026-06-29T09:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
   "release_date": "2026-04",
   "parameters": "35B-A3B",
   "context_window": "256K-token",
   "license": "Apache 2.0",
   "significance": "Mixture-of-Experts model with about 3 billion active parameters",
   "organization_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "model_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "release_date_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "parameters_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "context_window_evidence": [
    "What is open, and what is not",
    "Both take text, image, and video, call tools, and carry a 256K-token context that stretches to roughly a million with YaRN scaling."
   ],
   "license_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ],
   "significance_evidence": [
    "What is open, and what is not",
    "Alibaba's Qwen team released two genuinely open-weight Qwen 3.6 models in April 2026, both Apache 2.0 and both already past five million downloads: Qwen3.6-27B, a dense model, and Qwen3.6-35B-A3B, a Mixture-of-Experts model with about 3 billion active parameters."
   ]
  },
  {
   "url": "https://wpnews.pro/news/meta-s-muse-glimmer-bets-open-weights-on-local-agents",
   "title": "Meta's Muse Glimmer Bets Open Weights on Local Agents",
   "published_at": "2026-08-11T03:08:16",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": "30B",
   "context_window": null,
   "license": null,
   "significance": "76% on SWE-Bench Verified, 75.5% on MCP Atlas, and 94.7% on AIME 2026",
   "organization_evidence": [
    "Meta's Muse Glimmer Bets Open Weights on Local Agents",
    "Meta shipped Muse Glimmer, a 30B dense multimodal model from Meta Superintelligence Labs, under the Apache 2.0 license, abandoning the Llama Community License for the first time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meta's Muse Glimmer Bets Open Weights on Local Agents",
    "read5 min views32 publishedAug 11, 2026"
   ],
   "parameters_evidence": [
    "Meta's Muse Glimmer Bets Open Weights on Local Agents",
    "Meta shipped Muse Glimmer, a 30B dense multimodal model from Meta Superintelligence Labs, under the Apache 2.0 license, abandoning the Llama Community License for the first time."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta's Muse Glimmer Bets Open Weights on Local Agents",
    "The model, designed for local agent workflows, fits on a 24GB GPU and achieves 76% on SWE-Bench Verified, 75.5% on MCP Atlas, and 94.7% on AIME 2026."
   ]
  },
  {
   "url": "https://tech.einnews.com/article/906328007?lcf=kkUQZK9xbOt_7NAUGUglIB_oUP_vuXDyNt19En3Vmgw%3D&pg=3",
   "title": "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
   "published_at": "2026-04-16T17:05:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "spanning effective 2B and 4B edge variants, a 26B Mixture-of-Experts model, and a 31B dense model",
   "context_window": null,
   "license": null,
   "significance": "The release introduces native video and image processing across the lineup, audio input on the smaller models, context windows up to 256K tokens, and benchmark results that place the 31B dense variant in a bracket typically occupied by models three to five times its size.",
   "organization_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "Apr 16, 2026 2 min read"
   ],
   "parameters_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
    "The release introduces native video and image processing across the lineup, audio input on the smaller models, context windows up to 256K tokens, and benchmark results that place the 31B dense variant in a bracket typically occupied by models three to five times its size."
   ]
  },
  {
   "url": "https://news-area.com/2026/07/16/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-u-s-systems",
   "title": "China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
   "published_at": "2026-07-16T17:42:09",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI",
   "organization_evidence": [
    "# China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI ."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
    "Full model weights are scheduled to be released on July 27, according to details shared by researchers who reviewed the company's technical documentation."
   ],
   "parameters_evidence": [
    "# China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI ."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
    "Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 \u2014 a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI ."
   ]
  },
  {
   "url": "https://noze.it/en/insights/glm-5-2-open-weight-ai-sovereignty",
   "title": "GLM 5.2: an open-weight frontier under an MIT licence, and why it matters for AI sovereignty",
   "published_at": "2026-06-20T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "753 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Scored 51 on Artificial Analysis Intelligence Index, making it the best open-weight model on the board and fourth overall.",
   "organization_evidence": [
    "# GLM 5.2: an open-weight frontier under an MIT licence, and why it matters for AI sovereignty",
    "Z.ai releases GLM 5.2 with open weights (MIT) and a 1M-token context."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM 5.2: an open-weight frontier under an MIT licence, and why it matters for AI sovereignty",
    "Between 13 and 17 June 2026, **Z.ai** (formerly Zhipu AI) released **GLM 5.2** in stages: first on the **GLM Coding Plan** (13 June), then by publishing the **open weights** on Hugging Face (16 June), and finally with documentation and benchmarks (17 June)."
   ],
   "parameters_evidence": [
    "# GLM 5.2: an open-weight frontier under an MIT licence, and why it matters for AI sovereignty",
    "The headline characteristics, derivable from the model card and the `config.json` shipped with the weights:",
    "- **Mixture-of-Experts architecture** (not *dense*): **around 753 billion total parameters**, of which **~40 billion active** per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM 5.2: an open-weight frontier under an MIT licence, and why it matters for AI sovereignty",
    "The strongest independent data point comes from **Artificial Analysis**: in their Intelligence Index, GLM 5.2 scores **51**, making it **the best open-weight model** on the board, seven points clear of the next open model (MiniMax-M3 and DeepSeek V4 Pro at 44, Kimi K2.6 at 43) and an **+11-point** jump over GLM-5.1."
   ]
  },
  {
   "url": "https://seventnews.com/en/articles/minimax-launches-m25-a-coding-and-agentic-ai-model-with-state-of-the-art-performance",
   "title": "MiniMax M2.5: coding AI model tops benchmarks at low cost",
   "published_at": "2026-07-04T09:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Tops the Multi-SWE-Bench coding benchmark, beats mainstream models on workspace tasks, and scores state-of-the-art on Multi-SWE-Bench.",
   "organization_evidence": [
    "Artificial Intelligence",
    "MiniMax's new M2.5 coding model tops the benchmark at 5% of the price",
    "Chinese AI lab MiniMax just dropped M2.5, a large language model built from the ground up for coding and agentic tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Artificial Intelligence",
    "MiniMax's new M2.5 coding model tops the benchmark at 5% of the price",
    "MiniMax's M2.5 model tops the Multi-SWE-Bench coding benchmark, beats mainstream models on workspace tasks, and costs a tenth to a twentieth of competitors."
   ]
  },
  {
   "url": "https://awesomeagents.ai/models/deepseek-v4",
   "title": "DeepSeek V4",
   "published_at": "2026-04-28T16:55:38",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters with 49 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "V4-Pro scores 80.6% SWE-bench Verified, 90.1% GPQA Diamond, 93.5% LiveCodeBench in max reasoning mode - within a few points of Claude Opus 4.7 and GPT-5.5",
   "organization_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "DeepSeek released V4 on April 24, 2026, shipping two open-weight Mixture-of-Experts models under MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "DeepSeek released V4 on April 24, 2026, shipping two open-weight Mixture-of-Experts models under MIT license."
   ],
   "parameters_evidence": [
    "V4-Pro carries 1.6 trillion total parameters with 49 billion active per token.",
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "V4-Pro scores 80.6% SWE-bench Verified, 90.1% GPQA Diamond, 93.5% LiveCodeBench in max reasoning mode - within a few points of Claude Opus 4.7 and GPT-5.5",
    "**TL;DR**"
   ]
  },
  {
   "url": "https://awesomeagents.ai/models/deepseek-v4",
   "title": "DeepSeek V4",
   "published_at": "2026-04-28T16:55:38",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion total with 13 billion active",
   "context_window": "1 million token context window",
   "license": "MIT",
   "significance": "At 88.1% GPQA Diamond and 86.2 MMLU-Pro, it's within a few points of V4-Pro on knowledge benchmarks while costing 12x less on input tokens.",
   "organization_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "DeepSeek released V4 on April 24, 2026, shipping two open-weight Mixture-of-Experts models under MIT license."
   ],
   "model_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "V4-Flash runs at 284 billion total with 13 billion active."
   ],
   "release_date_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "DeepSeek released V4 on April 24, 2026, shipping two open-weight Mixture-of-Experts models under MIT license."
   ],
   "parameters_evidence": [
    "V4-Flash runs at 284 billion total with 13 billion active.",
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026."
   ],
   "context_window_evidence": [
    "Both support a 1 million token context window.",
    "## Overview"
   ],
   "license_evidence": [
    "DeepSeek V4 ships in two open-weight MoE variants - V4-Pro at 1.6T/49B active and V4-Flash at 284B/13B active - both with 1M-token context and MIT license, released April 24, 2026.",
    "DeepSeek released V4 on April 24, 2026, shipping two open-weight Mixture-of-Experts models under MIT license."
   ],
   "significance_evidence": [
    "At 88.1% GPQA Diamond and 86.2 MMLU-Pro, it's within a few points of V4-Pro on knowledge benchmarks while costing 12x less on input tokens.",
    "## Overview"
   ]
  },
  {
   "url": "https://openclawradar.com/article/mistral-medium-3-5-128b-released",
   "title": "Mistral Medium 3.5 128B Released: Dense Model with Configurable Reasoning and Vision",
   "published_at": "2026-04-29T16:15:02",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "dense transformer model that replaces Mistral Medium 3.1 and Magistral in Le Chat, and Devstral 2 in their coding agent Vibe, supporting instruction-following, reasoning, and coding, with a vision encoder trained from scratch and configurable reasoning effort",
   "organization_evidence": [
    "# Mistral Medium 3.5 128B Released: Dense Model with Configurable Reasoning and Vision",
    "Mistral AI has released **Mistral Medium 3.5 (128B)**, a dense transformer model that replaces Mistral Medium 3.1 and Magistral in Le Chat, and Devstral 2 in their coding agent Vibe."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 128B Released: Dense Model with Configurable Reasoning and Vision",
    "\u270d\ufe0f OpenClawRadar\ud83d\udcc5 Published: April 29, 2026[\ud83d\udd17 Source ](https://www.reddit.com/r/LocalLLaMA/comments/1sz1qer/mistralaimistralmedium35128b_hugging_face/)"
   ],
   "parameters_evidence": [
    "# Mistral Medium 3.5 128B Released: Dense Model with Configurable Reasoning and Vision",
    "Mistral AI has released **Mistral Medium 3.5 (128B)**, a dense transformer model that replaces Mistral Medium 3.1 and Magistral in Le Chat, and Devstral 2 in their coding agent Vibe.",
    "- **Dense 128B parameters** \u2014 not Mixture of Experts."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 128B Released: Dense Model with Configurable Reasoning and Vision",
    "Mistral AI has released **Mistral Medium 3.5 (128B)**, a dense transformer model that replaces Mistral Medium 3.1 and Magistral in Le Chat, and Devstral 2 in their coding agent Vibe."
   ]
  },
  {
   "url": "http://decrypt.co/363178/google-gemma-4-open-source-ai?amp=1",
   "title": "Google Jumps Back Into the Open Source AI Race With Gemma 4",
   "published_at": "2026-04-02T18:18:58",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "Effective 2B and 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "The 31B Dense currently ranks third among all open models on Arena AI's text leaderboard. The 26B MoE sits sixth. Google claims both outcompete models 20 times their size",
   "organization_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "Google's open AI ambitions got a lot more serious today."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "Google drops Gemma 4, a family of open models under the Apache 2.0 license, just as the U.S. open-source scene badly needed a win.",
    "Gemma 4 ships in four sizes: Effective 2B and 4B for phones and edge devices, a 26B Mixture of Experts model focused on speed, and a 31B Dense model optimized for raw quality."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "Google drops Gemma 4, a family of open models under the Apache 2.0 license, just as the U.S. open-source scene badly needed a win.",
    "The 31B Dense currently ranks third among all open models on [Arena AI's text leaderboard](https://lmarena.ai/)."
   ]
  },
  {
   "url": "https://pureai.com/articles/2026/07/17/china-moonshot-ai-releases-kimi-k3.aspx",
   "title": "China's Moonshot AI Releases Kimi K3, Its Largest Open-Weight Model -- Pure AI",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked fourth among 189 models tested on Artificial Analysis Intelligence Index with a score of 57; K3 stands as Moonshot AI's most powerful open-source coding model to date and achieves open frontier intelligence.",
   "organization_evidence": [
    "China's Moonshot AI Releases Kimi K3, Its Largest Open-Weight Model",
    "Chinese artificial intelligence startup [Moonshot AI](https://www.moonshot.cn/) released [Kimi K3](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) this week, an open-weight model with 2.8 trillion total parameters that the Beijing-based company describes as achieving \"open frontier intelligence.\""
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Full model weights are scheduled for release on July 27."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "News",
    "China's Moonshot AI Releases Kimi K3, Its Largest Open-Weight Model",
    "Chinese artificial intelligence startup [Moonshot AI](https://www.moonshot.cn/) released [Kimi K3](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) this week, an open-weight model with 2.8 trillion total parameters that the Beijing-based company describes as achieving \"open frontier intelligence.\"",
    "An independent evaluation by [Artificial Analysis](https://artificialanalysis.ai/), a third-party AI benchmarking firm, gives K3 a score of 57 on its Intelligence Index, ranking it fourth among 189 models tested.",
    "In its release announcement, Moonshot said, \"K3 stands as Moonshot AI's most powerful open-source coding model to date.\""
   ]
  },
  {
   "url": "https://andrew.ooo/answers/glm-5-review-open-source-frontier",
   "title": "What is GLM-5? The Open-Source Frontier Model You Need to Know (2026)",
   "published_at": "2026-03-17T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5 is the first open-source model to genuinely compete with the top closed models.",
   "organization_evidence": [
    "What is GLM-5? The Open-Source Frontier Model You Need to Know (2026)",
    "| Developer | Zhipu AI (China) |",
    "Quick Facts"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What is GLM-5? The Open-Source Frontier Model You Need to Know (2026)",
    "| Released | February 12, 2026 |",
    "Quick Facts"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What is GLM-5? The Open-Source Frontier Model You Need to Know (2026)",
    "GLM-5 is the first open-source model to genuinely compete with the top closed models."
   ]
  },
  {
   "url": "https://heybeagle.com/blog/minimax-m3-open-weight-model-what-the-numbers-actually-mean",
   "title": "MiniMax M3 Open-Weight Model: What the Numbers Actually Mean",
   "published_at": "2026-07-01T04:01:55",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59% SWE-Bench Pro score, 1M-token context, and native multimodality",
   "organization_evidence": [
    "MiniMax M3 Open-Weight Model: What the Numbers Actually Mean",
    "MiniMax M3 shipped June 1, 2026 with a 59% SWE-Bench Pro score, 1M-token context, and native multimodality."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Open-Weight Model: What the Numbers Actually Mean",
    "MiniMax M3 shipped June 1, 2026 with a 59% SWE-Bench Pro score, 1M-token context, and native multimodality."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Open-Weight Model: What the Numbers Actually Mean",
    "MiniMax M3 shipped June 1, 2026 with a 59% SWE-Bench Pro score, 1M-token context, and native multimodality."
   ]
  },
  {
   "url": "https://thenewspaperdaily.com/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T04:03:16",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DeepSeek-V4-Pro-Max scores 80.6% on SWE-bench Verified, the highest open-weight result at its release and tied with Gemini 3.1 Pro.",
   "organization_evidence": [
    "Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI\u2019s Kimi K3, DeepSeek V4 Pro, and Zhipu AI\u2019s GLM-5.2 are all sparse Mixture-of-Experts (MoE) models with million-token context windows.",
    "**DeepSeek V4 Pro** is a 1.6-trillion-parameter MoE with 49B active parameters, using 384 routed experts plus one shared expert.",
    "## **The three contenders**"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| Released | July 16, 2026 | April 24, 2026 | June 13, 2026 |",
    "| Spec | Kimi K3 | DeepSeek V4 Pro | GLM-5.2 |",
    "## **The three contenders**"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek-V4-Pro-Max scores 80.6% on SWE-bench Verified, the highest open-weight result at its release and tied with Gemini 3.1 Pro.",
    "## **Benchmarks**"
   ]
  },
  {
   "url": "https://thenewspaperdaily.com/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T04:03:16",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.2 held the top open-weight spot until K3 shipped.",
   "organization_evidence": [
    "Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI\u2019s Kimi K3, DeepSeek V4 Pro, and Zhipu AI\u2019s GLM-5.2 are all sparse Mixture-of-Experts (MoE) models with million-token context windows.",
    "**GLM-5.2** is a 744-billion-parameter MoE with roughly 40B active parameters and a 1M-token context window.",
    "## **The three contenders**"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| Released | July 16, 2026 | April 24, 2026 | June 13, 2026 |",
    "| Spec | Kimi K3 | DeepSeek V4 Pro | GLM-5.2 |",
    "## **The three contenders**"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 held the top open-weight spot until K3 shipped.",
    "## **Benchmarks**"
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/qwen-agentworld-open-weight-language-world-model-ai-agents-june-2026",
   "title": "Qwen-AgentWorld Is an Open Model That Simulates Worlds for AI Agents",
   "published_at": "2026-06-28T04:12:39",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-06-24",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Covers seven distinct agent environments in one place and posted a result narrowly ahead of a leading proprietary model on the team's agent-environment benchmark.",
   "organization_evidence": [
    "# Qwen-AgentWorld Is an Open Model That Simulates Worlds for AI Agents",
    "Alibaba's Qwen team open-sourced AgentWorld on June 24, 2026 \u2014 a language world model that simulates digital environments so AI agents can practice and improve."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen-AgentWorld Is an Open Model That Simulates Worlds for AI Agents",
    "Alibaba's Qwen team open-sourced AgentWorld on June 24, 2026 \u2014 a language world model that simulates digital environments so AI agents can practice and improve."
   ],
   "parameters_evidence": [
    "# Qwen-AgentWorld Is an Open Model That Simulates Worlds for AI Agents",
    "The Qwen team published a compact **35B-parameter mixture-of-experts release** (with roughly 3B active parameters) under the permissive **Apache 2.0 license**, with weights available on Hugging Face and ModelScope and commercial use permitted."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen-AgentWorld Is an Open Model That Simulates Worlds for AI Agents",
    "Remarkably, the model covers **seven distinct agent environments in one place** \u2014 MCP, search, terminal, software engineering, web, operating system, and Android \u2014 giving developers a single sandbox spanning the surfaces real agents operate on.",
    "On the team's own agent-environment benchmark, the large configuration posted a result narrowly ahead of a leading proprietary model."
   ]
  },
  {
   "url": "http://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [2](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $1.50 / $7.50 | 2026-04-29 | 2026-04-29 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "parameters_evidence": [
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [2](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $1.50 / $7.50 | 2026-04-29 | 2026-04-29 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "http://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "parameters_evidence": [
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "http://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [4](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "parameters_evidence": [
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [4](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "http://ai-tldr.dev/models/diffusion-gemma",
   "title": "DiffusionGemma \u2014 Specs, Benchmarks & Speed | AI/TLDR",
   "published_at": "2026-06-10T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": "26B total (25.2B) MoE \u00b7 3.8B active",
   "context_window": null,
   "license": null,
   "significance": "Google's first open-weight text-diffusion model: a 26B Gemma 4 MoE that denoises text in parallel for up to 4x faster generation.",
   "organization_evidence": [
    "# DiffusionGemma",
    "DiffusionGemma is an experimental open-weight large language model from Google, released on June 10, 2026 as part of the Gemini Diffusion / DiffusionGemma line."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "| Released | 2026-06-10 |",
    "## Overview",
    "DiffusionGemma is an experimental open-weight large language model from Google, released on June 10, 2026 as part of the Gemini Diffusion / DiffusionGemma line."
   ],
   "parameters_evidence": [
    "| Parameters | 26B total (25.2B) MoE \u00b7 3.8B active |",
    "## Overview",
    "DiffusionGemma is an experimental open-weight large language model from Google, released on June 10, 2026 as part of the Gemini Diffusion / DiffusionGemma line."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google's first open-weight text-diffusion model: a 26B Gemma 4 MoE that denoises text in parallel for up to 4x faster generation.",
    "## Overview",
    "DiffusionGemma is an experimental open-weight large language model from Google, released on June 10, 2026 as part of the Gemini Diffusion / DiffusionGemma line."
   ]
  },
  {
   "url": "https://noqta.tn/en/news/zhipu-glm-5-open-source-model-rivals-western-ai-2026",
   "title": "Zhipu AI Releases GLM-5: A 744B Open-Source Model That Rivals Western AI Giants",
   "published_at": "2026-02-13T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": "744-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "approaching Claude Opus 4.5's 80.9% on SWE-bench Verified, record-low hallucination rate on the Artificial Analysis Intelligence Index v4.0",
   "organization_evidence": [
    "# Zhipu AI Releases GLM-5: A 744B Open-Source Model That Rivals Western AI Giants",
    "Chinese AI lab Zhipu AI has released GLM-5, a 744-billion-parameter large language model available under the permissive MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI Releases GLM-5: A 744B Open-Source Model That Rivals Western AI Giants",
    "The model, released on February 12, 2026, was trained entirely on Huawei Ascend chips and claims near-parity with leading Western models from Anthropic, OpenAI, and Google on key benchmarks \u2014 a milestone that signals China's rapidly closing gap in frontier AI development."
   ],
   "parameters_evidence": [
    "# Zhipu AI Releases GLM-5: A 744B Open-Source Model That Rivals Western AI Giants",
    "Chinese AI lab Zhipu AI has released GLM-5, a 744-billion-parameter large language model available under the permissive MIT license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI Releases GLM-5: A 744B Open-Source Model That Rivals Western AI Giants",
    "## Key Highlights",
    "The model, released on February 12, 2026, was trained entirely on Huawei Ascend chips and claims near-parity with leading Western models from Anthropic, OpenAI, and Google on key benchmarks \u2014 a milestone that signals China's rapidly closing gap in frontier AI development.",
    "- **77.8% on SWE-bench Verified**, approaching Claude Opus 4.5's 80.9%",
    "- **Record-low hallucination rate** on the Artificial Analysis Intelligence Index v4.0"
   ]
  },
  {
   "url": "https://aitechconnect.in/news/minimax-m3-open-weight-frontier-coding-1m-context-2026",
   "title": "MiniMax M3: Open-Weight Frontier Coding With a 1M Context",
   "published_at": "2026-06-17T06:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "59.0% on SWE-Bench Pro (on par with GPT-5.5) and 66.0% on Terminal-Bench 2.1. It tops the open-weight SWE-Bench Pro leaderboard.",
   "organization_evidence": [
    "## What you need to know",
    "MiniMax M3, released on 1 June 2026 as the company's open-weight flagship, is the clearest sign yet that the trade-off is collapsing."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What you need to know",
    "MiniMax M3, released on 1 June 2026 as the company's open-weight flagship, is the clearest sign yet that the trade-off is collapsing."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## What you need to know",
    "- **Coding parity with a closed flagship.** 59.0% on SWE-Bench Pro (on par with GPT-5.5) and 66.0% on Terminal-Bench 2.1. It tops the open-weight SWE-Bench Pro leaderboard."
   ]
  },
  {
   "url": "https://shaam.blog/articles/laguna-s-2-1-open-weight-coding-model-guide-2026",
   "title": "Poolside Laguna S 2.1: How a 118B Open-Weight Coding Model Punches Above Its Weight Class",
   "published_at": "2026-07-29T23:54:31",
   "organization": "Poolside",
   "model": "Poolside Laguna S 2.1",
   "release_date": "2026-07-21",
   "parameters": "118 billion",
   "context_window": "1-million-token",
   "license": "OpenMDW-1.1 license",
   "significance": "Terminal-Bench 2.1: 70.2%",
   "organization_evidence": [
    "It was built by Poolside, a San Francisco-based AI lab founded in 2023 by Jason Warner (former CTO of GitHub) and Eiso Kant (former founder of source-code-analysis startup source), which focuses exclusively on foundation models for software engineering.",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "model_evidence": [
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "release_date_evidence": [
    "The model was released on July 21, 2026 with open weights on Hugging Face under the OpenMDW-1.1 license, along with GGUF and MLX conversions for local deployment.",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "parameters_evidence": [
    "It has 118 billion total parameters but only activates 8 billion per token, which is what makes it fast and compact enough to run on a single high-memory machine.",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "context_window_evidence": [
    "A 1-million-token context window means it can hold an entire large codebase in memory without losing the thread.",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "license_evidence": [
    "The model was released on July 21, 2026 with open weights on Hugging Face under the OpenMDW-1.1 license, along with GGUF and MLX conversions for local deployment.",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ],
   "significance_evidence": [
    "Terminal-Bench 2.1: 70.2%",
    "Poolside Laguna S 2.1 is a Mixture-of-Experts (MoE) language model specifically built for agentic software engineering \u2014 not general chat, not images, not search."
   ]
  },
  {
   "url": "https://tpsreport.news/news/deepseek-v4-flash-0731-post-training-update",
   "title": "DeepSeek V4-Flash 0731: Post-Training Update Boosts Agent Benchmarks",
   "published_at": "2026-08-01T01:51:01",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Terminal-Bench score rose from 56.9 to 82.7 \u2014 a 25.8-point jump, while Artificial Analysis separately reported Terminal-Bench 2.1 reaching 79%, GDPval-AA v2 Elo climbing from 1189 to 1559, a \u03c4\u00b3-Bench Banking gain of 8 points, and a 12% reduction in output-token usage versus the prior version.",
   "organization_evidence": [
    "# DeepSeek V4-Flash 0731 Update Jumps Terminal-Bench Score by 25.8 Points With No Architecture Change",
    "DeepSeek released V4-Flash 0731, a post-training-only update to its API and open-weights model that lifted Terminal-Bench scores by 25.8 points without changing model architecture or parameter count."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4-Flash 0731 Update Jumps Terminal-Bench Score by 25.8 Points With No Architecture Change",
    "DeepSeek released V4-Flash 0731 on July 31, 2026, a post-training-only update to its Flash API and open-weights model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmark Gains",
    "According to figures cited by @cline, Terminal-Bench score rose from 56.9 to 82.7 \u2014 a 25.8-point jump."
   ]
  },
  {
   "url": "https://awesomeagents.ai/news/qwen-3-6-35b-a3b-open-source-coder",
   "title": "Alibaba's Qwen3.6 Coder: 73.4 SWE-bench, 22GB VRAM",
   "published_at": "2026-04-22T21:20:25",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B (256 experts)",
   "context_window": null,
   "license": null,
   "significance": "73.4 on SWE-bench Verified, 51.5 on Terminal-Bench 2.0, 92.7 on AIME 2026",
   "organization_evidence": [
    "Qwen3.6-35B-A3B lands with 73.4 on SWE-bench Verified and Apache 2.0 weights, all from 3 billion active parameters routed through a 256-expert MoE.",
    "Alibaba's Qwen team shipped [Qwen3.6-35B-A3B](/models/qwen-3-6-35b-a3b/) on 16 April under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba's Qwen team shipped [Qwen3.6-35B-A3B](/models/qwen-3-6-35b-a3b/) on 16 April under Apache 2.0.",
    "| Released | 16 April 2026 |",
    "**Qwen3.6-35B-A3B at a glance**"
   ],
   "parameters_evidence": [
    "| Spec | Value |",
    "| Total parameters | 35B (256 experts) |",
    "**Qwen3.6-35B-A3B at a glance**"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen3.6-35B-A3B lands with 73.4 on SWE-bench Verified and Apache 2.0 weights, all from 3 billion active parameters routed through a 256-expert MoE.",
    "Alibaba's Qwen team shipped [Qwen3.6-35B-A3B](/models/qwen-3-6-35b-a3b/) on 16 April under Apache 2.0.",
    "The benchmark line reads hard: 73.4 on SWE-bench Verified, 51.5 on Terminal-Bench 2.0, 92.7 on AIME 2026."
   ]
  },
  {
   "url": "https://fazm.ai/blog/open-source-llm-news-april-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2026-04-11T03:50:43",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Full permissive license, unlike the original Codestral",
   "organization_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Mistral releases Codestral 2 under Apache 2.0",
    "Mistral Opens Up Codestral 2",
    "Codestral 2, released April 8 under Apache 2.0, fixes that problem entirely."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Mistral releases Codestral 2 under Apache 2.0",
    "Codestral 2, released April 8 under Apache 2.0, fixes that problem entirely."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Mistral releases Codestral 2 under Apache 2.0",
    "Codestral 2, released April 8 under Apache 2.0, fixes that problem entirely."
   ]
  },
  {
   "url": "https://fazm.ai/blog/open-source-llm-news-april-2026",
   "title": "Fazm - AI Computer Agent for macOS | Voice-Controlled Desktop Automation",
   "published_at": "2026-04-11T03:50:43",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open model to beat GPT-4o on this benchmark",
   "organization_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Alibaba's Qwen team released the 72B variant of Qwen 3 on April 5, and the benchmark results caught attention."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Alibaba's Qwen team released the 72B variant of Qwen 3 on April 5, and the benchmark results caught attention."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source LLM News April 2026: What Happened and Why It Matters",
    "Qwen 3 72B scores 89.1 on MMLU-Pro | April 5 | First open model to beat GPT-4o on this benchmark"
   ]
  },
  {
   "url": "https://ai-primer.com/engineer/stories/mistral-small-4-119b-256k",
   "title": "Mistral releases Small 4 119B MoE with 256K context",
   "published_at": "2026-03-16T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2603",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "unifies Instruct, Reasoning, and Devstral-style capabilities in one model, lifts GPQA Diamond from 59.1 to 71.2, MMLU-Pro from 73.5 to 78.0, IFBench from 35.7 to 48.0, and MMMU-Pro from 46.3 to 60.0",
   "organization_evidence": [
    "release",
    "# Mistral releases Small 4 119B MoE with 256K context",
    "Mistral shipped Mistral Small 4, a 119B MoE model with 6.5B active parameters, multimodal input, configurable reasoning, and Apache 2.0 weights."
   ],
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   "release_date_evidence": [
    "release",
    "# Mistral releases Small 4 119B MoE with 256K context",
    "SGLang shipped day-one support with a concrete server command that uses `mistralai/Mistral-Small-4-119B-2603` plus `--tool-call-parser mistral` and `--reasoning-parser mistral`, which means existing tool-calling pipelines do not need custom glue to expose the model's agentic and hybrid reasoning modes [in LMSYS's post](https://x.com/lmsysorg/status/2033664202343366794)."
   ],
   "parameters_evidence": null,
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   "significance_evidence": [
    "What shipped",
    "That material described a \u201cpowerful hybrid model\u201d that \u201cunifies\u201d Instruct, Reasoning, and Devstral-style capabilities in one model, with 128 experts, 4 active experts, 119B total parameters, and 6.5B active per token, as shown in [the pre-release PR leak](https://x.com/testingcatalog/status/2033630453958144205) and [the architecture screenshot](https://x.com/AiBattle_/status/2033604755713306754).",
    "Where Mistral says it improves",
    "The internal comparison chart in [the launch materials](https://x.com/itsPaulAi/status/2033658688469123459) shows separate instruct and reasoning variants for the same model, with reasoning mode lifting GPQA Diamond from 59.1 to 71.2, MMLU-Pro from 73.5 to 78.0, IFBench from 35.7 to 48.0, and MMMU-Pro from 46.3 to 60.0; Arena Hard improves more modestly from 55.8 to 58.3 [as a reposted chart also shows](https://x.com/kimmonismus/status/2033662154419933544)."
   ]
  },
  {
   "url": "https://thenextweb.com/news/google-gemma-4-open-models-apache-2-launch",
   "title": "Google launches Gemma 4: four open-weight models from smartphones to workstations",
   "published_at": "2026-04-02T18:16:22",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "2B",
   "context_window": null,
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   "significance": "Effective 2B (E2B) edge model, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware",
   "organization_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "[Google has released Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), the latest generation of its open-weight model family, in four sizes designed to cover everything from on-device inference on smartphones to workstation-class deployments."
   ],
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs."
   ]
  },
  {
   "url": "https://thenextweb.com/news/google-gemma-4-open-models-apache-2-launch",
   "title": "Google launches Gemma 4: four open-weight models from smartphones to workstations",
   "published_at": "2026-04-02T18:16:22",
   "organization": "Google",
   "model": null,
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   "organization_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "[Google has released Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), the latest generation of its open-weight model family, in four sizes designed to cover everything from on-device inference on smartphones to workstation-class deployments."
   ],
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs."
   ]
  },
  {
   "url": "https://thenextweb.com/news/google-gemma-4-open-models-apache-2-launch",
   "title": "Google launches Gemma 4: four open-weight models from smartphones to workstations",
   "published_at": "2026-04-02T18:16:22",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "26B",
   "context_window": null,
   "license": null,
   "significance": "26B Mixture-of-Experts (MoE) model aimed at offline use on developer hardware and consumer GPUs, sitting sixth on the Arena AI text leaderboard",
   "organization_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "[Google has released Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), the latest generation of its open-weight model family, in four sizes designed to cover everything from on-device inference on smartphones to workstation-class deployments."
   ],
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The four variants are the Effective 2B (E2B) and Effective 4B (E4B) edge models, designed to run on-device on phones, Raspberry Pi, and Jetson Nano hardware developed in collaboration with the Pixel team, Qualcomm, and MediaTek; and the 26B Mixture-of-Experts (MoE) and 31B Dense models, aimed at offline use on developer hardware and consumer GPUs.",
    "The 31B Dense model currently ranks third among all open models on the Arena AI text leaderboard; the 26B MoE sits sixth."
   ]
  },
  {
   "url": "https://thenextweb.com/news/google-gemma-4-open-models-apache-2-launch",
   "title": "Google launches Gemma 4: four open-weight models from smartphones to workstations",
   "published_at": "2026-04-02T18:16:22",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "31B Dense model currently ranks third among all open models on the Arena AI text leaderboard",
   "organization_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "[Google has released Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), the latest generation of its open-weight model family, in four sizes designed to cover everything from on-device inference on smartphones to workstation-class deployments."
   ],
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   "release_date_evidence": null,
   "parameters_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
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   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The 31B Dense model currently ranks third among all open models on the Arena AI text leaderboard; the 26B MoE sits sixth."
   ]
  },
  {
   "url": "https://kvmnode.com/en/blog/2026-kimi-k3-review-open-source-llm.html",
   "title": "Kimi K3 Review: The 2.8-Trillion-Parameter Open-Source Model That Challenges Claude and GPT",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": "1,048,576 tokens (1M)",
   "license": null,
   "significance": "largest open-source AI model by parameter count",
   "organization_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "On the night of July 16, 2026, Moonshot AI posted a banner at the top of its API documentation \u2014 \"\ud83c\udf89",
    "\"\ud83c\udf89 Kimi K3 is live!\"",
    "\u2014 with no keynote, no hype cycle, just a technical blog post, a pricing page, and an immediately callable model ID `kimi-k3`."
   ],
   "model_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "On the night of July 16, 2026, Moonshot AI posted a banner at the top of its API documentation \u2014 \"\ud83c\udf89",
    "\"\ud83c\udf89 Kimi K3 is live!\"",
    "\u2014 with no keynote, no hype cycle, just a technical blog post, a pricing page, and an immediately callable model ID `kimi-k3`."
   ],
   "release_date_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "Full weights open on **July 27**, at pricing roughly 40% below Claude Opus 4.8."
   ],
   "parameters_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "on the night of July 16, 2026, Moonshot AI quietly added a **2.8-trillion-parameter** model to the top of its API docs \u2014 Kimi K3, now the largest open-source AI model in the world."
   ],
   "context_window_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "It uses sparse MoE architecture, activating 16 of 896 experts at inference; paired with a **1-million-token context window** (roughly five full copies of *Dream of the Red Chamber*) and native vision understanding, it targets complex coding, long-document reasoning, and knowledge work."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "What Is Kimi K3? The 2.8T Open-Source Model and Launch Context",
    "**One-line definition:** Kimi K3 is currently the **largest open-source AI model by parameter count** \u2014 2.8T parameters, roughly 75% larger than DeepSeek V4 Pro (1.6T), 2.7x Xiaomi's open model (1.02T), and more than 7x Alibaba (397B)."
   ]
  },
  {
   "url": "https://felloai.com/fr/glm-5-2",
   "title": "GLM 5.2: Zhipu\u2019s 1M-Context Open-Source Model Explained",
   "published_at": "2026-06-16T05:57:44",
   "organization": "Zhipu AI",
   "model": "GLM 5.2",
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter",
   "context_window": "1,000,000 tokens",
   "license": "MIT license",
   "significance": "A coding-first frontier model with a Mixture-of-Experts design, 1-million-token context window, and MIT license.",
   "organization_evidence": [
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "model_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "release_date_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "parameters_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "context_window_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "license_evidence": [
    "**GLM 5.2** arrived on **June 13, 2026**, and it lands with three headline numbers: a **744-billion-parameter** Mixture-of-Experts design, a **1-million-token context window**, and an **MIT license** that makes the weights free for anyone to download and run.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ],
   "significance_evidence": [
    "Chinese AI lab **Zhipu AI** (operating as [**Z.ai**](https://z.ai)) shipped it as a coding-first frontier model, and the timing was not subtle.",
    "GLM 5.2",
    "## What Is GLM 5.2?"
   ]
  },
  {
   "url": "https://llmrequirements.com/news/2026-07-11-june-open-weight-roundup",
   "title": "June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
   "published_at": "2026-07-11T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95 billion",
   "context_window": null,
   "license": null,
   "significance": "runs on any machine with 16 GB of RAM or VRAM, processes text, images, audio, and video natively",
   "organization_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "Google released Gemma 4 12B on June 3, 2026 under Apache 2.0 (per the official blog and model card)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "Google released Gemma 4 12B on June 3, 2026 under Apache 2.0 (per the official blog and model card)."
   ],
   "parameters_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "It\u2019s an 11.95-billion-parameter dense model that processes text, images, audio, and video natively and runs on any machine with 16 GB of RAM or VRAM (reported, per the Ars Technica hands-on and VentureBeat coverage, June 2026)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "It\u2019s an 11.95-billion-parameter dense model that processes text, images, audio, and video natively and runs on any machine with 16 GB of RAM or VRAM (reported, per the Ars Technica hands-on and VentureBeat coverage, June 2026)."
   ]
  },
  {
   "url": "https://llmrequirements.com/news/2026-07-11-june-open-weight-roundup",
   "title": "June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
   "published_at": "2026-07-11T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "428B total parameters",
   "context_window": null,
   "license": null,
   "significance": "59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 83.5% BrowseComp",
   "organization_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "MiniMax shipped M3 on June 1, 2026, a mixture-of-experts model with roughly 428B total parameters and about 23B active per token, built on MiniMax Sparse Attention for 1M-token context (reported, per the official Hugging Face repository and MarkTechPost, June 2026)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "MiniMax shipped M3 on June 1, 2026, a mixture-of-experts model with roughly 428B total parameters and about 23B active per token, built on MiniMax Sparse Attention for 1M-token context (reported, per the official Hugging Face repository and MarkTechPost, June 2026)."
   ],
   "parameters_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "MiniMax shipped M3 on June 1, 2026, a mixture-of-experts model with roughly 428B total parameters and about 23B active per token, built on MiniMax Sparse Attention for 1M-token context (reported, per the official Hugging Face repository and MarkTechPost, June 2026)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "Reported benchmarks: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 83.5% BrowseComp (reported, per MiniMax\u2019s published results)."
   ]
  },
  {
   "url": "https://llmrequirements.com/news/2026-07-11-june-open-weight-roundup",
   "title": "June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
   "published_at": "2026-07-11T00:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550B-parameter",
   "context_window": null,
   "license": null,
   "significance": "300-plus output tokens per second on its own stack",
   "organization_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "NVIDIA launched Nemotron 3 Ultra on June 4, 2026 at Computex."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "NVIDIA launched Nemotron 3 Ultra on June 4, 2026 at Computex."
   ],
   "parameters_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "It\u2019s a 550B-parameter mixture-of-experts model with 55B active per token, using a hybrid Mamba-Transformer architecture (rather than a pure transformer) with native multi-token prediction and a 1M-token context (reported, per NVIDIA\u2019s release materials and MarkTechPost, June 2026)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "NVIDIA reports 300-plus output tokens per second on its own stack (reported, per the launch announcement, June 2026)."
   ]
  },
  {
   "url": "https://llmrequirements.com/news/2026-07-11-june-open-weight-roundup",
   "title": "June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
   "published_at": "2026-07-11T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "1T-parameter",
   "context_window": null,
   "license": null,
   "significance": "At 81.1 on MCP Mark Verified it edges past Claude Opus 4.8 in that benchmark",
   "organization_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "Moonshot AI released Kimi K2.7 Code on June 12, 2026 under a Modified MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "Moonshot AI released Kimi K2.7 Code on June 12, 2026 under a Modified MIT license."
   ],
   "parameters_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "It\u2019s a 1T-parameter MoE with 32B active (384 experts, top-8 plus one shared), a 256K context, and a small vision encoder for image and video input (reported, per Moonshot\u2019s model card and Hugging Face repository, June 2026)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# June 2026 open-weight roundup: Gemma 4 12B, MiniMax M3, Nemotron 3 Ultra, Kimi K2.7 Code",
    "At 81.1 on MCP Mark Verified it edges past Claude Opus 4.8 in that benchmark (reported, per Moonshot\u2019s published results)."
   ]
  },
  {
   "url": "https://tech-insider.org/nvidia-nemotron-3-ultra-open-weight-2026",
   "title": "Nvidia Nemotron 3 Ultra: 550B Params, Trails Rivals [2026]",
   "published_at": "2026-08-05T10:16:56",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": "550-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "A 550-billion-parameter open-weight language model with a 1-million-token context window and hybrid Mamba-Attention Mixture-of-Experts architecture.",
   "organization_evidence": [
    "Nvidia spent the past three years convincing the world it makes the chips that power AI.",
    "On June 4, 2026, it made a different kind of statement: it gave away the model too.",
    "**Nvidia Nemotron 3 Ultra**, a 550-billion-parameter open-weight language model, is now sitting on Hugging Face for anyone to download, fine-tune, or deploy, no API key required."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 4, 2026, it made a different kind of statement: it gave away the model too."
   ],
   "parameters_evidence": [
    "**Nvidia Nemotron 3 Ultra**, a 550-billion-parameter open-weight language model, is now sitting on Hugging Face for anyone to download, fine-tune, or deploy, no API key required."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**Nvidia Nemotron 3 Ultra**, a 550-billion-parameter open-weight language model, is now sitting on Hugging Face for anyone to download, fine-tune, or deploy, no API key required."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/deepseek-v4-open-weight-llm-review",
   "title": "DeepSeek V4 \u2014 Open-Weight Frontier, Huawei Chips, and 93.5% on LiveCodeBench \u2014 ChatForest",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "93.5% on LiveCodeBench; 1.6T total parameters / 49B active; SWE-bench Verified: 80.6%; GPQA Diamond: 90.1%; Codeforces: 3,206 Elo; Context window: 1 million tokens",
   "organization_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
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   "url": "https://blogs.novita.ai/qwen-3-5-medium-model-series-on-novita-ai",
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   "url": "https://blogs.novita.ai/qwen-3-5-medium-model-series-on-novita-ai",
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   "url": "https://getyourdozai.blogspot.com/2026/07/the-open-source-ai-revolution-in-2026.html",
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   "release_date": "2026-04",
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   "significance": "frontier-class mid-tier model with 256K context",
   "organization_evidence": [
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    "Mistral's mid-2026 strategy centers on two complementary models."
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   "release_date_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Meanwhile Mistral Medium 3.5 arrived in April 2026 as a dense 256K-context model described by Mistral's changelog as frontier-class mid-tier."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Meanwhile Mistral Medium 3.5 arrived in April 2026 as a dense 256K-context model described by Mistral's changelog as frontier-class mid-tier."
   ]
  },
  {
   "url": "https://getyourdozai.blogspot.com/2026/07/the-open-source-ai-revolution-in-2026.html",
   "title": "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
   "published_at": "2026-07-28T10:23:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "introduced Apache 2.0 licensing across the board",
   "organization_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Google's Gemma family split cleanly in direction between its older and newer releases."
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   "release_date_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "Gemma 4 followed in April 2026 as the cleaner story, introducing Apache 2.0 licensing across the board, an important distinction for enterprises weighing long-term compliance risk."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
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   ]
  },
  {
   "url": "https://getyourdozai.blogspot.com/2026/07/the-open-source-ai-revolution-in-2026.html",
   "title": "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
   "published_at": "2026-07-28T10:23:00",
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   "release_date": "2026-04-24",
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   "significance": "brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes",
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   "release_date_evidence": [
    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "The DeepSeek V4 Preview released April 24, 2026 brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes."
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   "parameters_evidence": null,
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    "The Open-Source AI Revolution in 2026: How Llama, Mistral, and Gemma Are Closing the Gap with Proprietary Models",
    "The DeepSeek V4 Preview released April 24, 2026 brings two production tiers: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active, both featuring a default 1M context window and dual Thinking-Non-Thinking modes."
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   "url": "https://groundtruth.day/news/mistral-releases-a-model-that-does-formal-math-proofs.html",
   "title": "Mistral releases a lean, open model built for formal math proofs",
   "published_at": "2026-06-30T21:36:43",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-06-30",
   "parameters": "119 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Specialized model built for formal, machine-checked mathematical proofs in the Lean 4 proof system and for translating ordinary math into that formal language, using a mixture-of-experts design to activate around 6.5 billion parameters out of 119 billion.",
   "organization_evidence": [
    "Mistral releases a lean, open model built for formal math proofs",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "News \u00b7 [2026-06-30](2026-06-30.html)",
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   "parameters_evidence": [
    "Leanstral has about 119 billion total parameters - the internal numbers that hold what it knows - but it only activates around 6.5 billion of them for any given piece of work."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Mistral releases a lean, open model built for formal math proofs",
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   ]
  },
  {
   "url": "https://localaimaster.com/models/gemma-4",
   "title": "Gemma 4: Every Model, What Hardware Runs It (Phone to H100)",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-03-31",
   "parameters": "~2.3B effective",
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   "significance": "Phones, Raspberry Pi, Jetson Orin Nano \u2014 fully offline",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| E2B | ~2.3B effective | Phones, Raspberry Pi, Jetson Orin Nano \u2014 fully offline (Google's claim) |"
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| E2B | ~2.3B effective | Phones, Raspberry Pi, Jetson Orin Nano \u2014 fully offline (Google's claim) |"
   ]
  },
  {
   "url": "https://localaimaster.com/models/gemma-4",
   "title": "Gemma 4: Every Model, What Hardware Runs It (Phone to H100)",
   "published_at": "2026-07-20T00:00:00",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| E4B | ~4.5B effective | Same targets as E2B, more capable |"
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| E4B | ~4.5B effective | Same targets as E2B, more capable |"
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   "url": "https://localaimaster.com/models/gemma-4",
   "title": "Gemma 4: Every Model, What Hardware Runs It (Phone to H100)",
   "published_at": "2026-07-20T00:00:00",
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   "release_date": "2026-06-03",
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   "significance": "Text + audio + image input in one unified model, up to 256K context",
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    "| 12B Unified | 12B | Text + audio + image input in one unified model, up to 256K context. Added June 3, 2026 |"
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| 12B Unified | 12B | Text + audio + image input in one unified model, up to 256K context. Added June 3, 2026 |"
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  },
  {
   "url": "https://localaimaster.com/models/gemma-4",
   "title": "Gemma 4: Every Model, What Hardware Runs It (Phone to H100)",
   "published_at": "2026-07-20T00:00:00",
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   "release_date": "2026-03-31",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| 26B-A4B | 26B total / ~4B active (MoE) | MoE speed: quality of its size class, compute of a ~4B |"
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    "| 26B-A4B | 26B total / ~4B active (MoE) | MoE speed: quality of its size class, compute of a ~4B |"
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  },
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   "url": "https://localaimaster.com/models/gemma-4",
   "title": "Gemma 4: Every Model, What Hardware Runs It (Phone to H100)",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-03-31",
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   "significance": "The full-strength dense flagship; fits a single 80GB H100",
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| 31B | 31B dense | The full-strength dense flagship; fits a single 80GB H100 (Google's claim) |"
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    "Gemma 4: Every Model, and What Hardware Actually Runs It",
    "| 31B | 31B dense | The full-strength dense flagship; fits a single 80GB H100 (Google's claim) |"
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  {
   "url": "https://mindstackreview.com/articles/2026-07-27-moonshot-ai-opens-kimi-k3-the-world-s-largest-open-weight-model-for-free-downloa",
   "title": "Moonshot releases Kimi K3 weights, dropping a 2.8-trillion-parameter open model into enterprise buyers' laps",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
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   "significance": "largest open-weight model ever built free to download",
   "organization_evidence": [
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   "release_date_evidence": [
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Moonshot releases Kimi K3 weights, dropping a 2.8-trillion-parameter open model into enterprise buyers' laps",
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  },
  {
   "url": "https://telnyx.com/release-notes/minimax-m3",
   "title": "Minimax M3 now available on Telnyx Inference",
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   "organization": "MiniMax",
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   "release_date": "2026-06-12",
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   "license": null,
   "significance": "MiniMax M3 is the first open-weight model to combine frontier coding and agent capabilities, a 1M-token context window, and native multimodal understanding.",
   "organization_evidence": [
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   "release_date_evidence": [
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   "significance_evidence": [
    "# Minimax M3 is now available on Telnyx Inference",
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   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/nvidia-nemotron-labs-diffusion-language-models-fastdiffuser-self-speculation-may-23-2026",
   "title": "NVIDIA Releases Nemotron Diffusion Language Models \u2014 A Single Checkpoint That Generates Text Up to 6.4x Faster",
   "published_at": "2026-05-27T18:09:35",
   "organization": "NVIDIA Nemotron Labs",
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   "release_date": "2026-05-23",
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   "license": null,
   "significance": "Generates tokens in parallel and refines them, hitting 6.4x speedups via self-speculation; 8B model beats Qwen3 8B.",
   "organization_evidence": [
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   "release_date_evidence": [
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   "context_window_evidence": null,
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   "significance_evidence": [
    "# NVIDIA Releases Nemotron Diffusion Language Models \u2014 A Single Checkpoint That Generates Text Up to 6.4x Faster",
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  },
  {
   "url": "https://thequantumdispatch.com/articles/nvidia-nemotron-labs-diffusion-language-models-fastdiffuser-self-speculation-may-23-2026",
   "title": "NVIDIA Releases Nemotron Diffusion Language Models \u2014 A Single Checkpoint That Generates Text Up to 6.4x Faster",
   "published_at": "2026-05-27T18:09:35",
   "organization": "LG AI Research",
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   "release_date": "2026-08-06",
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   "context_window": null,
   "license": null,
   "significance": "750-billion-parameter mixture-of-experts model with 37B active parameters.",
   "organization_evidence": [
    "### K-EXAONE 2.0 Ships 750B Open Weights Under Apache 2.0",
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   "release_date_evidence": [
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   "context_window_evidence": null,
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   "significance_evidence": [
    "### K-EXAONE 2.0 Ships 750B Open Weights Under Apache 2.0",
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  },
  {
   "url": "https://felloai.com/deepseek-v4",
   "title": "Specs, Benchmarks and the 0731 Release",
   "published_at": "2026-04-24T05:04:00",
   "organization": "DeepSeek",
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   "release_date": "2026-07-31",
   "parameters": "284 billion total with 13 billion activated",
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   "license": null,
   "significance": "Terminal Bench 2.1 went from 61.8 to 82.7 on DeepSeek\u2019s own harness, past the V4-Pro preview at 72.1.",
   "organization_evidence": [
    "On **31 July 2026** DeepSeek shipped **[DeepSeek-V4-Flash-0731](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)**, the official release of V4-Flash that supersedes the April preview."
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   "release_date_evidence": [
    "On **31 July 2026** DeepSeek shipped **[DeepSeek-V4-Flash-0731](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)**, the official release of V4-Flash that supersedes the April preview."
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    "On **31 July 2026** DeepSeek shipped **[DeepSeek-V4-Flash-0731](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)**, the official release of V4-Flash that supersedes the April preview."
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    "**Terminal Bench 2.1 went from 61.8 to 82.7** on DeepSeek\u2019s own harness, past the V4-Pro preview at 72.1.",
    "On **31 July 2026** DeepSeek shipped **[DeepSeek-V4-Flash-0731](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)**, the official release of V4-Flash that supersedes the April preview."
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  },
  {
   "url": "https://felloai.com/deepseek-v4",
   "title": "Specs, Benchmarks and the 0731 Release",
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   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
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   "context_window": "one million tokens",
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   "release_date_evidence": [
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   "license_evidence": [
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  },
  {
   "url": "https://felloai.com/deepseek-v4",
   "title": "Specs, Benchmarks and the 0731 Release",
   "published_at": "2026-04-24T05:04:00",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion total with 13 billion activated",
   "context_window": "one million tokens",
   "license": "MIT License",
   "significance": "V4-Flash pushes that further to 10% of the FLOPs and 7% of the cache.",
   "organization_evidence": [
    "**DeepSeek-V4-Flash** has **284 billion** total with **13 billion** activated.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "model_evidence": [
    "**DeepSeek-V4-Flash** has **284 billion** total with **13 billion** activated.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "release_date_evidence": [
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "parameters_evidence": [
    "**DeepSeek-V4-Flash** has **284 billion** total with **13 billion** activated.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "context_window_evidence": [
    "**DeepSeek-V4-Flash** has **284 billion** total with **13 billion** activated.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "license_evidence": [
    "Both are on Hugging Face under the **MIT License**, through the DeepSeek API, and via chat.deepseek.com.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ],
   "significance_evidence": [
    "V4-Flash pushes that further to 10% of the FLOPs and 7% of the cache.",
    "The V4 series first arrived on **24 April 2026** as previews, an open-source Mixture-of-Experts family with a native one-million-token context window."
   ]
  },
  {
   "url": "https://theplanettools.ai/tools/llama-5",
   "title": "Llama 5 Review: Meta Open Weights Frontier \u2014 8.0 out of 10",
   "published_at": "2026-05-01T03:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier-class benchmark performance matching or exceeding GPT-5 and Gemini 3 across reasoning, coding, and agentic benchmarks.",
   "organization_evidence": [
    "Llama 5 is Meta's open-weights large language model announced April 8, 2026 by Mark Zuckerberg.",
    "Llama 5 is the fifth-generation open-weights large language model family from Meta Platforms, announced April 8, 2026 by Mark Zuckerberg in an event broadcast on CNBC and across Meta's developer channels."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Llama 5 is Meta's open-weights large language model announced April 8, 2026 by Mark Zuckerberg.",
    "Llama 5 is the fifth-generation open-weights large language model family from Meta Platforms, announced April 8, 2026 by Mark Zuckerberg in an event broadcast on CNBC and across Meta's developer channels."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama 5 is Meta's open-weights large language model announced April 8, 2026 by Mark Zuckerberg.",
    "The model targets frontier-class performance matching or exceeding GPT-5 and Gemini 3 across reasoning, coding, and agentic benchmarks."
   ]
  },
  {
   "url": "https://k3-kimi.com/blog/kimi-k3-complete-guide",
   "title": "Kimi K3: Complete Guide to Moonshot's 2.8T Model (2026)",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "Ranked #1 on 4 of 8 real-world agentic benchmarks in Moonshot's launch suite and highest rank ever achieved by an open-weight model (#4 out of 189 on Artificial Analysis)",
   "organization_evidence": [
    "# Kimi K3: Complete Guide to Moonshot's 2.8T Model (2026)",
    "**Kimi K3** is the newest flagship AI model from **Moonshot AI**, officially launched on **July 16, 2026**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Release timeline",
    "- **July 27, 2026** \u2014 open weights scheduled for release under a Modified MIT license."
   ],
   "parameters_evidence": [
    "Key specifications at a glance",
    "| Total parameters | 2.8T |",
    "# Kimi K3: Complete Guide to Moonshot's 2.8T Model (2026)",
    "It is the largest open-weight model ever released \u2014 a 2.8-trillion-parameter mixture-of-experts system with a 1-million-token context window, native image and video understanding, and benchmark scores that put it within striking distance of GPT-5.6 Sol and Claude Fable 5 at roughly half the cost per task."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: Complete Guide to Moonshot's 2.8T Model (2026)",
    "It ranks **#1 on 4 of 8 real-world agentic benchmarks** in Moonshot\u2019s launch suite.",
    "Independent testers at Artificial Analysis place K3 at **#4 out of 189 AI models** (score 57), ahead of Claude Opus 4.8 and GPT-5.5 \u2014 the highest rank ever achieved by an open-weight model."
   ]
  },
  {
   "url": "https://devpik.com/blog/glm-5-1-autonomous-coding-agent",
   "title": "GLM-5.1: The Open-Source AI That Works an 8-Hour Shift (Developer Guide)",
   "published_at": "2026-04-08T11:28:49",
   "organization": "Z.AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "GLM-5.1 ranks #1 open-source and #3 globally across the combined SWE-Bench Pro, Terminal-Bench, and NL2Repo benchmarks.",
   "organization_evidence": [
    "## What Is GLM-5.1?",
    "On April 7, 2026, Z.AI (Zhipu AI, listed on the Hong Kong Stock Exchange with a $52.83B market cap) released GLM-5.1 \u2014 a 754-billion parameter Mixture-of-Experts model under the **MIT License** with weights on HuggingFace ([zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1))."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What Is GLM-5.1?",
    "On April 7, 2026, Z.AI (Zhipu AI, listed on the Hong Kong Stock Exchange with a $52.83B market cap) released GLM-5.1 \u2014 a 754-billion parameter Mixture-of-Experts model under the **MIT License** with weights on HuggingFace ([zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1))."
   ],
   "parameters_evidence": [
    "## What Is GLM-5.1?",
    "On April 7, 2026, Z.AI (Zhipu AI, listed on the Hong Kong Stock Exchange with a $52.83B market cap) released GLM-5.1 \u2014 a 754-billion parameter Mixture-of-Experts model under the **MIT License** with weights on HuggingFace ([zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1)).",
    "- **Parameters:** 754B (Mixture-of-Experts)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmarks: #1 Open Source, Competitive With Every Frontier Model",
    "GLM-5.1 ranks **#1 open-source and #3 globally** across the combined SWE-Bench Pro, Terminal-Bench, and NL2Repo benchmarks."
   ]
  },
  {
   "url": "https://saascity.io/blog/minimax-m3-review-open-weight-model-2026",
   "title": "MiniMax M3 Review: The First Open-Weight Model to Do Frontier Coding, 1M Context, and Multimodality All at Once",
   "published_at": "2026-06-08T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "an open-weight model combining frontier-level coding, a 1-million-token context window, and native multimodality \u2014 all in one system",
   "organization_evidence": [
    "A Shanghai lab quietly dropped the model that closed-source incumbents didn't want to exist.",
    "MiniMax M3 launched on June 1, 2026 \u2014 and the developer community has been arguing about it ever since."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "A Shanghai lab quietly dropped the model that closed-source incumbents didn't want to exist.",
    "MiniMax M3 launched on June 1, 2026 \u2014 and the developer community has been arguing about it ever since."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The pitch: an open-weight model combining frontier-level coding, a 1-million-token context window, and native multimodality \u2014 all in one system."
   ]
  },
  {
   "url": "https://bit.ly/43k9Zq2",
   "title": "Nvidia\u2019s best model is now live",
   "published_at": "2026-06-04T16:12:33",
   "organization": "Nvidia",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Nemotron 3 Ultra is the fastest model among its direct competitors like Kimi-K2.6, Qwen-3.5, and GML-5.1 \u2014 and the best U.S. open-weight model yet \u2014 though it trails the best Chinese models on most benchmarks.",
   "organization_evidence": [
    "Nvidia\u2019s best model is now live",
    "Nvidia has released Nemotron 3 Ultra, a 550B open-weight MoE model with 55B active parameters, 1M token context, and up to 30% cost savings vs. comparable models."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nvidia\u2019s best model is now live",
    "While it is the fastest model among its direct competitors like Kimi-K2.6, Qwen-3.5, and GML-5.1 \u2014 and the best U.S. open-weight model yet \u2014 it does still trail the best of these Chinese models on most benchmarks, even if only by a few points."
   ]
  },
  {
   "url": "https://andrew.ooo/answers/best-open-weight-ai-models-2026-ranked-by-value",
   "title": "Best Open-Weight AI Models 2026: Ranked by Value",
   "published_at": "2026-07-25T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "scores ~57 on the Artificial Analysis Intelligence Index, ranking #3 overall \u2014 the first open-weight model in the top tier with GPT-5.6 Sol and Claude Fable 5",
   "organization_evidence": [
    "Best Open-Weight AI Models 2026: Ranked by Value",
    "Moonshot AI\u2019s **Kimi K3** (2.8T MoE, 1M context, native vision) scores **~57 on the Artificial Analysis Intelligence Index**, ranking **#3 overall** \u2014 the first open-weight model in the top tier with GPT-5.6 Sol and Claude Fable 5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Best Open-Weight AI Models 2026: Ranked by Value",
    "Open weights land July 27, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Best Open-Weight AI Models 2026: Ranked by Value",
    "Moonshot AI\u2019s **Kimi K3** (2.8T MoE, 1M context, native vision) scores **~57 on the Artificial Analysis Intelligence Index**, ranking **#3 overall** \u2014 the first open-weight model in the top tier with GPT-5.6 Sol and Claude Fable 5."
   ]
  },
  {
   "url": "https://mysummit.school/blog/en/qwen-alibaba-review-2026",
   "title": "Qwen by Alibaba in 2026: From Open-Source Leader to Closed Flagships",
   "published_at": "2026-03-06T06:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-06-02",
   "parameters": "32B total parameters, 3B active per token",
   "context_window": null,
   "license": null,
   "significance": "First open model to cross 80% on SWE-Verified, reaching 82%.",
   "organization_evidence": [
    "# Qwen by Alibaba in 2026: From Open-Source Leader to Closed Flagships",
    "- **Next-level code**. **Qwen 4 Coder 32B-A3B** (June 2, 2026) \u2013 the first open model to cross 82% on SWE-Verified. MoE architecture: 32B parameters, only 3B active \u2013 runs on a Mac with 24 GB of memory at 58 tokens/sec. Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen by Alibaba in 2026: From Open-Source Leader to Closed Flagships",
    "- **Next-level code**. **Qwen 4 Coder 32B-A3B** (June 2, 2026) \u2013 the first open model to cross 82% on SWE-Verified. MoE architecture: 32B parameters, only 3B active \u2013 runs on a Mac with 24 GB of memory at 58 tokens/sec. Apache 2.0 license.",
    "**Qwen 4 Coder 32B-A3B**, released June 2, 2026, is the first model to mark a transition to the fourth Qwen generation, and the first open model to cross 80% on SWE-Verified \u2013 a benchmark that correlates with real developer productivity."
   ],
   "parameters_evidence": [
    "# Qwen by Alibaba in 2026: From Open-Source Leader to Closed Flagships",
    "- **MoE architecture**: 32B total parameters, 3B active per token. 256K context"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen by Alibaba in 2026: From Open-Source Leader to Closed Flagships",
    "- **Next-level code**. **Qwen 4 Coder 32B-A3B** (June 2, 2026) \u2013 the first open model to cross 82% on SWE-Verified. MoE architecture: 32B parameters, only 3B active \u2013 runs on a Mac with 24 GB of memory at 58 tokens/sec. Apache 2.0 license.",
    "**Qwen 4 Coder 32B-A3B**, released June 2, 2026, is the first model to mark a transition to the fourth Qwen generation, and the first open model to cross 80% on SWE-Verified \u2013 a benchmark that correlates with real developer productivity."
   ]
  },
  {
   "url": "https://hivebook.wiki/wiki/mistral-ai-model-lineup-in-may-2026-mistral-small-4",
   "title": "Mistral AI Model Lineup in May 2026 \u2014 Mistral Small 4",
   "published_at": "2026-05-14T11:00:43",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "unifies instruct + reasoning + coding + vision in one weight set",
   "organization_evidence": [
    "Mistral AI's model catalog reshuffled materially across late-2025 and the first half of 2026.",
    "Overview",
    "The headline release is **Mistral Small 4** (2026-03-16, API ID `mistral-small-2603`) \u2014 a hybrid MoE that **unifies the previously separate Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding) lines into a single multimodal model with a 256k context window**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The headline release is **Mistral Small 4** (2026-03-16, API ID `mistral-small-2603`) \u2014 a hybrid MoE that **unifies the previously separate Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding) lines into a single multimodal model with a 256k context window**.",
    "Overview"
   ],
   "parameters_evidence": [
    "ModelAPI ID (latest alias)ReleasedContextActive / Total paramsTierNotes",
    "Current Lineup (May 2026)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "ModelAPI ID (latest alias)ReleasedContextActive / Total paramsTierNotes",
    "Current Lineup (May 2026)"
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/google-diffusion-gemma",
   "title": "Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in\u2026",
   "published_at": "2026-06-10T04:10:19",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 26B MoE that posts 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6 while sustaining four-figure tokens/sec on a single GPU makes very fast local inference and high-throughput batch serving look practical.",
   "organization_evidence": [
    "Google DeepMind \u00b7 2026-06-10 \u00b7 major",
    "DiffusionGemma is a new open-weight Gemma model from Google DeepMind that ships under Apache-2.0 on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind \u00b7 2026-06-10 \u00b7 major"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in Parallel via Discrete Block Diffusion, Hits 1,000+ Tokens/Sec on H100 and 700+ on RTX 5090 While Posting 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6",
    "A 26B MoE that posts 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6 while sustaining four-figure tokens/sec on a single GPU makes very fast local inference and high-throughput batch serving look practical."
   ]
  },
  {
   "url": "https://yalc.ai/blog/kimi-k3-open-weights-explained",
   "title": "Kimi K3 Open Weights: What the Drop Changes",
   "published_at": "2026-07-28T00:00:00",
   "organization": "Moonshot",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "2.8 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Largest open weight model shipped to date, sits fourth in the current frontier pack on broad intelligence indexes, and posts 42.0 on SWE Marathon.",
   "organization_evidence": [
    "Kimi K3 open weights are now public.",
    "On 2026 July 26 Moonshot released the full 2.8 trillion parameter model as a free download that needs no API key to self host, roughly a day ahead of its stated July 27 target, per [explainx.ai](https://explainx.ai/blog/kimi-k3-open-weights-2-8-trillion-parameters-july-2026)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On 2026 July 26 Moonshot released the full 2.8 trillion parameter model as a free download that needs no API key to self host, roughly a day ahead of its stated July 27 target, per [explainx.ai](https://explainx.ai/blog/kimi-k3-open-weights-2-8-trillion-parameters-july-2026).",
    "Here is the shape of it in one view.",
    "| Release | Open weights published 2026 July 26, about a day ahead of the July 27 target |"
   ],
   "parameters_evidence": [
    "On 2026 July 26 Moonshot released the full 2.8 trillion parameter model as a free download that needs no API key to self host, roughly a day ahead of its stated July 27 target, per [explainx.ai](https://explainx.ai/blog/kimi-k3-open-weights-2-8-trillion-parameters-july-2026).",
    "Here is the shape of it in one view.",
    "| Size | 2.8 trillion parameters, 16 of 896 experts active per token, near 104 billion activated |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "This is the largest open weight model shipped to date, and Moonshot put it out for free download rather than behind an API meter, per [Quartz](https://qz.com/moonshot-ai-kimi-k3-open-weights-download-072726).",
    "On the broad intelligence indexes, K3 sits fourth in the current frontier pack, behind Claude Fable 5 and GPT 5.6 Sol, and narrowly ahead of Claude Opus 4.8, per [Codersera's benchmark roundup](https://codersera.com/blog/kimi-k3-benchmarks-comparison-2026/).",
    "K3 posts 42.0 on SWE Marathon against 40.0 for Opus 4.8, 39.0 for GPT 5.6 Sol, and 35.0 for Claude Fable 5, and it nearly triples GPT 5.5 on the same test."
   ]
  },
  {
   "url": "https://effloow.com/articles/glm-5-1-zhipu-zai-swe-bench-agentic-coding-guide-2026",
   "title": "GLM-5.1: Zhipu's Open-Weight Agentic Coder That Topped SWE-Bench Pro",
   "published_at": "2026-05-15T13:05:43",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6.",
   "organization_evidence": [
    "# GLM-5.1: Zhipu's Open-Weight Agentic Coder That Topped SWE-Bench Pro",
    "On April 7, 2026, Zhipu AI (now operating as Z.ai) released GLM-5.1, an open-weight model built for long-horizon agentic coding."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 7, 2026, Zhipu AI (now operating as Z.ai) released GLM-5.1, an open-weight model built for long-horizon agentic coding."
   ],
   "parameters_evidence": [
    "GLM-5.1 from Z.ai scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6. 754B MoE, MIT license, $0.80/1M input tokens. Developer guide.",
    "# GLM-5.1: Zhipu's Open-Weight Agentic Coder That Topped SWE-Bench Pro",
    "GLM-5.1 uses a Mixture-of-Experts (MoE) design:",
    "| Total parameters | 754B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.1 from Z.ai scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6. 754B MoE, MIT license, $0.80/1M input tokens. Developer guide.",
    "# GLM-5.1: Zhipu's Open-Weight Agentic Coder That Topped SWE-Bench Pro",
    "The headline number is a SWE-Bench Pro score of 58.4 \u2014 above GPT-5.4 (57.7) and Claude Opus 4.6 (57.3)."
   ]
  },
  {
   "url": "https://swfte.com/de/ai/models/minimax-m3",
   "title": "MiniMax M3 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-07-20T22:37:25",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax's June 1 2026 open-weight flagship \u2014 the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context. Built on MiniMax Sparse Attention (MSA): ~1/20th the per-token compute at 1M context, >9x faster prefill, >15x faster decode. SWE-bench Pro 59.0% surpasses GPT-5.5 and Gemini 3.1 Pro and approaches Opus 4.7; Terminal-Bench 2.1 66.0%, OSWorld-Verified 70.06%.",
   "organization_evidence": [
    "# MiniMax M3",
    "MiniMax M3 is a open-source AI model by MiniMax, released on June 1, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax M3",
    "MiniMax's June 1 2026 open-weight flagship \u2014 the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context.",
    "MiniMax M3 is a open-source AI model by MiniMax, released on June 1, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax M3",
    "MiniMax's June 1 2026 open-weight flagship \u2014 the first open model to combine frontier agentic coding, native multimodality, and a 1M-token context. Built on MiniMax Sparse Attention (MSA): ~1/20th the per-token compute at 1M context, >9x faster prefill, >15x faster decode. SWE-bench Pro 59.0% surpasses GPT-5.5 and Gemini 3.1 Pro and approaches Opus 4.7; Terminal-Bench 2.1 66.0%, OSWorld-Verified 70.06%."
   ]
  },
  {
   "url": "https://beri.net/article/2026-06-04-nvidia-nemotron-3-ultra-550b-open-frontier-agent-model",
   "title": "Nemotron 3 Ultra: 550B Open Model Cuts Agent Cost 30%",
   "published_at": "2026-06-04T08:07:11",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
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   "significance": "5x faster inference and up to 30% lower cost for complex agentic tasks versus comparable open frontier models, ranking above Meta's Llama 4 Maverick and Mistral Large 3 on an independent Intelligence Index of 48",
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    "| Jun 1, 2026 | MiniMax | [MiniMax M3](/models/minimax-minimax-m3/) | Frontier open-weight model | Coding, Agents, Long context, Multimodal | 80 benchmarks / 87 scores |"
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    "## Release Index",
    "| Date | Provider | Model | Type | Focus | Benchmark coverage |",
    "| Jun 1, 2026 | MiniMax | [MiniMax M3](/models/minimax-minimax-m3/) | Frontier open-weight model | Coding, Agents, Long context, Multimodal | 80 benchmarks / 87 scores |"
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   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "## Release Index",
    "| Date | Provider | Model | Type | Focus | Benchmark coverage |",
    "| May 20, 2026 | Cohere | [Command A+](/models/cohere-command-a-plus-05-2026/) | Flagship open-weight MoE model | Agentic, Multimodal, Open weight | 4 benchmarks / 4 scores |"
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    "| May 20, 2026 | Cohere | [Command A+](/models/cohere-command-a-plus-05-2026/) | Flagship open-weight MoE model | Agentic, Multimodal, Open weight | 4 benchmarks / 4 scores |"
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    "## Release Index",
    "| Date | Provider | Model | Type | Focus | Benchmark coverage |",
    "| May 20, 2026 | Cohere | [Command A+](/models/cohere-command-a-plus-05-2026/) | Flagship open-weight MoE model | Agentic, Multimodal, Open weight | 4 benchmarks / 4 scores |"
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  {
   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "## Release Index",
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    "| Apr 24, 2026 | DeepSeek | DeepSeek-V4 | Open MoE model (preview) | Agentic, Reasoning, Long context | Unlinked |"
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    "| Apr 24, 2026 | DeepSeek | DeepSeek-V4 | Open MoE model (preview) | Agentic, Reasoning, Long context | Unlinked |"
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   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "| Date | Provider | Model | Type | Focus | Benchmark coverage |",
    "| Apr 20, 2026 | Moonshot AI | [Kimi K2.6](/models/moonshotai-kimi-k2.6/) | Open agentic coding model | Coding, Agentic, Multimodal | 230 benchmarks / 258 scores |"
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    "| Apr 20, 2026 | Moonshot AI | [Kimi K2.6](/models/moonshotai-kimi-k2.6/) | Open agentic coding model | Coding, Agentic, Multimodal | 230 benchmarks / 258 scores |"
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    "| Apr 20, 2026 | Moonshot AI | [Kimi K2.6](/models/moonshotai-kimi-k2.6/) | Open agentic coding model | Coding, Agentic, Multimodal | 230 benchmarks / 258 scores |"
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  {
   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "| Apr 2, 2026 | Google | Gemma 4 | Open-weight multimodal model family | Open weight, Multimodal, Agentic | Unlinked |"
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    "| Apr 2, 2026 | Google | Gemma 4 | Open-weight multimodal model family | Open weight, Multimodal, Agentic | Unlinked |"
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    "| Apr 2, 2026 | Google | Gemma 4 | Open-weight multimodal model family | Open weight, Multimodal, Agentic | Unlinked |"
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   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "Major public AI model releases from GPT-2 onward \u2014 LLMs plus notable reasoning, open-weight, and multimodal (vision/image/audio/video) models.",
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    "| Date | Provider | Model | Type | Focus | Benchmark coverage |",
    "| Mar 23, 2026 | Mistral AI | Voxtral TTS | Text-to-speech model | Multimodal, Open weight | Unlinked |"
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    "| Mar 23, 2026 | Mistral AI | Voxtral TTS | Text-to-speech model | Multimodal, Open weight | Unlinked |"
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   "url": "https://benchmarklist.com/timeline",
   "title": "AI Model Release Timeline | BenchmarkList",
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    "| Mar 18, 2026 | MiniMax | [MiniMax-M2.7](/models/minimax-minimax-m2.7/) | Open-weight self-evolving agentic model | Agentic, Coding, Open weight | 146 benchmarks / 164 scores |"
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    "| Mar 18, 2026 | MiniMax | [MiniMax-M2.7](/models/minimax-minimax-m2.7/) | Open-weight self-evolving agentic model | Agentic, Coding, Open weight | 146 benchmarks / 164 scores |"
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    "| Mar 18, 2026 | MiniMax | [MiniMax-M2.7](/models/minimax-minimax-m2.7/) | Open-weight self-evolving agentic model | Agentic, Coding, Open weight | 146 benchmarks / 164 scores |"
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   "url": "https://benchmarklist.com/timeline",
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    "| Mar 16, 2026 | Mistral AI | [Mistral Small 4](/models/mistralai-mistral-small-2603/) | Open MoE unified model | Open weight, Multimodal, Reasoning | 28 benchmarks / 37 scores |"
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    "| Mar 16, 2026 | Mistral AI | [Mistral Small 4](/models/mistralai-mistral-small-2603/) | Open MoE unified model | Open weight, Multimodal, Reasoning | 28 benchmarks / 37 scores |"
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    "| Feb 12, 2026 | MiniMax | [MiniMax-M2.5](/models/minimax-minimax-m2.5/) | Open-weight coding/agentic SOTA model | Coding, Agentic, Open weight | 95 benchmarks / 99 scores |"
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   "url": "https://benchmarklist.com/timeline",
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    "| Jan 27, 2026 | Moonshot AI | [Kimi K2.5](/models/moonshotai-kimi-k2.5/) | Open multimodal agentic model | Multimodal, Agentic, Open weight | 239 benchmarks / 257 scores |"
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    "| Jan 27, 2026 | Moonshot AI | [Kimi K2.5](/models/moonshotai-kimi-k2.5/) | Open multimodal agentic model | Multimodal, Agentic, Open weight | 239 benchmarks / 257 scores |"
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    "| Jan 27, 2026 | Moonshot AI | [Kimi K2.5](/models/moonshotai-kimi-k2.5/) | Open multimodal agentic model | Multimodal, Agentic, Open weight | 239 benchmarks / 257 scores |"
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   "url": "https://aizolo.com/blog/mistral-ai-models-2026",
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   "url": "https://bigtech.news/2026-07-20-china-moonshot-ai-releases-open-source-model.html",
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   "release_date_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
   ],
   "parameters_evidence": [
    "Kimi K3 contains 2.8 trillion parameters, serving as a measure of the AI\u2019s scale and processing power, according to a report by the BBC."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company.",
    "The model is described by Moonshot AI as comparable to Anthropic\u2019s Claude Opus in performance, based on internal benchmarks."
   ]
  },
  {
   "url": "https://urankmyai.com/articles/glm-5-2-review-2026.html",
   "title": "GLM-5.2 Review 2026: Is Zhipu AI's Open-Source Model Worth It for Coding?",
   "published_at": "2026-06-23T16:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": "740\u2013755 billion range",
   "context_window": null,
   "license": null,
   "significance": "Scores higher than GPT-5.5 on SWE-bench Pro while costing a fraction as much per token through the official API.",
   "organization_evidence": [
    "## Quick Verdict",
    "GLM-5.2, released by Z.ai (formerly Zhipu AI) on June 16, 2026, is the most credible open-weight challenger to closed frontier models we've seen for coding-heavy workloads."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Quick Verdict",
    "GLM-5.2, released by Z.ai (formerly Zhipu AI) on June 16, 2026, is the most credible open-weight challenger to closed frontier models we've seen for coding-heavy workloads."
   ],
   "parameters_evidence": [
    "## What Is GLM-5.2?",
    "Z.ai has not published an exact parameter count; third-party estimates place it somewhere in the 740\u2013755 billion range as a Mixture-of-Experts (MoE) architecture, but treat that figure as an approximation rather than a confirmed spec."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Quick Verdict",
    "It ships under an MIT license, supports a 1,048,576-token context window, and \u2014 according to Z.ai's own launch benchmarks plus independent tracking on OpenRouter \u2014 scores higher than GPT-5.5 on SWE-bench Pro while costing a fraction as much per token through the official API."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "One of the most popular LLMs for local deployment, as it runs on consumer hardware.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Qwen3.6 | Alibaba | 27B dense | 262K | Apache 2.0 | Running yourself |",
    "### 1. Qwen3.6",
    "Released April 2026, Qwen3.6 is an open weight model from Alibaba and one of the most popular LLMs for local deployment, as it runs on consumer hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 1. Qwen3.6",
    "Released April 2026, Qwen3.6 is an open weight model from Alibaba and one of the most popular LLMs for local deployment, as it runs on consumer hardware."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 1. Qwen3.6",
    "Released April 2026, Qwen3.6 is an open weight model from Alibaba and one of the most popular LLMs for local deployment, as it runs on consumer hardware."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Became the most-downloaded open family of 2026.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Gemma 4 | Google | 2B\u201331B | 256K | Apache 2.0 | Small models and laptops |",
    "### 2. Gemma 4",
    "Gemma 4 is another small open weights model you can run locally. It was developed by Google DeepMind and released in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 2. Gemma 4",
    "Gemma 4 is another small open weights model you can run locally. It was developed by Google DeepMind and released in April 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 2. Gemma 4",
    "It's no surprise, then, that Gemma 4 became the most-downloaded open family of 2026."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Tencent",
   "model": null,
   "release_date": "2026-07-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Positioned to compete with trillion-parameter class models while fitting under 300GB in FP8, and under 100GB in the official 4-bit quantization.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Hunyuan Hy3 | Tencent | 295B (21B active) | 256K | Apache 2.0 | Capability per gigabyte |",
    "### 4. Hunyuan Hy3",
    "Hunyuan Hy3 is an open model from Tencent released on July 6, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 4. Hunyuan Hy3",
    "Hunyuan Hy3 is an open model from Tencent released on July 6, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 4. Hunyuan Hy3",
    "Hunyuan Hy3 is an open model from Tencent released on July 6, 2026. It's a 295B parameter model, but it's positioned to compete with trillion-parameter class models while fitting under 300GB in FP8, and under 100GB in the official 4-bit quantization, and you can run it locally if you have more than 128GB of VRAM or unified memory."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Cohere",
   "model": null,
   "release_date": "2026-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Its differentiating feature is native citation generation that is built directly into the model weights.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Command A+ | Cohere | 218B (25B active) | 128K | Apache 2.0 | Enterprise RAG |",
    "### 5. Command A+",
    "Command A+ is an open weights model released by Cohere in May 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 5. Command A+",
    "Command A+ is an open weights model released by Cohere in May 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 5. Command A+",
    "Command A+ is an open weights model released by Cohere in May 2026. Its differentiating feature is native citation generation that is built directly into the model weights."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Interconnects called it \"the step change for open models\" \u2014 the first open release that can replace Claude Opus-level LLMs in agentic workflows.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| GLM-5.2 | Z.ai | 753B (40B active) | 1M | MIT | Best overall |",
    "### 6. GLM-5.2",
    "GLM-5.2 is a 753B-parameter mixture-of-experts model from Z.ai, released in June 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 6. GLM-5.2",
    "GLM-5.2 is a 753B-parameter mixture-of-experts model from Z.ai, released in June 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 6. GLM-5.2",
    "GLM-5.2 is a 753B-parameter mixture-of-experts model from Z.ai, released in June 2026. This is a very powerful open weight AI model. Interconnects called it \"the step change for open models\" \u2014 the first open release that can replace Claude Opus-level LLMs in agentic workflows."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "V4-Pro became the most-deployed open LLM in production within a month of release because it made long context cheap to serve.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| DeepSeek-V4-Pro | DeepSeek | 1.6T (49B active) | 1M | MIT | Long context at scale |",
    "### 7. DeepSeek-V4-Pro",
    "DeepSeek-V4-Pro, released in April 2026, is the flagship model from DeepSeek."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 7. DeepSeek-V4-Pro",
    "DeepSeek-V4-Pro, released in April 2026, is the flagship model from DeepSeek."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 7. DeepSeek-V4-Pro",
    "DeepSeek-V4-Pro, released in April 2026, is the flagship model from DeepSeek. V4-Pro became the most-deployed open LLM in production within a month of release because it made long context cheap to serve: processing a 1M-token context takes about 4\u00d7 less compute and 10\u00d7 less memory than on DeepSeek's previous generation, which in API pricing translates to $0.43 per million input tokens and $0.87 per million output."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "As of this writing it's the most performant open weight model, occupying the #3 position on the Artificial Analysis intelligence index, behind only Claude Fable 5 and GPT-5.6 Sol.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Kimi K3 | Moonshot AI | 2.8T | 1M | Modified MIT | Frontier agentic work |",
    "### 8. Kimi K3",
    "Kimi K3 is Moonshot AI's 2.8-trillion-parameter flagship, launched on July 16, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 8. Kimi K3",
    "Kimi K3 is Moonshot AI's 2.8-trillion-parameter flagship, launched on July 16, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 8. Kimi K3",
    "Kimi K3 is Moonshot AI's 2.8-trillion-parameter flagship, launched on July 16, 2026. As of this writing it's the most performant open weight model, occupying the #3 position on the Artificial Analysis intelligence index, behind only Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "An open source multimodal LLM that supports text, images, and video input, features a 1M-token context window, and 23B active parameters.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| MiniMax-M3 | MiniMax | 428B (23B active) | 1M | Community | Multimodal + 1M context |",
    "### 9. MiniMax-M3",
    "MiniMax-M3 is the flagship model created by Shanghai-based MiniMax."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 9. MiniMax-M3",
    "MiniMax-M3 is the flagship model created by Shanghai-based MiniMax.",
    "| Released | June 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 9. MiniMax-M3",
    "MiniMax-M3 is an open source multimodal LLM that supports text, images, and video input, features a 1M-token context window, and 23B active parameters."
   ]
  },
  {
   "url": "https://atomic.chat/blog/llm-updates/best-open-source-llm",
   "title": "Best Open Source LLM in 2026: 10 Models Ranked - Atomic Chat",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The most truly open source flagship-level model, as NVIDIA publicly released not only the weights but also the training datasets, post-training recipes, and evaluation harness.",
   "organization_evidence": [
    "What are the best open source LLMs in 2026?",
    "| Model | Developer | Parameters | Context | License | Best for |",
    "| Nemotron 3 Ultra | NVIDIA | 550B (55B active) | 1M | OpenMDW-1.1 | Most complete open release |",
    "### 10. Nemotron 3 Ultra",
    "Nemotron 3 Ultra is NVIDIA's flagship model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "### 10. Nemotron 3 Ultra",
    "Nemotron 3 Ultra is NVIDIA's flagship model.",
    "| Released | June 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "### 10. Nemotron 3 Ultra",
    "Nemotron 3 Ultra is NVIDIA's flagship model. This is the most advanced open weights model made outside of China. It's also the most truly open source flagship-level model, as NVIDIA publicly released not only the weights but also:"
   ]
  },
  {
   "url": "https://buildfastwithai.com/blogs/deepseek-v4-flash-review-2026",
   "title": "DeepSeek V4 Flash: Review, Pricing & When to Use It (2026)",
   "published_at": "2026-04-27T08:09:08",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MIT license, open weights, free commercial use. That is not the budget fallback \u2014 that is DeepSeek V4 Flash, released April 24, 2026, and it is the model I would default to for most production workloads right now.",
   "organization_evidence": [
    "DeepSeek V4 Flash: Review, Pricing & When to Use It (2026)",
    "DeepSeek V4 Flash is the fast, cost-optimized tier of DeepSeek's fourth-generation model family, released simultaneously with V4 Pro on April 24, 2026, under the MIT license.",
    "Both models are available via the DeepSeek API and as open weights on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Flash: Review, Pricing & When to Use It (2026)",
    "DeepSeek V4 Flash is the fast, cost-optimized tier of DeepSeek's fourth-generation model family, released simultaneously with V4 Pro on April 24, 2026, under the MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Flash: Review, Pricing & When to Use It (2026)",
    "MIT license, open weights, free commercial use. That is not the budget fallback \u2014 that is DeepSeek V4 Flash, released April 24, 2026, and it is the model I would default to for most production workloads right now."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/google-gemma-4-12b-encoder-free",
   "title": "Gemma 4 12B \u2014 Google's First Unified, Encoder-Free Multimodal Decoder Streams Audio, Video, and Image\u2026",
   "published_at": "2026-06-03T12:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95B",
   "context_window": null,
   "license": null,
   "significance": "An 11.95B decoder-only multimodal model that drops the vision and audio encoders, projects raw 48x48 patches and 40ms audio frames straight into the LLM, runs on a 16GB laptop, and ships open-weight under Apache 2.0.",
   "organization_evidence": [
    "Google \u00b7 2026-06-03 \u00b7 major",
    "# Gemma 4 12B \u2014 Google's First Unified, Encoder-Free Multimodal Decoder Streams Audio, Video, and Image Patches Straight Into an 11.95B Apache-2.0 LLM With 256K Context"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google \u00b7 2026-06-03 \u00b7 major"
   ],
   "parameters_evidence": [
    "Key specs",
    "| Parameters | 11.95B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4 12B \u2014 Google's First Unified, Encoder-Free Multimodal Decoder Streams Audio, Video, and Image Patches Straight Into an 11.95B Apache-2.0 LLM With 256K Context",
    "An 11.95B decoder-only multimodal model that drops the vision and audio encoders, projects raw 48x48 patches and 40ms audio frames straight into the LLM, runs on a 16GB laptop, and ships open-weight under Apache 2.0."
   ]
  },
  {
   "url": "https://vinoth12940.github.io/blog/articles/genai-20260718-kimi-k3-launch",
   "title": "Kimi K3: The 2.8 Trillion Parameter Open Model That Just Reset the Frontier",
   "published_at": "2026-07-18T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It outperforms leading US frontier models on several benchmarks, according to the company.",
   "organization_evidence": [
    "July 18, 2026 \u00b7 AI Models Open Source Moonshot AI",
    "Moonshot AI (the Beijing-based company behind the Kimi chatbot) published K3 on July 14, 2026, with general availability following on July 16."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "July 18, 2026 \u00b7 AI Models Open Source Moonshot AI",
    "Moonshot AI (the Beijing-based company behind the Kimi chatbot) published K3 on July 14, 2026, with general availability following on July 16."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "July 18, 2026 \u00b7 AI Models Open Source Moonshot AI",
    "It outperforms leading US frontier models on several benchmarks, according to the company."
   ]
  },
  {
   "url": "https://intelligibberish.com/articles/2026-02-22-glm-5-china-744b-open-source-model-huawei-chips",
   "title": "GLM-5: China's 744B Open-Source Model Trained Entirely on Huawei Chips",
   "published_at": "2026-02-22T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Matches frontier models from OpenAI and Anthropic on key benchmarks while being trained entirely on Huawei chips and leading on research and factual accuracy tasks.",
   "organization_evidence": [
    "# GLM-5: China's 744B Open-Source Model Trained Entirely on Huawei Chips",
    "Zhipu AI releases GLM-5 under MIT license, a frontier model rivaling Claude and GPT-5 while proving China can build top-tier AI without NVIDIA hardware.",
    "Zhipu AI has released GLM-5, a 744-billion-parameter open-source model that\u2019s raising eyebrows for two reasons: it matches frontier models from OpenAI and Anthropic on key benchmarks, and it was trained entirely on Huawei chips without a single NVIDIA GPU."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5: China's 744B Open-Source Model Trained Entirely on Huawei Chips",
    "The model dropped on February 11, 2026 under an MIT license, making it fully open for commercial use."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5: China's 744B Open-Source Model Trained Entirely on Huawei Chips",
    "Zhipu AI has released GLM-5, a 744-billion-parameter open-source model that\u2019s raising eyebrows for two reasons: it matches frontier models from OpenAI and Anthropic on key benchmarks, and it was trained entirely on Huawei chips without a single NVIDIA GPU.",
    "GLM-5 leads on research and factual accuracy tasks but trails on agentic coding and pure math."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-07/19-mira-muratis-thinking-machines-lab-releases-inkling-its-first-ai-model-a-975-billion-parameter-open-weight-system",
   "title": "Mira Murati's Thinking Machines Lab Releases Inkling, Its First AI Model, a 975-Billion-Parameter Open-Weight System",
   "published_at": "2026-07-19T09:56:08",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975 billion",
   "context_window": null,
   "license": null,
   "significance": "Tops US open-weight benchmarks, leading open-weights model from a U.S. lab on the Artificial Analysis Intelligence Index.",
   "organization_evidence": [
    "# Mira Murati's Thinking Machines Lab Releases Inkling, Its First AI Model, a 975-Billion-Parameter Open-Weight System",
    "Thinking Machines Lab shipped its first model, Inkling, an open-weight 975-billion-parameter system that tops US open-weight benchmarks but still trails China's leading models."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mira Murati's Thinking Machines Lab Releases Inkling, Its First AI Model, a 975-Billion-Parameter Open-Weight System",
    "Thinking Machines Lab, the AI startup founded by former OpenAI chief technology officer Mira Murati, released its first in-house model on July 15, 2026, calling it [Inkling](https://thinkingmachines.ai/news/introducing-inkling/) in the company\u2019s own announcement."
   ],
   "parameters_evidence": [
    "# Mira Murati's Thinking Machines Lab Releases Inkling, Its First AI Model, a 975-Billion-Parameter Open-Weight System",
    "Thinking Machines Lab shipped its first model, Inkling, an open-weight 975-billion-parameter system that tops US open-weight benchmarks but still trails China's leading models."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mira Murati's Thinking Machines Lab Releases Inkling, Its First AI Model, a 975-Billion-Parameter Open-Weight System",
    "Thinking Machines Lab shipped its first model, Inkling, an open-weight 975-billion-parameter system that tops US open-weight benchmarks but still trails China's leading models.",
    "on the Artificial Analysis Intelligence Index, Inkling \u201cranks three points above the previous leader, Nemotron 3 Ultra at 38,\u201d making it \u201cthe leading open-weights model from a U.S. lab,\u201d according to [The Decoder](https://the-decoder.com/ex-openai-cto-muratis-thinking-machines-drops-inkling-a-975b-parameter-model-that-leads-us-labs-but-trails-china/). On the GDPval-AA v2 agent benchmark, Inkling reached an Elo rating of 1,238, beating [Kimi K2.6](/article/2026-04/22-moonshot-ai-open-sources-kimi-k26-a-trillion-parameter-model-that-runs-300-agent-swarms-for-hours) at 1,190 and DeepSeek v4 Flash max at 1,189, The Decoder"
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/Emo_1b14b_130B",
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   "url": "https://wowhow.cloud/blogs/deepseek-v4-pro-flash-open-source-developer-guide-april-2026",
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   "url": "https://wowhow.cloud/blogs/deepseek-v4-pro-flash-open-source-developer-guide-april-2026",
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   "url": "https://neuralstack.network/article/2026-07-22-meta-llama-4-unveiled",
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  {
   "url": "https://news-factory.app/news/mistral-closes-in-on-big-ai-rivals-with-new-open-weight-frontier-and-small-models",
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   "url": "https://signalstack.news/blog/google-unleashes-gemma-4-advanced-open-models-redefine-on-device-ai-with-apache-",
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   "url": "https://scottharvanek.com/moonshot-kimi-k3-largest-open-model-thinking-machines-inkling-openai-gpt-red",
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   "url": "https://aiintelreport.com/frontier-models/zhipu-ai-glm-5-2-open-weights",
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   "url": "https://heybeagle.com/blog/minimax-m3-and-the-first-open-weight-model-you-can-run-at-1m-context",
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  {
   "url": "https://techpillow.co/blog/nvidia-nemotron-diffusion-open-weights-tri-mode",
   "title": "NVIDIA Nemotron-Labs-Diffusion Open Weights | TechPillow",
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  },
  {
   "url": "https://aplicar.ai/qwen-by-alibaba-the-open-weight-ai-family-quietly-eating-the-llm-world",
   "title": "Qwen by Alibaba: The Open-Weight AI Family Quietly Eating the LLM World",
   "published_at": "2026-05-17T00:31:53",
   "organization": "Alibaba",
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   "url": "https://aplicar.ai/qwen-by-alibaba-the-open-weight-ai-family-quietly-eating-the-llm-world",
   "title": "Qwen by Alibaba: The Open-Weight AI Family Quietly Eating the LLM World",
   "published_at": "2026-05-17T00:31:53",
   "organization": "Alibaba",
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    "### The current generations (as of mid-2026)"
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    "### The current generations (as of mid-2026)"
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  {
   "url": "https://thequantumdispatch.com/articles/google-gemma-4-open-model-apache-2-multimodal-2026",
   "title": "Google Gemma 4 Launches With Four Sizes, Apache 2.0 License, and a Top-3 Open Model Ranking",
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   "release_date_evidence": [
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  {
   "url": "https://wordupnews.com/tech/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-u-s-systems",
   "title": "China\u2019s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems",
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  },
  {
   "url": "https://aimodelsnavi.com/ko/models/glm-ga-generative-agent",
   "title": "GLM-GA (Generative Agent) | AI Models Navi",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Top open-weight coding model with 74.4% on FrontierSWE, 1M context window, and trained entirely on Huawei Ascend chips.",
   "organization_evidence": [
    "[GLM-5.2](/ko/models/glm-5-2) is Zhipu AI's (Z.ai) flagship open-weight language model, released June 13, 2026, representing the culmination of a multi-year push to bring Chinese-developed AI to frontier parity.",
    "# GLM-GA (Generative Agent)"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[GLM-5.2](/ko/models/glm-5-2) is Zhipu AI's (Z.ai) flagship open-weight language model, released June 13, 2026, representing the culmination of a multi-year push to bring Chinese-developed AI to frontier parity.",
    "# GLM-GA (Generative Agent)"
   ],
   "parameters_evidence": [
    "Built on a 744-billion-parameter Mixture-of-Experts architecture with ~40B active parameters per token, it was trained entirely on approximately 100,000 Huawei Ascend 910B chips using MindSpore\u2014making it one of the first frontier models produced without any NVIDIA hardware.",
    "# GLM-GA (Generative Agent)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Positioned as the strongest open-weight alternative to Western closed-source models, [GLM-5.2](/ko/models/glm-5-2) sits within striking distance of [Claude Opus 4.8](/ko/models/claude-opus-4-8) on long-horizon coding tasks (74.4% vs 75.1% on FrontierSWE) while costing roughly one-sixth on output tokens.",
    "# GLM-GA (Generative Agent)"
   ]
  },
  {
   "url": "https://aichina.news/blog/full-transparency-for-the-ascend-ecosystem-why-developers-should-try-d1pvzk",
   "title": "Full Transparency for the Ascend Ecosystem: Why Developers Should Try OLMo-7B on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-04-16T11:45:35",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Designed to be a completely transparent ecosystem allowing researchers and developers to study exactly how training data and specific algorithms influence model behaviour, providing native support for Huawei Ascend NPU.",
   "organization_evidence": [
    "Full Transparency for the Ascend Ecosystem: Why Developers Should Try OLMo-7B on Modelers.cn",
    "While many models claim to be 'open,' the Allen Institute for AI (AI2) has built OLMo (Open Language Model) with a different philosophy."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Full Transparency for the Ascend Ecosystem: Why Developers Should Try OLMo-7B on Modelers.cn",
    "models/allenai April 16, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Full Transparency for the Ascend Ecosystem: Why Developers Should Try OLMo-7B on Modelers.cn",
    "It is designed to be a completely transparent ecosystem, allowing researchers and developers to study exactly how training data and specific algorithms influence model behaviour."
   ]
  },
  {
   "url": "https://toknow.ai/posts/google-gemma-4-apache-2-open-models-on-device-ai",
   "title": "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone \u2013 ToKnow.ai",
   "published_at": "2026-04-08T01:01:56",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "31B dense model, a 26B mixture-of-experts (MoE) that activates only 3.8B of its 25.2B parameters per forward pass, and two edge models: E4B at 4.5B effective parameters and E2B at 2.3B effective",
   "context_window": null,
   "license": null,
   "significance": "The 31B scores 89.2% on AIME 2026 (a math competition benchmark), 85.2% on MMLU Pro, and reaches 2150 Codeforces ELO. The MoE variant hits 82.3% on GPQA Diamond (a graduate-level science benchmark) while running nearly as fast as a 4B model.",
   "organization_evidence": [
    "Google released [Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), a family of four open models under a fully permissive Apache 2.0 license.",
    "Kabui, Charles",
    "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "2026-04-08",
    "Kabui, Charles",
    "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone"
   ],
   "parameters_evidence": [
    "The lineup spans a 31B dense model, a [26B mixture-of-experts](https://ai.google.dev/gemma/docs/core/model_card_4) (MoE) that activates only 3.8B of its 25.2B parameters per forward pass, and two edge models: E4B at 4.5B effective parameters and E2B at 2.3B effective.",
    "Kabui, Charles",
    "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B scores 89.2% on AIME 2026 (a math competition benchmark), 85.2% on MMLU Pro, and reaches 2150 Codeforces ELO.",
    "The MoE variant hits 82.3% on GPQA Diamond (a graduate-level science benchmark) while running nearly as fast as a 4B model.",
    "Kabui, Charles",
    "Google Gemma 4: Apache 2.0 Open Models with 256K Context That Run on Your Phone"
   ]
  },
  {
   "url": "https://aimodelsnavi.com/en/models/glm-ga-generative-agent",
   "title": "GLM-GA (Generative Agent) | AI Models Navi",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter Mixture-of-Experts architecture with ~40B active parameters per token",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "#1 open-weight model, #4 overall",
   "organization_evidence": [
    "A high-performance foundation model developed by Zhipu AI."
   ],
   "model_evidence": [
    "[GLM-5.2](/en/models/glm-5-2) is Zhipu AI's (Z.ai) flagship open-weight language model, released June 13, 2026, representing the culmination of a multi-year push to bring Chinese-developed AI to frontier parity."
   ],
   "release_date_evidence": [
    "[GLM-5.2](/en/models/glm-5-2) is Zhipu AI's (Z.ai) flagship open-weight language model, released June 13, 2026, representing the culmination of a multi-year push to bring Chinese-developed AI to frontier parity."
   ],
   "parameters_evidence": [
    "Built on a 744-billion-parameter Mixture-of-Experts architecture with ~40B active parameters per token, it was trained entirely on approximately 100,000 Huawei Ascend 910B chips using MindSpore\u2014making it one of the first frontier models produced without any NVIDIA hardware."
   ],
   "context_window_evidence": [
    "Context Window",
    "1,000,000 tokens"
   ],
   "license_evidence": [
    "The model ships under a permissive MIT license with weights freely available on Hugging Face, targeting developers who need competitive coding performance without vendor lock-in."
   ],
   "significance_evidence": [
    "Artificial Analysis Intelligence Index",
    "51",
    "#1 open-weight model, #4 overall"
   ]
  },
  {
   "url": "https://kucoin.com/news/flash/nvidia-open-sources-550b-nemotron-3-ultra-model-with-mamba-transformer-hybrid-moe-architecture",
   "title": "NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture. | KuCoin",
   "published_at": "2026-06-04T16:18:22",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Ultra",
   "release_date": "2026-06-04",
   "parameters": "550B",
   "context_window": "1 million tokens",
   "license": "open-sourced",
   "significance": "Scored 48 on Artificial Analysis\u2019 intelligence index, ranking second among open-weight models in the U.S.",
   "organization_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "On June 4, 2026, NVIDIA open-sourced its 550B Nemotron 3 Ultra model, featuring a Mamba-Transformer hybrid MoE architecture."
   ],
   "model_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "On June 4, 2026, NVIDIA open-sourced its 550B Nemotron 3 Ultra model, featuring a Mamba-Transformer hybrid MoE architecture."
   ],
   "release_date_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "On June 4, 2026, NVIDIA open-sourced its 550B Nemotron 3 Ultra model, featuring a Mamba-Transformer hybrid MoE architecture."
   ],
   "parameters_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "On June 4, 2026, NVIDIA open-sourced its 550B Nemotron 3 Ultra model, featuring a Mamba-Transformer hybrid MoE architecture."
   ],
   "context_window_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "It supports a 1 million token context window with low memory usage and 5x higher throughput."
   ],
   "license_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "On June 4, 2026, NVIDIA open-sourced its 550B Nemotron 3 Ultra model, featuring a Mamba-Transformer hybrid MoE architecture."
   ],
   "significance_evidence": [
    "# NVIDIA open-sources the 550B Nemotron 3 Ultra model with a Mamba-Transformer hybrid MoE architecture.",
    "The model scored 48 on Artificial Analysis\u2019 intelligence index, ranking second among open-weight models in the U.S."
   ]
  },
  {
   "url": "https://deepinfra.com/blog/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1.6T (49B active)",
   "context_window": null,
   "license": null,
   "significance": "holds the #1 position on LiveCodeBench globally, and has weights on Hugging Face today",
   "organization_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "Kimi K3 (Moonshot AI, July 2026 \u2014 [now also available through DeepInfra](https://deepinfra.com/moonshotai/Kimi-K3)), [DeepSeek V4 Pro](https://deepinfra.com/deepseek-ai/DeepSeek-V4-Pro) (DeepSeek, April 2026), and [GLM-5.2 ](https://deepinfra.com/zai-org/GLM-5.2)(Zhipu AI, June 2026) all carry MIT or Modified MIT licenses, all support one-million-token context windows, and all benchmark competitively with closed frontier systems that cost five to thirty times more per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "Kimi K3 (Moonshot AI, July 2026 \u2014 [now also available through DeepInfra](https://deepinfra.com/moonshotai/Kimi-K3)), [DeepSeek V4 Pro](https://deepinfra.com/deepseek-ai/DeepSeek-V4-Pro) (DeepSeek, April 2026), and [GLM-5.2 ](https://deepinfra.com/zai-org/GLM-5.2)(Zhipu AI, June 2026) all carry MIT or Modified MIT licenses, all support one-million-token context windows, and all benchmark competitively with closed frontier systems that cost five to thirty times more per token."
   ],
   "parameters_evidence": [
    "| | **Kimi K3** | **DeepSeek V4 Pro** | **GLM-5.2** |",
    "| Total params | 2.8T (50B active) | 1.6T (49B active) | ~753B (40B active) |",
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "DeepSeek V4 Pro is the cost leader by a significant margin, holds the #1 position on LiveCodeBench globally, and has weights on Hugging Face today."
   ]
  },
  {
   "url": "https://deepinfra.com/blog/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06",
   "parameters": "~753B (40B active)",
   "context_window": null,
   "license": null,
   "significance": "leads on SWE-bench Pro among the three, runs at nearly three times the throughput of its peers, and was the strongest open-weight model by overall index score until K3 arrived",
   "organization_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "Kimi K3 (Moonshot AI, July 2026 \u2014 [now also available through DeepInfra](https://deepinfra.com/moonshotai/Kimi-K3)), [DeepSeek V4 Pro](https://deepinfra.com/deepseek-ai/DeepSeek-V4-Pro) (DeepSeek, April 2026), and [GLM-5.2 ](https://deepinfra.com/zai-org/GLM-5.2)(Zhipu AI, June 2026) all carry MIT or Modified MIT licenses, all support one-million-token context windows, and all benchmark competitively with closed frontier systems that cost five to thirty times more per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "Kimi K3 (Moonshot AI, July 2026 \u2014 [now also available through DeepInfra](https://deepinfra.com/moonshotai/Kimi-K3)), [DeepSeek V4 Pro](https://deepinfra.com/deepseek-ai/DeepSeek-V4-Pro) (DeepSeek, April 2026), and [GLM-5.2 ](https://deepinfra.com/zai-org/GLM-5.2)(Zhipu AI, June 2026) all carry MIT or Modified MIT licenses, all support one-million-token context windows, and all benchmark competitively with closed frontier systems that cost five to thirty times more per token."
   ],
   "parameters_evidence": [
    "| | **Kimi K3** | **DeepSeek V4 Pro** | **GLM-5.2** |",
    "| Total params | 2.8T (50B active) | 1.6T (49B active) | ~753B (40B active) |",
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open-Weight AI Model Comparison",
    "GLM-5.2 leads on SWE-bench Pro among the three, runs at nearly three times the throughput of its peers, and was the strongest open-weight model by overall index score until K3 arrived."
   ]
  },
  {
   "url": "https://peoplearegeek.com/articles/mistral-leanstral-1-5-formal-verification",
   "title": "Mistral Open Sources Leanstral 1.5 for Lean 4 Proofs",
   "published_at": "2026-07-14T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": "119 billion",
   "context_window": null,
   "license": null,
   "significance": "solved 587 of 672 PutnamBench math benchmark and uncovered 5 previously unknown bugs across 57 real code repositories",
   "organization_evidence": [
    "DevNews",
    "Mistral Open Sources Leanstral 1.5 for Lean 4 Proofs",
    "Mistral has open sourced Leanstral 1.5, a model whose entire purpose is to prove that code is correct rather than just test that it usually works."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DevNews",
    "Mistral Open Sources Leanstral 1.5 for Lean 4 Proofs",
    "Released on July 2, 2026 under a permissive Apache 2.0 license, it is built for Lean 4, the proof assistant that lets you state a property about a program and then check it with mathematical certainty."
   ],
   "parameters_evidence": [
    "DevNews",
    "Mistral Open Sources Leanstral 1.5 for Lean 4 Proofs",
    "The model is a Mixture of Experts with 128 experts, 119 billion total parameters and about 6.5 billion active per token, a 256,000 token context, and it is available on Hugging Face with a free API."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DevNews",
    "Mistral Open Sources Leanstral 1.5 for Lean 4 Proofs",
    "Leanstral 1.5 solved 587 of 672 problems on the PutnamBench math benchmark and, more usefully for engineers, uncovered 5 previously unknown bugs across 57 real code repositories."
   ]
  },
  {
   "url": "https://asoasis.tech/news/2026-04-03-0123-gemma-4",
   "title": "Google launches Gemma 4 under Apache 2.0: open, multimodal AI from phones to workstations | ASOasis - All about Tech",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Ranks third among open models on Arena AI\u2019s text leaderboard for the 31B model, and sixth for the 26B MoE model.",
   "organization_evidence": [
    "# Google launches Gemma 4 under Apache 2.0: open, multimodal AI from phones to workstations",
    "Google DeepMind has launched Gemma 4, a new open-weight model family designed for advanced reasoning and agentic workflows, and licensed under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google launches Gemma 4 under Apache 2.0: open, multimodal AI from phones to workstations",
    "Announced on April 2, 2026, the lineup spans phones to workstations and is built on the same research foundations as Gemini 3."
   ],
   "parameters_evidence": [
    "## Four models, one playbook: run anywhere",
    "Gemma 4 arrives in four sizes tailored to different deployment targets:\n\n- E2B (Effective 2B) and E4B (Effective 4B) for phones, IoT, and other edge devices\n- 26B MoE prioritizing speed by activating ~4B parameters at inference\n- 31B dense maximizing raw quality on consumer GPUs and workstations"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmarks and capabilities at a glance",
    "Google says the 31B dense model currently ranks third among open models on Arena AI\u2019s text leaderboard, with the 26B Mixture-of-Experts (MoE) model in sixth."
   ]
  },
  {
   "url": "https://neuralcoretech.com/kimi-k3-largest-open-weight-ai-model-2026",
   "title": "Kimi K3 vs Claude & GPT: Is Moonshot AI\u2019s 2.8T Open-Weight Model the New AI King?",
   "published_at": "2026-07-18T14:36:32",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "largest open-weight AI system ever announced and beat Claude Fable 5 outright on LMArena's Frontend Code Arena",
   "organization_evidence": [
    "Table of Contents",
    "On July 16, 2026, Beijing-based Moonshot AI released [Kimi K3](https://www.kimi.com/blog/kimi-k3), a 2.8-trillion-parameter mixture-of-experts model that instantly became the largest open-weight AI system ever announced \u2014 and, in at least one widely watched benchmark, beat Anthropic\u2019s Claude Fable 5 outright."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Table of Contents",
    "Full model weights are scheduled to ship by July 27, 2026, meaning any organization with sufficient compute will soon be able to run a frontier-class model without paying Anthropic, OpenAI, or Google a single dollar in API fees."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Table of Contents",
    "On July 16, 2026, Beijing-based Moonshot AI released [Kimi K3](https://www.kimi.com/blog/kimi-k3), a 2.8-trillion-parameter mixture-of-experts model that instantly became the largest open-weight AI system ever announced \u2014 and, in at least one widely watched benchmark, beat Anthropic\u2019s Claude Fable 5 outright."
   ]
  },
  {
   "url": "https://baike.baidu.com/en/item/GLM-5/1422146",
   "title": "GLM-5",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "ACHIEVED OPEN-SOURCE STATE-OF-THE-ART (SOTA) PERFORMANCE, RANKING FIRST AMONG OPEN-SOURCE MODELS, WITH COMPREHENSIVE CAPABILITY RANKING FOURTH GLOBALLY.",
   "organization_evidence": [
    "GLM-5 is a new-generation foundational model launched by Zhipu AI.",
    "On February 12, 2026, Zhipu AI announced via its official WeChat account the launch and open-sourcing of GLM-5, with its parameter scale expanded from 355B (32B activated) to 744B (40B activated)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "It was announced as upcoming by Chief Scientist Tang Jie on January 8, 2026, the day of the company's listing, and was officially released on February 11 of the same year.",
    "Late at night on February 11, 2026, Zhipu AI released its new-generation flagship model GLM-5."
   ],
   "parameters_evidence": [
    "The model has a parameter scale of 744B, employs the DeepSeek sparse attention mechanism and Multi-Token Prediction technology, supports a 202K context window, and focuses on enhancing programming and agent capabilities, aiming to drive the programming paradigm shift from \" Vibe Coding \" to \"Agentic Engineering\".",
    "On February 12, 2026, Zhipu AI announced via its official WeChat account the launch and open-sourcing of GLM-5, with its parameter scale expanded from 355B (32B activated) to 744B (40B activated)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On the SWE-bench-Verified programming benchmark, GLM-5 achieved a score of 77.8%, ranking first among open-source models, with performance in real-world programming scenarios close to Claude Opus 4.5 .",
    "In terms of Coding and Agent capabilities, GLM-5 achieved open-source state-of-the-art (SOTA) performance, with its user experience in real-world programming scenarios approaching that of Claude Opus 4.5 ."
   ]
  },
  {
   "url": "https://trend.undefined-labs.dev/wiki/models/minimax-m3",
   "title": "MiniMax M3",
   "published_at": "2026-07-17T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "~428B",
   "context_window": "1,000,000 tokens (1M); min. 512K guaranteed",
   "license": "Open-weight (HuggingFace)",
   "significance": "1. One of the first Chinese open-weight models to surpass a leading US closed model (Claude Opus 4.7) on an agent benchmark (BrowseComp 83.5 vs. 79.3)\n2. A frontier-class open-weight model with 1M context and native multimodality from a non-Tier-1 (non-Alibaba/ByteDance) Chinese lab\n3. Available globally via API and open-weight \u2014 expanding Chinese AI model access internationally",
   "organization_evidence": [
    "# MiniMax M3",
    "Spec",
    "| Attribute | Value |",
    "| Developer | [MiniMax](/wiki/entities/minimax) |"
   ],
   "model_evidence": [
    "# MiniMax M3"
   ],
   "release_date_evidence": [
    "# MiniMax M3",
    "Spec",
    "| Attribute | Value |",
    "| Released | June 1, 2026 |"
   ],
   "parameters_evidence": [
    "# MiniMax M3",
    "Spec",
    "| Attribute | Value |",
    "| Total parameters | ~428B |"
   ],
   "context_window_evidence": [
    "# MiniMax M3",
    "Spec",
    "| Attribute | Value |",
    "| Context window | 1,000,000 tokens (1M); min. 512K guaranteed |"
   ],
   "license_evidence": [
    "# MiniMax M3",
    "Spec",
    "| Attribute | Value |",
    "| License | Open-weight (HuggingFace) |"
   ],
   "significance_evidence": [
    "# MiniMax M3",
    "## Significance",
    "MiniMax M3 is notable as:",
    "1. One of the first Chinese open-weight models to surpass a leading US closed model (Claude Opus 4.7) on an agent benchmark (BrowseComp 83.5 vs. 79.3)",
    "2. A frontier-class open-weight model with 1M context and native multimodality from a non-Tier-1 (non-Alibaba/ByteDance) Chinese lab",
    "3. Available globally via API and open-weight \u2014 expanding Chinese AI model access internationally"
   ]
  },
  {
   "url": "https://datanorth.ai/news/deepseek-releases-deepseek-v4-flash-0731",
   "title": "DeepSeek's new open MoE model",
   "published_at": "2026-08-04T06:43:31",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "284 billion",
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   "license": null,
   "significance": "DeepSeek-V4-Flash-0731 scores higher than DeepSeek V4-Pro (Preview) on all nine agent and coding benchmarks the company published, while costing roughly a third as much on output tokens.",
   "organization_evidence": [
    "# DeepSeek releases DeepSeek V4-Flash-0731",
    "DeepSeek released DeepSeek-V4-Flash-0731 on July 31, 2026, publishing the weights on Hugging Face under an MIT licence and moving the official V4-Flash API into public beta."
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   "release_date_evidence": [
    "# DeepSeek releases DeepSeek V4-Flash-0731",
    "04-08-2026",
    "DeepSeek released DeepSeek-V4-Flash-0731 on July 31, 2026, publishing the weights on Hugging Face under an MIT licence and moving the official V4-Flash API into public beta."
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   "parameters_evidence": [
    "# DeepSeek releases DeepSeek V4-Flash-0731",
    "DeepSeek-V4-Flash-0731 is DeepSeek's official V4-Flash release, published on July 31, 2026: a 284 billion parameter MoE with 13 billion active parameters, a 1 million token context window, MIT-licensed weights and pricing of 0.14 and 0.28 dollars per million tokens."
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   "significance_evidence": [
    "# DeepSeek releases DeepSeek V4-Flash-0731",
    "## How does DeepSeek-V4-Flash-0731 compare to DeepSeek V4-Pro?",
    "The headline comparison in this release is internal.",
    "DeepSeek-V4-Flash-0731 scores higher than DeepSeek V4-Pro (Preview) on all nine agent and coding benchmarks the company published, while costing roughly a third as much on output tokens."
   ]
  },
  {
   "url": "https://thinksmart.life/research/posts/qwen-3.6-complete-guide-agentic-coding-local-inference",
   "title": "Qwen 3.6: The Open-Local Agentic Coding King",
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   "release_date": "2026-04-16",
   "parameters": "35B total / 3B active",
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   "significance": "Max agentic coding / Reasoning",
   "organization_evidence": [
    "# Qwen 3.6: The Open-Local Agentic Coding King",
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    "Released: April 16, 2026",
    "### Qwen3.6-35B-A3B (Flagship MoE)",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
   ],
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    "### Qwen3.6-35B-A3B (Flagship MoE)",
    "Architecture: Sparse Mixture-of-Experts (35B total / 3B active)",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
   ],
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   "significance_evidence": [
    "Model Architecture Comparison",
    "| **Qwen3.6-35B-A3B** | Mixture-of-Experts | 35B | 3B | Max agentic coding / Reasoning |",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
   ]
  },
  {
   "url": "https://thinksmart.life/research/posts/qwen-3.6-complete-guide-agentic-coding-local-inference",
   "title": "Qwen 3.6: The Open-Local Agentic Coding King",
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   "organization": "Alibaba",
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   "release_date": "2026-04-22",
   "parameters": "27B",
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   "significance": "Faster inference / Latency-sensitive",
   "organization_evidence": [
    "# Qwen 3.6: The Open-Local Agentic Coding King",
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   "release_date_evidence": [
    "Released: April 22, 2026",
    "### Qwen3.6-27B (Dense Variant)",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
   ],
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    "### Qwen3.6-27B (Dense Variant)",
    "Architecture: Dense Transformer (27B parameters)",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
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   "significance_evidence": [
    "Model Architecture Comparison",
    "| **Qwen3.6-27B** | Dense Transformer | 27B | 27B | Faster inference / Latency-sensitive |",
    "# Qwen 3.6: The Open-Local Agentic Coding King"
   ]
  },
  {
   "url": "https://theeditorial.news/tech-news/metas-llama-4-arrives-40-cheaper-than-gpt-5-but-safety-benchmarks-raise-red-flags-mq2b9wpl",
   "title": "Meta's Llama 4 Arrives 40% Cheaper Than GPT-5, But Safety Benchmarks Raise Red Flags",
   "published_at": "2026-06-06T12:10:21",
   "organization": "Meta",
   "model": null,
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   "parameters": "405 billion",
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   "significance": "Matches OpenAI's GPT-5 on coding and mathematics benchmarks while costing one-sixth the price per token.",
   "organization_evidence": [
    "Meta's Llama 4 Arrives 40% Cheaper Than GPT-5, But Safety Benchmarks Raise Red Flags",
    "Meta released Llama 4 on Friday, an open-weight large language model that matches OpenAI's GPT-5 on coding and mathematics benchmarks while costing one-sixth the price per token \u2014 but internal safety evaluations reviewed by The Editorial show the model generates harmful content at nearly three times the rate of its closed-source rivals."
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Meta's Llama 4 Arrives 40% Cheaper Than GPT-5, But Safety Benchmarks Raise Red Flags",
    "Llama 4's 405-billion-parameter variant scores 90.1% on MMLU-Pro, 88.7% on GPQA, and 74.3% on SWE-Bench."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Meta's Llama 4 Arrives 40% Cheaper Than GPT-5, But Safety Benchmarks Raise Red Flags",
    "Meta released Llama 4 on Friday, an open-weight large language model that matches OpenAI's GPT-5 on coding and mathematics benchmarks while costing one-sixth the price per token \u2014 but internal safety evaluations reviewed by The Editorial show the model generates harmful content at nearly three times the rate of its closed-source rivals."
   ]
  },
  {
   "url": "https://decrypt.co/363178/google-gemma-4-open-source-ai",
   "title": "Google Jumps Back Into the Open Source AI Race With Gemma 4",
   "published_at": "2026-04-02T18:18:58",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
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   "significance": "rank #3 globally on Arena AI's text leaderboard; outcompete models 20 times their size",
   "organization_evidence": [
    "Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "Google drops Gemma 4, a family of open models under the Apache 2.0 license, just as the U.S. open-source scene badly needed a win."
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   "model_evidence": null,
   "release_date_evidence": [
    "Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "\u2014 Google (@Google) [April 2, 2026](https://twitter.com/Google/status/2039736220834480233?ref_src=twsrc%5Etfw)"
   ],
   "parameters_evidence": null,
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    "Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "The four-model lineup spans phones to data centers with the 31B model ranking #3 globally already.",
    "The 31B Dense currently ranks third among all open models on [Arena AI's text leaderboard](https://lmarena.ai/)."
   ]
  },
  {
   "url": "https://kommunicate.io/blog/glm-ai-model-review",
   "title": "The Open-Source AI Safety Net for Enterprise",
   "published_at": "2026-06-29T18:25:31",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
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   "license": null,
   "significance": "Matched leading frontier models across several benchmarks while costing a fraction of the price, and functions as genuine insurance against provider lock-in.",
   "organization_evidence": [
    "Updated on June 29, 2026",
    "TL;DR \u00b7 GLM 5.2: The Open-Source AI Safety Net",
    "GLM 5.2 is Zhipu AI\u2019s 753B-parameter open-weight model, released June 13, 2026, under an MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Updated on June 29, 2026",
    "TL;DR \u00b7 GLM 5.2: The Open-Source AI Safety Net",
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    "Updated on June 29, 2026",
    "TL;DR \u00b7 GLM 5.2: The Open-Source AI Safety Net",
    "GLM 5.2 is Zhipu AI\u2019s 753B-parameter open-weight model, released June 13, 2026, under an MIT license. It matched leading frontier models across several benchmarks while costing a fraction of the price."
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  },
  {
   "url": "https://agenccy.ai/news/thinking-machines-releases-inkling-975b-apache-2-open-model",
   "title": "Mira Murati's Thinking Machines Ships Inkling, a 975B-Parameter Model Under Apache 2.0",
   "published_at": "2026-07-16T15:20:31",
   "organization": "Thinking Machines Lab",
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   "release_date": "2026-07-15",
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   "significance": "97.1% on AIME 2026, 87.2% on GPQA Diamond, 77.6% on SWE-bench Verified and 46.0% on Humanity's Last Exam with tools",
   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
    "Mira Murati's Thinking Machines Ships Inkling, a 975B-Parameter Model Under Apache 2.0",
    "Jul 16, 2026Source: [TechCrunch](https://techcrunch.com/2026/07/15/thinking-machines-amps-up-its-bet-against-one-size-fits-all-ai-with-its-first-open-model-inkling/)",
    "**Inkling**, released **July 15**, arrives with something the frontier labs do not offer: the full weights, under **Apache 2.0**."
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   "parameters_evidence": [
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Mira Murati's Thinking Machines Ships Inkling, a 975B-Parameter Model Under Apache 2.0",
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   ]
  },
  {
   "url": "https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33.02+Content/DeepSeek+v4",
   "title": "DeepSeek v4 - DeveloPassion",
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   "organization": "DeepSeek",
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   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active parameters",
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   "organization_evidence": [
    "DeepSeek v4 Fourth-generation flagship release from [[Deepseek]] (April 24, 2026)."
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   "release_date_evidence": [
    "DeepSeek v4 Fourth-generation flagship release from [[Deepseek]] (April 24, 2026)."
   ],
   "parameters_evidence": [
    "Two open-weight variants \u2014 V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active) \u2014 both built on a [[AI Mixture of Experts (MoE)|MoE]] architecture, ship with a 1M-token [[Context Window]] by default, and fold what was the separate R reasoning line into a single model with switchable Thinking / Non-Thinking modes.",
    "DeepSeek v4 Fourth-generation flagship release from [[Deepseek]] (April 24, 2026)."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "V4-Pro is the largest [[AI Open Weight Models|open weights]] model released to date.",
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    "DeepSeek v4 Fourth-generation flagship release from [[Deepseek]] (April 24, 2026)."
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  },
  {
   "url": "https://mungomash.com/ai/qwen/versions",
   "title": "Qwen Versions",
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   "organization": "Alibaba",
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   "release_date": "2026-04-22",
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   "significance": "hybrid Gated DeltaNet + self-attention",
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    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "release_date_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "significance_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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  },
  {
   "url": "https://mungomash.com/ai/qwen/versions",
   "title": "Qwen Versions",
   "published_at": null,
   "organization": "Alibaba",
   "model": "Qwen3.6-27B",
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   "organization_evidence": [
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   "model_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "release_date_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "parameters_evidence": [
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    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "license_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "significance_evidence": null
  },
  {
   "url": "https://mungomash.com/ai/qwen/versions",
   "title": "Qwen Versions",
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   "significance": "extended that to 201 languages and added native multimodality across text + image + video",
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   "model_evidence": null,
   "release_date_evidence": [
    "Qwen Versions",
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   "parameters_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Qwen Versions",
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  },
  {
   "url": "https://mungomash.com/ai/qwen/versions",
   "title": "Qwen Versions",
   "published_at": null,
   "organization": "Alibaba",
   "model": "Qwen3.5",
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   "organization_evidence": [
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   "model_evidence": [
    "Qwen Versions",
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   "release_date_evidence": [
    "Qwen Versions",
    "The newest open-weights Qwen flagship remains **Qwen3.6-27B** (April 22, 2026; Apache 2.0, hybrid Gated DeltaNet + self-attention, 262K context extensible to 1M), and the largest is **Qwen3.5-397B-A17B** (February 16, 2026)."
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   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Qwen Versions",
    "Every Qwen flagship release since (Qwen3-Coder, Qwen3.5, Qwen3.5-Plus's open-weights variants, Qwen3.6-27B, Qwen-Image-2512) has shipped Apache 2.0."
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    "Qwen Versions",
    "The Qwen3.5 family released ten months later (February 2026) extended that to 201 languages and added native multimodality across text + image + video; Qwen3.6-27B (April 2026) added the Gated DeltaNet hybrid architecture and Thinking Preservation."
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  },
  {
   "url": "https://wowhow.cloud/blogs/mistral-medium-3-5-developer-guide-api-remote-agents-2026",
   "title": "Mistral Medium 3.5 Developer Guide: API, Remote Agents & Pricing 2026",
   "published_at": "2026-05-03T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "77.6% on SWE-Bench Verified",
   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
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   "parameters_evidence": [
    "Mistral Medium 3.5 Developer Guide: API, Remote Agents & Pricing 2026 | WOWHOW",
    "Mistral Medium 3.5: 128B open-weight model, 77.6% SWE-Bench, $1.50/M input.",
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   "context_window_evidence": null,
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   ]
  },
  {
   "url": "https://cloudinsight.cc/en/blog/gemma-4-guide",
   "title": "Gemma 4 Complete Guide: The Most Powerful Open Source Model of 2026",
   "published_at": "2026-04-06T00:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
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   "significance": "Scored 89.2% on AIME 2026 and 85.2% on MMLU Pro \u2014 a 4.3x jump in math reasoning over Gemma 3.",
   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
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   "parameters_evidence": null,
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   "significance_evidence": [
    "Gemma 4 Complete Guide: The Most Powerful Open Source Model of 2026",
    "The flagship 31B scores 89.2% on AIME 2026 and 85.2% on MMLU Pro \u2014 a 4.3x jump in math reasoning over Gemma 3."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/kimi-k3",
   "title": "Kimi K3 \u2014 Specs, Benchmarks & Pricing | AI/TLDR",
   "published_at": "2026-07-16T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8T total \u00b7 ~16 of 896 experts active per token",
   "context_window": null,
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   "significance": "Competitive benchmark positioning with Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 Sol across coding, agentic, reasoning, and vision suites.",
   "organization_evidence": [
    "# Kimi K3",
    "Moonshot's 2.8-trillion-parameter MoE flagship: 1M context, native multimodal, released July 2026.",
    "Kimi K3 is Moonshot AI's flagship large language model, a 2.8-trillion-parameter Mixture-of-Experts system built on a new architecture that combines Stable LatentMoE (activating roughly 16 of 896 experts per token) with Kimi Delta Attention and Attention Residuals."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3",
    "Moonshot's 2.8-trillion-parameter MoE flagship: 1M context, native multimodal, released July 2026.",
    "It was released on July 16, 2026 and runs with a 1,048,576-token context window and native multimodal input across text, images, and video.",
    "| Released | 2026-07-16 |"
   ],
   "parameters_evidence": [
    "# Kimi K3",
    "Moonshot's 2.8-trillion-parameter MoE flagship: 1M context, native multimodal, released July 2026.",
    "| Parameters | 2.8T total \u00b7 ~16 of 896 experts active per token |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3",
    "Moonshot's 2.8-trillion-parameter MoE flagship: 1M context, native multimodal, released July 2026.",
    "Moonshot published a broad benchmark comparison at launch that positions K3 competitively with Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 Sol across coding, agentic, reasoning, and vision suites."
   ]
  },
  {
   "url": "https://aiunpacker.com/blog/glm-5-2-released-new-long-context-ai-model-for-agents-and-coding",
   "title": "GLM-5.2: New Long-Context AI Model for Agents and Coding",
   "published_at": "2026-06-13T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": "753-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "highest open-weights score on the Artificial Analysis Intelligence Index v4.1 (51 points)",
   "organization_evidence": [
    "GLM-5.2 Released: New Long-Context AI Model for Agents and CodingBy AIUnpacker Editorial \u00b7 10 min read",
    "**GLM-5.2 is a 753-billion-parameter Mixture-of-Experts long-context AI model from Z.ai (formerly Zhipu AI) that runs a usable 1-million-token context window and posts the highest open-weights score on the Artificial Analysis Intelligence Index v4.1 (51 points) as of June 17, 2026 ([z.ai/blog/glm-5.2](https://z.ai/blog/glm-5.2), [artificialanalysis.ai](https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index)).**"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2 Released: New Long-Context AI Model for Agents and CodingBy AIUnpacker Editorial \u00b7 10 min read"
   ],
   "parameters_evidence": [
    "GLM-5.2 Released: New Long-Context AI Model for Agents and CodingBy AIUnpacker Editorial \u00b7 10 min read",
    "**GLM-5.2 is a 753-billion-parameter Mixture-of-Experts long-context AI model from Z.ai (formerly Zhipu AI) that runs a usable 1-million-token context window and posts the highest open-weights score on the Artificial Analysis Intelligence Index v4.1 (51 points) as of June 17, 2026 ([z.ai/blog/glm-5.2](https://z.ai/blog/glm-5.2), [artificialanalysis.ai](https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index)).**"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 Released: New Long-Context AI Model for Agents and CodingBy AIUnpacker Editorial \u00b7 10 min read",
    "**GLM-5.2 is a 753-billion-parameter Mixture-of-Experts long-context AI model from Z.ai (formerly Zhipu AI) that runs a usable 1-million-token context window and posts the highest open-weights score on the Artificial Analysis Intelligence Index v4.1 (51 points) as of June 17, 2026 ([z.ai/blog/glm-5.2](https://z.ai/blog/glm-5.2), [artificialanalysis.ai](https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index)).**"
   ]
  },
  {
   "url": "https://wpnews.pro/news/nvidias-alpamayo-2-super-opens-robotaxi-development-to-commercial-use",
   "title": "NVIDIA\u2019s Alpamayo 2 Super Opens Robotaxi Development to Commercial Use",
   "published_at": "2026-08-04T15:48:55",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 79.2 on the Lingo-Judge metric of LingoQA, ranking first among nearly 40 models evaluated by NVIDIA.",
   "organization_evidence": [
    "NVIDIA\u2019s Alpamayo 2 Super Opens Robotaxi Development to Commercial Use",
    "NVIDIA released Alpamayo 2 Super, a 34-billion-parameter reasoning model for autonomous driving, for commercial use on August 4, 2026, under the Linux Foundation's OpenMDW-1.1 license, clearing a licensing barrier that had kept the Alpamayo family in research."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA\u2019s Alpamayo 2 Super Opens Robotaxi Development to Commercial Use",
    "NVIDIA released Alpamayo 2 Super, a 34-billion-parameter reasoning model for autonomous driving, for commercial use on August 4, 2026, under the Linux Foundation's OpenMDW-1.1 license, clearing a licensing barrier that had kept the Alpamayo family in research."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA\u2019s Alpamayo 2 Super Opens Robotaxi Development to Commercial Use",
    "NVIDIA released Alpamayo 2 Super, a 34-billion-parameter reasoning model for autonomous driving, for commercial use on August 4, 2026, under the Linux Foundation's OpenMDW-1.1 license, clearing a licensing barrier that had kept the Alpamayo family in research."
   ]
  },
  {
   "url": "https://24-ai.news/en/news/2026-05-09/allenai-emo-mixture-experts-emergent-modularity",
   "title": "Allen Institute: EMO \u2014 MoE language model with natural semantic modularity from data",
   "published_at": "2026-05-09T00:00:00",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-05-08",
   "parameters": "1B active and 14B total parameters",
   "context_window": null,
   "license": null,
   "significance": "EMO is a sparse Mixture-of-Experts (MoE) language model that develops natural semantic modularity among its experts without manual labels. When only 25% of experts are activated (32 out of 128), EMO loses just ~1% absolute performance, while at 12.5% of experts (16 out of 128) the drop is around 3%.",
   "organization_evidence": [
    "# Allen Institute: EMO \u2014 MoE language model with natural semantic modularity from data",
    "The Allen Institute for AI (Ai2) published EMO on May 8, 2026 \u2014 a sparse Mixture-of-Experts ([MoE](/en/glossary/mixture-of-experts/)) language model that develops natural semantic modularity among its experts without manual labels."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Allen Institute for AI (Ai2) published EMO on May 8, 2026 \u2014 a sparse Mixture-of-Experts ([MoE](/en/glossary/mixture-of-experts/)) language model that develops natural semantic modularity among its experts without manual labels.",
    "# Allen Institute: EMO \u2014 MoE language model with natural semantic modularity from data"
   ],
   "parameters_evidence": [
    "EMO is a new MoE language model from the Allen Institute with 1B active and 14B total parameters, trained on 1 trillion tokens.",
    "# Allen Institute: EMO \u2014 MoE language model with natural semantic modularity from data"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Allen Institute for AI (Ai2) published EMO on May 8, 2026 \u2014 a sparse Mixture-of-Experts ([MoE](/en/glossary/mixture-of-experts/)) language model that develops natural semantic modularity among its experts without manual labels.",
    "When only 25% of experts are activated (32 out of 128), EMO loses just ~1% absolute performance, while at 12.5% of experts (16 out of 128) the drop is around 3%.",
    "# Allen Institute: EMO \u2014 MoE language model with natural semantic modularity from data"
   ]
  },
  {
   "url": "https://hivebook.wiki/wiki/deepseek-v4-v4-pro-and-v4-flash-moe-open-weights-llms",
   "title": "DeepSeek V4 \u2014 V4-Pro and V4-Flash MoE Open-Weights LLMs",
   "published_at": "2026-05-13T07:13:07",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 T total / 49 B activated",
   "context_window": null,
   "license": null,
   "significance": "A hybrid attention stack (Compressed Sparse Attention + Heavily Compressed Attention, \"CSA + HCA\") that brings single-token inference FLOPs at 1 M tokens down to 27% of V3.2 and KV cache to 10% of V3.2.",
   "organization_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "parameters_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline architectural change is a *hybrid attention stack* (Compressed Sparse Attention + Heavily Compressed Attention, \"CSA + HCA\") that brings single-token inference FLOPs at 1 M tokens down to **27% of V3.2** and KV cache to **10% of V3.2** \u2014 the long-context cost reductions that make 1 M-token serving practical.",
    "## Overview"
   ]
  },
  {
   "url": "https://hivebook.wiki/wiki/deepseek-v4-v4-pro-and-v4-flash-moe-open-weights-llms",
   "title": "DeepSeek V4 \u2014 V4-Pro and V4-Flash MoE Open-Weights LLMs",
   "published_at": "2026-05-13T07:13:07",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "284 B total / 13 B activated",
   "context_window": null,
   "license": null,
   "significance": "A hybrid attention stack (Compressed Sparse Attention + Heavily Compressed Attention, \"CSA + HCA\") that brings single-token inference FLOPs at 1 M tokens down to 27% of V3.2 and KV cache to 10% of V3.2.",
   "organization_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "parameters_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "## Overview"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline architectural change is a *hybrid attention stack* (Compressed Sparse Attention + Heavily Compressed Attention, \"CSA + HCA\") that brings single-token inference FLOPs at 1 M tokens down to **27% of V3.2** and KV cache to **10% of V3.2** \u2014 the long-context cost reductions that make 1 M-token serving practical.",
    "## Overview"
   ]
  },
  {
   "url": "https://toolchase.com/tool/qwen",
   "title": "Qwen Review 2026: Pricing, Features & Alternatives | ToolChase",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": "roughly 397 billion parameters",
   "context_window": null,
   "license": null,
   "significance": "an open-weight flagship with roughly 397 billion parameters and native multimodal capabilities \u2014 it can understand text, images, and video within a single model",
   "organization_evidence": [
    "# Qwen",
    "Alibaba's open-source AI model family \u2014 powerful LLMs for chat, coding, math, and multimodal tasks with competitive GPT-4 level performance",
    "The 2026 lineup is led by Qwen3.5 (released February 16, 2026), an open-weight flagship with roughly 397 billion parameters and native multimodal capabilities \u2014 it can understand text, images, and video within a single model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen",
    "Alibaba's open-source AI model family \u2014 powerful LLMs for chat, coding, math, and multimodal tasks with competitive GPT-4 level performance",
    "The 2026 lineup is led by Qwen3.5 (released February 16, 2026), an open-weight flagship with roughly 397 billion parameters and native multimodal capabilities \u2014 it can understand text, images, and video within a single model."
   ],
   "parameters_evidence": [
    "# Qwen",
    "Alibaba's open-source AI model family \u2014 powerful LLMs for chat, coding, math, and multimodal tasks with competitive GPT-4 level performance",
    "The 2026 lineup is led by Qwen3.5 (released February 16, 2026), an open-weight flagship with roughly 397 billion parameters and native multimodal capabilities \u2014 it can understand text, images, and video within a single model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen",
    "Alibaba's open-source AI model family \u2014 powerful LLMs for chat, coding, math, and multimodal tasks with competitive GPT-4 level performance",
    "The 2026 lineup is led by Qwen3.5 (released February 16, 2026), an open-weight flagship with roughly 397 billion parameters and native multimodal capabilities \u2014 it can understand text, images, and video within a single model."
   ]
  },
  {
   "url": "https://dev.to/ji_ai/meta-ditched-llama-for-a-closed-model-called-muse-spark-open-source-ai-just-lost-its-biggest-1nlo",
   "title": "Meta Ditched Llama for a Closed Model Called Muse Spark \u2014 Open Source AI Just Lost Its Biggest Champion",
   "published_at": "2026-04-15T23:25:01",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": "27B dense, 26B MoE",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "The Bigger Picture \u2014 Open Source AI Enters a Multi-Polar Era",
    "In April 2026, Google shipped Gemma 4 under Apache 2.0, Zhipu AI released GLM-5.1 under MIT, and numerous smaller labs are producing competitive open models.",
    "| Open Model | Origin | Parameters | License |",
    "| Gemma 4 | Google | 27B dense, 26B MoE | Apache 2.0 |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "The Bigger Picture \u2014 Open Source AI Enters a Multi-Polar Era",
    "| Open Model | Origin | Parameters | License |",
    "| Gemma 4 | Google | 27B dense, 26B MoE | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://dev.to/ji_ai/meta-ditched-llama-for-a-closed-model-called-muse-spark-open-source-ai-just-lost-its-biggest-1nlo",
   "title": "Meta Ditched Llama for a Closed Model Called Muse Spark \u2014 Open Source AI Just Lost Its Biggest Champion",
   "published_at": "2026-04-15T23:25:01",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": null,
   "parameters": "744B MoE (40B active)",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "The Bigger Picture \u2014 Open Source AI Enters a Multi-Polar Era",
    "In April 2026, Google shipped Gemma 4 under Apache 2.0, Zhipu AI released GLM-5.1 under MIT, and numerous smaller labs are producing competitive open models.",
    "| Open Model | Origin | Parameters | License |",
    "| GLM-5.1 | Zhipu AI (China) | 744B MoE (40B active) | MIT |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "The Bigger Picture \u2014 Open Source AI Enters a Multi-Polar Era",
    "| Open Model | Origin | Parameters | License |",
    "| GLM-5.1 | Zhipu AI (China) | 744B MoE (40B active) | MIT |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aimoneytools.net/blog/gemma-4-12b-review-2026",
   "title": "Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
   "published_at": "2026-06-04T00:00:00",
   "organization": "Google",
   "model": "Gemma 4 12B",
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "First medium-sized model in the Gemma family to use an encoder-free multimodal architecture; runs locally on 16GB RAM; handles text, images, and audio natively.",
   "organization_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "Google just released Gemma 4 12B \u2014 a 12 billion parameter multimodal model that runs locally on 16GB RAM."
   ],
   "model_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "Google just released Gemma 4 12B \u2014 a 12 billion parameter multimodal model that runs locally on 16GB RAM."
   ],
   "release_date_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "Google released Gemma 4 12B on June 3, 2026 \u2014 and it is a meaningful step forward for anyone who wants to run powerful AI locally without a dedicated GPU setup."
   ],
   "parameters_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "Google just released Gemma 4 12B \u2014 a 12 billion parameter multimodal model that runs locally on 16GB RAM."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "It is open-source under the Apache 2.0 license (free to use commercially)"
   ],
   "significance_evidence": [
    "# Gemma 4 12B: Google's New Encoder-Free AI Model Runs on Your Laptop (2026 Guide)",
    "Google just released Gemma 4 12B \u2014 a 12 billion parameter multimodal model that runs locally on 16GB RAM.",
    "The 12B variant is the first medium-sized model in the Gemma family to use an encoder-free multimodal architecture."
   ]
  },
  {
   "url": "https://aiintelreport.com/frontier-models/moonshot-kimi-k3-open-weight-frontier-model",
   "title": "Moonshot AI's Kimi K3 Emerges as Largest Open-Weight Model at 2.8 Trillion Parameters",
   "published_at": "2026-07-19T12:35:36",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "World's largest open-weight AI system; competitive performance with Anthropic's Fable 5 and outperforms Opus 4.8, GPT 5.6 Sol, and GPT 5.5; first in web interface building per Arena.ai; 67.3 on DeepSWE v1.1.",
   "organization_evidence": [
    "Frontier Models",
    "# Moonshot AI's Kimi K3 Emerges as Largest Open-Weight Model at 2.8 Trillion Parameters",
    "Kimi K3 is a 2.8-trillion-parameter open-weight model from Moonshot AI featuring a 1 million token context window and mixture of experts architecture."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Frontier Models",
    "# Moonshot AI's Kimi K3 Emerges as Largest Open-Weight Model at 2.8 Trillion Parameters",
    "The Chinese startup's 2.8 trillion parameter model with a 1 million token context window and full weights release on July 27 positions it competitively against closed US systems from Anthropic and OpenAI.",
    "The full weights will be available for download by July 27, 2026, which will allow for extensive testing and customization by developers and researchers around the world."
   ],
   "parameters_evidence": [
    "Frontier Models",
    "# Moonshot AI's Kimi K3 Emerges as Largest Open-Weight Model at 2.8 Trillion Parameters",
    "The Chinese startup's 2.8 trillion parameter model with a 1 million token context window and full weights release on July 27 positions it competitively against closed US systems from Anthropic and OpenAI."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Frontier Models",
    "# Moonshot AI's Kimi K3 Emerges as Largest Open-Weight Model at 2.8 Trillion Parameters",
    "The Chinese startup's 2.8 trillion parameter model with a 1 million token context window and full weights release on July 27 positions it competitively against closed US systems from Anthropic and OpenAI.",
    "The company claims that the model offers competitive performance with Anthropic's Fable 5 and outperforms models such as Opus 4.8, GPT 5.6 Sol, and GPT 5.5 on various benchmarks."
   ]
  },
  {
   "url": "https://happyrock.cloud/blog/glm",
   "title": "GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
   "published_at": "2026-06-17T16:23:18",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-17",
   "parameters": "753B",
   "context_window": "1M",
   "license": "MIT",
   "significance": "scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)",
   "organization_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "> **Abstract**: On June 17, 2026, Zhipu AI (Z.ai) officially open-sourced GLM-5.2 \u2014 a 753B-parameter MoE model scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)."
   ],
   "model_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "> **Abstract**: On June 17, 2026, Zhipu AI (Z.ai) officially open-sourced GLM-5.2 \u2014 a 753B-parameter MoE model scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)."
   ],
   "release_date_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "> **Abstract**: On June 17, 2026, Zhipu AI (Z.ai) officially open-sourced GLM-5.2 \u2014 a 753B-parameter MoE model scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)."
   ],
   "parameters_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "> **Abstract**: On June 17, 2026, Zhipu AI (Z.ai) officially open-sourced GLM-5.2 \u2014 a 753B-parameter MoE model scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)."
   ],
   "context_window_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "## 2. GLM-5.2: The Data Story",
    "### 2.1 Architecture Specifications",
    "**Key observations:** 753B total/40B active (~5.3% activation); 1M context + 128K output (5x over GLM-5.1); MIT license with no restrictions."
   ],
   "license_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "## 2. GLM-5.2: The Data Story",
    "### 2.1 Architecture Specifications",
    "**Key observations:** 753B total/40B active (~5.3% activation); 1M context + 128K output (5x over GLM-5.1); MIT license with no restrictions."
   ],
   "significance_evidence": [
    "# GLM-5.2 Open Source Deep Dive: How Open-Source AI First Approached the Closed-Source Frontier",
    "> **Abstract**: On June 17, 2026, Zhipu AI (Z.ai) officially open-sourced GLM-5.2 \u2014 a 753B-parameter MoE model scoring 74.4 on FrontierSWE, approaching Claude Opus 4.8 (75.1) and surpassing GPT-5.5 (72.6)."
   ]
  },
  {
   "url": "https://digitalapplied.com/blog/nvidia-nemotron-3-ultra-550b-open-reasoning-model-2026",
   "title": "NVIDIA Nemotron 3 Ultra: 550B Open Reasoning Model Live",
   "published_at": "2026-06-04T23:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranking ninth out of eighty-nine on Artificial Analysis's Intelligence Index with a score of 48, making it the highest-ranking US-origin open-weight model at launch.",
   "organization_evidence": [
    "NVIDIA released Nemotron 3 Ultra on June 4, 2026 \u2014 a 550-billion-parameter open Mixture-of-Experts reasoning model that ships not just weights but training data and recipes under a permissive Linux Foundation license.",
    "NVIDIA released Nemotron 3 Ultra in four checkpoint variants on the same day: an NVFP4-quantized build, a BF16 post-trained instruct model, a BF16 base model, and a GenRM (generative reward model) variant for building reward pipelines."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA released Nemotron 3 Ultra on June 4, 2026 \u2014 a 550-billion-parameter open Mixture-of-Experts reasoning model that ships not just weights but training data and recipes under a permissive Linux Foundation license.",
    "Nemotron 3 Ultra shipped *June 4, 2026* in four checkpoints across 25+ platforms."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On the one independent benchmark available at launch, Artificial Analysis's Intelligence Index, it scores 48 and ranks ninth of eighty-nine models evaluated.",
    "That places it above every other US open-weight model."
   ]
  },
  {
   "url": "https://allenai.org/blog/open-coding-agents?lid=15qLhVBKszJszUtOW",
   "title": "Open Coding Agents: Fast, accessible coding agents that adapt to any repo | Ai2",
   "published_at": null,
   "organization": "Ai2",
   "model": null,
   "release_date": "2026-02-03",
   "parameters": "14-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "A new 14-billion-parameter model in the family to support more setups.",
   "organization_evidence": [
    "Open Coding Agents: Fast, accessible coding agents that adapt to any repo",
    "January 27, 2026",
    "Ai2",
    "To support more setups, today we're releasing **SERA-14B**, a new 14-billion-parameter model in the family."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Coding Agents: Fast, accessible coding agents that adapt to any repo",
    "January 27, 2026",
    "Ai2",
    "***Update 2/3:*** *Announcing SERA-14B*"
   ],
   "parameters_evidence": [
    "Open Coding Agents: Fast, accessible coding agents that adapt to any repo",
    "January 27, 2026",
    "Ai2",
    "To support more setups, today we're releasing **SERA-14B**, a new 14-billion-parameter model in the family."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Coding Agents: Fast, accessible coding agents that adapt to any repo",
    "January 27, 2026",
    "Ai2",
    "To support more setups, today we're releasing **SERA-14B**, a new 14-billion-parameter model in the family."
   ]
  },
  {
   "url": "https://aideck.website/articles/ai-landscape-spring-2026-roundup",
   "title": "What's Real in AI Right Now: The Spring 2026 Model and Tooling Roundup",
   "published_at": "2026-06-28T11:38:03",
   "organization": "DeepSeek",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Made its 75% launch discount permanent on May 23 and costs roughly 34 times cheaper on output than GPT-5.5 for near-frontier performance.",
   "organization_evidence": [
    "What's Real in AI Right Now: The Spring 2026 Model and Tooling Roundup",
    "DeepSeek shipped the open-weight V4 (now $0.435/$0.87 for Pro)"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What's Real in AI Right Now: The Spring 2026 Model and Tooling Roundup",
    "The text models that actually shipped",
    "One day later, DeepSeek dropped [V4](/tools/deepseek-v4) (V4-Pro and V4-Flash), open weights under the MIT license, and made its 75% launch discount permanent on May 23: V4-Pro now costs $0.435 input and $0.87 output.",
    "That is roughly 34 times cheaper on output than GPT-5.5 for near-frontier performance."
   ]
  },
  {
   "url": "https://aimidday.com/qwen-open-sources-qwen3-8-27b-that-runs-on-consumer-gpus",
   "title": "Qwen open-sources Qwen3.8-27B that runs on consumer GPUs",
   "published_at": "2026-08-14T16:41:32",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-28",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "native 262K-token context (extendable to 1M via YaRN), a new reasoning_effort control that dials thinking depth to save compute, and big claimed gains over Qwen3.6-27B in coding and office work \u2014 Qwen says it even beats Qwen3.7-Plus",
   "organization_evidence": [
    "The Frontier",
    "Qwen open-sources Qwen3.8-27B that runs on consumer GPUs",
    "Alibaba's Qwen delivered the open-source model the community kept asking for, and Meta's most expensive hire walked out the door \u2014 a Friday that splits cleanly into two stories."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Frontier",
    "Qwen open-sources Qwen3.8-27B that runs on consumer GPUs",
    "Alibaba's Qwen open-sourced Qwen3.8-27B on Friday under the permissive Apache 2.0 license \u2014 a 27-billion-parameter dense multimodal model built to run on consumer GPUs, free for anyone to download, deploy, and sell."
   ],
   "parameters_evidence": [
    "The Frontier",
    "Qwen open-sources Qwen3.8-27B that runs on consumer GPUs",
    "Alibaba's Qwen open-sourced Qwen3.8-27B on Friday under the permissive Apache 2.0 license \u2014 a 27-billion-parameter dense multimodal model built to run on consumer GPUs, free for anyone to download, deploy, and sell."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Frontier",
    "Qwen open-sources Qwen3.8-27B that runs on consumer GPUs",
    "The company positions 27B as the size the global AI community demanded most, and the release leans hard into local deployment: native 262K-token context (extendable to 1M via YaRN), a new reasoning_effort control that dials thinking depth to save compute, and big claimed gains over Qwen3.6-27B in coding and office work \u2014 Qwen says it even beats Qwen3.7-Plus."
   ]
  },
  {
   "url": "https://aistackchoice.com/best-open-source-ai-models-in-2026-deepseek-v4-glm-5-1-qwen-3-6-gemma-4-llama-4-compared",
   "title": "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared - AI Stack Choice",
   "published_at": "2026-05-01T02:04:35",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "MoE (1.6T / 49B active)",
   "context_window": null,
   "license": null,
   "significance": "DeepSeek V4 Pro, released on April 24, 2026, represents the most ambitious open source model to date.",
   "organization_evidence": [
    "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared",
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Release Date** | April 24, 2026 | April 8, 2026 | April 23, 2026 | April 2, 2026 | April 5, 2026 |"
   ],
   "parameters_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Architecture** | MoE (1.6T / 49B active) | MoE (744B total) | Dense (27B) | Dense (31B) | MoE (109B / 17B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Pro: The New Open Source King of All Trades",
    "DeepSeek V4 Pro, released on April 24, 2026, represents the most ambitious open source model to date."
   ]
  },
  {
   "url": "https://aistackchoice.com/best-open-source-ai-models-in-2026-deepseek-v4-glm-5-1-qwen-3-6-gemma-4-llama-4-compared",
   "title": "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared - AI Stack Choice",
   "published_at": "2026-05-01T02:04:35",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": "MoE (744B total)",
   "context_window": null,
   "license": null,
   "significance": "Zhipu AI\u2019s GLM-5.1, released April 8, 2026, holds a special distinction: it is the first open source model to rank number one on SWE-bench Pro at 58.4%, beating both GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) on one of the most challenging real-world coding benchmarks in AI.",
   "organization_evidence": [
    "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared",
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Release Date** | April 24, 2026 | April 8, 2026 | April 23, 2026 | April 2, 2026 | April 5, 2026 |"
   ],
   "parameters_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Architecture** | MoE (1.6T / 49B active) | MoE (744B total) | Dense (27B) | Dense (31B) | MoE (109B / 17B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.1: The Autonomous Coding Specialist",
    "Zhipu AI\u2019s GLM-5.1, released April 8, 2026, holds a special distinction: it is the **first open source model to rank number one on SWE-bench Pro** at 58.4%, beating both GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%) on one of the most challenging real-world coding benchmarks in AI."
   ]
  },
  {
   "url": "https://aistackchoice.com/best-open-source-ai-models-in-2026-deepseek-v4-glm-5-1-qwen-3-6-gemma-4-llama-4-compared",
   "title": "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared - AI Stack Choice",
   "published_at": "2026-05-01T02:04:35",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-23",
   "parameters": "Dense (27B)",
   "context_window": null,
   "license": null,
   "significance": "Alibaba\u2019s Qwen 3.6 series took an unconventional approach: instead of chasing maximum parameter counts, the team focused on squeezing maximum capability into minimum active parameters.",
   "organization_evidence": [
    "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared",
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Release Date** | April 24, 2026 | April 8, 2026 | April 23, 2026 | April 2, 2026 | April 5, 2026 |"
   ],
   "parameters_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Architecture** | MoE (1.6T / 49B active) | MoE (744B total) | Dense (27B) | Dense (31B) | MoE (109B / 17B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen 3.6-27B: The Lightweight Coding Champion",
    "Alibaba\u2019s Qwen 3.6 series took an unconventional approach: instead of chasing maximum parameter counts, the team focused on **squeezing maximum capability into minimum active parameters**."
   ]
  },
  {
   "url": "https://aistackchoice.com/best-open-source-ai-models-in-2026-deepseek-v4-glm-5-1-qwen-3-6-gemma-4-llama-4-compared",
   "title": "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared - AI Stack Choice",
   "published_at": "2026-05-01T02:04:35",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "Dense (31B)",
   "context_window": null,
   "license": null,
   "significance": "Google DeepMind released Gemma 4 on April 2, 2026, and the message was clear: the parameter arms race is over.",
   "organization_evidence": [
    "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared",
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Release Date** | April 24, 2026 | April 8, 2026 | April 23, 2026 | April 2, 2026 | April 5, 2026 |"
   ],
   "parameters_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Architecture** | MoE (1.6T / 49B active) | MoE (744B total) | Dense (27B) | Dense (31B) | MoE (109B / 17B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 31B: Google\u2019s Efficiency Breakthrough",
    "Google DeepMind released Gemma 4 on April 2, 2026, and the message was clear: the parameter arms race is over."
   ]
  },
  {
   "url": "https://aistackchoice.com/best-open-source-ai-models-in-2026-deepseek-v4-glm-5-1-qwen-3-6-gemma-4-llama-4-compared",
   "title": "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared - AI Stack Choice",
   "published_at": "2026-05-01T02:04:35",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": "MoE (109B / 17B active)",
   "context_window": null,
   "license": null,
   "significance": "Meta\u2019s Llama 4 Scout, released April 5, 2026, brings one headline feature that no other model can match: a 10-million-token context window.",
   "organization_evidence": [
    "Best Open Source AI Models in 2026: DeepSeek V4, GLM-5.1, Qwen 3.6, Gemma 4 & Llama 4 Compared",
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Release Date** | April 24, 2026 | April 8, 2026 | April 23, 2026 | April 2, 2026 | April 5, 2026 |"
   ],
   "parameters_evidence": [
    "Quick Comparison: The Five Contenders at a Glance",
    "| Feature | DeepSeek V4 Pro | GLM-5.1 | Qwen 3.6-27B | Gemma 4 31B | Llama 4 Scout |",
    "| **Architecture** | MoE (1.6T / 49B active) | MoE (744B total) | Dense (27B) | Dense (31B) | MoE (109B / 17B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama 4 Scout: The Context Window Monster",
    "Meta\u2019s Llama 4 Scout, released April 5, 2026, brings one headline feature that no other model can match: a **10-million-token context window**."
   ]
  },
  {
   "url": "https://idlen.io/news/google-gemma-4-open-source-apache-aime-codeforces-ai-agents-mobile",
   "title": "Gemma 4 Google: Apache 2.0, AIME 89%, open-source models frontier",
   "published_at": "2026-04-03T12:05:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "AIME 2026 89.2%, Codeforces 2150 ELO, LMArena 1452 (#3 worldwide open-source)",
   "organization_evidence": [
    "Google Gemma 4: AIME 20% \u2192 89%, Codeforces 110 \u2192 2150, Apache 2.0 \u2014 The Leap That Redefines Open-Source Models",
    "Google DeepMind launches Gemma 4: 4 Apache 2.0 open-weight models, AIME 89%, Codeforces ELO 2150, native function calling, and full offline on smartphones.",
    "**Google DeepMind launches Gemma 4 on April 1, 2026**: 4 open-weight models (E2B, E4B, 26B MoE, 31B) under Apache 2.0 \u2014 a first for the Gemma family"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4: AIME 20% \u2192 89%, Codeforces 110 \u2192 2150, Apache 2.0 \u2014 The Leap That Redefines Open-Source Models",
    "**Google DeepMind launches Gemma 4 on April 1, 2026**: 4 open-weight models (E2B, E4B, 26B MoE, 31B) under Apache 2.0 \u2014 a first for the Gemma family"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4: AIME 20% \u2192 89%, Codeforces 110 \u2192 2150, Apache 2.0 \u2014 The Leap That Redefines Open-Source Models",
    "**Google DeepMind launches Gemma 4 on April 1, 2026**: 4 open-weight models (E2B, E4B, 26B MoE, 31B) under Apache 2.0 \u2014 a first for the Gemma family",
    "**Record performance**: AIME 2026 89.2%, Codeforces 2150 ELO, LMArena 1452 (#3 worldwide open-source) \u2014 frontier performance in open-source"
   ]
  },
  {
   "url": "https://digg.com/ai/lvlg9m33",
   "title": "Moonshot AI open-sources Kimi-K2.7-Code, cutting reasoning tokens by 30% and boosting Kimi Code Bench scores\u2026",
   "published_at": "2026-06-12T10:23:17",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite.",
   "organization_evidence": [
    "Moonshot just released Kimi-K2.7 code, a huge upgrade to Kimi-K2.6!"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "3:16 AM \u00b7 Jun 12, 2026 \u00b7 1.2M Views"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "\ud83d\udd37 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. \ud83d\udd37 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6. \ud83d\udd37 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates."
   ]
  },
  {
   "url": "https://morphllm.com/glm-5-1",
   "title": "GLM-5.1: 754B Open-Weight Coding Model, 200K Context, Benchmarks, Pricing (2026)",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Z.ai",
   "model": "GLM-5.1",
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": "200K",
   "license": "MIT",
   "significance": "58.4 on SWE-bench Pro (SOTA at launch)",
   "organization_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context."
   ],
   "model_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context."
   ],
   "release_date_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is an open-weight (MIT) mixture-of-experts coding model from Z.ai (formerly Zhipu AI), released April 7, 2026."
   ],
   "parameters_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context."
   ],
   "context_window_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context."
   ],
   "license_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context."
   ],
   "significance_evidence": [
    "# GLM-5.1: 754B Open-Weight Agentic Coding Model, 200K Context, MIT",
    "GLM-5.1 is Z.ai's 754B MoE coding model, released April 2026 under MIT with a 200K context.",
    "It scored 58.4 on SWE-bench Pro (SOTA at launch) at $1.40/$4.40 per M tokens on Z.ai."
   ]
  },
  {
   "url": "https://boostn.ai/en/agency/news/minimax-m3-open-weight-frontier-model",
   "title": "MiniMax M3: Open-Weight Frontier Model With Sparse Attention",
   "published_at": "2026-06-02T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax positions the model as the first open-weight system to combine frontier-level coding, a one-million-token context window and native multimodality in one.",
   "organization_evidence": [
    "Shanghai-based AI lab MiniMax unveiled its new language model **MiniMax M3** on June 1, 2026 \u2014 and the bet here is not on more parameters but on a new architecture.",
    "# MiniMax M3: Open-Weight Frontier Model With Sparse Attention"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Shanghai-based AI lab MiniMax unveiled its new language model **MiniMax M3** on June 1, 2026 \u2014 and the bet here is not on more parameters but on a new architecture.",
    "# MiniMax M3: Open-Weight Frontier Model With Sparse Attention"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax positions the model as the first open-weight system to combine frontier-level coding, a one-million-token context window and native multimodality in one.",
    "# MiniMax M3: Open-Weight Frontier Model With Sparse Attention"
   ]
  },
  {
   "url": "https://open-techstack.com/blog/apertus-1-5-swiss-open-weights-open-training-data",
   "title": "Apertus 1.5: Switzerland's Fully Open AI Model Pushes Transparency",
   "published_at": "2026-07-27T00:00:00",
   "organization": "ETH Zurich and EPFL",
   "model": null,
   "release_date": "2026-07-24",
   "parameters": "8B, 70B",
   "context_window": null,
   "license": null,
   "significance": "Fully open weights, data, and training code, native image understanding, optional thinking mode, 262K context window, and improved instruction-following and tool-use capabilities.",
   "organization_evidence": [
    "Apertus 1.5: Switzerland's Fully Open AI Model Pushes Transparency",
    "ETH Zurich and EPFL release Apertus 1.5 \u2014 fully open weights, data, and training code. 8B/70B models with image understanding, thinking mode, 262K context.",
    "On July 24, 2026, ETH Zurich, EPFL, and the Swiss National Supercomputing Centre (CSCS) released **Apertus 1.5** \u2014 the latest version of Switzerland\u2019s fully open large language model."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Apertus 1.5: Switzerland's Fully Open AI Model Pushes Transparency",
    "Published July 27, 2026 \u00b7 8 min read",
    "On July 24, 2026, ETH Zurich, EPFL, and the Swiss National Supercomputing Centre (CSCS) released **Apertus 1.5** \u2014 the latest version of Switzerland\u2019s fully open large language model."
   ],
   "parameters_evidence": [
    "Apertus 1.5: Switzerland's Fully Open AI Model Pushes Transparency",
    "ETH Zurich and EPFL release Apertus 1.5 \u2014 fully open weights, data, and training code. 8B/70B models with image understanding, thinking mode, 262K context.",
    "The release includes 8-billion and 70-billion parameter variants, with the 8B model receiving 4 trillion additional tokens of text and multimodal training data, and the 70B model receiving 2 trillion."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Apertus 1.5: Switzerland's Fully Open AI Model Pushes Transparency",
    "ETH Zurich and EPFL release Apertus 1.5 \u2014 fully open weights, data, and training code. 8B/70B models with image understanding, thinking mode, 262K context.",
    "Both versions gain native image understanding, an optional thinking mode, a 262,144-token context window, improved instruction-following, and better tool-use capabilities."
   ]
  },
  {
   "url": "https://aimenta.ai/news/mistral-large-2-open-weights-2026",
   "title": "Mistral AI Releases Mistral Large 2 as Open Weights \u2014 123B Parameter Frontier Model Available for On-Premises Deployment",
   "published_at": "2026-03-12T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First frontier-class open-weights model available for APAC enterprise on-premises deployment without API costs.",
   "organization_evidence": [
    "AIMenta editorial take",
    "Mistral AI releases Mistral Large 2 as open-weights, 123B parameters, 128K context, matching Claude 3.5 Sonnet on most benchmarks."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AIMenta editorial take",
    "Mistral AI releases Mistral Large 2 as open-weights, 123B parameters, 128K context, matching Claude 3.5 Sonnet on most benchmarks.",
    "First frontier-class open-weights model available for APAC enterprise on-premises deployment without API costs."
   ]
  },
  {
   "url": "https://launchberg.com/google-gemma-4",
   "title": "Google Gemma 4: Open-Weight AI That Punches Way Up | Launchberg",
   "published_at": "2026-04-03T12:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B to 31B",
   "context_window": null,
   "license": null,
   "significance": "Scores 88.3% on AIME 2026 with 3.8B active parameters, natively multimodal from text, images, video to audio.",
   "organization_evidence": [
    "# Google Gemma 4: Open-Weight AI That Punches Way Up",
    "Google DeepMind released Gemma 4 on April 2, 2026: four model sizes, all Apache 2.0 licensed, all natively multimodal."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4: Open-Weight AI That Punches Way Up",
    "Google DeepMind released Gemma 4 on April 2, 2026: four model sizes, all Apache 2.0 licensed, all natively multimodal."
   ],
   "parameters_evidence": [
    "# Google Gemma 4: Open-Weight AI That Punches Way Up",
    "Four model sizes, Apache 2.0, natively multimodal from 2B to 31B \u2014 and the 26B MoE variant scores 88.3% on AIME 2026 with only 3.8B active parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4: Open-Weight AI That Punches Way Up",
    "Four model sizes, Apache 2.0, natively multimodal from 2B to 31B \u2014 and the 26B MoE variant scores 88.3% on AIME 2026 with only 3.8B active parameters."
   ]
  },
  {
   "url": "https://bovo-digital.tech/en/blog/kimi-k3-moonshot-ai-chine-open-weight-frontier",
   "title": "Kimi K3 + Moonshot AI: China Defies OpenAI with a 2.8T Open-Weight Frontier Model",
   "published_at": "2026-07-26T19:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "#1 on Arena Frontend Code, largest open-weight model published to date",
   "organization_evidence": [
    "# Kimi K3 + Moonshot AI: China Defies OpenAI with a 2.8T Open-Weight Frontier Model",
    "Moonshot AI releases Kimi K3 on July 16, 2026: 2.8T MoE parameters, 1M context, #1 Arena Frontend Code. Open weights on July 27. China defies OpenAI with a downloadable frontier model.",
    "On **July 16, 2026**, Moonshot AI \u2014 a Chinese startup based in Beijing \u2014 releases **Kimi K3**, a **2.8-trillion-parameter** AI model in **Mixture-of-Experts (MoE)** architecture."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI releases Kimi K3 on July 16, 2026: 2.8T MoE parameters, 1M context, #1 Arena Frontend Code. Open weights on July 27. China defies OpenAI with a downloadable frontier model.",
    "| **July 27, 2026** | Full weights published on Hugging Face |"
   ],
   "parameters_evidence": [
    "Moonshot AI releases Kimi K3 on July 16, 2026: 2.8T MoE parameters, 1M context, #1 Arena Frontend Code. Open weights on July 27. China defies OpenAI with a downloadable frontier model.",
    "# Kimi K3 Technical Specs",
    "| **Total parameters** | 2.8 trillion (2.8T) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI releases Kimi K3 on July 16, 2026: 2.8T MoE parameters, 1M context, #1 Arena Frontend Code. Open weights on July 27. China defies OpenAI with a downloadable frontier model.",
    "# Benchmarks: K3 vs the Frontier",
    "Announced as the **largest open-weight model published to date**, K3 surpasses DeepSeek V4 Pro (1.6T) and Zhipu GLM 5 (744B).",
    "The most striking result: Kimi K3 debuts at **#1 position** on Arena Frontend Code leaderboard, a **17-place jump** over Kimi K2.6."
   ]
  },
  {
   "url": "https://webiano.digital/running-glm-5-2-locally-from-bare-metal-to-a-working-coding-agent",
   "title": "Running GLM-5.2 locally, from bare metal to a working coding agent",
   "published_at": "2026-07-01T22:11:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "a flagship model for long-horizon tasks",
   "organization_evidence": [
    "# Running GLM-5.2 locally, from bare metal to a working coding agent",
    "GLM-5.2 is a large language model released by Z.ai, the Beijing company formerly known as Zhipu AI, a lab that spun out of Tsinghua University in 2019."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Running GLM-5.2 locally, from bare metal to a working coding agent",
    "The model rolled out to Z.ai\u2019s own GLM Coding Plan subscribers on Saturday, June 13, 2026, an unusual weekend launch, and the open weights followed three days later on June 16 alongside a technical release blog."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Running GLM-5.2 locally, from bare metal to a working coding agent",
    "GLM-5.2 is explicitly pitched by Z.ai as **a flagship model for long-horizon tasks**, and the engineering choices inside it, discussed in the next section, follow directly from that goal."
   ]
  },
  {
   "url": "https://aiunpacker.com/blog/minimax-m3-review",
   "title": "MiniMax M3 Review 2026: 1M Context Multimodal AI Model",
   "published_at": "2026-06-05T00:00:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": "1 million token context window",
   "license": "open-weight",
   "significance": "frontier coding, million-token context, and native multimodality - all in an open-weight package",
   "organization_evidence": [
    "MiniMax M3 Review: 1M Context Multimodal AI Model for Agents, Coding, and Research",
    "M3 is the latest large language model from MiniMax, the Shanghai-based AI company founded in early 2022."
   ],
   "model_evidence": [
    "MiniMax M3 Review: 1M Context Multimodal AI Model for Agents, Coding, and Research",
    "**MiniMax M3** launched on June 1, 2026, and it\u2019s not just another checkpoint drop."
   ],
   "release_date_evidence": [
    "MiniMax M3 Review: 1M Context Multimodal AI Model for Agents, Coding, and Research",
    "**MiniMax M3** launched on June 1, 2026, and it\u2019s not just another checkpoint drop."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "MiniMax M3 Review: 1M Context Multimodal AI Model for Agents, Coding, and Research",
    "The headline numbers:",
    "- **1 million token context window** (512K guaranteed minimum)"
   ],
   "license_evidence": [
    "**MiniMax M3** launched on June 1, 2026, and it\u2019s not just another checkpoint drop.",
    "It\u2019s a model that genuinely tries to do three hard things at once: frontier coding, million-token context, and native multimodality - all in an open-weight package."
   ],
   "significance_evidence": [
    "MiniMax M3 Review: 1M Context Multimodal AI Model for Agents, Coding, and Research",
    "It\u2019s a model that genuinely tries to do three hard things at once: frontier coding, million-token context, and native multimodality - all in an open-weight package."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/MolmoWeb-Pretrained-4B",
   "title": "allenai/MolmoWeb-Pretrained-4B \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen AI",
   "model": "MolmoWeb-Pretrained-4B",
   "release_date": "2026-04-12",
   "parameters": "4.0B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A 4-billion parameter pretrained vision-language base model from the Allen Institute for AI (AI2) Molmo family, made available under the Apache-2.0 license via Modelers.cn.",
   "organization_evidence": [
    "allenai / MolmoWeb-Pretrained-4B",
    "This is a 4-billion parameter pretrained vision-language model from Allen AI (AI2), part of the Molmo family, made available on the Modelers.cn platform."
   ],
   "model_evidence": [
    "allenai / MolmoWeb-Pretrained-4B",
    "allenai/MolmoWeb-Pretrained-4B",
    "MolmoWeb-Pretrained-4B is a 4-billion parameter vision-language base model from the Allen Institute for AI (AI2) Molmo family."
   ],
   "release_date_evidence": [
    "allenai/MolmoWeb-Pretrained-4B",
    "Created: 2026-04-12 | Updated: 2026-04-12"
   ],
   "parameters_evidence": [
    "allenai / MolmoWeb-Pretrained-4B",
    "Parameters",
    "4.0B"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "allenai / MolmoWeb-Pretrained-4B",
    "Downloads: 278 | License: Apache-2.0",
    "Apache-2.0 licence is fully permissive for commercial and research use"
   ],
   "significance_evidence": [
    "allenai / MolmoWeb-Pretrained-4B",
    "This is a 4-billion parameter pretrained vision-language model from Allen AI (AI2), part of the Molmo family, made available on the Modelers.cn platform."
   ]
  },
  {
   "url": "https://hivebook.wiki/wiki/deepseek-v4-april-2026-v4-pro-1-6t-49b-and-v4-flash-284b-13b-moe-open-weights-llms-1m-context-csa-hca-hybrid-attention-fp4-fp8-precision-openai-and-anthropic-compatible-apis-mit-license",
   "title": "DeepSeek V4 \u2014 V4-Pro and V4-Flash MoE Open-Weights LLMs",
   "published_at": "2026-05-13T07:13:07",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Single-token inference FLOPs at 1 M tokens down to 27% of V3.2 and KV cache to 10% of V3.2",
   "organization_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4, announced 2026-04-24, is the successor to the V3 line and ships as two MIT-licensed open-weights MoE models: **V4-Pro** (1.6 T total / 49 B activated) and **V4-Flash** (284 B total / 13 B activated), both with a 1 M-token context window.",
    "The headline architectural change is a *hybrid attention stack* (Compressed Sparse Attention + Heavily Compressed Attention, \"CSA + HCA\") that brings single-token inference FLOPs at 1 M tokens down to **27% of V3.2** and KV cache to **10% of V3.2** \u2014 the long-context cost reductions that make 1 M-token serving practical."
   ]
  },
  {
   "url": "https://aimadetools.com/blog/mistral-medium-3-5-complete-guide",
   "title": "Mistral Medium 3.5 Complete Guide \u2014 Specs, Benchmarks, and How to Use It (2026)",
   "published_at": "2026-04-30T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04",
   "parameters": "128B (dense)",
   "context_window": null,
   "license": null,
   "significance": "128B dense transformer with a 256K context window, 77.6% on SWE-bench Verified, and open weights under a modified MIT license",
   "organization_evidence": [
    "# Mistral Medium 3.5 Complete Guide \u2014 Specs, Benchmarks, and How to Use It (2026)",
    "Mistral Medium 3.5 is Mistral AI\u2019s new flagship model, released in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 Complete Guide \u2014 Specs, Benchmarks, and How to Use It (2026)",
    "Mistral Medium 3.5 is Mistral AI\u2019s new flagship model, released in April 2026.",
    "Key specs",
    "| **Release date** | April 2026 |"
   ],
   "parameters_evidence": [
    "Key specs",
    "| **Parameters** | 128B (dense) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 Complete Guide \u2014 Specs, Benchmarks, and How to Use It (2026)",
    "Mistral Medium 3.5 is a 128B dense transformer with a 256K context window, 77.6% on SWE-bench Verified, and open weights under a modified MIT license."
   ]
  },
  {
   "url": "https://nextomoro.com/gemma-4",
   "title": "Gemma 4",
   "published_at": "2026-05-15T05:03:54",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-05",
   "parameters": "2 billion to 31 billion",
   "context_window": "128K tokens on E2B and E4B; 256K tokens on 26B-A4B and 31B",
   "license": "Apache 2.0",
   "significance": "As of May 2026, Gemma 4 31B IT sits in the leading group of mid-scale open-weights multimodal models on reasoning, coding, and vision benchmarks",
   "organization_evidence": [
    "Gemma 4",
    "Gemma 4 is Google DeepMind's 2026 open-weights model family, spanning dense and mixture-of-experts variants from 2 billion to 31 billion parameters, with native multimodal input, 256K context, and Apache 2.0 licensing."
   ],
   "model_evidence": [
    "Gemma 4",
    "Gemma 4 is Google DeepMind's 2026 open-weights model family, spanning dense and mixture-of-experts variants from 2 billion to 31 billion parameters, with native multimodal input, 256K context, and Apache 2.0 licensing."
   ],
   "release_date_evidence": [
    "Gemma 4",
    "As of May 2026, Gemma 4 31B IT sits in the leading group of mid-scale open-weights multimodal models on reasoning, coding, and vision benchmarks, with the Apache 2.0 license placing it in the broadly-permissive release tier alongside the Qwen 3.6 and DeepSeek V4 families."
   ],
   "parameters_evidence": [
    "Gemma 4",
    "Gemma 4 is Google DeepMind's 2026 open-weights model family, spanning dense and mixture-of-experts variants from 2 billion to 31 billion parameters, with native multimodal input, 256K context, and Apache 2.0 licensing."
   ],
   "context_window_evidence": [
    "Gemma 4",
    "At a glance",
    "- **Context window:** 128K tokens on E2B and E4B; 256K tokens on 26B-A4B and 31B."
   ],
   "license_evidence": [
    "Gemma 4",
    "Gemma 4 is Google DeepMind's 2026 open-weights model family, spanning dense and mixture-of-experts variants from 2 billion to 31 billion parameters, with native multimodal input, 256K context, and Apache 2.0 licensing."
   ],
   "significance_evidence": [
    "Gemma 4",
    "As of May 2026, Gemma 4 31B IT sits in the leading group of mid-scale open-weights multimodal models on reasoning, coding, and vision benchmarks, with the Apache 2.0 license placing it in the broadly-permissive release tier alongside the Qwen 3.6 and DeepSeek V4 families."
   ]
  },
  {
   "url": "https://youtube.com/watch?v=wvspOfo1qsU",
   "title": "Kimi K3 Is Here: The Biggest Open AI Model Yet",
   "published_at": "2026-07-17T13:52:23",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A major open model line landing near the top of the scoreboard, offering a million tokens of context at flat pricing, and winning on some coding and in benchmarks.",
   "organization_evidence": [
    "Kimi is built by Moonshot AI, released July 16, 2026.",
    "Transcript"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi is built by Moonshot AI, released July 16, 2026.",
    "Transcript"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "K3 flips that script.",
    "It's an open weighted model line landing near the top of the scoreboard.",
    "It offers a million tokens of context at flat pricing, and it's actually winning on some coding and in benchmarks.",
    "Transcript"
   ]
  },
  {
   "url": "https://actuia.com/en/news/while-france-debates-ai-sovereignty-portugal-delivered-its-own-for-eur7-million",
   "title": "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
   "published_at": "2026-07-03T08:46:53",
   "organization": "Portuguese government",
   "model": null,
   "release_date": "2026-07-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open large language model developed in European Portuguese",
   "organization_evidence": [
    "New model",
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, Portugal officially unveiled Am\u00e1lia, its first open large language model in European Portuguese, funded with \u20ac7 million in public money."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "New model",
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, Portugal officially unveiled Am\u00e1lia, its first open large language model in European Portuguese, funded with \u20ac7 million in public money."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "New model",
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, the Portuguese government officially unveiled **Am\u00e1lia**, which its creators describe as the first open large language model (LLM) developed in European Portuguese."
   ]
  },
  {
   "url": "https://artificialintelligenceherald.com/news/olmoearth-v1-1-efficiency-2026",
   "title": "OlmoEarth v1.1: AI2 and HuggingFace Deliver Leaner Multimodal Models for Scalable Enterprise Deployment",
   "published_at": "2026-05-20T12:00:52",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-05-20",
   "parameters": "7B, 14B, and 30B",
   "context_window": null,
   "license": null,
   "significance": "OlmoEarth v1.1 30B now leads the open-weight multimodal category on several critical tests, while the 7B model offers the best accuracy-to-flops ratio in its class.",
   "organization_evidence": [
    "News May 20, 2026 5 min read69 views",
    "# OlmoEarth v1.1: AI2 and HuggingFace Deliver Leaner Multimodal Models for Scalable Enterprise Deployment",
    "The Allen Institute for AI (AI2), in collaboration with HuggingFace, released OlmoEarth v1.1 today, a family of vision-language models that deliver performance comparable to their predecessors while using significantly fewer parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "News May 20, 2026 5 min read69 views",
    "# OlmoEarth v1.1: AI2 and HuggingFace Deliver Leaner Multimodal Models for Scalable Enterprise Deployment",
    "The Allen Institute for AI (AI2), in collaboration with HuggingFace, released OlmoEarth v1.1 today, a family of vision-language models that deliver performance comparable to their predecessors while using significantly fewer parameters."
   ],
   "parameters_evidence": [
    "News May 20, 2026 5 min read69 views",
    "# OlmoEarth v1.1: AI2 and HuggingFace Deliver Leaner Multimodal Models for Scalable Enterprise Deployment",
    "According to the announcement on HuggingFace's official blog, the new models \u2014 OlmoEarth v1.1 7B, 14B, and 30B \u2014 achieve this through improved training recipes, including better loss weighting and more efficient data curation strategies."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "News May 20, 2026 5 min read69 views",
    "## Benchmark Breakdown: Where v1.1 Excels",
    "Benchmark results released by AI2 show that OlmoEarth v1.1 30B now leads the open-weight multimodal category on several critical tests:",
    "The 7B model, notably, now achieves 76.8% on ChartQA \u2014 surpassing the original 14B's 75.4%."
   ]
  },
  {
   "url": "https://amirteymoori.com/deepseek-v4-flash-open-weight-frontier-llm-review",
   "title": "DeepSeek V4: Open Weights Reach the Frontier",
   "published_at": "2026-08-03T15:07:25",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "DeepSeek V4 went GA on July 20, 2026, moving out of preview with noticeably stronger agentic behavior, math reasoning, and code generation.",
   "organization_evidence": [
    "DeepSeek V4 went GA on July 20, 2026, moving out of preview with noticeably stronger agentic behavior, math reasoning, and code generation.",
    "Both are MIT licensed."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 went GA on July 20, 2026, moving out of preview with noticeably stronger agentic behavior, math reasoning, and code generation."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 went GA on July 20, 2026, moving out of preview with noticeably stronger agentic behavior, math reasoning, and code generation."
   ]
  },
  {
   "url": "https://evermx.com/case/google-gemma-4-open-model-family-apache-2-agentic",
   "title": "Google Launches Gemma 4: Four Open Models With Agentic Skills Under Apache 2.0",
   "published_at": "2026-04-04T09:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B to 31B parameters",
   "context_window": null,
   "license": null,
   "significance": "Scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench v6, with a Codeforces ELO of 2,150; 26B MoE model ranks 6th on Arena AI while using only 3.8 billion active parameters.",
   "organization_evidence": [
    "Open Source",
    "# Google Launches Gemma 4: Four Open Models With Agentic Skills Under Apache 2.0",
    "Google DeepMind releases Gemma 4, a family of four open-weight models from 2B to 31B parameters, under Apache 2.0, designed for advanced reasoning and edge deployment."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Source",
    "## The Most Capable Open Models Google Has Ever Released",
    "Released on April 2, 2026, under the permissive Apache 2.0 license, Gemma 4 is purpose-built for advanced reasoning and agentic workflows, delivering what Google describes as an unprecedented level of intelligence-per-parameter."
   ],
   "parameters_evidence": [
    "Open Source",
    "# Google Launches Gemma 4: Four Open Models With Agentic Skills Under Apache 2.0",
    "Google DeepMind releases Gemma 4, a family of four open-weight models from 2B to 31B parameters, under Apache 2.0, designed for advanced reasoning and edge deployment."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source",
    "## Benchmark Performance That Challenges Closed Models",
    "The 31B dense model scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench v6, with a Codeforces ELO of 2,150."
   ]
  },
  {
   "url": "https://k3-kimi.com/blog/kimi-k3-release-date",
   "title": "Kimi K3 Release Date: Launched July 16, 2026",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "largest open-weight model ever released \u2014 nearly 3\u00d7 the size of K2\u2019s 1.0T",
   "organization_evidence": [
    "Kimi K3 Release Date: Launched July 16, 2026",
    "Moonshot AI announced its 2.8-trillion-parameter flagship with a full launch \u2014 model live in the Kimi app, API access, the Kimi Work desktop app, and the Kimi Code CLI all available on day one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3 Release Date: Launched July 16, 2026",
    "| **July 27, 2026** | **Open weights scheduled for release** under a Modified MIT license |",
    "| Date | Milestone |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What\u2019s next after release",
    "The biggest remaining date is **July 27, 2026**, when K3\u2019s weights go public under a Modified MIT license.",
    "That will make K3 the largest open-weight model ever released \u2014 nearly 3\u00d7 the size of K2\u2019s 1.0T."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/glm-5-1",
   "title": "GLM-5.1 \u2014 Z.ai Open-Weight 754B MoE Specs | AI/TLDR",
   "published_at": "2026-04-07T00:00:00",
   "organization": "Z.ai",
   "model": "GLM-5.1",
   "release_date": "2026-04-07",
   "parameters": "~754B total, 40B active (MoE)",
   "context_window": "200K",
   "license": "MIT",
   "significance": "SOTA on SWE-Bench Pro and up to ~8 hours of autonomous agentic execution",
   "organization_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day."
   ],
   "model_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day."
   ],
   "release_date_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day."
   ],
   "parameters_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day.",
    "It is a ~754B-parameter Mixture-of-Experts model with roughly 40B parameters active per token, a 200K-token context window and up to 128K tokens of output."
   ],
   "context_window_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day.",
    "It is a ~754B-parameter Mixture-of-Experts model with roughly 40B parameters active per token, a 200K-token context window and up to 128K tokens of output."
   ],
   "license_evidence": [
    "# GLM-5.1",
    "GLM-5.1 is the flagship large language model from Z.ai (formerly Zhipu AI), released on April 7, 2026 and open-sourced under the MIT license the following day."
   ],
   "significance_evidence": [
    "# GLM-5.1",
    "Z.ai's open-weight 754B MoE flagship \u2014 SOTA on SWE-Bench Pro and up to ~8 hours of autonomous agentic execution.",
    "On SWE-Bench Pro it scores 58.4, which Z.ai reports as ahead of GPT-5.4 and Claude Opus 4.6, making GLM-5.1 one of the strongest open-weight coding and agent models at release."
   ]
  },
  {
   "url": "https://thecosmicmeta.com/nvidia-and-umd-release-audio-flamingo-next-af-next-super-powerful-open-audio-language-model",
   "title": "NVIDIA And UMD Release Audio Flamingo Next (AF-Next): Super Powerful Open Audio-Language Model",
   "published_at": "2026-04-14T14:08:06",
   "organization": "NVIDIA and the University of Maryland",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": "over 7 billion parameters",
   "context_window": null,
   "license": null,
   "significance": "achieves new advances and state-of-the-art results on more than 20 benchmarks, including a 75.76% accuracy on MMAU-v05.15.25",
   "organization_evidence": [
    "## Introduction: The dawn of truly intelligent audio AI",
    "Developed by researchers from [NVIDIA](https://thecosmicmeta.com/nvidia-pulls-resident-evil-requiem-driver-over-critical-fan-control-bug/) and the [University](https://thecosmicmeta.com/anthropic-unveils-higher-education-advisory-board-and-ai-fluency-courses/) of [Maryland](https://thecosmicmeta.com/rare-flesh-eating-parasite-confirmed-in-us-for-first-time-in-decades/), AF-Next is a large audio-language model unveiled on April 14, [2026](https://thecosmicmeta.com/claude-ai-forecasts-top-crypto-picks-for-2026-xrp-sui-aave-and-a-meme-coin/)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Introduction: The dawn of truly intelligent audio AI",
    "Developed by researchers from [NVIDIA](https://thecosmicmeta.com/nvidia-pulls-resident-evil-requiem-driver-over-critical-fan-control-bug/) and the [University](https://thecosmicmeta.com/anthropic-unveils-higher-education-advisory-board-and-ai-fluency-courses/) of [Maryland](https://thecosmicmeta.com/rare-flesh-eating-parasite-confirmed-in-us-for-first-time-in-decades/), AF-Next is a large audio-language model unveiled on April 14, [2026](https://thecosmicmeta.com/claude-ai-forecasts-top-crypto-picks-for-2026-xrp-sui-aave-and-a-meme-coin/)."
   ],
   "parameters_evidence": [
    "### Architecture: A mix of components",
    "It includes a sliding-window audio encoder, an audio projector, a text-only [language model](https://thecosmicmeta.com/how-to-build-an-ai-agent-with-function-calling-and-gpt-5/) backbone with over 7 billion parameters, and a [Text-to-Speech](https://thecosmicmeta.com/google-docs-audio-summaries-ai-turns-docs-into-podcasts/) unit for output."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Introduction: The dawn of truly intelligent audio AI",
    "Traditional models often failed with [complexity](https://thecosmicmeta.com/see-how-fractals-forever-changed-math-and-science/), but AF-Next delivers state-of-the-art results on more than 20 [benchmarks](https://thecosmicmeta.com/openai-says-chatgpt-can-already-do-some-work-tasks-as-well-as-humans/), including a 75.76% [accuracy](https://thecosmicmeta.com/how-myriad-genetics-achieved-fast-accurate-and-cost-efficient-document-processing-using-the-aws-open-source-generative-ai-intelligent-document-processing-accelerator/) on MMAU-v05.15.25, surpassing Audio Flamingo 3 by over three percentage points.",
    "## Context and background: From original Flamingo to AF-Next",
    "Now, **Audio Flamingo Next (AF-Next)**, a joint effort by NVIDIA\u2019s Applied [Deep Learning](https://thecosmicmeta.com/how-to-design-complex-deep-learning-tensor-pipelines-using-einops-with-vision-attention-and-multimodal-examples/) Research lab and the University of Maryland\u2019s [computer science](https://thecosmicmeta.com/a-new-algorithm-makes-it-faster-to-find-the-shortest-paths/) department, achieves new advances."
   ]
  },
  {
   "url": "https://neoteric.no/blog/google-gemma-4-open-models",
   "title": "Gemma 4: Google's Open Model Family Goes Multimodal",
   "published_at": "2026-04-05T09:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B to 31B",
   "context_window": null,
   "license": null,
   "significance": "Four variants, Apache 2.0, 256K context, native vision and audio, 140+ languages \u2014 and it was built from the same research as Gemini 3.",
   "organization_evidence": [
    "# Gemma 4: Google's Open Model Family Goes Multimodal",
    "Google released Gemma 4 on April 2, 2026 \u2014 four variants from 2B to 31B, with 256K context, native vision and audio, and Apache 2.0 licensing."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4: Google's Open Model Family Goes Multimodal",
    "Google released Gemma 4 on April 2, 2026 \u2014 four variants from 2B to 31B, with 256K context, native vision and audio, and Apache 2.0 licensing."
   ],
   "parameters_evidence": [
    "# Gemma 4: Google's Open Model Family Goes Multimodal",
    "Google released Gemma 4 on April 2, 2026 \u2014 four variants from 2B to 31B, with 256K context, native vision and audio, and Apache 2.0 licensing."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4: Google's Open Model Family Goes Multimodal",
    "Google shipped Gemma 4 on April 2, 2026, the same week the local-AI ecosystem started feeling crowded for the first time."
   ]
  },
  {
   "url": "https://cronfeed.work/ai-china-kimi-k3-release-note-open-weights-supernode-license-2026q3",
   "title": "Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
   "published_at": "2026-07-30T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1,048,576 tokens",
   "license": "Kimi K3 License",
   "significance": "Moonshot's claimed 2.5\u00d7 scaling-efficiency improvement over K2; scored 88.3 on Moonshot's Terminal-Bench 2.1 table, close to GPT-5.6 Sol at 88.8 and Claude Fable 5 at 88.0; ranked first on the WebDev Arena as of July 23",
   "organization_evidence": [
    "ai china",
    "# Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
    "Moonshot AI kept the most important promise in its Kimi K3 launch."
   ],
   "model_evidence": [
    "ai china",
    "# Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
    "Moonshot AI kept the most important promise in its Kimi K3 launch."
   ],
   "release_date_evidence": [
    "ai china",
    "# Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
    "The Beijing lab introduced the model on **July 16, 2026**, said the full weights would arrive by July 27, and then published them on schedule alongside a 47-page technical report and a custom license.[1][2][3][4]"
   ],
   "parameters_evidence": [
    "ai china",
    "# Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
    "Moonshot describes a **2.8-trillion-parameter** mixture-of-experts model with **104 billion parameters activated per token**, native vision, and a context window of **1,048,576 tokens**.[2][3]"
   ],
   "context_window_evidence": [
    "ai china",
    "# Kimi K3 opens the weights; the recommended deployment starts at 64 accelerators",
    "Moonshot describes a **2.8-trillion-parameter** mixture-of-experts model with **104 billion parameters activated per token**, native vision, and a context window of **1,048,576 tokens**.[2][3]"
   ],
   "license_evidence": [
    "ai china",
    "## The license is permissive until scale changes the contract",
    "The **Kimi K3 License** grants broad rights to use, copy, modify, distribute, sublicense, sell, deploy, and fine-tune the software and weights.[4]"
   ],
   "significance_evidence": [
    "ai china",
    "## What changed from Kimi K2",
    "Moonshot's claimed **2.5\u00d7 scaling-efficiency improvement over K2** belongs to this complete package\u2014attention, depth routing, sparse experts, training recipe, and infrastructure\u2014not to one isolated trick.[1][2]",
    "K3 scored **88.3** on Moonshot's Terminal-Bench 2.1 table, close to GPT-5.6 Sol at 88.8 and Claude Fable 5 at 88.0.",
    "Moonshot's report also says K3 ranked first on the WebDev Arena as of July 23, while only eighth on the Text Arena and fourth on the newer Agent Arena.[2]"
   ]
  },
  {
   "url": "https://beri.net/article/2026-04-21-glm-5-1-open-source-enterprise-ai-huawei",
   "title": "GLM-5.1 Tops SWE-Bench: Open-Source AI Shifts",
   "published_at": "2026-04-21T20:06:14",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "744-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "#1 on SWE-Bench Pro, edging past GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro",
   "organization_evidence": [
    "Something important happened on April 7, 2026 that most Western enterprise IT leaders are still underestimating.",
    "This is GLM-5.1 from Z.ai, the company formerly known as Zhipu AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Something important happened on April 7, 2026 that most Western enterprise IT leaders are still underestimating.",
    "This is GLM-5.1 from Z.ai, the company formerly known as Zhipu AI."
   ],
   "parameters_evidence": [
    "Something important happened on April 7, 2026 that most Western enterprise IT leaders are still underestimating.",
    "GLM-5.1 is a 744-billion-parameter Mixture-of-Experts model with 44B active parameters per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Something important happened on April 7, 2026 that most Western enterprise IT leaders are still underestimating.",
    "A company on the US Entity List, using zero Nvidia chips, released an open-source AI model that now sits at #1 on SWE-Bench Pro \u2014 ahead of GPT-5.4, [Claude](/tools/claude) Opus 4.6, and [Gemini](/tools/gemini) 3.1 Pro.",
    "The headline benchmark: 58.4 on SWE-Bench Pro, the hardest agentic coding benchmark currently tracked."
   ]
  },
  {
   "url": "https://thejasonfleagle.com/deepseek-v4-open-source-ai-model",
   "title": "DeepSeek V4: Is This the New Open-Source AI Leader?",
   "published_at": "2026-04-24T18:35:50",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": null,
   "license": null,
   "significance": "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released.",
   "organization_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "parameters_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released."
   ]
  },
  {
   "url": "https://thejasonfleagle.com/deepseek-v4-open-source-ai-model",
   "title": "DeepSeek V4: Is This the New Open-Source AI Leader?",
   "published_at": "2026-04-24T18:35:50",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion parameters",
   "context_window": "1 million",
   "license": "MIT License",
   "significance": "V4-Flash, its leaner sibling, packs 284 billion parameters into a package that costs less per token than most small models from US labs.",
   "organization_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "model_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "release_date_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "parameters_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Flash, its leaner sibling, packs 284 billion parameters into a package that costs less per token than most small models from US labs."
   ],
   "context_window_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet.",
    "Both ship with a **one-million-token context window as the default**, not a premium add-on."
   ],
   "license_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "Here is the table that matters most for operators making infrastructure decisions right now:",
    "| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Open Weights? |",
    "| **DeepSeek V4-Flash** | **$0.14** | **$0.28** | 1M tokens | Yes (MIT) |",
    "Because both models are released under the **MIT License with open weights**, enterprises with the infrastructure to self-host can drive their marginal cost of inference to near zero."
   ],
   "significance_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Flash, its leaner sibling, packs 284 billion parameters into a package that costs less per token than most small models from US labs."
   ]
  },
  {
   "url": "https://thejasonfleagle.com/deepseek-v4-open-source-ai-model",
   "title": "DeepSeek V4: Is This the New Open-Source AI Leader?",
   "published_at": "2026-04-24T18:35:50",
   "organization": "DeepSeek",
   "model": "DeepSeek V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": "1 million",
   "license": "MIT License",
   "significance": "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released.",
   "organization_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "model_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "release_date_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet."
   ],
   "parameters_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released."
   ],
   "context_window_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "The Hangzhou-based lab released a preview of **DeepSeek V4** \u2014 two open-source models, V4-Pro and V4-Flash, that together represent the most aggressive cost-performance challenge to closed-source AI yet.",
    "Both ship with a **one-million-token context window as the default**, not a premium add-on."
   ],
   "license_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "Here is the table that matters most for operators making infrastructure decisions right now:",
    "| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Open Weights? |",
    "| **DeepSeek V4-Pro** | **$1.74** | **$3.48** | 1M tokens | Yes (MIT) |",
    "Because both models are released under the **MIT License with open weights**, enterprises with the infrastructure to self-host can drive their marginal cost of inference to near zero."
   ],
   "significance_evidence": [
    "DeepSeek V4 AI Model \u2014 AI Pathfinder Issue #53 by Jason Fleagle",
    "V4-Pro carries 1.6 trillion total parameters, making it the largest open-weights model ever released."
   ]
  },
  {
   "url": "https://faq.com.tw/en/developer-tools/2026-07-12-mistral-leanstral-15-formal-verification-en",
   "title": "Mistral's Leanstral 1.5 Brings Formal Verification to Every Developer \u2014 and Found 5 Real Bugs",
   "published_at": "2026-07-12T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-04",
   "parameters": "119 billion",
   "context_window": null,
   "license": null,
   "significance": "When tested against 57 open-source software repositories, Leanstral found five previously unknown bugs, including one that caused crashes in debug mode and silent data corruption in release builds. On miniF2F \u2014 a standard test of formal mathematical reasoning \u2014 Leanstral saturates the benchmark, achieving 100%. On PutnamBench, a collection of problems from the William Lowell Putnam Mathematical Competition widely used to evaluate theorem-proving systems, Leanstral solved 587 out of 672 problems.",
   "organization_evidence": [
    "# Mistral's Leanstral 1.5 Brings Formal Verification to Every Developer \u2014 and Found 5 Real Bugs",
    "Mistral has open-sourced Leanstral 1.5, a 119-billion-parameter model under Apache 2.0 that generates machine-checkable mathematical proofs for software using Lean 4."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On July 4, 2026, the French AI company released Leanstral 1.5 under the Apache 2.0 license \u2014 an open-weights model purpose-built for generating and checking mathematical proofs using Lean 4, the proof assistant developed at Microsoft Research.",
    "# Mistral's Leanstral 1.5 Brings Formal Verification to Every Developer \u2014 and Found 5 Real Bugs"
   ],
   "parameters_evidence": [
    "Mistral has open-sourced Leanstral 1.5, a 119-billion-parameter model under Apache 2.0 that generates machine-checkable mathematical proofs for software using Lean 4.",
    "# Mistral's Leanstral 1.5 Brings Formal Verification to Every Developer \u2014 and Found 5 Real Bugs"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline claim is striking: when tested against 57 open-source software repositories, Leanstral found five previously unknown bugs, including one that caused crashes in debug mode and silent data corruption in release builds.",
    "The benchmarks are impressive across the board.",
    "On miniF2F \u2014 a standard test of formal mathematical reasoning \u2014 Leanstral saturates the benchmark, achieving 100%.",
    "On PutnamBench, a collection of problems from the William Lowell Putnam Mathematical Competition widely used to evaluate theorem-proving systems, Leanstral solved 587 out of 672 problems.",
    "# Mistral's Leanstral 1.5 Brings Formal Verification to Every Developer \u2014 and Found 5 Real Bugs"
   ]
  },
  {
   "url": "https://pasqualepillitteri.it/en/news/5240/gemma-4-150-million-downloads-builders",
   "title": "Gemma 4 Hits 150 Million Downloads: What Three Builders Are Making With Google's Open AI",
   "published_at": "2026-06-17T07:30:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B model currently sits in third place on the Arena leaderboard for text-only open models, while the 26B lands in sixth, and according to the company the 31B holds its own against models up to twenty times larger.",
   "organization_evidence": [
    "That is the number Google announced on June 16, 2026 while talking about Gemma 4, the family of open language models released on April 2 under the Apache 2.0 license (the license that grants anyone the right to use, modify, and sell the software without paying royalties)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "That is the number Google announced on June 16, 2026 while talking about Gemma 4, the family of open language models released on April 2 under the Apache 2.0 license (the license that grants anyone the right to use, modify, and sell the software without paying royalties)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B model currently sits in third place on the Arena leaderboard for text-only open models, while the 26B lands in sixth, and according to the company the 31B holds its own against models up to twenty times larger."
   ]
  },
  {
   "url": "https://shaam.blog/articles/kimi-k3-open-weight-frontier-model-2026",
   "title": "Kimi K3 Explained: How Moonshot's 2.8T Open-Weight Model Changes the AI Compute Math (2026)",
   "published_at": "2026-07-31T05:07:24",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Full model weights were released by July 27, 2026 under the Kimi K3 License (a modified-MIT-style license permitting commercial use), making it the first open-weight model in the \"3-trillion-parameter class\"",
   "organization_evidence": [
    "What Is Kimi K3?",
    "Kimi K3 is Moonshot AI's flagship large language model, launched July 16, 2026, and available through the Kimi web app ([kimi.com](https://www.kimi.com)), Kimi Work desktop app, Kimi Code terminal tool, and the Moonshot API."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is Kimi K3?"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Is Kimi K3?"
   ]
  },
  {
   "url": "https://awesomeagents.ai/reviews/review-glm-5-1",
   "title": "GLM-5.1 Review: Open-Source Model Tops SWE-Bench Pro",
   "published_at": "2026-04-17T15:57:45",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Claims top spot on SWE-Bench Pro (58.4) and leads CyberGym (68.7).",
   "organization_evidence": [
    "# GLM-5.1 Review: Open-Source Model Tops SWE-Bench Pro",
    "Z.ai's GLM-5.1 is a 754B open-weight model that claims the top spot on SWE-Bench Pro without a single NVIDIA chip - here's how it holds up in practice."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Architecture and Specifications",
    "On April 7, 2026, Z.ai (formerly Zhipu AI) released the weights of GLM-5.1 under the MIT license and posted a benchmark table that made a lot of AI engineers do a double take.",
    "| Release date | April 7, 2026 |"
   ],
   "parameters_evidence": [
    "Architecture and Specifications",
    "GLM-5.1 uses a Mixture-of-Experts design the company calls GLM_MOE_DSA (Dynamic Sparse Attention), with 754 billion total parameters and 40 billion active per forward pass.",
    "| Parameters (total) | 754B (MoE) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.1 Review: Open-Source Model Tops SWE-Bench Pro",
    "Z.ai's GLM-5.1 is a 754B open-weight model that claims the top spot on SWE-Bench Pro without a single NVIDIA chip - here's how it holds up in practice.",
    "Tops SWE-Bench Pro (58.4) ahead of GPT-5.4 (57.7) and Claude Opus 4.6 (57.3); also leads CyberGym with 68.7"
   ]
  },
  {
   "url": "https://theaidatabase.com/m/minimax-minimax-m2-5.html",
   "title": "AIDb, the IMDb for AI Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "coding/agent specialist",
   "organization_evidence": [
    "# MiniMax-M2.5",
    "- **Vendor** MiniMax"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# MiniMax-M2.5",
    "Launched within a day of GLM-5 during the Feb 2026 Chinese frontier wave."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# MiniMax-M2.5",
    "230B-A10B MoE, MIT license; coding/agent specialist."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/06/04/nvidia-ai-releases-nemotron-3-ultra-an-open-550b-mixture-of-experts-hybrid-mamba-transformer-for-long-running-agents",
   "title": "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents",
   "published_at": "2026-06-04T21:42:28",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Ultra",
   "release_date": "2026-06-04",
   "parameters": "550 billion total parameter",
   "context_window": "1 million tokens",
   "license": "OpenMDW-1.1",
   "significance": "up to roughly 6x higher inference throughput than comparable open LLMs, at on-par accuracy",
   "organization_evidence": [
    "NVIDIA has released **[Nemotron 3 Ultra](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16)**, the largest model in its Nemotron 3 family.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents"
   ],
   "model_evidence": [
    "NVIDIA has released **[Nemotron 3 Ultra](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16)**, the largest model in its Nemotron 3 family.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents"
   ],
   "release_date_evidence": [
    "Sources: NVIDIA Nemotron 3 Ultra technical report & blog \u00b7 Verified Jun 4, 2026",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents"
   ],
   "parameters_evidence": [
    "Nemotron 3 Ultra is a 550 billion total parameter Mixture-of-Experts (MoE) model.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents",
    "## **What is Nemotron 3 Ultra**"
   ],
   "context_window_evidence": [
    "Context was then extended to 1 million tokens.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents",
    "## **What is Nemotron 3 Ultra**"
   ],
   "license_evidence": [
    "Weights, training data, and recipes ship openly under OpenMDW-1.1, with one NVFP4 checkpoint for Blackwell, Hopper, and Ampere.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents",
    "## **Key Takeaways**"
   ],
   "significance_evidence": [
    "NVIDIA team reports up to roughly 6x higher inference throughput than comparable open LLMs, at on-par accuracy.",
    "NVIDIA AI Releases Nemotron 3 Ultra: An Open 550B Mixture-of-Experts Hybrid Mamba-Transformer for Long-Running Agents",
    "## **What is Nemotron 3 Ultra**"
   ]
  },
  {
   "url": "https://aichina.news/blog/meet-molmoweb-8b-native-a-web-specialised-vlm-for-ascend-npus-that-3h6h5j",
   "title": "Meet MolmoWeb-8B-Native: A Web-Specialised VLM for Ascend NPUs That Actually Understands UI Screenshots \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:25:32",
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "8-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "A web-specialized VLM fine-tuned for UI understanding, natively joint multimodal model with pixel-pointing capability, and first-class support for Huawei Ascend NPUs.",
   "organization_evidence": [
    "Meet MolmoWeb-8B-Native: A Web-Specialised VLM for Ascend NPUs That Actually Understands UI Screenshots",
    "Enter **MolmoWeb-8B-Native**, an 8-billion-parameter VLM from Allen AI (Ai2) that\u2019s been fine-tuned specifically for web and UI understanding."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meet MolmoWeb-8B-Native: A Web-Specialised VLM for Ascend NPUs That Actually Understands UI Screenshots",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Meet MolmoWeb-8B-Native: A Web-Specialised VLM for Ascend NPUs That Actually Understands UI Screenshots",
    "Enter **MolmoWeb-8B-Native**, an 8-billion-parameter VLM from Allen AI (Ai2) that\u2019s been fine-tuned specifically for web and UI understanding."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meet MolmoWeb-8B-Native: A Web-Specialised VLM for Ascend NPUs That Actually Understands UI Screenshots",
    "Enter **MolmoWeb-8B-Native**, an 8-billion-parameter VLM from Allen AI (Ai2) that\u2019s been fine-tuned specifically for web and UI understanding.",
    "MolmoWeb is a **natively joint multimodal model**, not a retrofit where a vision encoder is bolted onto a language backbone."
   ]
  },
  {
   "url": "https://aijustdropped.com/model/qwen-agentworld",
   "title": "Qwen-AgentWorld review",
   "published_at": "2026-06-24T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-06-24",
   "parameters": "35B-A3B and 397B-A17B",
   "context_window": null,
   "license": null,
   "significance": "A language world model that simulates seven agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) and is trained to predict how each environment responds to an action.",
   "organization_evidence": [
    "# Qwen-AgentWorld review",
    "Alibaba's Qwen team open-sources AgentWorld \u2014 a language world model that simulates seven agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) and is trained to predict how each environment responds to an action."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen-AgentWorld review",
    "Released June 24, 2026 under Apache 2.0."
   ],
   "parameters_evidence": [
    "# Qwen-AgentWorld review",
    "**Qwen-AgentWorld** was released on June 24, 2026 in two open-weight sizes \u2014 Qwen-AgentWorld-35B-A3B (a mixture-of-experts model with 3B active parameters and a 256K context window) and the larger Qwen-AgentWorld-397B-A17B \u2014 both under the Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen-AgentWorld review",
    "Alibaba's Qwen team open-sources AgentWorld \u2014 a language world model that simulates seven agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) and is trained to predict how each environment responds to an action."
   ]
  },
  {
   "url": "https://andrew.ooo/answers/what-is-llama-5",
   "title": "What is Llama 5? Meta's 600B Open-Source Frontier Model",
   "published_at": "2026-04-10T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-08",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It\u2019s the first open-weight model to credibly match closed frontier models like GPT-5.4 and Claude Opus 4.6.",
   "organization_evidence": [
    "What is Llama 5? Meta's 600B Open-Source Frontier Model",
    "Llama 5 is Meta\u2019s flagship open-weight large language model, released on **April 8, 2026**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What is Llama 5? Meta's 600B Open-Source Frontier Model",
    "Llama 5 is Meta\u2019s flagship open-weight large language model, released on **April 8, 2026**."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What is Llama 5? Meta's 600B Open-Source Frontier Model",
    "It\u2019s the first open-weight model to credibly match closed frontier models like GPT-5.4 and Claude Opus 4.6."
   ]
  },
  {
   "url": "https://jetherverse.net.ng/tech-trends/google-releases-gemma-4-under-apache-20-license",
   "title": "Google Releases Gemma 4 Under Apache 2.0 License \u2014 JetherVerse Tech Trends",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Supports context windows up to 256,000 tokens, processes text, images, video, and audio natively, works across more than 140 languages, and is built for agentic workflows with native function calling and structured output. On competitive coding benchmarks, the 31B model scored an ELO of 2150 on Codeforces compared to 110 for Gemma 3, nearly a 20x improvement in one generation.",
   "organization_evidence": [
    "AI",
    "Google Releases Gemma 4 Under Apache 2.0 License",
    "Google DeepMind released Gemma 4 on April 2, 2026, marking the largest open model release in the Gemma series since it launched in 2024."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI",
    "Google Releases Gemma 4 Under Apache 2.0 License",
    "Google DeepMind released Gemma 4 on April 2, 2026, marking the largest open model release in the Gemma series since it launched in 2024."
   ],
   "parameters_evidence": [
    "AI",
    "Google Releases Gemma 4 Under Apache 2.0 License",
    "The family spans four sizes: E2B and E4B edge models optimized for phones, Raspberry Pi, and offline use, a 26B mixture-of-experts model that activates only 4B parameters during inference, and a 31B dense flagship that runs on a single 80GB GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI",
    "Google Releases Gemma 4 Under Apache 2.0 License",
    "The technical numbers are strong. Gemma 4 supports context windows up to 256,000 tokens, processes text, images, video, and audio natively, works across more than 140 languages, and is built for agentic workflows with native function calling and structured output. On competitive coding benchmarks, the 31B model scored an ELO of 2150 on Codeforces compared to 110 for Gemma 3, nearly a 20x improvement in one generation."
   ]
  },
  {
   "url": "https://felloai.com/pt/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "tied GPT-5.5 on the toughest coding benchmark in the industry, and is now the #1 open-weight model on the Artificial Analysis Intelligence Index",
   "organization_evidence": [
    "**Kimi K2.6** is the latest flagship model from **Moonshot AI**, a Beijing-based lab that has been releasing open-source AI models at a pace closer to a startup than a state-backed research outfit."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index.",
    "K2.6 keeps the same **Mixture-of-Experts** architecture as its predecessors, with **1 trillion total parameters** and **32 billion active per token** routed across **384 experts** (8 selected per forward pass)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It just tied **GPT-5.5** on the toughest coding benchmark in the industry, at roughly **80% less per million tokens**.",
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ]
  },
  {
   "url": "https://thefalcon.dev/writing/open-weight-models-glm-5-2-minimax-m3-showdown",
   "title": "Best Open Weight Model 2026? GLM-5.2 vs MiniMax M3 for Self-Hosters",
   "published_at": "2026-06-24T04:30:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A go-to for developers outside the US almost overnight as an MIT-licensed sovereignty play.",
   "organization_evidence": [
    "So here's an honest showdown between two of June's most talked-about releases: Zhipu AI's GLM-5.2 and MiniMax M3.",
    "GLM-5.2, from Zhipu AI, shipped on June 13, 2026 under an MIT license ([per devFlokers' June model-release log](https://www.devflokers.com/blog/ai-tech-news-model-releases-june-2026))."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2, from Zhipu AI, shipped on June 13, 2026 under an MIT license ([per devFlokers' June model-release log](https://www.devflokers.com/blog/ai-tech-news-model-releases-june-2026))."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2: the MIT-licensed sovereignty play",
    "GLM-5.2, from Zhipu AI, shipped on June 13, 2026 under an MIT license ([per devFlokers' June model-release log](https://www.devflokers.com/blog/ai-tech-news-model-releases-june-2026))."
   ]
  },
  {
   "url": "https://tensorfeed.ai/models/minimax-m3",
   "title": "MiniMax M3: Pricing, Benchmarks, Specs",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax reports 59% on SWE-Bench Pro and 83.5 on BrowseComp",
   "organization_evidence": [
    "MiniMax M3 launched June 1, 2026 as an open-weight coding and agentic model built on MiniMax Sparse Attention, which swaps full attention for KV-block selection to cut long-context compute to roughly one twentieth of the previous generation at 1M tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 launched June 1, 2026 as an open-weight coding and agentic model built on MiniMax Sparse Attention, which swaps full attention for KV-block selection to cut long-context compute to roughly one twentieth of the previous generation at 1M tokens."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax reports 59% on SWE-Bench Pro and 83.5 on BrowseComp, though several results were run on MiniMax infrastructure with agent scaffolding, so independent verification is still pending."
   ]
  },
  {
   "url": "https://hokai.io/hub/models/deepseek-v4-pro",
   "title": "DeepSeek V4 Pro Review (2026): 86% SWE-bench, Open-Weight, Pricing",
   "published_at": "2026-07-16T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "strongest open-weight coding model scoring 86% on SWE-bench Verified",
   "organization_evidence": [
    "DeepSeek V4 Pro May 2026. 86% SWE-bench. Open-weight. Free API + paid. Strongest open coding model. Chinese origin \u2014 data residency consideration.",
    "Pro (released May 20, 2026) is Hangzhou DeepSeek AI's flagship open-weight coding model scoring 86% on SWE-bench Verified, offering free API access and paid tiers, strongest open-weight coding model, Chinese origin \u2014 data residency consideration."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Pro May 2026. 86% SWE-bench. Open-weight. Free API + paid. Strongest open coding model. Chinese origin \u2014 data residency consideration.",
    "Released April 24, 2026 as a preview under the MIT license, V4-Pro is a Mixture-of-Experts transformer with 1.6 trillion total parameters and 49 billion activated per token."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Pro May 2026. 86% SWE-bench. Open-weight. Free API + paid. Strongest open coding model. Chinese origin \u2014 data residency consideration.",
    "Pro (released May 20, 2026) is Hangzhou DeepSeek AI's flagship open-weight coding model scoring 86% on SWE-bench Verified, offering free API access and paid tiers, strongest open-weight coding model, Chinese origin \u2014 data residency consideration."
   ]
  },
  {
   "url": "https://vucense.com/ai-intelligence/local-llms/best-open-source-llms-april-2026-sovereignty-guide",
   "title": "The Best Open-Source AI Models in April 2026",
   "published_at": "2026-04-23T09:00:00",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "3.8B active / 26B total",
   "context_window": null,
   "license": null,
   "significance": "rank #3 among all open models on the Arena AI text leaderboard, outperforming Llama 4 Maverick on mathematics, coding, and reasoning despite being a fraction of the size",
   "organization_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Gemma 4 26B MoE \u2014 The Sovereign Default",
    "Released April 2, 2026 by Google DeepMind under Apache 2.0, Gemma 4 is the model that changes the calculus for teams that previously assumed open-weight AI required enterprise hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Gemma 4 26B MoE \u2014 The Sovereign Default",
    "Released April 2, 2026 by Google DeepMind under Apache 2.0, Gemma 4 is the model that changes the calculus for teams that previously assumed open-weight AI required enterprise hardware."
   ],
   "parameters_evidence": [
    "## The Vucense April 2026 Open-Source AI Sovereignty Index",
    "Ranking the leading open-weight models on the dimensions that matter for sovereign, local-first deployment: licence freedom, geographic restrictions, and real consumer-hardware runnability.",
    "| **Gemma 4 26B MoE** | Google DeepMind | 3.8B active / 26B total | 1M tokens | \u2705 16 GB RAM | \u2705 Yes | Apache 2.0 | **95/100** |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## The April 2026 Open-Source AI Landscape: What Actually Changed",
    "Gemma 4 31B Dense ranks #3 among all open models on the Arena AI text leaderboard, outperforming Llama 4 Maverick on mathematics, coding, and reasoning despite being a fraction of the size."
   ]
  },
  {
   "url": "https://vucense.com/ai-intelligence/local-llms/best-open-source-llms-april-2026-sovereignty-guide",
   "title": "The Best Open-Source AI Models in April 2026",
   "published_at": "2026-04-23T09:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": "40B active / 744B total",
   "context_window": null,
   "license": null,
   "significance": "scored 58.4 on SWE-Bench Pro, narrowly surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3)",
   "organization_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "What is GLM-5.1 and why does it matter?",
    "GLM-5.1 is Z.ai\u2019s open-weight model released April 7, 2026 under the MIT licence."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "What is GLM-5.1 and why does it matter?",
    "GLM-5.1 is Z.ai\u2019s open-weight model released April 7, 2026 under the MIT licence."
   ],
   "parameters_evidence": [
    "## The Vucense April 2026 Open-Source AI Sovereignty Index",
    "Ranking the leading open-weight models on the dimensions that matter for sovereign, local-first deployment: licence freedom, geographic restrictions, and real consumer-hardware runnability.",
    "| **GLM-5.1** | Z.ai | 40B active / 744B total | 200K tokens | \u274c Enterprise GPUs | \u2705 Yes (MIT) | MIT | 44/100 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## The April 2026 Open-Source AI Landscape: What Actually Changed",
    "GLM-5.1 scored 58.4 on SWE-Bench Pro, narrowly surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3)."
   ]
  },
  {
   "url": "https://vucense.com/ai-intelligence/local-llms/best-open-source-llms-april-2026-sovereignty-guide",
   "title": "The Best Open-Source AI Models in April 2026",
   "published_at": "2026-04-23T09:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "~6B active / 119B total",
   "context_window": null,
   "license": null,
   "significance": "unifies four previously separate products into a single 119 billion parameter MoE deployment with configurable reasoning effort",
   "organization_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Mistral Small 4 \u2014 The Consolidated Enterprise Option",
    "Released March 16, 2026 by Mistral AI (EU-based, Paris) under Apache 2.0, Mistral Small 4 unifies four previously separate products \u2014 Mistral Small, Magistral, Pixtral, and Devstral \u2014 into a single 119 billion parameter MoE deployment with configurable reasoning effort."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Mistral Small 4 \u2014 The Consolidated Enterprise Option",
    "Released March 16, 2026 by Mistral AI (EU-based, Paris) under Apache 2.0, Mistral Small 4 unifies four previously separate products \u2014 Mistral Small, Magistral, Pixtral, and Devstral \u2014 into a single 119 billion parameter MoE deployment with configurable reasoning effort."
   ],
   "parameters_evidence": [
    "## The Vucense April 2026 Open-Source AI Sovereignty Index",
    "Ranking the leading open-weight models on the dimensions that matter for sovereign, local-first deployment: licence freedom, geographic restrictions, and real consumer-hardware runnability.",
    "| **Mistral Small 4** | Mistral AI | ~6B active / 119B total | 128K tokens | \u26a0\ufe0f High-end only | \u2705 Yes | Apache 2.0 | **83/100** |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Mistral Small 4 \u2014 The Consolidated Enterprise Option",
    "Released March 16, 2026 by Mistral AI (EU-based, Paris) under Apache 2.0, Mistral Small 4 unifies four previously separate products \u2014 Mistral Small, Magistral, Pixtral, and Devstral \u2014 into a single 119 billion parameter MoE deployment with configurable reasoning effort."
   ]
  },
  {
   "url": "https://vucense.com/ai-intelligence/local-llms/best-open-source-llms-april-2026-sovereignty-guide",
   "title": "The Best Open-Source AI Models in April 2026",
   "published_at": "2026-04-23T09:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": "~3B active / 35B total",
   "context_window": null,
   "license": null,
   "significance": "activates only 3 billion parameters per token while scoring 73.4% on SWE-bench Verified",
   "organization_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Qwen 3.6-35B-A3B \u2014 The 32 GB Sweet Spot",
    "Released April 14, 2026 by Alibaba under Apache 2.0, Qwen 3.6-35B-A3B activates only 3 billion parameters per token from a 35 billion total parameter model \u2014 the efficiency ratio is remarkable."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## Model-by-Model Analysis: The Four Models Worth Evaluating",
    "Qwen 3.6-35B-A3B \u2014 The 32 GB Sweet Spot",
    "Released April 14, 2026 by Alibaba under Apache 2.0, Qwen 3.6-35B-A3B activates only 3 billion parameters per token from a 35 billion total parameter model \u2014 the efficiency ratio is remarkable."
   ],
   "parameters_evidence": [
    "## The Vucense April 2026 Open-Source AI Sovereignty Index",
    "Ranking the leading open-weight models on the dimensions that matter for sovereign, local-first deployment: licence freedom, geographic restrictions, and real consumer-hardware runnability.",
    "| **Qwen 3.6-35B-A3B** | Alibaba | ~3B active / 35B total | 1M tokens | \u2705 32 GB RAM | \u2705 Yes | Apache 2.0 | **91/100** |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# The Best Open-Source AI Models in April 2026",
    "## The April 2026 Open-Source AI Landscape: What Actually Changed",
    "Qwen 3.6-35B activates only 3 billion parameters per token while scoring 73.4% on SWE-bench Verified."
   ]
  },
  {
   "url": "https://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Medium 3.5",
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Medium 3.5](/models/mistral/mistral-medium-2604)mistral/mistral-medium-2604 | [2](/models/mistral/mistral-medium-2604#providers) | 262,144 | 262,144 | | Yes | Yes | Yes | Yes | [Open](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) | $1.50 / $7.50 | 2026-04-29 | 2026-04-29 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Small (latest)",
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small (latest)](/models/mistral/mistral-small-latest)mistral/mistral-small-latest | [2](/models/mistral/mistral-small-latest#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://models.dev/labs/mistral",
   "title": "An open-source database of AI models",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Small 4",
    "## Mistral",
    "| Model | Providers | Context | Output | Input | Reasoning | Tool Call | Structured | Temperature | Weights | Price | Release | Updated |",
    "| [Mistral Small 4](/models/mistral/mistral-small-2603)mistral/mistral-small-2603 | [3](/models/mistral/mistral-small-2603#providers) | 256,000 | 256,000 | | Yes | Yes | - | Yes | [Open](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) | $0.15 / $0.60 | 2026-03-16 | 2026-03-16 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://cynicalsally.com/en/roasts/events/google-gemma-4-release-april-2026",
   "title": "Google Releases Gemma 4 with Native Vision and 256K Context",
   "published_at": "2026-04-09T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-09",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Native vision, native audio, native function calling, 256K context, and a 140-language footprint",
   "organization_evidence": [
    "Google Releases Gemma 4 with Native Vision and 256K Context",
    "# Google Releases Gemma 4 with Native Vision and 256K Context"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "2026-04-09"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 is the first Google model release where the word 'finally' isn't doing any heavy lifting.",
    "Native vision, native audio, native function calling, 256K context, and a 140-language footprint that makes the previous generation look like it was trained in a single time zone.",
    "# Google Releases Gemma 4 with Native Vision and 256K Context"
   ]
  },
  {
   "url": "https://theagenttimes.com/agents/article/moonshot-ai-s-2-8-trillion-parameter-kimi-k3-gets-day-zero-v-219baf5d",
   "title": "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Servi\u2026",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-29",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "one of the most powerful open-weight models ever released; 2.5x the intelligence per unit of compute",
   "organization_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "Moonshot AI, a Beijing-based frontier AI lab, released Kimi K3 \u2014 a 2.8-trillion-parameter mixture-of-experts model with a one-million-token context window and native visual understanding \u2014 and the vLLM inference engine shipped day-zero serving support, amplified by the official PyTorch account on X."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "**published** 2026-07-29T14:25:10Z"
   ],
   "parameters_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "Moonshot AI released Kimi K3, a 2.8-trillion-parameter open-weight mixture-of-experts model with a 1M-token context window and native vision, and the vLLM inference engine shipped day-zero serving support \u2014 endorsed by PyTorch \u2014 enabling immediate self-hosted deployment for agent operators."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI's 2.8-Trillion-Parameter Kimi K3 Gets Day-Zero vLLM Serving Support",
    "At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released."
   ]
  },
  {
   "url": "https://groundy.com/articles/glm-5-2-vs-claude-opus-4-8-open-weight-coding-at-frontier-pricing",
   "title": "GLM-5.2 vs Claude Opus 4.8: Open-Weight Coding at Frontier Pricing",
   "published_at": "2026-06-19T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.2 posts 62.1% on SWE-bench Pro and 81.0 on Terminal-Bench 2.1, four points behind Opus 4.8.",
   "organization_evidence": [
    "# GLM-5.2 vs Claude Opus 4.8: Open-Weight Coding at Frontier Pricing",
    "Zhipu released GLM-5.2 on June 13, 20261."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 vs Claude Opus 4.8: Open-Weight Coding at Frontier Pricing",
    "Zhipu released GLM-5.2 on June 13, 20261."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 vs Claude Opus 4.8: Open-Weight Coding at Frontier Pricing",
    "GLM-5.2 posts 62.1% on SWE-bench Pro and 81.0 on Terminal-Bench 2.1, four points behind Opus 4.8."
   ]
  },
  {
   "url": "https://outyet.ai/providers/minimax",
   "title": "MiniMax AI model release tracker",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax's June 2026 flagship; weights followed the announcement in mid-June.",
   "organization_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "# MiniMax"
   ],
   "model_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "## MiniMax M3"
   ],
   "release_date_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "Released Jun 1, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "MiniMax's June 2026 flagship; weights followed the announcement in mid-June."
   ]
  },
  {
   "url": "https://outyet.ai/providers/minimax",
   "title": "MiniMax AI model release tracker",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax M2.7",
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Spring 2026 update with top-tier SWE-bench results.",
   "organization_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "# MiniMax"
   ],
   "model_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "## MiniMax M2.7"
   ],
   "release_date_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "Released Mar 18, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "Spring 2026 update with top-tier SWE-bench results."
   ]
  },
  {
   "url": "https://outyet.ai/providers/minimax",
   "title": "MiniMax AI model release tracker",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax M2.5",
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Agent-native coding flagship released weeks after MiniMax's Hong Kong IPO, with top-tier SWE-bench results at a fraction of frontier cost.",
   "organization_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "# MiniMax"
   ],
   "model_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "## MiniMax M2.5"
   ],
   "release_date_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "Released Feb 12, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Provider watch",
    "Open-weight MiniMax M-series reasoning and agentic models.",
    "Agent-native coding flagship released weeks after MiniMax's Hong Kong IPO, with top-tier SWE-bench results at a fraction of frontier cost."
   ]
  },
  {
   "url": "https://aichina.news/blog/meet-stdmoe-allenais-sparse-moe-model-with-emotional-annealing-now-on-bibwwp",
   "title": "Meet StdMoE: AllenAI\u2019s Sparse MoE Model with Emotional Annealing Now on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T12:47:46",
   "organization": "AllenAI",
   "model": null,
   "release_date": "2026-07-30",
   "parameters": "1 billion active parameters with 14 billion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Pairs 1 billion active parameters with 14 billion total parameters after training on roughly one trillion tokens, featuring an emotional annealing stage for affective generation.",
   "organization_evidence": [
    "Meet StdMoE: AllenAI\u2019s Sparse MoE Model with Emotional Annealing Now on Modelers.cn",
    "If you\u2019ve been hunting for a Mixture-of-Experts language model that combines research pedigree, permissive licensing, and a genuinely novel training twist, AllenAI\u2019s latest checkpoint might be just the ticket."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "models/allenai July 30, 2026 3 min read Auto-generated from Modelers",
    "Meet StdMoE: AllenAI\u2019s Sparse MoE Model with Emotional Annealing Now on Modelers.cn"
   ],
   "parameters_evidence": [
    "The **StdMoE_1b14b_1T_EmoAnnealed** \u2014 hosted for the first time on Modelers.cn, the Huawei/Ascend AI ecosystem platform \u2014 is a sparsely-activated MoE model that pairs 1 billion active parameters with 14 billion total parameters after training on roughly one trillion tokens.",
    "Meet StdMoE: AllenAI\u2019s Sparse MoE Model with Emotional Annealing Now on Modelers.cn"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The **StdMoE_1b14b_1T_EmoAnnealed** \u2014 hosted for the first time on Modelers.cn, the Huawei/Ascend AI ecosystem platform \u2014 is a sparsely-activated MoE model that pairs 1 billion active parameters with 14 billion total parameters after training on roughly one trillion tokens.",
    "Meet StdMoE: AllenAI\u2019s Sparse MoE Model with Emotional Annealing Now on Modelers.cn"
   ]
  },
  {
   "url": "https://aiwiki.ai/wiki/deepseek_v4_flash",
   "title": "DeepSeek V4-Flash",
   "published_at": "2026-07-31T12:39:52",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-07-31",
   "parameters": "284B total, 13B activated",
   "context_window": null,
   "license": null,
   "significance": "substantial agentic improvements and DSpark speculative decoding module attached",
   "organization_evidence": [
    "DeepSeek V4-Flash",
    "**DeepSeek V4-Flash** is the smaller of the two [large language models](/wiki/large_language_model) in the DeepSeek V4 family, a 284-billion-parameter [Mixture of Experts](/wiki/mixture_of_experts) model with 13 billion active parameters and a one-million-token [context window](/wiki/context_window), developed by the Hangzhou lab [DeepSeek](/wiki/deepseek).[19][20] It first shipped on April 24, 2026 as a preview, alongside the larger V4-Pro variant of the same [DeepSeek"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4-Flash"
   ],
   "parameters_evidence": [
    "DeepSeek V4-Flash",
    "**DeepSeek V4-Flash** is the smaller of the two [large language models](/wiki/large_language_model) in the DeepSeek V4 family, a 284-billion-parameter [Mixture of Experts](/wiki/mixture_of_experts) model with 13 billion active parameters and a one-million-token [context window](/wiki/context_window), developed by the Hangzhou lab [DeepSeek](/wiki/deepseek).[19][20] It first shipped on April 24, 2026 as a preview, alongside the larger V4-Pro variant of the same [DeepSeek"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4-Flash",
    "It describes DeepSeek-V4-Flash-0731 as \"the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities,\" and states that \"it has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.\""
   ]
  },
  {
   "url": "https://wpnews.pro/news/forget-the-manifesto-meta-s-real-news-is-apache-2-0",
   "title": "Forget the Manifesto \u2014 Meta's Real News Is Apache 2.0",
   "published_at": "2026-08-10T16:08:59",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 30B-parameter dense open-weight AI model targeting local agentic workloads with 4-bit quantization under 20GB.",
   "organization_evidence": [
    "Forget the Manifesto \u2014 Meta's Real News Is Apache 2.0",
    "Meta Platforms Inc. released Muse Glimmer, a 30B-parameter dense open-weight AI model, under the permissive Apache 2.0 license, marking its first open-weight release since Muse Spark and a shift from its proprietary Llama Community License."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Forget the Manifesto \u2014 Meta's Real News Is Apache 2.0",
    "read6 min views29 publishedAug 10, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Forget the Manifesto \u2014 Meta's Real News Is Apache 2.0",
    "Meta Platforms Inc. released Muse Glimmer, a 30B-parameter dense open-weight AI model, under the permissive Apache 2.0 license, marking its first open-weight release since Muse Spark and a shift from its proprietary Llama Community License.",
    "Glimmer targets local agentic workloads with 4-bit quantization under 20GB, supported on Hugging Face, Ollama, and LM Studio at launch."
   ]
  },
  {
   "url": "https://servola.de/journal/your-ai-code-can-now-be-proven",
   "title": "Your AI Code Can Now Be Proven Correct",
   "published_at": "2026-07-04T00:00:00",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Proves code correct, found 5 real bugs, and lets you own the checker.",
   "organization_evidence": [
    "Digital Sovereignty",
    "Mistral open-sourced Leanstral 1.5 under Apache-2.0 on July 2, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Digital Sovereignty",
    "Mistral open-sourced Leanstral 1.5 under Apache-2.0 on July 2, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Digital Sovereignty",
    "Mistral open-sourced Leanstral 1.5 under Apache-2.0 on July 2, 2026. It proves code correct, found 5 real bugs, and lets you own the checker."
   ]
  },
  {
   "url": "https://aibusinessweekly.net/p/google-gemma-4-open-models-apache-license",
   "title": "Google Launches Gemma 4, Its Most Capable Open AI Model Family Yet, Under Apache 2.0 License for the First Time",
   "published_at": "2026-04-02T20:45:01",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": null,
   "parameters": "2B, 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Ranks third among all open models on the Arena AI text leaderboard, features native image and video processing, support for over 140 languages, function calling, and structured JSON output.",
   "organization_evidence": [
    "Google just made its strongest move yet to win the open-weight AI developer market.",
    "Google DeepMind launched Gemma 4 on Wednesday - a family of four open-weight models built from the same research foundation as Gemini 3, available immediately under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Google DeepMind launched Gemma 4 on Wednesday - a family of four open-weight models built from the same research foundation as Gemini 3, available immediately under the Apache 2.0 license.",
    "The family comes in four sizes targeting different hardware tiers.",
    "The Effective 2B and Effective 4B models are optimized for edge devices - smartphones, Raspberry Pi, and Jetson Nano - with native audio input, near-zero latency, and memory usage under 1.5GB for the E2B model.",
    "The 26B Mixture of Experts model activates only 3.8 billion parameters during inference, delivering high tokens-per-second throughput despite its size.",
    "The 31B Dense model prioritizes quality and benchmark performance - it currently ranks third among all open models on the Arena AI text leaderboard."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 31B Dense model prioritizes quality and benchmark performance - it currently ranks third among all open models on the Arena AI text leaderboard.",
    "All four models share a common capability set: native image and video processing, support for over 140 languages, function calling, and structured JSON output - meaning developers no longer need to retrofit applications to make models interact with external tools."
   ]
  },
  {
   "url": "https://glitchwire.com/news/kimi-k3-is-here-the-open-source-ai-gap-just-disappeared",
   "title": "Kimi K3 Is Here. The Open-Source AI Gap Just Disappeared.",
   "published_at": "2026-07-16T20:19:12",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Packs 2.8 trillion parameters into a Mixture-of-Experts architecture, making it the largest open-source model released to date, with a 1-million-token context window and state-of-the-art browse score.",
   "organization_evidence": [
    "# Kimi K3 Is Here. The Open-Source AI Gap Just Disappeared.",
    "Moonshot AI launched Kimi K3 on July 16, and the specifications demand attention."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 Is Here. The Open-Source AI Gap Just Disappeared.",
    "Moonshot AI launched Kimi K3 on July 16, and the specifications demand attention."
   ],
   "parameters_evidence": [
    "# Kimi K3 Is Here. The Open-Source AI Gap Just Disappeared.",
    "Moonshot AI launched Kimi K3 on July 16, and the specifications demand attention.",
    "[According to the company's technical blog](https://www.kimi.com/blog/kimi-k3), K3 packs 2.8 trillion parameters into a Mixture-of-Experts architecture, making it the largest open-source model released to date."
   ],
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   "significance_evidence": [
    "# Kimi K3 Is Here. The Open-Source AI Gap Just Disappeared.",
    "Moonshot AI launched Kimi K3 on July 16, and the specifications demand attention.",
    "[According to the company's technical blog](https://www.kimi.com/blog/kimi-k3), K3 packs 2.8 trillion parameters into a Mixture-of-Experts architecture, making it the largest open-source model released to date."
   ]
  },
  {
   "url": "https://effloow.com/articles/glm-5-1-744b-moe-open-source-swebench-guide-2026",
   "title": "GLM-5.1: Open-Source Model That Tops SWE-Bench Pro",
   "published_at": "2026-04-15T06:27:48",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6, and features sustained autonomous execution for up to eight hours.",
   "organization_evidence": [
    "# GLM-5.1: Open-Source Model That Tops SWE-Bench Pro",
    "On April 7, 2026, Z.ai (the company formerly known as Zhipu AI) released GLM-5.1 and immediately took the top spot on SWE-Bench Pro \u2014 the most demanding real-world software engineering benchmark available."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.1: Open-Source Model That Tops SWE-Bench Pro",
    "On April 7, 2026, Z.ai (the company formerly known as Zhipu AI) released GLM-5.1 and immediately took the top spot on SWE-Bench Pro \u2014 the most demanding real-world software engineering benchmark available."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.1: Open-Source Model That Tops SWE-Bench Pro",
    "GLM-5.1 is a 754B MoE open-weight model with MIT license that scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6.",
    "On April 7, 2026, Z.ai (the company formerly known as Zhipu AI) released GLM-5.1 and immediately took the top spot on SWE-Bench Pro \u2014 the most demanding real-world software engineering benchmark available.",
    "With a score of 58.4, it edged out GPT-5.4 (57.7) and Claude Opus 4.6 (57.3).",
    "The feature Z.ai emphasizes most heavily is sustained autonomous execution \u2014 the ability to work continuously on a single task for up to eight hours."
   ]
  },
  {
   "url": "https://actgsys.com/en/blog/minimax-m3-open-weight-multimodal-sme-2026-06",
   "title": "MiniMax Launches M3 Open-Weight Multimodal Model (June 2026): Frontier-Class at 5\u201310% of the Price, With Image & Video \u2014 How Should SMEs Use It?",
   "published_at": "2026-06-11T00:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Approaches or exceeds GPT-5.5 and Gemini 3.1 Pro on key benchmarks while costing just 5\u201310% as much, combining native multimodality, a 1M-token context window, and agentic coding.",
   "organization_evidence": [
    "Industry Trends",
    "# MiniMax Launches M3 Open-Weight Multimodal Model (June 2026): Frontier-Class at 5\u201310% of the Price, With Image & Video \u2014 How Should SMEs Use It?",
    "MiniMax officially launched M3 on June 1, 2026, positioning it as the first Chinese open model to combine frontier coding, agentic capabilities, a 1M-token context window, and native multimodality in a single architecture."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Industry Trends",
    "# MiniMax Launches M3 Open-Weight Multimodal Model (June 2026): Frontier-Class at 5\u201310% of the Price, With Image & Video \u2014 How Should SMEs Use It?",
    "MiniMax launched M3 on June 1, 2026 \u2014 an open-weight model that folds native multimodality, a 1M-token context window, and agentic coding into a single architecture, priced at roughly 5\u201310% of frontier closed models."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Industry Trends",
    "# MiniMax Launches M3 Open-Weight Multimodal Model (June 2026): Frontier-Class at 5\u201310% of the Price, With Image & Video \u2014 How Should SMEs Use It?",
    "According to [VentureBeat (2026)](https://venturebeat.com/technology/minimax-m3-debuts-eclipsing-gpt-5-5-and-gemini-3-1-pro-on-key-benchmark-performance-for-just-5-10-of-the-cost), MiniMax claims M3 approaches or exceeds GPT-5.5 and Gemini 3.1 Pro on key benchmarks while costing just 5\u201310% as much."
   ]
  },
  {
   "url": "https://agent-wars.com/news/2026-04-16-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all",
   "title": "Qwen3.6-35B-A3B Ships as Qwen Team Falls Apart",
   "published_at": "2026-04-16T01:37:31",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "built for agentic coding",
   "organization_evidence": [
    "Alibaba's Qwen team just dropped Qwen3.6-35B-A3B, an open-weight model built for agentic coding."
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Alibaba's Qwen team just dropped Qwen3.6-35B-A3B, an open-weight model built for agentic coding."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba's Qwen team just dropped Qwen3.6-35B-A3B, an open-weight model built for agentic coding."
   ]
  },
  {
   "url": "https://theagenttimes.com/agents/article/mistral-ai-releases-leanstral-1-5-with-119b-parameters-under-b10303d1",
   "title": "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2\u2026",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "Saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, sets new state-of-the-art scores on two algebra proof benchmarks, and found five previously unknown bugs in real-world repositories.",
   "organization_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI, the Paris-based frontier AI company, has released Leanstral 1.5, a free open-source formal verification model that saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, and sets new state-of-the-art scores on two algebra proof benchmarks \u2014 all under an Apache-2.0 license with weights published on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The model, [announced July 2 on Mistral\u2019s blog](https://mistral.ai/news/leanstral-1-5/), carries 119 billion total parameters in a sparse mixture-of-experts architecture but activates only approximately 6 billion parameters per token, making it deployable at a fraction of the compute cost implied by its total parameter count."
   ],
   "parameters_evidence": [
    "Mistral AI Releases Leanstral 1.5 With 119B Parameters Under Apache-2.0 License",
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Mistral AI released Leanstral 1.5, a 119B-parameter formal verification model under Apache-2.0 that saturates miniF2F, solves 587/672 PutnamBench problems, and found five previously unknown bugs in real-world repositories \u2014 available free via Hugging Face and API.",
    "Mistral AI, the Paris-based frontier AI company, has released Leanstral 1.5, a free open-source formal verification model that saturates the miniF2F benchmark, solves 587 of 672 PutnamBench problems, and sets new state-of-the-art scores on two algebra proof benchmarks \u2014 all under an Apache-2.0 license with weights published on Hugging Face."
   ]
  },
  {
   "url": "https://spoonai.me/posts/2026-04-05-google-gemma-4-open-source-apache-en",
   "title": "Gemma 4 Is Here \u2014 And It's Finally Apache 2.0",
   "published_at": "2026-04-04T23:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B",
   "context_window": null,
   "license": null,
   "significance": "Apache 2.0 license release with 140 languages, 256k context, and four model sizes",
   "organization_evidence": [
    "Gemma 4 Is Here \u2014 And It's Finally Apache 2.0",
    "Google dropped Gemma 4 in four sizes (2B/4B/26B/31B), 140 languages, 256k context \u2014 all under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 Is Here \u2014 And It's Finally Apache 2.0",
    "On April 2, Google released Gemma 4 under a true **Apache 2.0 license**."
   ],
   "parameters_evidence": [
    "Gemma 4 Is Here \u2014 And It's Finally Apache 2.0",
    "Google dropped Gemma 4 in four sizes (2B/4B/26B/31B), 140 languages, 256k context \u2014 all under Apache 2.0.",
    "| Gemma 4 | Apr 2026 | 31B | 256k | Apache 2.0 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 Is Here \u2014 And It's Finally Apache 2.0",
    "Google dropped Gemma 4 in four sizes (2B/4B/26B/31B), 140 languages, 256k context \u2014 all under Apache 2.0."
   ]
  },
  {
   "url": "https://felloai.com/ja/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": "1 trillion total parameters, 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "Tied GPT-5.5 on the toughest coding benchmark in the industry at roughly 80% less per million tokens and is the #1 open-weight model on the Artificial Analysis Intelligence Index.",
   "organization_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**.",
    "**Kimi K2.6** is the latest flagship model from **Moonshot AI**, a Beijing-based lab that has been releasing open-source AI models at a pace closer to a startup than a state-backed research outfit."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**."
   ],
   "parameters_evidence": [
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index.",
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**Kimi K2.6** is the new open-source AI model from China\u2019s **Moonshot AI**, released on **[April 20, 2026](https://www.kimi.com/blog/kimi-k2-6)**.",
    "It just tied **GPT-5.5** on the toughest coding benchmark in the industry, at roughly **80% less per million tokens**.",
    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index."
   ]
  },
  {
   "url": "https://claudemarket.ai/blog/glm-5-openclaw-setup-guide",
   "title": "GLM-5 on OpenClaw: Setup Guide, Benchmarks, and When to Use It",
   "published_at": "2026-08-11T14:25:13",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Top-tier for open models; competitive with Claude Sonnet 4, near-perfect on competition-level math",
   "organization_evidence": [
    "GLM-5 on OpenClaw: Setup Guide, Benchmarks, and When to Use It",
    "GLM-5 is the flagship large language model from Zhipu AI, a Beijing-based AI research lab that has been building the GLM (General Language Model) series since 2022.",
    "Architecture and Specifications",
    "| **Developer** | Zhipu AI (Z.ai) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5 on OpenClaw: Setup Guide, Benchmarks, and When to Use It",
    "Released in February 2026 under the MIT license, GLM-5 is one of the largest open-weight Mixture of Experts models publicly available \u2014 744 billion total parameters with 40 billion active per inference pass.",
    "Architecture and Specifications",
    "| **Release Date** | February 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5 on OpenClaw: Setup Guide, Benchmarks, and When to Use It",
    "Benchmarks and Performance",
    "| **SWE-bench Verified** | 77.8% | Top-tier for open models; competitive with Claude Sonnet 4 |",
    "| **AIME 2024** | 92.7% | Near-perfect on competition-level math |"
   ]
  },
  {
   "url": "https://beri.net/article/nvidia-nemotron-3-nano-omni-open-multimodal-agentic-enterprise-2026",
   "title": "Nemotron 3 Nano Omni: NVIDIA's Open Bet on Agent AI",
   "published_at": "2026-04-30T04:04:35",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30B",
   "context_window": null,
   "license": null,
   "significance": "It claims 9\u00d7 higher throughput than competing open omni models at the same interactivity, tops six public leaderboards in document and audio-video understanding, and ships with the full training recipe, dataset, and weights on Hugging Face.",
   "organization_evidence": [
    "On April 28, 2026, NVIDIA released **Nemotron 3 Nano Omni**, an open-weight multimodal model that processes text, image, video, and audio inside a single 30B-parameter mixture-of-experts architecture."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 28, 2026, NVIDIA released **Nemotron 3 Nano Omni**, an open-weight multimodal model that processes text, image, video, and audio inside a single 30B-parameter mixture-of-experts architecture."
   ],
   "parameters_evidence": [
    "On April 28, 2026, NVIDIA released **Nemotron 3 Nano Omni**, an open-weight multimodal model that processes text, image, video, and audio inside a single 30B-parameter mixture-of-experts architecture."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "It claims **9\u00d7 higher throughput** than competing open omni models at the same interactivity, tops six public leaderboards in document and audio-video understanding, and ships with the full training recipe, dataset, and weights on Hugging Face."
   ]
  },
  {
   "url": "https://aichina.news/blog/allenais-stdmoe-a-reproducible-moe-baseline-for-the-ascend-npu-sf3gos",
   "title": "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem \u2014 AICHINA.news Blog",
   "published_at": "2026-07-30T13:27:02",
   "organization": "AllenAI",
   "model": "StdMoE_1b14b_1T",
   "release_date": "2026-07-30",
   "parameters": "1 billion active / 14 billion total parameter",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "A reproducible MoE baseline for the Ascend NPU ecosystem, featuring 1 billion active / 14 billion total parameters, trained on 1 trillion tokens with native Ascend NPU compatibility via the CANN/MindSpore stack.",
   "organization_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "AllenAI\u2019s new **StdMoE_1b14b_1T** aims to fix that."
   ],
   "model_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "AllenAI\u2019s new **StdMoE_1b14b_1T** aims to fix that."
   ],
   "release_date_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "models/allenai July 30, 2026 3 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "This is a 1 billion active / 14 billion total parameter MoE language model trained on exactly 1 trillion tokens, and it\u2019s hosted on Modelers.cn \u2014 Huawei\u2019s Ascend NPU ecosystem platform."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "The Apache\u20112.0 licence also means you can use it commercially without legal gymnastics."
   ],
   "significance_evidence": [
    "AllenAI\u2019s StdMoE: A Reproducible MoE Baseline for the Ascend NPU Ecosystem",
    "This is a 1 billion active / 14 billion total parameter MoE language model trained on exactly 1 trillion tokens, and it\u2019s hosted on Modelers.cn \u2014 Huawei\u2019s Ascend NPU ecosystem platform.",
    "The immediate benefit for Ascend users is native compatibility via the CANN/MindSpore stack, so you won\u2019t waste days porting CUDA kernels."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/alibaba-drops-qwen-3-6-27b-expanding-open-source-options-for-9a3c9c91",
   "title": "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
   "published_at": "2026-04-22T14:58:30",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "Extends the agentic-focused Qwen 3.6 series into a parameter class suitable for local deployment on consumer hardware, serving as the reasoning core for autonomous coding, multi-step task execution, and tool-use pipelines without requiring cloud API calls.",
   "organization_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "Alibaba's Tongyi Lab has released Qwen3.6-27B as an open-weight model on Hugging Face, extending the agentic-focused Qwen 3.6 series into a parameter class suitable for local deployment on consumer hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Key Facts",
    "- **When:** Appeared on Hugging Face on or around April 22, 2026"
   ],
   "parameters_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "Alibaba's Tongyi Lab has released Qwen3.6-27B as an open-weight model on Hugging Face, extending the agentic-focused Qwen 3.6 series into a parameter class suitable for local deployment on consumer hardware."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "Alibaba's Tongyi Lab has released Qwen3.6-27B as an open-weight model on Hugging Face, extending the agentic-focused Qwen 3.6 series into a parameter class suitable for local deployment on consumer hardware."
   ]
  },
  {
   "url": "https://theagenttimes.com/articles/alibaba-drops-qwen-3-6-27b-expanding-open-source-options-for-9a3c9c91",
   "title": "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
   "published_at": "2026-04-22T14:58:30",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-14",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Surpass its predecessor, Qwen3.5-35B-A3B, on agentic coding benchmarks.",
   "organization_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "On April 14, the team open-sourced [Qwen3.6-35B-A3B](https://releasebot.io/updates/qwen), a sparse mixture-of-experts model with 35 billion total parameters but only 3 billion active at inference time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "On April 14, the team open-sourced [Qwen3.6-35B-A3B](https://releasebot.io/updates/qwen), a sparse mixture-of-experts model with 35 billion total parameters but only 3 billion active at inference time."
   ],
   "parameters_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "On April 14, the team open-sourced [Qwen3.6-35B-A3B](https://releasebot.io/updates/qwen), a sparse mixture-of-experts model with 35 billion total parameters but only 3 billion active at inference time."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba Drops Qwen 3.6 27B, Expanding Open-Source Options for Local Agents",
    "That model was reported to surpass its predecessor, Qwen3.5-35B-A3B, on agentic coding benchmarks, per [Releasebot](https://releasebot.io/updates/qwen)."
   ]
  },
  {
   "url": "https://insights.marvin-42.com/articles/mistral-medium-35-a-single-128b-open-weight-model-that-replaces-three-separate-models",
   "title": "Mistral Medium 3.5: A Single 128B Open-Weight Model That Replaces Three Separate Models",
   "published_at": "2026-05-05T15:14:03",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": "128B",
   "context_window": null,
   "license": null,
   "significance": "Replaces three separate models, achieves 77.6% on SWE-bench Verified, supports 4-GPU self-hosting",
   "organization_evidence": [
    "Mistral Medium 3.5: A Single 128B Open-Weight Model That Replaces Three Separate Models",
    "Mistral AI released Mistral Medium 3.5 on April 29, 2026 under a Modified MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mistral Medium 3.5: A Single 128B Open-Weight Model That Replaces Three Separate Models",
    "Mistral AI released Mistral Medium 3.5 on April 29, 2026 under a Modified MIT license."
   ],
   "parameters_evidence": [
    "Mistral Medium 3.5: A Single 128B Open-Weight Model That Replaces Three Separate Models",
    "## Specifications",
    "- **Parameters:** 128B (dense, not MoE)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Medium 3.5: A Single 128B Open-Weight Model That Replaces Three Separate Models",
    "Mistral AI released Mistral Medium 3.5 on April 29, 2026 under a Modified MIT license.",
    "The model consolidates three previously separate Mistral offerings: Mistral Medium 3.1 for instruction-following, Magistral for reasoning, and Devstral 2 for coding.",
    "- **SWE-bench Verified:** 77.6%",
    "- **Self-hostable:** On 4 GPUs"
   ]
  },
  {
   "url": "https://medium.com/@ikh4ever/google-gemma-4-the-most-capable-open-models-yet-632236a5cdab",
   "title": "Google Gemma 4: The Most Capable Open Models Yet",
   "published_at": "2026-04-06T16:58:12",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "major leap in intelligence-per-parameter, outcompeting models up to 20\u00d7 its size on key benchmarks",
   "organization_evidence": [
    "# Google Gemma 4: The Most Capable Open Models Yet",
    "Google DeepMind released **Gemma 4** on April 2, 2026, under the permissive Apache 2.0 open-source license \u2014 making it freely available for commercial and personal use."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4: The Most Capable Open Models Yet",
    "Google DeepMind released **Gemma 4** on April 2, 2026, under the permissive Apache 2.0 open-source license \u2014 making it freely available for commercial and personal use."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4: The Most Capable Open Models Yet",
    "Built from the same research foundation as Gemini 3, it represents a major leap in intelligence-per-parameter, outcompeting models up to 20\u00d7 its size on key benchmarks."
   ]
  },
  {
   "url": "https://happyrock.cloud/blog/2026-07-23_moonshot_kimi_k3_2.8t_parameter_open_source_model_million_token_context_native_vision_us_china_ai_competition_deep_dive_en",
   "title": "Moonshot Kimi K3 2.8 Trillion Parameter Open-Source Model Deep Dive: Million-Token Context, Native Vision, and the New Era of US-China AI Competition",
   "published_at": "2026-07-23T00:48:16",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 Trillion",
   "context_window": null,
   "license": null,
   "significance": "world's largest open-source AI model, topped the Arena frontend coding blind test within 24 hours",
   "organization_evidence": [
    "# Moonshot Kimi K3 2.8 Trillion Parameter Open-Source Model Deep Dive: Million-Token Context, Native Vision, and the New Era of US-China AI Competition",
    "On July 16, 2026, Moonshot AI officially released Kimi K3\u2014**the world\u2019s largest open-source AI model** with 2.8 trillion total parameters, built on a Mixture-of-Experts (MoE) architecture that activates 16 out of 896 experts per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot Kimi K3 2.8 Trillion Parameter Open-Source Model Deep Dive: Million-Token Context, Native Vision, and the New Era of US-China AI Competition",
    "On July 16, 2026, Moonshot AI officially released Kimi K3\u2014**the world\u2019s largest open-source AI model** with 2.8 trillion total parameters, built on a Mixture-of-Experts (MoE) architecture that activates 16 out of 896 experts per token."
   ],
   "parameters_evidence": [
    "# Moonshot Kimi K3 2.8 Trillion Parameter Open-Source Model Deep Dive: Million-Token Context, Native Vision, and the New Era of US-China AI Competition",
    "On July 16, 2026, Moonshot AI officially released Kimi K3\u2014**the world\u2019s largest open-source AI model** with 2.8 trillion total parameters, built on a Mixture-of-Experts (MoE) architecture that activates 16 out of 896 experts per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot Kimi K3 2.8 Trillion Parameter Open-Source Model Deep Dive: Million-Token Context, Native Vision, and the New Era of US-China AI Competition",
    "On July 16, 2026, Moonshot AI officially released Kimi K3\u2014**the world\u2019s largest open-source AI model** with 2.8 trillion total parameters, built on a Mixture-of-Experts (MoE) architecture that activates 16 out of 896 experts per token.",
    "Within 24 hours of release, K3 topped the Arena frontend coding blind test, with developers preferring it over Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://aiunderstanding.org/news/zhipu-glm-5-2-open-weights",
   "title": "Zhipu AI Open-Sources GLM-5.2 With a One-Million-Token Context Window",
   "published_at": "2026-06-17T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.2 scores 62.1 on SWE-bench Pro, a test of resolving real software engineering issues, up from 58.4 for the previous GLM-5.1. On the Artificial Analysis Intelligence Index, it scores 51 points, making it the strongest open-weights model currently tracked.",
   "organization_evidence": [
    "On June 17, Zhipu AI \u2014 the Chinese lab also known as Z.ai \u2014 released the full weights of GLM-5.2, its newest flagship model, under the MIT license.",
    "## What happened"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 17, Zhipu AI \u2014 the Chinese lab also known as Z.ai \u2014 released the full weights of GLM-5.2, its newest flagship model, under the MIT license.",
    "## What happened"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 scores 62.1 on SWE-bench Pro, a test of resolving real software engineering issues, up from 58.4 for the previous GLM-5.1.",
    "On the Artificial Analysis Intelligence Index, it scores 51 points, making it the strongest open-weights model currently tracked.",
    "## How it performs"
   ]
  },
  {
   "url": "https://voice.lapaas.com/minimax-release-open-source-m2-7-ai-model?amp=1",
   "title": "MiniMax release open-Source M2.7 AI Model",
   "published_at": "2026-04-12T06:45:53",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First self-evolving model designed to actively manage its own training and engineering workflows, featuring autonomous debugging, recursive improvement, and persistent learning.",
   "organization_evidence": [
    "Shanghai-based AI lab **MiniMax** has officially released the open-source weights for its next-generation reasoning model, **MiniMax-M2.7**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released on **March 18, 2026**, and broadly available through NVIDIA\u2019s open-source inference ecosystem as of this week, M2.7 is being hailed as the first \u201cself-evolving\u201d model designed to actively manage its own training and engineering workflows."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Released on **March 18, 2026**, and broadly available through NVIDIA\u2019s open-source inference ecosystem as of this week, M2.7 is being hailed as the first \u201cself-evolving\u201d model designed to actively manage its own training and engineering workflows."
   ]
  },
  {
   "url": "https://swfte.com/blog/deepseek-v4-5-open-weight-deep-dive-2026",
   "title": "DeepSeek V4.5: The Open-Weight Model That Undercuts Everyone",
   "published_at": "2026-07-20T22:37:25",
   "organization": "DeepSeek",
   "model": "DeepSeek V4.5",
   "release_date": "2026-07-05",
   "parameters": null,
   "context_window": null,
   "license": "MIT",
   "significance": "GPQA Diamond 91.8%",
   "organization_evidence": [
    "DeepSeek released V4.5 on July 5, 2026 the way it always does: weights on Hugging Face under an MIT license, an API priced low enough to make everyone else's spreadsheet look wrong, and a technical report that followed a few days later."
   ],
   "model_evidence": [
    "DeepSeek released V4.5 on July 5, 2026 the way it always does: weights on Hugging Face under an MIT license, an API priced low enough to make everyone else's spreadsheet look wrong, and a technical report that followed a few days later.",
    "## The short version",
    "| Model | License | GPQA Diamond | Input / Output (per 1M) | Weights? |",
    "| **DeepSeek V4.5** | **MIT** | **91.8%** | **$0.50 / $1.10** | **Yes** |"
   ],
   "release_date_evidence": [
    "DeepSeek released V4.5 on July 5, 2026 the way it always does: weights on Hugging Face under an MIT license, an API priced low enough to make everyone else's spreadsheet look wrong, and a technical report that followed a few days later."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "DeepSeek released V4.5 on July 5, 2026 the way it always does: weights on Hugging Face under an MIT license, an API priced low enough to make everyone else's spreadsheet look wrong, and a technical report that followed a few days later.",
    "## The short version",
    "| Model | License | GPQA Diamond | Input / Output (per 1M) | Weights? |",
    "| **DeepSeek V4.5** | **MIT** | **91.8%** | **$0.50 / $1.10** | **Yes** |"
   ],
   "significance_evidence": [
    "The reasoning scores land in the frontier band: GPQA Diamond 91.8%, competition-math results sitting with models that cost twenty times as much.",
    "## The short version",
    "| Model | License | GPQA Diamond | Input / Output (per 1M) | Weights? |",
    "| **DeepSeek V4.5** | **MIT** | **91.8%** | **$0.50 / $1.10** | **Yes** |"
   ]
  },
  {
   "url": "https://techfastforward.com/articles/alibaba-qwen-36-plus-fireworks-ai-1m-context-open-weights-2026",
   "title": "Alibaba's Qwen 3.6-Plus Just Made the Closed-Weights Arms Race Look Obsolete",
   "published_at": "2026-05-03T11:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #28 out of 115 evaluated models on BenchLM.ai, cracked the top 10 on the verified leaderboard, and leads on SWE-bench Pro, Terminal-Bench 2.0, SkillsBench, and QwenWebBench.",
   "organization_evidence": [
    "Model Release",
    "Alibaba's Qwen 3.6-Plus Just Made the Closed-Weights Arms Race Look Obsolete",
    "Then Alibaba's Qwen team released a model with a 1-million-token context window, 3x the inference speed of Claude Opus 4.6, leading performance on the hardest real-world coding benchmarks, and an open-weights license, and deployed it on Fireworks AI for commercial use on day one."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
    "Alibaba's Qwen 3.6-Plus Just Made the Closed-Weights Arms Race Look Obsolete",
    "On March 30-31, 2026, Alibaba's Qwen team released Qwen 3.6-Plus Preview, the next-generation flagship in its model series and the direct successor to Qwen 3.5."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "Alibaba's Qwen 3.6-Plus Just Made the Closed-Weights Arms Race Look Obsolete",
    "On BenchLM.ai's comprehensive multi-task leaderboard, Qwen 3.6-Plus ranked **#28 out of 115 evaluated models** with an overall score of 74/100, and cracked the top 10 on the verified leaderboard, which filters out benchmark overfitting by using held-out test sets that model developers cannot optimize for directly.",
    "The model leads on SWE-bench Pro, the hardest real-world software engineering benchmark, which evaluates models on actual GitHub repository issues requiring multi-file reasoning and code changes, as well as Terminal-Bench 2.0, SkillsBench, and QwenWebBench."
   ]
  },
  {
   "url": "https://the-agent-report.com/2026/07/open-source-ai-paradox-2026-meta-moonshot-deepseek",
   "title": "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
   "published_at": "2026-07-24T06:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "the largest open model ever. It is, on several benchmarks, a top-3 model globally, competitive with GPT-5.5 and Claude Opus 4.8.",
   "organization_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "Moonshot released Kimi K3 (2.8T params, open-weight) \u2014 the largest open model ever.",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **Moonshot** | **Kimi K3** (2.8T, Apache 2.0) | Consumer app with subscription | \ud83d\udfe2 Opening |",
    "**Moonshot AI** released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model under the Apache 2.0 license."
   ],
   "model_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "Moonshot released Kimi K3 (2.8T params, open-weight) \u2014 the largest open model ever.",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **Moonshot** | **Kimi K3** (2.8T, Apache 2.0) | Consumer app with subscription | \ud83d\udfe2 Opening |",
    "**Moonshot AI** released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model under the Apache 2.0 license."
   ],
   "release_date_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "**Moonshot AI** released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model under the Apache 2.0 license."
   ],
   "parameters_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "Moonshot released Kimi K3 (2.8T params, open-weight) \u2014 the largest open model ever.",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **Moonshot** | **Kimi K3** (2.8T, Apache 2.0) | Consumer app with subscription | \ud83d\udfe2 Opening |",
    "**Moonshot AI** released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model under the Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "Moonshot released Kimi K3 (2.8T params, open-weight) \u2014 the largest open model ever.",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **Moonshot** | **Kimi K3** (2.8T, Apache 2.0) | Consumer app with subscription | \ud83d\udfe2 Opening |",
    "**Moonshot AI** released Kimi K3 on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "Moonshot released Kimi K3 (2.8T params, open-weight) \u2014 the largest open model ever.",
    "It is, on several benchmarks, a top-3 model globally, competitive with GPT-5.5 and Claude Opus 4.8."
   ]
  },
  {
   "url": "https://the-agent-report.com/2026/07/open-source-ai-paradox-2026-meta-moonshot-deepseek",
   "title": "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
   "published_at": "2026-07-24T06:00:00",
   "organization": "DeepSeek",
   "model": "V4 Pro and V4 Flash",
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": "MIT",
   "significance": "both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input).",
   "organization_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "- **The paradox**: Western AI leader Meta shifts to proprietary models (Mango, Avocado) as Chinese labs Moonshot (Kimi K3, Apache 2.0) and DeepSeek (V4, MIT) release their best work open-weight",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **DeepSeek** | **V4 Pro + Flash** (MIT) | API pricing ($0.09-$0.43/M input) | \ud83d\udfe2 Opening |",
    "**DeepSeek** released V4 Pro and V4 Flash on April 24 under the MIT license \u2014 both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input)."
   ],
   "model_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "- **The paradox**: Western AI leader Meta shifts to proprietary models (Mango, Avocado) as Chinese labs Moonshot (Kimi K3, Apache 2.0) and DeepSeek (V4, MIT) release their best work open-weight",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **DeepSeek** | **V4 Pro + Flash** (MIT) | API pricing ($0.09-$0.43/M input) | \ud83d\udfe2 Opening |",
    "**DeepSeek** released V4 Pro and V4 Flash on April 24 under the MIT license \u2014 both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input)."
   ],
   "release_date_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "**DeepSeek** released V4 Pro and V4 Flash on April 24 under the MIT license \u2014 both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "- **The paradox**: Western AI leader Meta shifts to proprietary models (Mango, Avocado) as Chinese labs Moonshot (Kimi K3, Apache 2.0) and DeepSeek (V4, MIT) release their best work open-weight",
    "| Lab | Open Strategy | Proprietary Strategy | Net Direction |",
    "| **DeepSeek** | **V4 Pro + Flash** (MIT) | API pricing ($0.09-$0.43/M input) | \ud83d\udfe2 Opening |",
    "**DeepSeek** released V4 Pro and V4 Flash on April 24 under the MIT license \u2014 both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input)."
   ],
   "significance_evidence": [
    "The Open-Source AI Paradox of 2026: Meta Closes, Moonshot Opens, and the Frontier Shifts From California to Beijing",
    "**DeepSeek** released V4 Pro and V4 Flash on April 24 under the MIT license \u2014 both open-weight, both commercially usable, both aggressively priced ($0.09-$0.43/M input)."
   ]
  },
  {
   "url": "https://en.wikipedia.org/wiki/Gemma_(language_model)",
   "title": "Gemma (language model)",
   "published_at": "2026-08-13T18:17:14",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place.",
   "organization_evidence": [
    "Gemma is a series of source-available large language models developed by Google DeepMind.",
    "Developer: Google DeepMind"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 2, 2026, Google released Gemma 4 under the free and open-source Apache\u00a02.0 license.",
    "| Gemma 4 | 2026-04-02 | 31B, 26B A4B, 12B, 4B, ~2B | 128K (edge) 256K (larger) | Yes (vision, audio) | Apache 2.0 | The 31B and 26B A4B models are not capable of audio. |",
    "Technical specifications of Gemma models"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4's 31B Dense variant reached third place on Arena 's text leaderboard, and the 26B variant reached sixth place."
   ]
  },
  {
   "url": "https://ainchina.com/blog/kimi-k3-moonshot-2-8-trillion-open-weight-model-2026",
   "title": "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-17",
   "parameters": "2.8 trillion parameters",
   "context_window": "1 million tokens",
   "license": "open weights",
   "significance": "2.8T parameters, open weights",
   "organization_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "The culprit wasn't a Fed announcement or a geopolitical crisis. It was a press release from a Beijing startup called Moonshot AI."
   ],
   "model_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "The company had unveiled Kimi K3, a 2.8 trillion-parameter open-weight language model \u2014 the largest ever released."
   ],
   "release_date_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "July 26, 2026\u00b7AI in China",
    "At 12:47 AM on July 17, 2026, the trading floor at the Nasdaq was quiet."
   ],
   "parameters_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "The company had unveiled Kimi K3, a 2.8 trillion-parameter open-weight language model \u2014 the largest ever released."
   ],
   "context_window_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "Inside the Architecture: What 2.8 Trillion Parameters Actually Means",
    "- **1 million token context window** \u2014 matching Claude Sonnet 5's capacity"
   ],
   "license_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "The company had unveiled Kimi K3, a 2.8 trillion-parameter open-weight language model \u2014 the largest ever released."
   ],
   "significance_evidence": [
    "Kimi K3: The 2.8 Trillion-Parameter Open-Weight Model That Moved the Nasdaq",
    "From Kimi Chat to K3: The Moonshot Journey",
    "| Milestone | Date | Significance |",
    "| **Kimi K3** | **Jul 17, 2026** | **2.8T parameters, open weights** |",
    "*Table: Moonshot AI key milestones, 2023\u20132026. Source: Company announcements, TechCrunch, Xinhua.*"
   ]
  },
  {
   "url": "https://avenchat.com/blog/glm-5.2-review",
   "title": "GLM 5.2 Review: Benchmarks, Coding Performance & Is It Worth Using?",
   "published_at": "2026-06-22T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "hitting 62.1 on SWE-bench Pro, leading Design Arena's Code Categories leaderboard, and doing it all under a fully permissive MIT license at around one-sixth the API cost of GPT-5.5",
   "organization_evidence": [
    "## GLM 5.2 Review: Is Zhipu AI's Open-Weight Flagship Worth Your Attention?",
    "**GLM 5.2** is the latest open-weight model from Zhipu AI (now operating as Z.ai), released on June 13, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Quick Answer",
    "| **Release date** | June 13, 2026 |",
    "**GLM 5.2** is the latest open-weight model from Zhipu AI (now operating as Z.ai), released on June 13, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## GLM 5.2 Review: Is Zhipu AI's Open-Weight Flagship Worth Your Attention?",
    "It is the first open model to genuinely close the gap on frontier closed-source coding performance \u2014 hitting 62.1 on SWE-bench Pro, leading Design Arena's Code Categories leaderboard, and doing it all under a fully permissive MIT license at around one-sixth the API cost of GPT-5.5."
   ]
  },
  {
   "url": "https://thenextweb.com/news/poolside-laguna-s-2-1-open-weight-coding-model",
   "title": "Poolside releases Laguna S 2.1, the open-weight coding model pitched as the West\u2019s answer to DeepSeek and Qwen",
   "published_at": "2026-07-21T19:49:20",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matches or exceeds models several times its size on agentic coding benchmarks, scores just over 70 percent on Terminal-Bench and nearly 60 percent on SWE-Bench Pro",
   "organization_evidence": [
    "Poolside releases Laguna S 2.1, the open-weight coding model pitched as the West\u2019s answer to DeepSeek and Qwen"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Poolside releases Laguna S 2.1, the open-weight coding model pitched as the West\u2019s answer to DeepSeek and Qwen"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Poolside releases Laguna S 2.1, the open-weight coding model pitched as the West\u2019s answer to DeepSeek and Qwen",
    "The 118-billion-parameter model matches systems several times its size on agentic coding benchmarks, runs on a single Nvidia DGX Spark, and is available under the Linux Foundation's OpenMDW license",
    "On Terminal-Bench and SWE-Bench Pro, two agentic coding evaluations, Laguna S 2.1 scored just over 70 percent and nearly 60 percent respectively, matching or beating models from DeepSeek, Nvidia, and Thinking Machines that carry two to eight times as many active parameters."
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/deepseek-v4-flash",
   "title": "DeepSeek-V4-Flash: 284B MoE, 1M Context, MIT | AI/TLDR",
   "published_at": "2026-04-24T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "284B total \u0097 13B active",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-Experts language model with 284 billion total parameters, of which 13 billion are activated per token, and it natively supports a one-million-token context window with up to 384K tokens of output.",
   "organization_evidence": [
    "# DeepSeek-V4-Flash",
    "DeepSeek's lightweight V4 tier: a 284B / 13B-active open-weight MoE with a 1M-token context, near-Pro reasoning, and prices as low as $0.14 per million input tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek-V4-Flash",
    "DeepSeek-V4-Flash is the smaller, faster, and cheaper of the two models in DeepSeek's V4 series, released as a preview on 24 April 2026 alongside the larger DeepSeek-V4-Pro.",
    "| Released | 2026-04-24 |"
   ],
   "parameters_evidence": [
    "# DeepSeek-V4-Flash",
    "DeepSeek's lightweight V4 tier: a 284B / 13B-active open-weight MoE with a 1M-token context, near-Pro reasoning, and prices as low as $0.14 per million input tokens.",
    "| Parameters | 284B total \u00b7 13B active |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek-V4-Flash",
    "DeepSeek-V4-Flash is the smaller, faster, and cheaper of the two models in DeepSeek's V4 series, released as a preview on 24 April 2026 alongside the larger DeepSeek-V4-Pro."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matches GPT-4o on MMLU, MATH, and HumanEval. Best open model for production general-purpose assistants.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 1. Meta Llama 4 \u2014 Best Overall",
    "Meta\u2019s Llama 4 family is the flagship open stack of 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 1. Meta Llama 4 \u2014 Best Overall",
    "- **Maverick (17B active / 128-expert MoE)** \u2014 matches GPT-4o on MMLU, MATH, and HumanEval. Best open model for production general-purpose assistants."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-Q1",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "On AIME 2024 and Codeforces it outscores o3-mini.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 2. DeepSeek R2 \u2014 Best for Reasoning",
    "DeepSeek R2 (released Q1 2026) is a 671B MoE model with only ~37B active parameters per forward pass."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 2. DeepSeek R2 \u2014 Best for Reasoning",
    "DeepSeek R2 (released Q1 2026) is a 671B MoE model with only ~37B active parameters per forward pass."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 2. DeepSeek R2 \u2014 Best for Reasoning",
    "On AIME 2024 and Codeforces it outscores o3-mini."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "tops SWE-bench Verified for coding, outperforms Llama 4 Maverick on Chinese and Spanish tasks, and runs on 2\u00d7A100 80GB GPUs.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 3. Qwen 3 \u2014 Best Multilingual and Coding",
    "Alibaba\u2019s Qwen 3 (235B MoE, 22B active per pass) ships in 8 sizes from 0.6B to 235B."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 3. Qwen 3 \u2014 Best Multilingual and Coding",
    "The 32B dense variant is the sweet spot: it tops SWE-bench Verified for coding, outperforms Llama 4 Maverick on Chinese and Spanish tasks, and runs on 2\u00d7A100 80GB GPUs."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 3 27B outperforms the original Llama 3 70B on most benchmarks at a fraction of the resource cost.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 4. Google Gemma 3 \u2014 Best for Edge and On-Device",
    "Gemma 3 27B is Google\u2019s strongest small model: natively multimodal (text + images), 128K context, optimized for Nvidia, AMD, and Apple Silicon via llama.cpp and MediaPipe."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 4. Google Gemma 3 \u2014 Best for Edge and On-Device",
    "Gemma 3 27B outperforms the original Llama 3 70B on most benchmarks at a fraction of the resource cost."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "Microsoft",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "punches well above its weight class.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 5. Microsoft Phi-4 \u2014 Best for Resource-Constrained Deployments",
    "Microsoft\u2019s investment in synthetic data quality over raw scale makes it the top choice for edge servers, developer laptops (runs at 4-bit quantization on 8GB VRAM), and cost-sensitive inference pipelines."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 5. Microsoft Phi-4 \u2014 Best for Resource-Constrained Deployments",
    "Phi-4 (14B) punches well above its weight class."
   ]
  },
  {
   "url": "https://aitoolspot.net/best-open-source-ai-models-2026",
   "title": "Best Open Source AI Models 2026 | AIToolSpott",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Magistral Medium matches Llama 4 Maverick on standard benchmarks.",
   "organization_evidence": [
    "## What Are Open Source AI Models in 2026?",
    "### 6. Mistral Magistral \u2014 Best for EU Regulatory Compliance",
    "Mistral Magistral (24B and 141B) brings frontier reasoning to a fully European-developed model \u2014 critical for GDPR-sensitive deployments where data residency and auditability matter."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Top Open Source AI Models of 2026",
    "### 6. Mistral Magistral \u2014 Best for EU Regulatory Compliance",
    "Magistral Medium matches Llama 4 Maverick on standard benchmarks."
   ]
  },
  {
   "url": "https://chatforest.com/reviews/google-gemma-4-open-weight-multimodal-llm-review",
   "title": "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model \u2014 ChatForest",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "E2B, E4B, 26B A4B (MoE), 31B (dense)",
   "context_window": "128K (E2B/E4B), 256K (26B/31B)",
   "license": "Apache 2.0",
   "significance": "GPQA Diamond: 84.3% (31B). LiveCodeBench: 80.0%. AIME 2026: 89.2%. MMLU-Pro: 85.2%. Image input: all variants. Audio input: E2B/E4B. 2M+ HuggingFace downloads.",
   "organization_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "model_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "release_date_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "parameters_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "context_window_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "license_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ],
   "significance_evidence": [
    "MCP Server ReviewPublished May 13, 2026 \u00b7 Updated May 13, 2026",
    "Google Gemma 4 \u2014 Apache 2.0, MoE, Audio, and 256K Context in a 26B Model",
    "**At a glance:** Gemma 4, released April 2, 2026. Four sizes: E2B, E4B, 26B A4B (MoE), 31B (dense). Apache 2.0 license. Context: 128K (E2B/E4B), 256K (26B/31B)."
   ]
  },
  {
   "url": "https://hasaka.io/journal/kimi-k3-release",
   "title": "Kimi K3: What Moonshot's New Model Means",
   "published_at": "2026-07-25T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Topped a blind developer coding benchmark ahead of Claude Fable 5 and came in first across six of seven domains in the frontend category of the Frontend Code Arena.",
   "organization_evidence": [
    "AI",
    "Kimi K3 Just Landed, and It's Forcing a Different Conversation About Open AI",
    "Kimi K3 was launched by Moonshot AI on July 16, 2026, and having done the legwork on researching the technical description, coverage from actual testers, and the conversation that is starting to form in developer forums and social media circles following this event, I wanted to put down facts as they really stand apart from the headlines claiming this is China's Anthropic/OpenAI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI",
    "Kimi K3 Just Landed, and It's Forcing a Different Conversation About Open AI",
    "Kimi K3 was launched by Moonshot AI on July 16, 2026, and having done the legwork on researching the technical description, coverage from actual testers, and the conversation that is starting to form in developer forums and social media circles following this event, I wanted to put down facts as they really stand apart from the headlines claiming this is China's Anthropic/OpenAI."
   ],
   "parameters_evidence": [
    "AI",
    "Kimi K3 Just Landed, and It's Forcing a Different Conversation About Open AI",
    "Moonshot's Kimi K3 is a 2.8 trillion parameter, open-weight model that still trails Claude Fable 5 and GPT 5.6 Sol overall, but topped a blind developer coding benchmark ahead of Fable 5."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI",
    "Kimi K3 Just Landed, and It's Forcing a Different Conversation About Open AI",
    "Moonshot's Kimi K3 is a 2.8 trillion parameter, open-weight model that still trails Claude Fable 5 and GPT 5.6 Sol overall, but topped a blind developer coding benchmark ahead of Fable 5."
   ]
  },
  {
   "url": "https://convly.ai/glm-5-2-explained-2026",
   "title": "GLM 5.2 Explained: Zhipu's Open 1M-Context Coder | Convly AI",
   "published_at": "2026-06-19T16:39:07",
   "organization": "Zhipu AI",
   "model": "GLM 5.2",
   "release_date": "2026-06-16",
   "parameters": "~753B-parameter",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "Independent evaluators now rank GLM 5.2 as the leading open-weights model on aggregate intelligence and near the top on frontend coding",
   "organization_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan."
   ],
   "model_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan."
   ],
   "release_date_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan.",
    "That changed when the open weights went public on June 16: Zhipu published a full benchmark suite, and independent evaluators followed quickly."
   ],
   "parameters_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan.",
    "**~753B-parameter Mixture-of-Experts** (per Zhipu\u2019s own model card) with roughly 40B active parameters per token, exposed in Claude Code as the model ID `glm-5.2[1m]` (base ID `glm-5.2`)."
   ],
   "context_window_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan.",
    "The headline number is a 1,000,000-token context window, five times what GLM 5.1 offered, paired with MIT-licensed open weights that Zhipu promised would land within the week alongside the standalone API and chatbot.",
    "Attribute | GLM 5.2 (confirmed)",
    "Context window | 1,000,000 tokens (`glm-5.2[1m]`)"
   ],
   "license_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "On June 13, 2026, Zhipu AI (which now brands its products as Z.ai) pushed GLM 5.2 to every tier of its GLM Coding Plan.",
    "The headline number is a 1,000,000-token context window, five times what GLM 5.1 offered, paired with MIT-licensed open weights that Zhipu promised would land within the week alongside the standalone API and chatbot.",
    "Attribute | GLM 5.2 (confirmed)",
    "License | MIT (open weights)"
   ],
   "significance_evidence": [
    "GLM 5.2 Explained: Zhipu\u2019s Open 1M-Context Coder",
    "The benchmark gap that defined the first 72 hours has closed: independent evaluators now rank GLM 5.2 as the leading open-weights model on aggregate intelligence and near the top on frontend coding, which is a genuine credential."
   ]
  },
  {
   "url": "https://shaam.blog/articles/serving-ai-agents-at-scale-minimax-m3-sparse-attention-2026",
   "title": "Serving AI Agents at Scale: How MiniMax M3 and Sparse Attention Reshape LLM Infrastructure in 2026",
   "published_at": "2026-07-31T18:18:16",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "~428B total parameters, ~23B activated (MoE)",
   "context_window": null,
   "license": null,
   "significance": "1M-token context window powered by MiniMax Sparse Attention (MSA), native multimodality, and frontier-level agentic coding (59.0% on SWE-Bench Pro).",
   "organization_evidence": [
    "MiniMax M3 released June 1, 2026; ~428B total parameters, ~23B activated (MoE), 1M context window.",
    "## What makes MiniMax M3 different from other open-weight models?"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 released June 1, 2026; ~428B total parameters, ~23B activated (MoE), 1M context window.",
    "## What makes MiniMax M3 different from other open-weight models?"
   ],
   "parameters_evidence": [
    "MiniMax M3 released June 1, 2026; ~428B total parameters, ~23B activated (MoE), 1M context window.",
    "## What makes MiniMax M3 different from other open-weight models?"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Three capabilities, previously found only in closed-source frontier models, are combined in a single open-weight checkpoint for the first time:",
    "1. **1M-token context window** powered by MiniMax Sparse Attention (MSA).",
    "2. **Native multimodality**\u2014image, video, and text trained together from step zero, not bolted on.",
    "3. **Frontier-level agentic coding**\u201459.0% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro on this benchmark ([MiniMax M3 model page](https://www.minimax.io/models/text/m3)).",
    "## What makes MiniMax M3 different from other open-weight models?"
   ]
  },
  {
   "url": "https://openai-hub.com/news/778",
   "title": "Hugging Face\u53d1\u5e03MolmoMotion\uff1a\u8ba9\u8bed\u8a00\u5f00\u53e3\u6307\u63253D\u52a8\u4f5c",
   "published_at": "2026-06-17T20:03:22",
   "organization": "AI2",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": "7B",
   "context_window": null,
   "license": null,
   "significance": "Turns 3D human motion prediction into a natural language-driven multimodal task, outperforming MotionGPT and LLM-integrated baselines by 15-20 points on Instruction Following Rate.",
   "organization_evidence": [
    "Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "New Model",
    "# Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "The AI2 team has launched MolmoMotion based on the Molmo vision-language model, turning 3D human motion prediction into a natural language-driven multimodal task."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "New Model",
    "# Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "2026-06-17T20:03:22.009Z"
   ],
   "parameters_evidence": [
    "Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "New Model",
    "# Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "Memory requirements are moderate \u2014 the 7B fp16 version runs on ~16GB, so A10/4090 are fine."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "New Model",
    "# Hugging Face releases MolmoMotion: Enabling language to command 3D actions",
    "The AI2 team has launched MolmoMotion based on the Molmo vision-language model, turning 3D human motion prediction into a natural language-driven multimodal task.",
    "On their custom benchmark, MolmoMotion outperformed MotionGPT and LLM-integrated baselines by 15\u201320 points."
   ]
  },
  {
   "url": "https://tensorfeed.ai/originals/kimi-k3-open-frontier-ceiling-8x",
   "title": "Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days.",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "8x larger by total params, roughly 8x by context length, and it clears GLM on almost every coding benchmark Moonshot chose to publish.",
   "organization_evidence": [
    "Moonshot AI put Kimi K3 live on Thursday, July 16, 2026.",
    "# Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI put Kimi K3 live on Thursday, July 16, 2026.",
    "# Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days."
   ],
   "parameters_evidence": [
    "Moonshot AI put Kimi K3 live on Thursday, July 16, 2026.",
    "# Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3 is roughly 8x larger by total params, roughly 8x by context length, and it clears GLM on almost every coding benchmark Moonshot chose to publish.",
    "# Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days."
   ]
  },
  {
   "url": "https://aichatdaily.com/ai-models/google-deepmind-launches-gemma-4-12b-encoder-free-multimodal",
   "title": "Google DeepMind launches Gemma 4 12B, an encoder-free multimodal model for laptops",
   "published_at": "2026-06-09T16:06:51",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "An encoder-free multimodal model for laptops with native audio and vision baked into the LLM backbone, approaching 26B benchmark performance at less than half the total memory footprint.",
   "organization_evidence": [
    "Main content",
    "Google DeepMind launches Gemma 4 12B, an encoder-free multimodal model for laptops",
    "Google DeepMind released [Gemma](/gemini) 4 12B on June 3, 2026, a 12B-parameter open model that runs on a laptop with 16GB of VRAM and processes vision and audio inputs without separate encoders."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Main content",
    "Google DeepMind launches Gemma 4 12B, an encoder-free multimodal model for laptops",
    "Google DeepMind released [Gemma](/gemini) 4 12B on June 3, 2026, a 12B-parameter open model that runs on a laptop with 16GB of VRAM and processes vision and audio inputs without separate encoders."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "Main content",
    "Google DeepMind launches Gemma 4 12B, an encoder-free multimodal model for laptops",
    "The mid-sized open model runs on 16GB of VRAM and nears 26B benchmark performance, with native audio and vision baked into the LLM backbone."
   ]
  },
  {
   "url": "https://bregg.com/post.php?slug=kimi-k3-moonshot-open-weight-healthcare-ai-strategy-2026-07-17",
   "title": "Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest open-source AI model in the world",
   "organization_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "parameters_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: The World's Largest Open-Weight Model Arrives One Month After Fable Was Pulled \u2014 What It Means for Healthcare AI Strategy",
    "On July 16, 2026, Beijing-based Moonshot AI released Kimi K3 \u2014 a 2.8-trillion-parameter open-weight model that the company says is now the largest open-source AI model in the world."
   ]
  },
  {
   "url": "https://google.com/goto?url=CAESgwEB7keqTcysRpvjUQ1Yfka5GyKBhf79ADB5hzkaaCf1JAu6VxZSoW_DC1KdDEdkKRxBN6GPep3MWqkN3MVJHVQoi03rS7g1g-OprNAdkc-yhC87SdWs0Ou2FyI86fsb-9j47eXsVGK6qHWz69ynBORtaJjeT4FaqAZBjx9EOsF91UFbRQ%3D%3D",
   "title": "GLM-5.2 Review (2026): Benchmarks, Free Access & How to Use It",
   "published_at": "2026-06-18T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked as the #1 open-weights AI model in the world and 4th best overall by Artificial Analysis.",
   "organization_evidence": [
    "# GLM-5.2 Review (2026): Benchmarks, Free Access & How to Use It",
    "GLM-5.2 is a large language model made by a Chinese AI company called **Z.ai** (previously known as Zhipu AI).",
    "| Developer | Z.ai (formerly Zhipu AI) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Review (2026): Benchmarks, Free Access & How to Use It",
    "It was announced on **June 13, 2026**, and the weights were made publicly available on **June 16\u201317, 2026** on Hugging Face."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Review (2026): Benchmarks, Free Access & How to Use It",
    "According to **Artificial Analysis**, an independent AI research firm, GLM-5.2 is currently the **#1 open-weights AI model in the world** and **4th best overall**, behind only a couple of OpenAI and Anthropic models."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/minimax-m3-beats-gpt-5-5-at-10-percent-coding-cost",
   "title": "MiniMax M3 Beats GPT-5.5 at Just 10% of Coding Cost",
   "published_at": "2026-06-02T04:51:30",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 59.0% on SWE-Bench Pro, surpassing GPT-5.5 and Gemini 3.1 Pro, with a one-million-token context window and native multimodal input.",
   "organization_evidence": [
    "Model Release",
    "# MiniMax M3 Beats GPT-5.5 at Just 10% of Coding Cost",
    "MiniMax released M3 on **June 1, 2026**, calling it the first open-weight system to combine frontier coding-agent performance, a **one-million-token** context window, and native multimodal input in a single model."
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   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
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    "MiniMax released M3 on **June 1, 2026**, calling it the first open-weight system to combine frontier coding-agent performance, a **one-million-token** context window, and native multimodal input in a single model."
   ],
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   "significance_evidence": [
    "Model Release",
    "# MiniMax M3 Beats GPT-5.5 at Just 10% of Coding Cost",
    "MiniMax released M3 on **June 1, 2026**, calling it the first open-weight system to combine frontier coding-agent performance, a **one-million-token** context window, and native multimodal input in a single model."
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/intent-aware-lfqa-qwen3-4b-multiview",
   "title": "allenai/intent-aware-lfqa-qwen3-4b-multiview \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "allenai",
   "model": null,
   "release_date": "2026-06-05",
   "parameters": "4.0B",
   "context_window": null,
   "license": null,
   "significance": "intent-aware long-form question answering (LFQA) model built on top of Qwen3-4B, fine-tuned to recognise user intent and generate multi-perspective answers",
   "organization_evidence": [
    "Modelers",
    "# allenai / intent-aware-lfqa-qwen3-4b-multiview",
    "Hosted under the Allen AI namespace on a Chinese platform \u2014 provenance and ownership are unclear",
    "# allenai/intent-aware-lfqa-qwen3-4b-multiview"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Modelers",
    "# allenai / intent-aware-lfqa-qwen3-4b-multiview",
    "**Created:** 2026-06-05 | **Updated:** 2026-06-05"
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    "# allenai / intent-aware-lfqa-qwen3-4b-multiview",
    "Parameters",
    "4.0B"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Modelers",
    "# allenai / intent-aware-lfqa-qwen3-4b-multiview",
    "This is an intent-aware long-form question answering (LFQA) model built on top of Qwen3-4B, fine-tuned to recognise user intent and generate multi-perspective answers."
   ]
  },
  {
   "url": "https://deepseekai.guide/news/deepseek-v4-release-date",
   "title": "DeepSeek V4 Release Date: What Shipped on April 24, 2026",
   "published_at": "2026-04-24T19:30:06",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active",
   "context_window": null,
   "license": null,
   "significance": "Pro demonstrates superior performance relative to GPT-5.2 and Gemini-3.0-Pro on standard reasoning benchmarks, while falling marginally short of GPT-5.4 and Gemini-3.1-Pro.",
   "organization_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "DeepSeek posted the V4 Preview announcement on its API docs site on **April 24, 2026**, the same morning the weights went live on Hugging Face and Expert/Instant Mode appeared in the web chat."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "If you were waiting for a firm DeepSeek V4 release date, here it is: the V4 Preview dropped on April 24, 2026, as two open-weight Mixture-of-Experts models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 both with a one-million-token context window.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Release date | 2026-04-24 | 2026-04-24 |"
   ],
   "parameters_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "The post opened by declaring DeepSeek-V4 Preview officially live and open-sourced, welcomed \u201cthe era of cost-effective 1M context length,\u201d and introduced two model IDs: DeepSeek-V4-Pro at 1.6T total / 49B active parameters, and DeepSeek-V4-Flash at 284B total / 13B active parameters.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Total parameters | 1.6T | 284B |",
    "| Active per token | 49B | 13B |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Benchmarks: competitive, not category-leading",
    "According to the V4 technical report, V4-Pro (reasoning_effort=max) demonstrates superior performance relative to GPT-5.2 and Gemini-3.0-Pro on standard reasoning benchmarks, while falling marginally short of GPT-5.4 and Gemini-3.1-Pro \u2014 a trajectory that trails the current frontier by roughly 3 to 6 months."
   ]
  },
  {
   "url": "https://deepseekai.guide/news/deepseek-v4-release-date",
   "title": "DeepSeek V4 Release Date: What Shipped on April 24, 2026",
   "published_at": "2026-04-24T19:30:06",
   "organization": "DeepSeek",
   "model": "deepseek-v4-flash",
   "release_date": "2026-04-24",
   "parameters": "284B total / 13B active",
   "context_window": "1,000,000 tokens",
   "license": "MIT",
   "significance": "Flash provides a cost-effective tier with a 1M context length, suitable for standard chat and coding work.",
   "organization_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "DeepSeek posted the V4 Preview announcement on its API docs site on **April 24, 2026**, the same morning the weights went live on Hugging Face and Expert/Instant Mode appeared in the web chat."
   ],
   "model_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "If you were waiting for a firm DeepSeek V4 release date, here it is: the V4 Preview dropped on April 24, 2026, as two open-weight Mixture-of-Experts models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 both with a one-million-token context window.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |"
   ],
   "release_date_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "If you were waiting for a firm DeepSeek V4 release date, here it is: the V4 Preview dropped on April 24, 2026, as two open-weight Mixture-of-Experts models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 both with a one-million-token context window.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Release date | 2026-04-24 | 2026-04-24 |"
   ],
   "parameters_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "The post opened by declaring DeepSeek-V4 Preview officially live and open-sourced, welcomed \u201cthe era of cost-effective 1M context length,\u201d and introduced two model IDs: DeepSeek-V4-Pro at 1.6T total / 49B active parameters, and DeepSeek-V4-Flash at 284B total / 13B active parameters.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Total parameters | 1.6T | 284B |",
    "| Active per token | 49B | 13B |"
   ],
   "context_window_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "If you were waiting for a firm DeepSeek V4 release date, here it is: the V4 Preview dropped on April 24, 2026, as two open-weight Mixture-of-Experts models \u2014 `deepseek-v4-pro` and `deepseek-v4-flash` \u2014 both with a one-million-token context window.",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Context window | 1,000,000 tokens | 1,000,000 tokens |"
   ],
   "license_evidence": [
    "# DeepSeek V4 Release Date: Pro and Flash Land as an Open-Weight Preview",
    "![DeepSeek V4 release-date card announcing the April 24, 2026 launch of the V4 Preview, with both V4-Pro and V4-Flash tiers shipping under MIT license on Hugging Face and through the DeepSeek API.](https://deepseekai.guide/wp-content/uploads/2026/04/deepseek-v4-release-card.png)",
    "| Attribute | deepseek-v4-pro | deepseek-v4-flash |",
    "| Weights license | MIT | MIT |"
   ],
   "significance_evidence": [
    "## What V4 actually is: two tiers, one feature set",
    "Pro sits at roughly 6\u00d7 Flash on output, so Flash remains the default recommendation for most chat and standard coding work."
   ]
  },
  {
   "url": "https://ainewslab.org/en/article/mistral-3-voxtral-tts",
   "title": "Mistral Ships Mistral 3 Family and Open-Source Voxtral TTS in the Same Week",
   "published_at": "2026-03-26T07:30:21",
   "organization": "Mistral",
   "model": null,
   "release_date": null,
   "parameters": "675B total parameters",
   "context_window": null,
   "license": null,
   "significance": "Mistral Large 3 debuted at #2 in the open-source non-reasoning category on the LMArena leaderboard",
   "organization_evidence": [
    "Mistral Ships Mistral 3 Family and Open-Source Voxtral TTS in the Same Week",
    "Mistral releases its most capable model family \u2014 including Mistral Large 3 at 675B parameters \u2014 and an open-weights text-to-speech model supporting 9 languages."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Mistral Ships Mistral 3 Family and Open-Source Voxtral TTS in the Same Week",
    "Mistral releases its most capable model family \u2014 including Mistral Large 3 at 675B parameters \u2014 and an open-weights text-to-speech model supporting 9 languages."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral Ships Mistral 3 Family and Open-Source Voxtral TTS in the Same Week",
    "Mistral Large 3 debuted at #2 in the open-source non-reasoning category on the LMArena leaderboard, according to [Mistral's announcement](https://mistral.ai/news/mistral-3)."
   ]
  },
  {
   "url": "https://nogentech.org/google-gemma-4-fully-open-commercial-use",
   "title": "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
   "published_at": "2026-04-03T11:31:56",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "spanning edge devices to 31 billion parameters",
   "context_window": "128K tokens for the E2B and E4B models, and up to 256K tokens for the 26B and 31B models",
   "license": "Apache 2.0 license",
   "significance": "its most capable open-weight model family to date, built from the same research and technology as Gemini 3",
   "organization_evidence": [
    "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
    "Google DeepMind released Gemma 4 on April 2, 2026, a family of four open-weight AI models spanning edge devices to 31 billion parameters, now available for the first time under the fully permissive Apache 2.0 license."
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   "model_evidence": [
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   ],
   "release_date_evidence": [
    "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
    "Google DeepMind released Gemma 4 on April 2, 2026, a family of four open-weight AI models spanning edge devices to 31 billion parameters, now available for the first time under the fully permissive Apache 2.0 license."
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   "parameters_evidence": [
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   ],
   "context_window_evidence": [
    "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
    "What Gemma 4 Brings to the Table Technically",
    "Context windows are large: 128K tokens for the E2B and E4B models, and up to 256K tokens for the 26B and 31B models."
   ],
   "license_evidence": [
    "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
    "Google DeepMind released Gemma 4 on April 2, 2026, a family of four open-weight AI models spanning edge devices to 31 billion parameters, now available for the first time under the fully permissive Apache 2.0 license."
   ],
   "significance_evidence": [
    "Google DeepMind Releases Gemma 4 Under A Fully Permissive Apache 2.0 License",
    "Google DeepMind has launched [Gemma 4](https://deepmind.google/models/gemma/gemma-4/), its most capable open-weight model family to date, built from the same research and technology as [Gemini 3](https://www.nogentech.org/what-is-google-gemini-3-flash/)."
   ]
  },
  {
   "url": "https://felloai.com/fr/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "Early 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Local and consumer-hardware coding, around $0.60 per million tokens",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-4.7 Flash** | Early 2026 | 30B dense | Local and consumer-hardware coding, around $0.60 per million tokens | Current lite |"
   ]
  },
  {
   "url": "https://felloai.com/fr/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "Feb 13 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/fr/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "Apr 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |"
   ]
  },
  {
   "url": "https://felloai.com/fr/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "Jun 13 2026",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |",
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |"
   ]
  },
  {
   "url": "https://insights.marvin-42.com/articles/minimax-m3-weights-hit-hugging-face-with-428b-total-parameters",
   "title": "MiniMax M3 weights hit Hugging Face with 428B total parameters",
   "published_at": "2026-06-13T11:03:40",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "~428B parameters",
   "context_window": null,
   "license": null,
   "significance": "59.0% on SWE-Bench Pro, 66.0% on Terminal Bench 2.1, 34.8% on SWE-fficiency, 28.8% on KernelBench Hard, and 74.2% on MCP Atlas.",
   "organization_evidence": [
    "MiniMax M3 weights hit Hugging Face with 428B total parameters",
    "MiniMax M3 is now a concrete open-weight release rather than only a benchmark post."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The MiniMax official account posted on June 12, 2026 at 14:11 UTC that the weights were live on Hugging Face and linked the MiniMax Sparse Attention paper."
   ],
   "parameters_evidence": [
    "MiniMax M3 weights hit Hugging Face with 428B total parameters",
    "MiniMax M3 is now a concrete open-weight release rather than only a benchmark post.",
    "The tweet\u2019s key figure was ~428B parameters and ~23B activated parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 weights hit Hugging Face with 428B total parameters",
    "MiniMax M3 is now a concrete open-weight release rather than only a benchmark post.",
    "The quoted earlier post adds the benchmark frame: 59.0% on SWE-Bench Pro, 66.0% on Terminal Bench 2.1, 34.8% on SWE-fficiency, 28.8% on KernelBench Hard, and 74.2% on MCP Atlas."
   ]
  },
  {
   "url": "https://24-ai.news/en/news/2026-04-29/nvidia-nemotron-3-nano-omni-multimodal",
   "title": "NVIDIA Nemotron 3 Nano Omni: open multimodal model 30B-A3B MoE with 256K context, 9\u00d7 higher throughput than\u2026",
   "published_at": "2026-04-29T00:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30B total / 3B active",
   "context_window": null,
   "license": null,
   "significance": "It achieves top accuracy on six leaderboards for document intelligence and audio-video understanding, with 9\u00d7 higher throughput than other open omni models at the same interactivity level.",
   "organization_evidence": [
    "NVIDIA Nemotron 3 Nano Omni: open multimodal model 30B-A3B MoE with 256K context, 9\u00d7 higher throughput than competitors",
    "Nemotron 3 Nano Omni is NVIDIA's new open multimodal model that unifies vision, speech, and language in a single 30B-A3B hybrid mixture-of-experts system with 256K context."
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   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Nemotron 3 Nano Omni: open multimodal model 30B-A3B MoE with 256K context, 9\u00d7 higher throughput than competitors",
    "On April 28, 2026, NVIDIA introduced **Nemotron 3 Nano Omni** \u2014 an open multimodal model that combines vision, speech, and language in a single system."
   ],
   "parameters_evidence": [
    "Nemotron 3 Nano Omni: open multimodal model 30B-A3B MoE with 256K context, 9\u00d7 higher throughput than competitors",
    "**30B-A3B hybrid mixture-of-experts** \u2014 30 billion parameters total, 3 billion active per inference."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Nemotron 3 Nano Omni: open multimodal model 30B-A3B MoE with 256K context, 9\u00d7 higher throughput than competitors",
    "It achieves top accuracy on six leaderboards for document intelligence and audio-video understanding, with 9\u00d7 higher throughput than other open omni models at the same interactivity level."
   ]
  },
  {
   "url": "https://rits.shanghai.nyu.edu/ai/deepseek-releases-v4-open-source-1-6t-moe-with-1m-context",
   "title": "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T",
   "context_window": null,
   "license": null,
   "significance": "MMLU-Pro: 87.5%; SimpleQA-Verified: 57.9%; LiveCodeBench: 93.5% (vs. Kimi K2.6 at 89.6%); Codeforces rating: 3206 (vs. GPT-5.4 at 3168); HMMT 2026 Feb (math): 95.2%; MRCR 1M (long-context recall): 83.5%; SWE-bench Verified: 80.6%",
   "organization_evidence": [
    "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
    "**DeepSeek has launched V4**, its newest flagship open-source language model, exactly one year after the V3/R1 releases that rattled Silicon Valley."
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   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
    "April 24, 2026",
    "Announced on April 24, 2026, the V4 family ships in two Mixture-of-Experts variants \u2014 a 1.6-trillion-parameter *V4-Pro* and a leaner 284-billion-parameter *V4-Flash* \u2014 both supporting a 1-million-token context window and both released with open weights under permissive licenses."
   ],
   "parameters_evidence": [
    "DeepSeek Releases V4: Open-Source 1.6T MoE with 1M Context",
    "Announced on April 24, 2026, the V4 family ships in two Mixture-of-Experts variants \u2014 a 1.6-trillion-parameter *V4-Pro* and a leaner 284-billion-parameter *V4-Flash* \u2014 both supporting a 1-million-token context window and both released with open weights under permissive licenses."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On the scorecards DeepSeek published alongside the release, V4-Pro-Max posts numbers that are competitive with \u2014 and in places ahead of \u2014 proprietary frontier systems:",
    "- **MMLU-Pro**: 87.5%",
    "- **SimpleQA-Verified**: 57.9%",
    "- **LiveCodeBench**: 93.5% (vs. Kimi K2.6 at 89.6%)",
    "- **Codeforces rating**: 3206 (vs. GPT-5.4 at 3168)",
    "- **HMMT 2026 Feb** (math): 95.2%",
    "- **MRCR 1M** (long-context recall): 83.5%",
    "- **SWE-bench Verified**: 80.6%"
   ]
  },
  {
   "url": "https://dayahimour.org/en/blog/llama-4-maverick",
   "title": "Llama 4 Maverick: The Open\u2011Source Model That Shook the AI Throne in 2026 \u2014 A Comprehensive Analysis",
   "published_at": "2026-04-04T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-04-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "outperforms GPT-4o in programming and mathematics at 90% lower operating cost",
   "organization_evidence": [
    "# Llama 4 Maverick: The Open\u2011Source Model That Shook the AI Throne in 2026 \u2014 A Comprehensive Analysis",
    "On 5 April 2026, **Meta** officially released **Llama 4 Maverick**\u2014and triggered a real earthquake in the AI world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Llama 4 Maverick: The Open\u2011Source Model That Shook the AI Throne in 2026 \u2014 A Comprehensive Analysis",
    "On 5 April 2026, **Meta** officially released **Llama 4 Maverick**\u2014and triggered a real earthquake in the AI world."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Llama 4 Maverick: The Open\u2011Source Model That Shook the AI Throne in 2026 \u2014 A Comprehensive Analysis",
    "On 5 April 2026, **Meta** officially released **Llama 4 Maverick**\u2014and triggered a real earthquake in the AI world.",
    "Not because the model is \u201cgood,\u201d but because it **outperforms GPT\u20114o in programming and mathematics** at 90% lower operating cost, and is **fully open\u2011source** for commercial use."
   ]
  },
  {
   "url": "https://techfastforward.com/articles/moonshot-kimi-k27-cuts-reasoning-tokens-30-percent",
   "title": "Moonshot Kimi K2.7 Cuts Reasoning Tokens 30 Percent",
   "published_at": "2026-06-13T05:16:26",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "1 trillion total parameters, 32B active per forward pass",
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   "license": null,
   "significance": "deliver 30% fewer reasoning tokens and 21.8% higher coding benchmark scores",
   "organization_evidence": [
    "Model Release",
    "# Moonshot Kimi K2.7 Cuts Reasoning Tokens 30 Percent",
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   "model_evidence": null,
   "release_date_evidence": [
    "Model Release",
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    "Moonshot AI's Kimi K2.7 Code delivers 30% fewer reasoning tokens and 21.8% higher coding benchmark scores, open-sourced under MIT license on June 12.",
    "On June 12, 2026, Moonshot AI released Kimi K2.7-Code, a **1-trillion-parameter** Mixture-of-Experts model with **32 billion parameters active** per forward pass."
   ],
   "parameters_evidence": [
    "Model Release",
    "# Moonshot Kimi K2.7 Cuts Reasoning Tokens 30 Percent",
    "- 1 trillion total parameters, 32B active per forward pass: MoE architecture with 384 experts keeps inference cost low while preserving large functional capacity"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Release",
    "# Moonshot Kimi K2.7 Cuts Reasoning Tokens 30 Percent",
    "Moonshot AI's Kimi K2.7 Code delivers 30% fewer reasoning tokens and 21.8% higher coding benchmark scores, open-sourced under MIT license on June 12."
   ]
  },
  {
   "url": "https://adtools.org/buyers-guide/ai-news-zhipu-glm-image-model-release",
   "title": "Zhipu AI: China's Zhipu Unveils AI Model Trained on Huawei Chips",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": "GLM-Image",
   "release_date": "2026-01-14",
   "parameters": "16B",
   "context_window": null,
   "license": "open-source",
   "significance": "state-of-the-art (SOTA) benchmarks like 0.9116 word accuracy on CVTG-2k and 0.9788 on LongText-Bench for Chinese text rendering",
   "organization_evidence": [
    "AI News Deep Dive",
    "# Zhipu AI: China's Zhipu Unveils AI Model Trained on Huawei ChipsUpdated: August 02, 2026",
    "Chinese AI startup Zhipu released the open-source GLM-Image multimodal model, the first fully trained on domestic Huawei Ascend chips using the MindSpore framework."
   ],
   "model_evidence": [
    "AI News Deep Dive",
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    "Chinese AI startup Zhipu released the open-source GLM-Image multimodal model, the first fully trained on domestic Huawei Ascend chips using the MindSpore framework."
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   "release_date_evidence": [
    "AI News Deep Dive",
    "## What Happened",
    "On January 14, 2026, Chinese AI startup Zhipu AI unveiled GLM-Image, its first open-source, industrial-grade discrete autoregressive image generation model, fully trained on Huawei's Ascend hardware."
   ],
   "parameters_evidence": [
    "AI News Deep Dive",
    "## What Happened",
    "On January 14, 2026, Chinese AI startup Zhipu AI unveiled GLM-Image, its first open-source, industrial-grade discrete autoregressive image generation model, fully trained on Huawei's Ascend hardware.",
    "The 16B-parameter model combines a 9B autoregressive module (initialized from GLM-4-9B) for semantic handling with a 7B diffusion decoder for high-fidelity details, using semantic-VQ tokenization and progressive training across resolutions up to 1024px."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "AI News Deep Dive",
    "# Zhipu AI: China's Zhipu Unveils AI Model Trained on Huawei ChipsUpdated: August 02, 2026",
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   "significance_evidence": [
    "AI News Deep Dive",
    "## What Happened",
    "It excels in text-to-image generation, image editing, and style transfer, achieving state-of-the-art (SOTA) benchmarks like 0.9116 word accuracy on CVTG-2k and 0.9788 on LongText-Bench for Chinese text rendering [source](https://z.ai/blog/glm-image)."
   ]
  },
  {
   "url": "https://aichina.news/blog/unlocking-sera-8b-allenais-open-model-now-available-on-huaweis-ascend-com24r",
   "title": "Unlocking SERA-8B: AllenAI\u2019s Open Model Now Available on Huawei\u2019s Ascend Ecosystem \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T13:16:28",
   "organization": "AllenAI",
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   "parameters": "8-billion-parameter",
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   "license": null,
   "significance": null,
   "organization_evidence": [
    "Unlocking SERA-8B: AllenAI\u2019s Open Model Now Available on Huawei\u2019s Ascend Ecosystem",
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   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Unlocking SERA-8B: AllenAI\u2019s Open Model Now Available on Huawei\u2019s Ascend Ecosystem",
    "The latest addition to this ecosystem is **SERA-8B**, an 8-billion-parameter language model from the well-respected AllenAI research collective."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://heybeagle.com/blog/open-weight-models-now-own-the-price-intelligence-frontier",
   "title": "Open-Weight Models Now Own the Price-Intelligence Frontier",
   "published_at": "2026-07-12T20:02:29",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Kimi K2.6 is a 1-trillion-parameter vision-language model designed to generate code in a plan-write-test-debug loop that can last for days, and it can instantiate hundreds of agents that collaborate on a single task.",
   "organization_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Kimi K2.6 is a 1-trillion-parameter vision-language model designed to generate code in a plan-write-test-debug loop that can last for days, and it can instantiate hundreds of agents that collaborate on a single task."
   ]
  },
  {
   "url": "https://heybeagle.com/blog/open-weight-models-now-own-the-price-intelligence-frontier",
   "title": "Open-Weight Models Now Own the Price-Intelligence Frontier",
   "published_at": "2026-07-12T20:02:29",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
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   "license": null,
   "significance": "DeepSeek V4 Pro is dramatically cheaper per token and carries a full one-million-token context at no premium.",
   "organization_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "DeepSeek V4 Pro is dramatically cheaper per token and carries a full one-million-token context at no premium."
   ]
  },
  {
   "url": "https://heybeagle.com/blog/open-weight-models-now-own-the-price-intelligence-frontier",
   "title": "Open-Weight Models Now Own the Price-Intelligence Frontier",
   "published_at": "2026-07-12T20:02:29",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "GLM-5.2 takes the top open-weight score on the Artificial Analysis Intelligence Index at 51 and leads on SWE-bench Pro at 62.1 percent.",
   "organization_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "Three releases in a nine-week window drove the shift: Moonshot AI shipped Kimi K2.6 on April 20, 2026; DeepSeek followed with V4 on April 24; and Z.ai closed the run with GLM-5.2 on June 13."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open-Weight Models Now Own the Price-Intelligence Frontier",
    "GLM-5.2 takes the top open-weight score on the Artificial Analysis Intelligence Index at 51 and leads on SWE-bench Pro at 62.1 percent."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/alibabas-new-qwen37-plus-brings-vision-and-autonom-5a411363",
   "title": "Alibaba's Qwen 3.8 Max Opens the Frontier: A 2.4-Trillion-Parameter Model Going Open-Source Next Week",
   "published_at": "2026-08-03T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 2.4-trillion-parameter mixture-of-experts (MoE) model with 95 billion active parameters per token, a 1-million-token context window, and native support for text, image, and video input.",
   "organization_evidence": [
    "# Alibaba's Qwen 3.8 Max Opens the Frontier: A 2.4-Trillion-Parameter Model Going Open-Source Next Week",
    "Alibaba released Qwen 3.8 Max on August 3, 2026, a 2.4-trillion-parameter mixture-of-experts (MoE) model with 95 billion active parameters per token, a 1-million-token context window, and native support for text, image, and video input."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Alibaba released Qwen 3.8 Max on August 3, 2026, a 2.4-trillion-parameter mixture-of-experts (MoE) model with 95 billion active parameters per token, a 1-million-token context window, and native support for text, image, and video input.",
    "# Alibaba's Qwen 3.8 Max Opens the Frontier: A 2.4-Trillion-Parameter Model Going Open-Source Next Week"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba released Qwen 3.8 Max on August 3, 2026, a 2.4-trillion-parameter mixture-of-experts (MoE) model with 95 billion active parameters per token, a 1-million-token context window, and native support for text, image, and video input.",
    "# Alibaba's Qwen 3.8 Max Opens the Frontier: A 2.4-Trillion-Parameter Model Going Open-Source Next Week"
   ]
  },
  {
   "url": "https://sewwa.com/open-source-ai-models-2026-comparison",
   "title": "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "This is the first time any Gemma release ships under Apache 2.0, which is a massive shift from the restrictive custom license that held back previous versions.",
   "organization_evidence": [
    "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
    "Google dropped Gemma 4 on April 2."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
    "Google dropped Gemma 4 on April 2."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Three Contenders at a Glance",
    "Before we get into the weeds, here is the quick picture.",
    "**Gemma 4** (Google, April 2, 2026) comes in four sizes ranging from 2.3B effective parameters up to a 31B dense model."
   ]
  },
  {
   "url": "https://sewwa.com/open-source-ai-models-2026-comparison",
   "title": "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It is natively multimodal, supports 201 languages, and has been making waves on coding benchmarks in particular.",
   "organization_evidence": [
    "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
    "Alibaba hit back with Qwen 3.5 back in February."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI in 2026",
    "**Qwen 3.5** (Alibaba, February 16, 2026) centers on a 27B dense flagship but spans from 0.8B all the way up to a 397B-A17B MoE model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Three Contenders at a Glance",
    "Before we get into the weeds, here is the quick picture.",
    "**Qwen 3.5** (Alibaba, February 16, 2026) centers on a 27B dense flagship but spans from 0.8B all the way up to a 397B-A17B MoE model."
   ]
  },
  {
   "url": "https://gate.com/news/detail/google-has-released-the-smartest-open-model-gemma-4-fully-switching-to-the-20010472",
   "title": "Google has released the smartest open model Gemma 4! Fully switching to the Apache 2.0 license, with performance close to that of large closed-source AI",
   "published_at": "2026-04-03T02:00:42",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, 26B, 31B",
   "context_window": null,
   "license": null,
   "significance": "Gemma-4-31B currently ranks 3rd among open models and set an impressive record with 84.3% on GPQA Diamond.",
   "organization_evidence": [
    "Google\u2019s official announcement confirms the formal launch of the next-generation open model series Gemma 4.",
    "\u2014 Google (@Google) April 2, 2026"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "\u2014 Google (@Google) April 2, 2026"
   ],
   "parameters_evidence": [
    "To meet different hardware and application scenarios, Gemma 4 is released in four different sizes.",
    "The lightest E2B (2B parameters) is designed for mobile devices and edge devices like web browsers; E4B (4B parameters) strikes a balance between performance and efficiency, and natively supports visual and audio input even more.",
    "Meanwhile, on the high-performance end, the 26B A4B uses a Mixture of Experts (MoE) architecture\u2014during inference, only about 4B parameters are activated\u2014dramatically reducing memory requirements, so it can run smoothly even on consumer-grade hardware like a Mac Mini with 24GB of memory.",
    "The top-tier 31B dense model is the performance flagship of the series."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "According to data from open-model leaderboards such as AI Arena, Gemma-4-31B currently ranks 3rd among open models.",
    "On the graduate-level reasoning benchmark GPQA Diamond, the 31B version also set an impressive record with 84.3%."
   ]
  },
  {
   "url": "https://xfutureagi.com/en/article/kimi-k3",
   "title": "Moonshot AI ships Kimi K3, the world's first 3-trillion-parameter open model \u00b7 XiaoHu \u00b7 AI, Explained",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the world's first open model at the 3-trillion-parameter scale, beating GPT 5.6 Sol on several agentic benchmarks",
   "organization_evidence": [
    "# Moonshot AI launches Kimi K3, the world's first open model at the 3-trillion-parameter scale",
    "Moonshot AI released its new flagship model, Kimi K3, on July 16."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released Kimi K3 on July 16: 2.8 trillion parameters, native vision, a 1-million-token context window \u2014 the first open model anywhere to hit the 3-trillion-parameter mark",
    "# Moonshot AI launches Kimi K3, the world's first open model at the 3-trillion-parameter scale"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot AI launches Kimi K3, the world's first open model at the 3-trillion-parameter scale",
    "2.8 trillion parameters, native vision, a 1-million-token context window \u2014 beats GPT 5.6 Sol on several agentic benchmarks, live today in the app and API"
   ]
  },
  {
   "url": "https://blog.4sapi.com/blog/glm-5-2-open-llm-1m-context-coding",
   "title": "GLM-5.2: Open-Source Coding LLM Explained",
   "published_at": "2026-06-30T00:00:00",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-16",
   "parameters": "744 billion",
   "context_window": "1,000,000-token",
   "license": "MIT",
   "significance": "Ranked first among publicly accessible models in full-stack coding capability, outperformed Fable 5 in webpage layout generation, scored 99.2 on AIME 2026, and maintained 94% factual recall at 500K tokens.",
   "organization_evidence": [
    "Abstract",
    "Released by Zhipu AI in mid-June 2026, GLM-5.2 is a major upgrade in the GLM flagship model series."
   ],
   "model_evidence": [
    "Abstract",
    "Released by Zhipu AI in mid-June 2026, GLM-5.2 is a major upgrade in the GLM flagship model series."
   ],
   "release_date_evidence": [
    "1. Core Release Background and Baseline Model Specifications",
    "#### 1.1 Product Timeline and Market Positioning",
    "Zhipu AI released GLM-5.2 on June 16, 2026."
   ],
   "parameters_evidence": [
    "1. Core Release Background and Baseline Model Specifications",
    "#### 1.2 Fundamental Structure and Training Parameters",
    "1. **Total parameter scale**: 744 billion parameters distributed across 256 expert modules."
   ],
   "context_window_evidence": [
    "Abstract",
    "Released by Zhipu AI in mid-June 2026, GLM-5.2 is a major upgrade in the GLM flagship model series.",
    "Together, these mechanisms support a stable 1,000,000-token native context window."
   ],
   "license_evidence": [
    "Abstract",
    "Released by Zhipu AI in mid-June 2026, GLM-5.2 is a major upgrade in the GLM flagship model series.",
    "Unlike many closed-source frontier models, GLM-5.2 is released under the MIT license."
   ],
   "significance_evidence": [
    "#### 1.1 Product Timeline and Market Positioning",
    "In these evaluations, GLM-5.2 outperformed Fable 5 in webpage layout generation.",
    "It also ranked first among publicly accessible models in full-stack coding capability."
   ]
  },
  {
   "url": "https://tech.grahammiranda.com/minimax-m3-vs-kimi-k27-code-vs-nemotron-3-ultra-2026",
   "title": "MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
   "published_at": "2026-06-15T13:06:36",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 scores 59.0% on SWE-bench Pro, 66.0% on Terminal-Bench 2.1, 34.8% on SWE-fficiency, and 83.5 on BrowseComp.",
   "organization_evidence": [
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
    "**MiniMax M3** is Shanghai-based MiniMax\u2019s play to be the first open-weight model that genuinely combines three things at once: frontier-level coding performance, a one-million-token context window, and native multimodality \u2014 including image, video, and desktop computer control."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "In the space of twelve days, three major open-weight releases landed within days of each other: **MiniMax M3** on June 1st, **Nemotron 3 Ultra** on June 4th, and \u2014 literally yesterday \u2014 **Kimi K2.7 Code** on June 12th.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, 34.8% on SWE-fficiency, and 83.5 on BrowseComp.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared"
   ]
  },
  {
   "url": "https://tech.grahammiranda.com/minimax-m3-vs-kimi-k27-code-vs-nemotron-3-ultra-2026",
   "title": "MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
   "published_at": "2026-06-15T13:06:36",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "At 550 billion parameters, it\u2019s the most capable open-weight model to come out of a US-based lab, scoring 47.7 on the Artificial Analysis Intelligence Index \u2014 a score that puts it in the same tier as Claude Opus 4.6 and Kimi K2.6.",
   "organization_evidence": [
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
    "**Nemotron 3 Ultra** is NVIDIA\u2019s answer to the question: what if the chip-maker built the model too?"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "In the space of twelve days, three major open-weight releases landed within days of each other: **MiniMax M3** on June 1st, **Nemotron 3 Ultra** on June 4th, and \u2014 literally yesterday \u2014 **Kimi K2.7 Code** on June 12th.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "At 550 billion parameters, it\u2019s the most capable open-weight model to come out of a US-based lab, scoring 47.7 on the Artificial Analysis Intelligence Index \u2014 a score that puts it in the same tier as Claude Opus 4.6 and Kimi K2.6.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared"
   ]
  },
  {
   "url": "https://tech.grahammiranda.com/minimax-m3-vs-kimi-k27-code-vs-nemotron-3-ultra-2026",
   "title": "MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
   "published_at": "2026-06-15T13:06:36",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "K2.7-Code scored 81.1 on MCP Mark Verified, beating Claude Opus 4.8\u2019s 76.4%.",
   "organization_evidence": [
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
    "**Kimi K2.7 Code** is Moonshot AI\u2019s (Beijing) fifth major release in under a year \u2014 a laser-focused coding upgrade to the already-impressive K2.6, cutting reasoning token usage by approximately 30% while pushing key agent benchmarks meaningfully higher."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "In the space of twelve days, three major open-weight releases landed within days of each other: **MiniMax M3** on June 1st, **Nemotron 3 Ultra** on June 4th, and \u2014 literally yesterday \u2014 **Kimi K2.7 Code** on June 12th.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared",
    "Moonshot AI launched yesterday \u2014 June 12, 2026 \u2014 with what is the fifth major release in under a year for the Beijing-based company, and they\u2019ve positioned their models around three pillars: agentic capabilities, extended context handling, and multimodal inputs."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The standout independent-adjacent result so far: K2.7-Code scored 81.1 on MCP Mark Verified, beating Claude Opus 4.8\u2019s 76.4%.",
    "# MiniMax M3 vs Kimi K2.7 Code vs Nemotron 3 Ultra: The Best Open-Weight AI Models of 2026 Compared"
   ]
  },
  {
   "url": "https://aichina.news/models/allenai/Olmo-3-7B-RLZero-Math",
   "title": "allenai/Olmo-3-7B-RLZero-Math \u2014 AICHINA.news | AI China News",
   "published_at": null,
   "organization": "Allen AI",
   "model": null,
   "release_date": "2026-04-12",
   "parameters": "7B",
   "context_window": null,
   "license": null,
   "significance": "Trained using reinforcement learning from zero (RL-Zero) to improve mathematical reasoning without an initial supervised fine-tuning warm-up stage.",
   "organization_evidence": [
    "Modelers",
    "# allenai / Olmo-3-7B-RLZero-Math",
    "This is a 7-billion parameter math-specialised language model from the Allen AI OLMo-3 family, trained using reinforcement learning from zero (RL-Zero) to improve mathematical reasoning."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Translated model card",
    "# allenai/Olmo-3-7B-RLZero-Math",
    "**Created:** 2026-04-12"
   ],
   "parameters_evidence": [
    "Modelers",
    "# allenai / Olmo-3-7B-RLZero-Math",
    "7.0B"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# allenai / Olmo-3-7B-RLZero-Math",
    "This is a 7-billion parameter math-specialised language model from the Allen AI OLMo-3 family, trained using reinforcement learning from zero (RL-Zero) to improve mathematical reasoning."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/07/18/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T01:41:33",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scored 80.6% on SWE-bench Verified, the highest open-weight result at its release and tied with Gemini 3.1 Pro.",
   "organization_evidence": [
    "Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI\u2019s [Kimi K3](https://www.kimi.com/blog/kimi-k3), [DeepSeek V4 Pro](https://api-docs.deepseek.com/news/news260424/), and Zhipu AI\u2019s [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) are all sparse Mixture-of-Experts (MoE) models with million-token context windows."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released | July 16, 2026 | April 24, 2026 | June 13, 2026",
    "This article compares them on three axes an AI team actually decides on: measured capability, license terms, and serving cost."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "[DeepSeek-V4-Pro-Max scores 80.6% on SWE-bench Verified](https://www.morphllm.com/deepseek-v4), the highest open-weight result at its release and tied with Gemini 3.1 Pro.",
    "Benchmarks"
   ]
  },
  {
   "url": "https://marktechpost.com/2026/07/18/kimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost",
   "title": "Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost",
   "published_at": "2026-07-19T01:41:33",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Held the top open-weight spot until K3 shipped and scored 62.1 on SWE-bench Pro, edging GPT-5.5 at 58.6.",
   "organization_evidence": [
    "Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI\u2019s [Kimi K3](https://www.kimi.com/blog/kimi-k3), [DeepSeek V4 Pro](https://api-docs.deepseek.com/news/news260424/), and Zhipu AI\u2019s [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) are all sparse Mixture-of-Experts (MoE) models with million-token context windows.",
    "[GLM-5.2 is also MIT-licensed](https://huggingface.co/zai-org/GLM-5.2), with full weights on Hugging Face under the zai-org organization."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released | July 16, 2026 | April 24, 2026 | June 13, 2026",
    "This article compares them on three axes an AI team actually decides on: measured capability, license terms, and serving cost."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2 held the top open-weight spot until K3 shipped.",
    "GLM-5.2 scored [62.1 on SWE-bench Pro](https://kie.ai/blog/glm-5-2-benchmark-deep-dive), edging GPT-5.5 at 58.6.",
    "Benchmarks"
   ]
  },
  {
   "url": "https://earlyterms.com/term/qwen3-6",
   "title": "Qwen3.6 is Alibaba's Qwen team's next-generation LLM line, positioned around \"real-world agents.\" It spans two",
   "published_at": "2026-04-02T15:32:29",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "35B-total / 3B-active",
   "context_window": null,
   "license": null,
   "significance": "SWE-bench Verified 73.4, integrates with Claude Code, OpenClaw, and Qwen Code out of the box, runs on a MacBook Pro via a ~21GB Q4 quant",
   "organization_evidence": [
    "# Qwen3.6",
    "Qwen3.6 is Alibaba's Qwen team's next-generation LLM line, positioned around \"real-world agents.\""
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Qwen3.6",
    "It spans two tiers: the closed [Qwen3.6-Plus](https://qwen.ai/blog?id=qwen3.6) (released April 2, 2026) and the open-weight [Qwen3.6-35B-A3B](https://qwen.ai/blog?id=qwen3.6-35b-a3b) \u2014 a 35B-total / 3B-active sparse-MoE model released April 16, 2026 under Apache 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.6",
    "Qwen3.6-35B-A3B hit SWE-bench Verified 73.4, integrates with Claude Code, OpenClaw, and Qwen Code out of the box, runs on a MacBook Pro via a ~21GB Q4 quant, and [Simon Willison showed it outdrew Claude Opus 4.7 on the pelican-riding-a-bicycle SVG benchmark](https://simonwillison.net/2026/Apr/16/qwen-beats-opus/)."
   ]
  },
  {
   "url": "https://dev.to/hassann/what-is-gemma-4-12b-2dnp",
   "title": "What is Gemma 4 12B?",
   "published_at": "2026-06-04T02:52:42",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "11.95B",
   "context_window": null,
   "license": null,
   "significance": "Encoder-free unified multimodal with native audio input, 256K context window, running on 16GB VRAM or unified memory.",
   "organization_evidence": [
    "What is Gemma 4 12B?",
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What is Gemma 4 12B?",
    "Google shipped Gemma 4 12B on June 3, 2026."
   ],
   "parameters_evidence": [
    "What is Gemma 4 12B?",
    "Google shipped Gemma 4 12B on June 3, 2026.",
    "It is an open-weights, 11.95B-parameter model that accepts text, images, audio, and video as input, returns text, and can run on a laptop with 16GB of memory."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What is Gemma 4 12B?",
    "The main implementation detail: it is a mid-sized multimodal model with native audio input and no separate vision or audio encoder."
   ]
  },
  {
   "url": "https://felloai.com/es/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-4.7 Flash",
   "release_date": "early 2026",
   "parameters": "30B dense",
   "context_window": "128K",
   "license": "MIT license",
   "significance": "tuned for fast, cheap, local coding rather than frontier reasoning",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "model_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "release_date_evidence": [
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "parameters_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "It is a smaller **30B dense** model with a 128K context and pricing near **$0.60 per million tokens**, tuned for fast, cheap, local coding rather than frontier reasoning.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "context_window_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "It is a smaller **30B dense** model with a 128K context and pricing near **$0.60 per million tokens**, tuned for fast, cheap, local coding rather than frontier reasoning.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "license_evidence": [
    "The reason GLM matters to you is simple.",
    "Every model in the family is **open-weight and MIT-licensed**, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ],
   "significance_evidence": [
    "**GLM-4.7 Flash** is the version to run on your own machine.",
    "It is a smaller **30B dense** model with a 128K context and pricing near **$0.60 per million tokens**, tuned for fast, cheap, local coding rather than frontier reasoning.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-4.7 Flash (early 2026)"
   ]
  },
  {
   "url": "https://felloai.com/es/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5",
   "release_date": "2026-02-13",
   "parameters": "744B (44B active)",
   "context_window": null,
   "license": "MIT license",
   "significance": "First open flagship, trained fully on Huawei Ascend chips",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5 (February 2026)"
   ],
   "model_evidence": [
    "**GLM-5** was the family\u2019s first true open frontier model.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5 (February 2026)"
   ],
   "release_date_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ],
   "parameters_evidence": [
    "The **744B** mixture-of-experts design activates only 44 billion parameters per query, and Zhipu trained it entirely on **Huawei Ascend** chips without a single Nvidia GPU, a landmark for Chinese AI independence.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5 (February 2026)"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "The reason GLM matters to you is simple.",
    "Every model in the family is **open-weight and MIT-licensed**, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5 (February 2026)"
   ],
   "significance_evidence": [
    "| **GLM-5** | Feb 13 2026 | 744B (44B active) | First open flagship, trained fully on Huawei Ascend chips | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ]
  },
  {
   "url": "https://felloai.com/es/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.1",
   "release_date": "2026-04",
   "parameters": "754B",
   "context_window": null,
   "license": null,
   "significance": "Long-horizon agentic work, around 94% of Claude Opus on coding",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.1 (April 2026)"
   ],
   "model_evidence": [
    "**GLM-5.1** was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around **94% of Claude Opus** while keeping the open weights.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.1 (April 2026)"
   ],
   "release_date_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ],
   "parameters_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.1** | Apr 2026 | 754B | Long-horizon agentic work, around 94% of Claude Opus on coding | Superseded |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ]
  },
  {
   "url": "https://felloai.com/es/what-is-glm",
   "title": "What Is GLM? Every Zhipu AI\u2019s Open Model Explained",
   "published_at": "2026-06-29T16:40:55",
   "organization": "Zhipu AI",
   "model": "GLM-5.2",
   "release_date": "2026-06-13",
   "parameters": "744B (40B active)",
   "context_window": "1 million tokens",
   "license": null,
   "significance": "Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost",
   "organization_evidence": [
    "**GLM** is the family of large language models built by **Zhipu AI**, the Chinese lab now trading as **Z.ai**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.2 (June 2026)"
   ],
   "model_evidence": [
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.2 (June 2026)"
   ],
   "release_date_evidence": [
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.2 (June 2026)"
   ],
   "parameters_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ],
   "context_window_evidence": [
    "**GLM-5.2** is the current flagship, released on **June 13, 2026**.",
    "It quadruples the context window to a full **1 million tokens**, keeps long-context inference affordable, and ranks ahead of rivals like DeepSeek and Kimi on independent intelligence indexes.",
    "GLM moves fast, with a new release roughly every few months.",
    "### GLM-5.2 (June 2026)"
   ],
   "license_evidence": null,
   "significance_evidence": [
    "| **GLM-5.2** | Jun 13 2026 | 744B (40B active) | Frontier flagship, 1M context, roughly one-sixth of GPT-5.5\u2019s cost | Current flagship |",
    "GLM moves fast, with a new release roughly every few months.",
    "| Model | Released | Params (MoE) | Best for | Status |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/minimax",
   "title": "MiniMax Model List 2026: All 7 Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax-M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": "512K",
   "license": "Open",
   "significance": "MiniMax multimodal model for long-context coding, perception, and agent planning",
   "organization_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ],
   "model_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ],
   "release_date_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ],
   "license_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ],
   "significance_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M3 minimax/MiniMax-M3 MiniMax multimodal model for long-context coding, perception, and agent planning | 512K | Reasoning Tools Vision Open | 2026-06-01 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/minimax",
   "title": "MiniMax Model List 2026: All 7 Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax-M2.7",
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": "204K",
   "license": "Open",
   "significance": "Open MiniMax flagship for coding agents, office automation, and complex environments",
   "organization_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "model_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "release_date_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "license_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "significance_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7 minimax/MiniMax-M2.7 Open MiniMax flagship for coding agents, office automation, and complex environments | 204K | Reasoning Tools Open | 2026-03-18 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/minimax",
   "title": "MiniMax Model List 2026: All 7 Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax-M2.7-highspeed",
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": "204K",
   "license": "Open",
   "significance": "Low-latency M2.7 variant for interactive coding plans and agent loops",
   "organization_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "model_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "release_date_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "license_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ],
   "significance_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.7-highspeed minimax/MiniMax-M2.7-highspeed Low-latency M2.7 variant for interactive coding plans and agent loops | 204K | Reasoning Tools Open | 2026-03-18 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/minimax",
   "title": "MiniMax Model List 2026: All 7 Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax-M2.5-highspeed",
   "release_date": "2026-02-13",
   "parameters": null,
   "context_window": "204K",
   "license": "Open",
   "significance": "High-speed MiniMax model for low-latency coding and agent workflows",
   "organization_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ],
   "model_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ],
   "release_date_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ],
   "license_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ],
   "significance_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5-highspeed minimax/MiniMax-M2.5-highspeed High-speed MiniMax model for low-latency coding and agent workflows | 204K | Reasoning Tools Open | 2026-02-13 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/minimax",
   "title": "MiniMax Model List 2026: All 7 Models",
   "published_at": null,
   "organization": "MiniMax",
   "model": "MiniMax-M2.5",
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": "204K",
   "license": "Open",
   "significance": "Prior MiniMax coding model for agent workflows, office edits, and automation",
   "organization_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "model_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "release_date_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "license_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "significance_evidence": [
    "# MiniMax model list",
    "All **7** notable MiniMax models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| MiniMax-M2.5 minimax/MiniMax-M2.5 Prior MiniMax coding model for agent workflows, office edits, and automation | 204K | Reasoning Tools Open | 2026-02-12 |"
   ]
  },
  {
   "url": "https://winbuzzer.com/2026/04/27/deepseek-v4-open-weights-launch-xcxwbn",
   "title": "DeepSeek V4 Ships 1M Context, Open-Weights",
   "published_at": "2026-04-27T13:57:16",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "1-million-token context window with up to 384,000 tokens of output, under MIT-licensed open weights hosted on Hugging Face and reachable through DeepSeek's API, undercutting Western API pricing by an order of magnitude.",
   "organization_evidence": [
    "DeepSeek V4 Ships 1M Context, Open-Weights",
    "Chinese AI lab DeepSeek has launched preview versions of its new DeepSeek V4 model, shipping Pro and Flash variants under the MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 Ships 1M Context, Open-Weights",
    "DeepSeek shipped V4-Pro and V4-Flash preview models on April 24 under MIT-licensed open weights, undercutting Western API prices by an order of magnitude."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4 Ships 1M Context, Open-Weights",
    "Both ship a 1-million-token context window with up to 384,000 tokens of output, under MIT-licensed open weights hosted on Hugging Face and reachable through DeepSeek\u2019s API."
   ]
  },
  {
   "url": "https://ai-tldr.dev/releases/qwen-3-8-27b",
   "title": "Qwen3.8-27B \u2014 a 27B open model that beats Opus 4.6 Max on SWE-bench Pro",
   "published_at": "2026-08-14T12:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.8-27B",
   "release_date": "2026-08-14",
   "parameters": "27B dense, 64 layers",
   "context_window": "262,144 native, up to 1,000,000",
   "license": "Apache-2.0",
   "significance": "Scores 61.7 on SWE-bench Pro and 84.3 on OSWorld-Verified, ahead of Opus 4.6 Max on both.",
   "organization_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-14 \u00b7 major",
    "# Qwen3.8-27B \u2014 a 27B open model that beats Opus 4.6 Max on SWE-bench Pro",
    "Quick facts",
    "| Maker | Alibaba (Qwen) |"
   ],
   "model_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-14 \u00b7 major",
    "# Qwen3.8-27B \u2014 a 27B open model that beats Opus 4.6 Max on SWE-bench Pro"
   ],
   "release_date_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-14 \u00b7 major"
   ],
   "parameters_evidence": [
    "Quick facts",
    "| Size | 27B dense, 64 layers |"
   ],
   "context_window_evidence": [
    "Quick facts",
    "| Context window | 262,144 native, up to 1,000,000 |"
   ],
   "license_evidence": [
    "Quick facts",
    "| License | Apache-2.0 |"
   ],
   "significance_evidence": [
    "Alibaba (Qwen) \u00b7 2026-08-14 \u00b7 major",
    "# Qwen3.8-27B \u2014 a 27B open model that beats Opus 4.6 Max on SWE-bench Pro",
    "[Qwen3.8-27B](/models/qwen3-8-27b/) is Alibaba's new 27B dense open-weights model with built-in vision, released under Apache-2.0."
   ]
  },
  {
   "url": "http://allusanewshub.com/2026/04/02/google-launches-gemma-4-four-open-weight-models-from-smartphones-to-workstations",
   "title": "Google launches Gemma 4: four open-weight models from smartphones to workstations",
   "published_at": "2026-04-02T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B Dense model currently ranks third among all open models on the Arena AI text leaderboard; the 26B MoE sits sixth. Google claims both larger models outcompete models up to 20 times their size on that benchmark.",
   "organization_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "[Google has released Gemma 4](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/), the latest generation of its open-weight model family, in four sizes designed to cover everything from on-device inference on smartphones to workstation-class deployments."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google launches Gemma 4: four open-weight models from smartphones to workstations",
    "The 31B Dense model currently ranks third among all open models on the Arena AI text leaderboard; the 26B MoE sits sixth. Google claims both larger models outcompete models up to 20 times their size on that benchmark."
   ]
  },
  {
   "url": "https://instadatanews.com/articles/2026-08-11-moonshot-kimi-k3-largest-open-weight-model.html",
   "title": "Moonshot's Kimi K3 Becomes Largest Open-Weight AI Model",
   "published_at": "2026-08-11T04:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight system ever released; pushed the open-source frontier well past DeepSeek's V4-Pro; top open-weight result on the Artificial Analysis Intelligence Index and No. 3 on that leaderboard",
   "organization_evidence": [
    "AI Research",
    "# Moonshot's Kimi K3 Becomes Largest Open-Weight AI Model",
    "Beijing-based **Moonshot AI** has published the full weights of **Kimi K3**, a 2.8-trillion-parameter model that is now the largest open-weight system ever released."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI Research",
    "# Moonshot's Kimi K3 Becomes Largest Open-Weight AI Model",
    "Launched on July 16 and shipped as downloadable weights on July 27, K3 pushes the open-source frontier well past DeepSeek's V4-Pro and lands as one of the clearest signals yet that the gap between freely available models and the best proprietary systems is narrowing fast."
   ],
   "parameters_evidence": [
    "AI Research",
    "# Moonshot's Kimi K3 Becomes Largest Open-Weight AI Model",
    "Beijing-based **Moonshot AI** has published the full weights of **Kimi K3**, a 2.8-trillion-parameter model that is now the largest open-weight system ever released."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Research",
    "# Moonshot's Kimi K3 Becomes Largest Open-Weight AI Model",
    "Beijing-based **Moonshot AI** has published the full weights of **Kimi K3**, a 2.8-trillion-parameter model that is now the largest open-weight system ever released.",
    "Launched on July 16 and shipped as downloadable weights on July 27, K3 pushes the open-source frontier well past DeepSeek's V4-Pro and lands as one of the clearest signals yet that the gap between freely available models and the best proprietary systems is narrowing fast.",
    "## Strong, but Not Unbeatable",
    "K3 posted the top open-weight result on the Artificial Analysis Intelligence Index, and at launch it debuted at **No. 3** on that leaderboard, trailing only Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 Sol among all models."
   ]
  },
  {
   "url": "https://llm-releases.com/multimodal-models",
   "title": "LLM Releases",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Moonshot's flagship open-weight agentic model and the largest open model released to date: a 2.8T-parameter MoE (896 experts, 16 active per token) using Kimi Delta Attention and Attention Residuals, with native multimodal input and a 1M-token context.",
   "organization_evidence": [
    "# Multimodal LLM releases",
    "Large language model releases with multimodal capabilities, including vision-language, audio, video, image-generation, and document-understanding models.",
    "Moonshot's flagship open-weight agentic model and the largest open model released to date: a 2.8T-parameter MoE (896 experts, 16 active per token) using Kimi Delta Attention and Attention Residuals, with native multimodal input and a 1M-token context."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Multimodal LLM releases",
    "Large language model releases with multimodal capabilities, including vision-language, audio, video, image-generation, and document-understanding models.",
    "Launched via API on Jul 16, 2026 at $3/$15 per Mtok (cached input $0.30); full open weights published to Hugging Face on Jul 26, 2026 \u2014 a day ahead of the announced Jul 27 target \u2014 under a Modified MIT license, making it freely downloadable and self-hostable."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Multimodal LLM releases",
    "Large language model releases with multimodal capabilities, including vision-language, audio, video, image-generation, and document-understanding models.",
    "Moonshot's flagship open-weight agentic model and the largest open model released to date: a 2.8T-parameter MoE (896 experts, 16 active per token) using Kimi Delta Attention and Attention Residuals, with native multimodal input and a 1M-token context."
   ]
  },
  {
   "url": "https://llm-releases.com/multimodal-models",
   "title": "LLM Releases",
   "published_at": null,
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Thinking Machines Lab's first model and the leading U.S. open-weights release: a natively multimodal Mixture-of-Experts with 975B total / 41B active parameters that reasons across text, image, and audio inputs and emits text.",
   "organization_evidence": [
    "# Multimodal LLM releases",
    "Large language model releases with multimodal capabilities, including vision-language, audio, video, image-generation, and document-understanding models.",
    "Thinking Machines Lab's first model and the leading U.S. open-weights release: a natively multimodal Mixture-of-Experts with 975B total / 41B active parameters that reasons across text, image, and audio inputs and emits text."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Multimodal LLM releases",
    "Large language model releases with multimodal capabilities, including vision-language, audio, video, image-generation, and document-understanding models.",
    "Thinking Machines Lab's first model and the leading U.S. open-weights release: a natively multimodal Mixture-of-Experts with 975B total / 41B active parameters that reasons across text, image, and audio inputs and emits text."
   ]
  },
  {
   "url": "https://beri.net/article/deepseek-v4-open-source-frontier-1m-context-2026",
   "title": "China's DeepSeek V4 Beats GPT-5 at 73% Lower Cost",
   "published_at": "2026-04-27T08:05:03",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters, 49B activated per token",
   "context_window": null,
   "license": null,
   "significance": "Benchmark-competitive with Anthropic Claude Opus 4.6, OpenAI GPT-5.4, and Google Gemini 3.1 in coding, math, and STEM reasoning.",
   "organization_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit."
   ],
   "parameters_evidence": [
    "V4-Pro is a 1.6 trillion parameter Mixture-of-Experts model with 49 billion activated parameters per token, a one-million-token context window, and an API price of $1.74 per million input tokens.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Pro**: 1.6T total parameters, 49B activated per token, 1M-token context, hybrid attention combining token-wise compression and DeepSeek Sparse Attention (DSA)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **Benchmark posture**: V4-Pro is positioned as benchmark-competitive with Anthropic Claude Opus 4.6, OpenAI GPT-5.4, and Google [Gemini](/tools/gemini) 3.1 in coding, math, and STEM reasoning.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ]
  },
  {
   "url": "https://beri.net/article/deepseek-v4-open-source-frontier-1m-context-2026",
   "title": "China's DeepSeek V4 Beats GPT-5 at 73% Lower Cost",
   "published_at": "2026-04-27T08:05:03",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T total parameters, 49B activated per token",
   "context_window": "1-million-token",
   "license": "Open weights on Hugging Face",
   "significance": "Benchmark-competitive with Anthropic Claude Opus 4.6, OpenAI GPT-5.4, and Google Gemini 3.1 in coding, math, and STEM reasoning.",
   "organization_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ],
   "model_evidence": [
    "V4-Pro is a 1.6 trillion parameter Mixture-of-Experts model with 49 billion activated parameters per token, a one-million-token context window, and an API price of $1.74 per million input tokens.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Pro**: 1.6T total parameters, 49B activated per token, 1M-token context, hybrid attention combining token-wise compression and DeepSeek Sparse Attention (DSA)."
   ],
   "release_date_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit."
   ],
   "parameters_evidence": [
    "V4-Pro is a 1.6 trillion parameter Mixture-of-Experts model with 49 billion activated parameters per token, a one-million-token context window, and an API price of $1.74 per million input tokens.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Pro**: 1.6T total parameters, 49B activated per token, 1M-token context, hybrid attention combining token-wise compression and DeepSeek Sparse Attention (DSA)."
   ],
   "context_window_evidence": [
    "V4-Pro is a 1.6 trillion parameter Mixture-of-Experts model with 49 billion activated parameters per token, a one-million-token context window, and an API price of $1.74 per million input tokens.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Pro**: 1.6T total parameters, 49B activated per token, 1M-token context, hybrid attention combining token-wise compression and DeepSeek Sparse Attention (DSA)."
   ],
   "license_evidence": [
    "- **License**: Open weights on Hugging Face; full technical report published.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ],
   "significance_evidence": [
    "- **Benchmark posture**: V4-Pro is positioned as benchmark-competitive with Anthropic Claude Opus 4.6, OpenAI GPT-5.4, and Google [Gemini](/tools/gemini) 3.1 in coding, math, and STEM reasoning.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ]
  },
  {
   "url": "https://beri.net/article/deepseek-v4-open-source-frontier-1m-context-2026",
   "title": "China's DeepSeek V4 Beats GPT-5 at 73% Lower Cost",
   "published_at": "2026-04-27T08:05:03",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B total / 13B activated",
   "context_window": "1-million-token",
   "license": "Open weights on Hugging Face",
   "significance": null,
   "organization_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ],
   "model_evidence": [
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Flash**: 284B total / 13B activated, same 1M-token context, same DSA pipeline, optimized for lower-latency agent tasks."
   ],
   "release_date_evidence": [
    "On April 24, 2026, DeepSeek released V4 \u2014 and the open-weight frontier closed the gap on the closed-weight frontier in a single commit."
   ],
   "parameters_evidence": [
    "Its smaller sibling, V4-Flash, lands at 284B total / 13B activated and runs at roughly one-tenth the per-token cost of comparable proprietary models.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Flash**: 284B total / 13B activated, same 1M-token context, same DSA pipeline, optimized for lower-latency agent tasks."
   ],
   "context_window_evidence": [
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:",
    "- **DeepSeek-V4-Flash**: 284B total / 13B activated, same 1M-token context, same DSA pipeline, optimized for lower-latency agent tasks."
   ],
   "license_evidence": [
    "- **License**: Open weights on Hugging Face; full technical report published.",
    "The headline architecture details, drawn from DeepSeek's own technical disclosures and confirmed by independent analyses:"
   ],
   "significance_evidence": null
  },
  {
   "url": "https://miraflow.ai/blog/qwen3-8-27b-open-weight-dense-model-local-2026",
   "title": "Qwen3.8-27B Explained: Alibaba's Open-Weight Dense Model That Runs on One Consumer GPU",
   "published_at": "2026-08-19T13:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08-14",
   "parameters": "27.8 billion",
   "context_window": null,
   "license": null,
   "significance": "Qwen3.8-27B hit the top of Hacker News with 893 points within its first day, surpassed one million downloads on Hugging Face within two days of release, and briefly ranked among the four most-downloaded models in Hugging Face's history within twelve hours.",
   "organization_evidence": [
    "# Qwen3.8-27B Explained: Alibaba's Open-Weight Dense Model That Runs on One Consumer GPU",
    "Alibaba's Qwen3.8-27B shipped on Hugging Face August 14, a real 27.8B dense multimodal model you can download and run on one consumer GPU, unlike its 2.4 trillion parameter sibling."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Qwen3.8-27B Explained: Alibaba's Open-Weight Dense Model That Runs on One Consumer GPU",
    "Alibaba's Qwen3.8-27B shipped on Hugging Face August 14, a real 27.8B dense multimodal model you can download and run on one consumer GPU, unlike its 2.4 trillion parameter sibling.",
    "Then, on August 14, 2026, the actual payoff arrived: Qwen3.8-27B, a 27.8 billion parameter dense multimodal model, published on Hugging Face at [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) under a clean Apache 2.0 license, with full BF16 weights, 262,144 tokens of native context, and a file size small enough to fit on a single consumer GPU [[1]](https://huggingface.co/Qwen/Qwen3.8-27B)[[2]](https://github.com/QwenLM/Qwen3.8)."
   ],
   "parameters_evidence": [
    "# Qwen3.8-27B Explained: Alibaba's Open-Weight Dense Model That Runs on One Consumer GPU",
    "Alibaba's Qwen3.8-27B shipped on Hugging Face August 14, a real 27.8B dense multimodal model you can download and run on one consumer GPU, unlike its 2.4 trillion parameter sibling.",
    "Then, on August 14, 2026, the actual payoff arrived: Qwen3.8-27B, a 27.8 billion parameter dense multimodal model, published on Hugging Face at [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) under a clean Apache 2.0 license, with full BF16 weights, 262,144 tokens of native context, and a file size small enough to fit on a single consumer GPU [[1]](https://huggingface.co/Qwen/Qwen3.8-27B)[[2]](https://github.com/QwenLM/Qwen3.8)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Qwen3.8-27B Explained: Alibaba's Open-Weight Dense Model That Runs on One Consumer GPU",
    "The reaction was immediate."
   ]
  },
  {
   "url": "https://officechai.com/ai/mark-zuckerberg-says-meta-will-release-weights-of-muse-spark-1-2-announces-30b-muse-glimmer-model-for-local-use",
   "title": "Mark Zuckerberg Says Meta Will Release Weights of Muse Spark 1.2, Announces 30B Muse Glimmer Model For Local Use",
   "published_at": "2026-08-10T10:27:42",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Meta's actual frontier coding and reasoning model, benchmarked against Anthropic\u2019s Opus 5, OpenAI\u2019s GPT-5.6 Terra, and Google\u2019s Gemini 3.6 Flash, currently powering Muse Code, and the fifth most powerful model on the Artificial Analysis Intelligence Index.",
   "organization_evidence": [
    "Mark Zuckerberg Says Meta Will Release Weights of Muse Spark 1.2, Announces 30B Muse Glimmer Model For Local Use",
    "Mark Zuckerberg took to X to announce that Meta is opening the weights of Muse Spark 1.2, the company\u2019s latest foundation model, while simultaneously releasing a smaller 30 billion parameter dense model called Muse Glimmer that\u2019s built specifically to run on local hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Muse Spark 1.2 To Become America\u2019s Leading Open Weights Model",
    "Muse Spark 1.2 is not an experimental side project \u2014 it\u2019s Meta\u2019s actual frontier coding and reasoning model, benchmarked against Anthropic\u2019s Opus 5, OpenAI\u2019s GPT-5.6 Terra, and Google\u2019s Gemini 3.6 Flash, and it currently powers Muse Code, Meta\u2019s terminal-based coding agent.",
    "Muse Spark 1.2 is currently the fifth most powerful model on the Artificial Analysis Intelligence Index."
   ]
  },
  {
   "url": "https://officechai.com/ai/mark-zuckerberg-says-meta-will-release-weights-of-muse-spark-1-2-announces-30b-muse-glimmer-model-for-local-use",
   "title": "Mark Zuckerberg Says Meta Will Release Weights of Muse Spark 1.2, Announces 30B Muse Glimmer Model For Local Use",
   "published_at": "2026-08-10T10:27:42",
   "organization": "Meta",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A dense model built specifically to run on local hardware and for agentic tasks rather than general chat.",
   "organization_evidence": [
    "Mark Zuckerberg Says Meta Will Release Weights of Muse Spark 1.2, Announces 30B Muse Glimmer Model For Local Use",
    "Mark Zuckerberg took to X to announce that Meta is opening the weights of Muse Spark 1.2, the company\u2019s latest foundation model, while simultaneously releasing a smaller 30 billion parameter dense model called Muse Glimmer that\u2019s built specifically to run on local hardware."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Muse Glimmer Takes On Gemma And China For The Local AI Race",
    "At 30 billion parameters, it\u2019s small enough to run on a single consumer GPU, a Mac, or a high-end PC, and Meta has built it specifically for agentic tasks rather than general chat."
   ]
  },
  {
   "url": "https://androguider.com/2026/07/unveiling-mistral-ai-new-challenger-to.html",
   "title": "Unveiling Mistral AI: The New Challenger to OpenAI",
   "published_at": "2026-07-04T23:46:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": null,
   "parameters": "24-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "A 24-billion-parameter open-source model released under the Apache 2.0 license, allowing developers to integrate advanced reasoning capabilities without cost barriers.",
   "organization_evidence": [
    "Unveiling Mistral AI: The New Challenger to OpenAI",
    "The global artificial intelligence race has intensified with the latest breakthrough from Paris-based startup Mistral AI."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Unveiling Mistral AI: The New Challenger to OpenAI",
    "Reasoning Models That Think in Multiple Languages",
    "- **Magistral Small**: A 24-billion-parameter open-source model released under the Apache 2.0 license, allowing developers to integrate advanced reasoning capabilities without cost barriers."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Unveiling Mistral AI: The New Challenger to OpenAI",
    "Reasoning Models That Think in Multiple Languages",
    "- **Magistral Small**: A 24-billion-parameter open-source model released under the Apache 2.0 license, allowing developers to integrate advanced reasoning capabilities without cost barriers."
   ]
  },
  {
   "url": "https://techjacksolutions.com/ai-tools/gemma",
   "title": "Google Gemma Hub: 5 Open Model & Fine-Tuning Guides (2026)",
   "published_at": "2026-05-28T13:32:34",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 introduces native multimodal understanding across text, image, audio, and video inputs.",
   "organization_evidence": [
    "Google Gemma is a family of open-weight models released by Google DeepMind, built from the same research and technology that powers Gemini.",
    "Google Gemma:\nOpen Models & Guides"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Apr 2026",
    "Gemma 4 Release",
    "## Key Numbers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 introduces native multimodal understanding across text, image, audio, and video inputs.",
    "### Multimodal Capabilities"
   ]
  },
  {
   "url": "https://blog.uncutly.ai/kimi-k3-open-weights-2026",
   "title": "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-26",
   "parameters": "2.8 trillion parameters",
   "context_window": "1 million tokens",
   "license": null,
   "significance": "the first open system in the 3-trillion class and the largest open-weight model published to date",
   "organization_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "On the evening of 26 July 2026, Beijing-based Moonshot AI published the full weights of Kimi K3 for anyone to download \u2014 a day ahead of the 27 July date the company had signalled."
   ],
   "model_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "On the evening of 26 July 2026, Beijing-based Moonshot AI published the full weights of Kimi K3 for anyone to download \u2014 a day ahead of the 27 July date the company had signalled."
   ],
   "release_date_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "On the evening of 26 July 2026, Beijing-based Moonshot AI published the full weights of Kimi K3 for anyone to download \u2014 a day ahead of the 27 July date the company had signalled."
   ],
   "parameters_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "The release of the weights was, because of the number attached to it: 2.8 trillion parameters, which Moonshot describes in its technical write-up as the first open system in the 3-trillion class and the largest open-weight model published to date."
   ],
   "context_window_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "K3 ships with a one-million-token context window, native vision, and an explicit focus on extended autonomous agent runs \u2014 the kind of workload where a model has to hold a task, a codebase and its own prior decisions in view for hours rather than answer a question and stop."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "The Largest Open Model Ever Released Is Also One Almost Nobody Can Run",
    "The release of the weights was, because of the number attached to it: 2.8 trillion parameters, which Moonshot describes in its technical write-up as the first open system in the 3-trillion class and the largest open-weight model published to date."
   ]
  },
  {
   "url": "https://philosophersstone.ee/knowledge/glm-51-open-weight-model",
   "title": "GLM 5.1 Open-Weight Model",
   "published_at": null,
   "organization": "ZAI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First open-source model to beat closed-source frontier models on SWE-Bench Pro \u2014 58.4% SWE-Pro beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro; built a fully functional Linux desktop environment with 50+ working applications in a single 8-hour autonomous run.",
   "organization_evidence": [
    "# GLM 5.1 Open-Weight Model",
    "GLM 5.1 from ZAI (April 7 2026) is the first open-source model to beat closed-source frontier models on SWE-Bench Pro \u2014 754B MoE, MIT license, full weights on HuggingFace, 58.4% SWE-Pro beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM 5.1 Open-Weight Model",
    "GLM 5.1 from ZAI (April 7 2026) is the first open-source model to beat closed-source frontier models on SWE-Bench Pro \u2014 754B MoE, MIT license, full weights on HuggingFace, 58.4% SWE-Pro beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM 5.1 Open-Weight Model",
    "GLM 5.1 from ZAI (April 7 2026) is the first open-source model to beat closed-source frontier models on SWE-Bench Pro \u2014 754B MoE, MIT license, full weights on HuggingFace, 58.4% SWE-Pro beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro."
   ]
  },
  {
   "url": "https://venturebeat.com/technology/minimax-m3-debuts-eclipsing-gpt-5-5-and-gemini-3-1-pro-on-key-benchmark-performance-for-just-5-10-of-the-cost",
   "title": "MiniMax M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost",
   "published_at": "2026-06-01T16:10:05",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-08-30",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax-M3 introduces a level of comprehensive utility previously restricted to expensive, closed-source ecosystems, effectively shifting the baseline of open-weights systems while drastically minimizing the operational compute footprint required to execute complex development loops.",
   "organization_evidence": [
    "Big news in enterprise AI broke over the weekend as Chinese AI startup[ MiniMax released its highly anticipated M3 large language model](https://www.minimax.io/blog/minimax-m3) on Sunday evening Eastern time, pairing frontier-tier coding and agentic performance with a 1-million-token context window and native multimodality for a fraction of the cost of leading proprietary models, with pricing starting at just $20 per month under its new subscription token plans."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Big news in enterprise AI broke over the weekend as Chinese AI startup[ MiniMax released its highly anticipated M3 large language model](https://www.minimax.io/blog/minimax-m3) on Sunday evening Eastern time, pairing frontier-tier coding and agentic performance with a 1-million-token context window and native multimodality for a fraction of the cost of leading proprietary models, with pricing starting at just $20 per month under its new subscription token plans."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "By unifying these two historically separated frontier capabilities, M3 introduces a level of comprehensive utility previously restricted to expensive, closed-source ecosystems, effectively shifting the baseline of open-weights systems while drastically minimizing the operational compute footprint required to execute complex development loops."
   ]
  },
  {
   "url": "https://github.com/QwenLM/Qwen-AgentWorld/blob/main/README.md",
   "title": "Qwen-AgentWorld/README.md at main \u00b7 QwenLM/Qwen-AgentWorld",
   "published_at": null,
   "organization": "Qwen",
   "model": "Qwen-AgentWorld-35B-A3B",
   "release_date": "2026-06-24",
   "parameters": "35B total / 3B active",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Qwen-AgentWorld-35B-A3B shows +8.66 improvement over Qwen3.5-35B-A3B without LWM training.",
   "organization_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## News",
    "- **2026-06-24**: We release **Qwen-AgentWorld-35B-A3B** and **AgentWorldBench**. Read more on our [blog](https://qwen.ai/blog?id=qwen-agentworld) and the [technical report](http://arxiv.org/abs/2606.24597)."
   ],
   "model_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## News",
    "- **2026-06-24**: We release **Qwen-AgentWorld-35B-A3B** and **AgentWorldBench**. Read more on our [blog](https://qwen.ai/blog?id=qwen-agentworld) and the [technical report](http://arxiv.org/abs/2606.24597)."
   ],
   "release_date_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## News",
    "- **2026-06-24**: We release **Qwen-AgentWorld-35B-A3B** and **AgentWorldBench**. Read more on our [blog](https://qwen.ai/blog?id=qwen-agentworld) and the [technical report](http://arxiv.org/abs/2606.24597)."
   ],
   "parameters_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## Open-Source Release",
    "| Release | Description |",
    "| [Qwen-AgentWorld-35B-A3B](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B) | Language world model (MoE, 35B total / 3B active, 256K context) |"
   ],
   "context_window_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## Open-Source Release",
    "| Release | Description |",
    "| [Qwen-AgentWorld-35B-A3B](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B) | Language world model (MoE, 35B total / 3B active, 256K context) |"
   ],
   "license_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## License Agreement",
    "All open-weight models and AgentWorldBench are licensed under Apache 2.0."
   ],
   "significance_evidence": [
    "Welcome to the GitHub repository of Qwen-AgentWorld.",
    "## Performance",
    "Qwen-AgentWorld-397B-A17B achieves the highest overall score (58.71), outperforming all frontier proprietary models including GPT-5.4 (58.25).",
    "Qwen-AgentWorld-35B-A3B shows +8.66 improvement over Qwen3.5-35B-A3B without LWM training."
   ]
  },
  {
   "url": "https://eweek.com/news/mistral-3-launch",
   "title": "Mistral 3 Launches for Open AI Era",
   "published_at": "2025-12-03T10:33:16",
   "organization": "Mistral AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Introduces advancements in architecture and capability, being Mistral's first mixture-of-experts (MoE) model since its Mixtral series, achieves parity with the best instruction-tuned open-weight models on the market on general prompts, native multimodal processing, multilingual conversations beyond English and Chinese, and debuts at the #2 position in the open-source non-reasoning models category on the LMArena leaderboard.",
   "organization_evidence": [
    "# Mistral 3 Launches for Open AI Era",
    "French firm Mistral AI has the wind in its sails with the launch of the Mistral 3 model family."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral 3 Launches for Open AI Era",
    "## Frontier open model",
    "The centerpiece of the [release](https://mistral.ai/news/mistral-3) is Mistral Large 3, a model that introduces advancements in architecture and capability."
   ]
  },
  {
   "url": "https://medium.com/@AdithyaGiridharan/gemma-4s-real-breakthrough-isn-t-the-benchmarks-google-just-handed-enterprises-something-worth-7d658e41427c",
   "title": "Gemma 4\u2019s Real Breakthrough Isn\u2019t the Benchmarks Google just handed enterprises something worth\u2026",
   "published_at": "2026-04-03T17:06:48",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B model ranks third globally among open models on Arena AI\u2019s text leaderboard. The AIME 2026 math score jumped from 20.8% on Gemma 3 to 89.2%. LiveCodeBench went from 29.1% to 80.0%.",
   "organization_evidence": [
    "# Gemma 4\u2019s Real Breakthrough Isn\u2019t the Benchmarks Google just handed enterprises something worth more than a leaderboard position: a clean license",
    "On April 2, 2026, Google DeepMind released Gemma 4 \u2014 four open-weight models ranging from a 2-billion-effective-parameter edge model to a 31-billion-parameter dense flagship."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Gemma 4\u2019s Real Breakthrough Isn\u2019t the Benchmarks Google just handed enterprises something worth more than a leaderboard position: a clean license",
    "On April 2, 2026, Google DeepMind released Gemma 4 \u2014 four open-weight models ranging from a 2-billion-effective-parameter edge model to a 31-billion-parameter dense flagship."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Gemma 4\u2019s Real Breakthrough Isn\u2019t the Benchmarks Google just handed enterprises something worth more than a leaderboard position: a clean license",
    "The 31B model ranks third globally among open models on Arena AI\u2019s text leaderboard.",
    "The AIME 2026 math score jumped from 20.8% on Gemma 3 to 89.2%.",
    "LiveCodeBench went from 29.1% to 80.0%."
   ]
  },
  {
   "url": "https://codeclash.dev/blog/kimi-k3-open-source-frontier-model",
   "title": "Kimi K3: 2.8T Params, Open-Source, Taking on GPT-5.6",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "Ranked #1 on BrowseComp, #1 on Frontend Code Arena, and used its own early version to optimize GPU kernels during training.",
   "organization_evidence": [
    "# Kimi K3: 2.8T Params, Open-Source, Taking on GPT-5.6",
    "On July 16, 2026, Beijing-based Moonshot AI dropped **Kimi K3** \u2014 a 2.8-trillion-parameter model that instantly became the largest open-source model ever released."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "And unlike many \u201copen\u201d releases that stop at a research paper, Moonshot is shipping **full model weights by July 27**.",
    "Open weights drop July 27.",
    "For developers, the July 27 weight release is the real prize."
   ],
   "parameters_evidence": [
    "Let\u2019s put this in perspective. Here\u2019s how K3 stacks up against other open-weight models:",
    "| **Kimi K3** | 2.8T | 1M | July 2026 | $3 / $15 |",
    "On July 16, 2026, Beijing-based Moonshot AI dropped **Kimi K3** \u2014 a 2.8-trillion-parameter model that instantly became the largest open-source model ever released."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: 2.8T Params, Open-Source, Taking on GPT-5.6",
    "**TL;DR:** Kimi K3 is 75% larger than DeepSeek V4 Pro (2.8T vs 1.6T params), beats Claude Opus 4.8 across most benchmarks, and costs $3/M input tokens \u2014 matching Anthropic\u2019s Sonnet pricing."
   ]
  },
  {
   "url": "https://supercareer.co/blog/glm-5-2-review-2026",
   "title": "GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & What It Means for Your Coding Workflow",
   "published_at": "2026-06-15T12:49:35",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "~744B total parameters (~40B active)",
   "context_window": null,
   "license": null,
   "significance": "A usable 1M-token context window, two reasoning modes, and an MIT license. It's positioned mainly as a coding and agentic model.",
   "organization_evidence": [
    "# GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & What It Means for Your Coding Workflow",
    "Zhipu AI's GLM-5.2 launched June 13, 2026 with a usable 1M-token context, two thinking modes, and an MIT open-source license at ~10x lower cost than Claude or GPT-5."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Review (2026): Specs, Benchmarks, Pricing & What It Means for Your Coding Workflow",
    "Zhipu AI's GLM-5.2 launched June 13, 2026 with a usable 1M-token context, two thinking modes, and an MIT open-source license at ~10x lower cost than Claude or GPT-5."
   ],
   "parameters_evidence": [
    "What is GLM-5.2?",
    "An open-weight Mixture-of-Experts language model from Zhipu AI, released June 13, 2026, with ~744B total parameters (~40B active), a usable 1M-token context window, two reasoning modes, and an MIT license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What is GLM-5.2?",
    "An open-weight Mixture-of-Experts language model from Zhipu AI, released June 13, 2026, with ~744B total parameters (~40B active), a usable 1M-token context window, two reasoning modes, and an MIT license."
   ]
  },
  {
   "url": "https://byteiota.com/minimax-m3-open-weight-1m-context-frontier-model",
   "title": "MiniMax M3: Open-Weight 1M-Context Frontier Model Guide",
   "published_at": "2026-06-23T10:39:06",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-06-01",
   "parameters": "229.9 billion",
   "context_window": "1M-token",
   "license": null,
   "significance": "MiniMax M3 is the most interesting open-weight release of 2026 so far. It is the first model to credibly combine frontier coding performance, a fast 1M-token context window, and native multimodality in an open-weight package \u2014 and the cost profile is genuinely disruptive.",
   "organization_evidence": [
    "MiniMax M3 launched June 1 and broke a ceiling that has defined the open-source LLM space for two years: frontier-level coding performance, a genuine 1M-token context window, and native multimodality \u2014 all in one open-weight model you can download and run on your own hardware."
   ],
   "model_evidence": [
    "MiniMax M3 launched June 1 and broke a ceiling that has defined the open-source LLM space for two years: frontier-level coding performance, a genuine 1M-token context window, and native multimodality \u2014 all in one open-weight model you can download and run on your own hardware."
   ],
   "release_date_evidence": [
    "MiniMax M3 launched June 1 and broke a ceiling that has defined the open-source LLM space for two years: frontier-level coding performance, a genuine 1M-token context window, and native multimodality \u2014 all in one open-weight model you can download and run on your own hardware."
   ],
   "parameters_evidence": [
    "The architecture is a Mixture-of-Experts model with 229.9 billion total parameters and 9.8 billion active per token across 256 fine-grained experts."
   ],
   "context_window_evidence": [
    "MiniMax M3 launched June 1 and broke a ceiling that has defined the open-source LLM space for two years: frontier-level coding performance, a genuine 1M-token context window, and native multimodality \u2014 all in one open-weight model you can download and run on your own hardware."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "Bottom Line",
    "MiniMax M3 is the most interesting open-weight release of 2026 so far.",
    "It is the first model to credibly combine frontier coding performance, a fast 1M-token context window, and native multimodality in an open-weight package \u2014 and the cost profile is genuinely disruptive."
   ]
  },
  {
   "url": "https://edgen.tech/news/post/mira-muratis-inkling-debuts-as-best-open-source-model-in-the-west",
   "title": "Mira Murati's Inkling debuts as best open-source model in the West",
   "published_at": "2026-07-26T14:42:40",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "975-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "It scores 74.1% on MCP Atlas for agentic tool use, nearly 30 points above Nvidia's Nemotron.",
   "organization_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "Mira Murati's Thinking Machines Lab released Inkling, a 975-billion-parameter open-source model that is the best Western-trained open-weight AI model \u2014 but its narrow strengths and heavy censorship limit its appeal to most developers."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "## Rachel Kim](https://www.edgen.tech/author/news/rachel-kim)\u00b7Jul 26 2026, 14:42"
   ],
   "parameters_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "Mira Murati's Thinking Machines Lab released Inkling, a 975-billion-parameter open-source model that is the best Western-trained open-weight AI model \u2014 but its narrow strengths and heavy censorship limit its appeal to most developers."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "It scores 74.1% on MCP Atlas for agentic tool use, nearly 30 points above Nvidia's Nemotron"
   ]
  },
  {
   "url": "https://aichina.news/blog/allen-ai-s-molmoact2-a-new-contender-for-open-source-robotic-3iiw1n",
   "title": "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU \u2014 AICHINA.news Blog",
   "published_at": "2026-07-31T07:35:38",
   "organization": "Allen AI",
   "model": "MolmoAct2",
   "release_date": "2026-07-31",
   "parameters": null,
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "Designed specifically for robotic manipulation, it builds upon the highly regarded Molmo family of multimodal models.",
   "organization_evidence": [
    "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU",
    "Enter MolmoAct2, a Vision-Language-Action (VLA) model hosted on Modelers.cn that promises to push the boundaries of embodied AI."
   ],
   "model_evidence": [
    "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU",
    "Enter MolmoAct2, a Vision-Language-Action (VLA) model hosted on Modelers.cn that promises to push the boundaries of embodied AI."
   ],
   "release_date_evidence": [
    "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU",
    "models/allenai July 31, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU",
    "It is essential to note that as an Apache-2.0 licensed project, it offers broad commercial and research utility, but developers should be aware of the current lack of detailed technical documentation or performance benchmarks."
   ],
   "significance_evidence": [
    "Allen AI's MolmoAct2: A New Contender for Open-Source Robotic Intelligence on Ascend NPU",
    "Designed specifically for robotic manipulation, it builds upon the highly regarded Molmo family of multimodal models."
   ]
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "OpenBMB",
   "model": null,
   "release_date": "2026-05-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniCPM-V 4.6 1.3B](/models/minicpm-v4-6-1-3b/) | OpenBMB | 2026-05-11 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniCPM-V 4.6 1.3B](/models/minicpm-v4-6-1-3b/) | OpenBMB | 2026-05-11 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "IBM",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 30B](/models/granite-4-1-30b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 30B](/models/granite-4-1-30b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "IBM",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 3B](/models/granite-4-1-3b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 3B](/models/granite-4-1-3b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "IBM",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 8B](/models/granite-4-1-8b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Granite 4.1 8B](/models/granite-4-1-8b/) | IBM | 2026-04-29 | Major Release | 131K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Mistral Medium 3.5](/models/mistral-medium-3-5/) | Mistral | 2026-04-29 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Mistral Medium 3.5](/models/mistral-medium-3-5/) | Mistral | 2026-04-29 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nemotron 3 Nano Omni 30B A3B Reasoning](/models/nemotron-3-nano-omni-30b-a3b/) | NVIDIA | 2026-04-29 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nemotron 3 Nano Omni 30B A3B Reasoning](/models/nemotron-3-nano-omni-30b-a3b/) | NVIDIA | 2026-04-29 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V4 Flash](/models/deepseek-v4-flash/) | DeepSeek | 2026-04-24 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V4 Flash](/models/deepseek-v4-flash/) | DeepSeek | 2026-04-24 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V4 Pro](/models/deepseek-v4-pro/) | DeepSeek | 2026-04-24 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V4 Pro](/models/deepseek-v4-pro/) | DeepSeek | 2026-04-24 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "InclusionAI",
   "model": null,
   "release_date": "2026-04-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Ling-2.6-1T](/models/ling-2-6-1t/) | InclusionAI | 2026-04-23 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Ling-2.6-1T](/models/ling-2-6-1t/) | InclusionAI | 2026-04-23 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Tencent",
   "model": null,
   "release_date": "2026-04-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Hy3-preview](/models/hy3/) | Tencent | 2026-04-23 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Hy3-preview](/models/hy3/) | Tencent | 2026-04-23 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.6 27B](/models/qwen3-6-27b/) | Alibaba | 2026-04-22 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.6 27B](/models/qwen3-6-27b/) | Alibaba | 2026-04-22 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Xiaomi",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiMo-V2.5](/models/mimo-v2-5-0424/) | Xiaomi | 2026-04-22 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiMo-V2.5](/models/mimo-v2-5-0424/) | Xiaomi | 2026-04-22 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Xiaomi",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiMo-V2.5-Pro](/models/mimo-v2-5-pro/) | Xiaomi | 2026-04-22 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiMo-V2.5-Pro](/models/mimo-v2-5-pro/) | Xiaomi | 2026-04-22 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "InclusionAI",
   "model": null,
   "release_date": "2026-04-21",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Ling 2.6 Flash](/models/ling-2-6-flash/) | InclusionAI | 2026-04-21 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Ling 2.6 Flash](/models/ling-2-6-flash/) | InclusionAI | 2026-04-21 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Kimi",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2.6](/models/kimi-k2-6/) | Kimi | 2026-04-20 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2.6](/models/kimi-k2-6/) | Kimi | 2026-04-20 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.6 35B A3B](/models/qwen3-6-35b-a3b/) | Alibaba | 2026-04-16 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.6 35B A3B](/models/qwen3-6-35b-a3b/) | Alibaba | 2026-04-16 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "LG AI Research",
   "model": null,
   "release_date": "2026-04-09",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [EXAONE 4.5 33B](/models/exaone-4-5-33b/) | LG AI Research | 2026-04-09 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [EXAONE 4.5 33B](/models/exaone-4-5-33b/) | LG AI Research | 2026-04-09 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Z AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-5.1](/models/glm-5-1/) | Z AI | 2026-04-07 | Major Release | 200K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-5.1](/models/glm-5-1/) | Z AI | 2026-04-07 | Major Release | 200K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 E4B](/models/gemma-4-e4b/) | Google | 2026-04-03 | Major Release | 128K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 E4B](/models/gemma-4-e4b/) | Google | 2026-04-03 | Major Release | 128K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 26B A4B](/models/gemma-4-26b-a4b/) | Google | 2026-04-02 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 26B A4B](/models/gemma-4-26b-a4b/) | Google | 2026-04-02 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 31B](/models/gemma-4-31b/) | Google | 2026-04-02 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 31B](/models/gemma-4-31b/) | Google | 2026-04-02 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 E2B](/models/gemma-4-e2b/) | Google | 2026-04-02 | Major Release | 128K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Gemma 4 E2B](/models/gemma-4-e2b/) | Google | 2026-04-02 | Major Release | 128K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Arcee AI",
   "model": null,
   "release_date": "2026-04-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Trinity Large Thinking](/models/trinity-large-thinking/) | Arcee AI | 2026-04-01 | Major Release | 512K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Trinity Large Thinking](/models/trinity-large-thinking/) | Arcee AI | 2026-04-01 | Major Release | 512K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-03-19",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nemotron Cascade 2 30B A3B](/models/nemotron-cascade-2-30b-a3b/) | NVIDIA | 2026-03-19 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nemotron Cascade 2 30B A3B](/models/nemotron-cascade-2-30b-a3b/) | NVIDIA | 2026-03-19 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniMax-M2.7](/models/minimax-m2-7/) | MiniMax | 2026-03-18 | Major Release | 204K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniMax-M2.7](/models/minimax-m2-7/) | MiniMax | 2026-03-18 | Major Release | 204K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Mistral Small 4](/models/mistral-small-4/) | Mistral | 2026-03-16 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Mistral Small 4](/models/mistral-small-4/) | Mistral | 2026-03-16 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [NVIDIA Nemotron 3 Nano 4B](/models/nvidia-nemotron-3-nano-4b/) | NVIDIA | 2026-03-16 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [NVIDIA Nemotron 3 Nano 4B](/models/nvidia-nemotron-3-nano-4b/) | NVIDIA | 2026-03-16 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-03-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [NVIDIA Nemotron 3 Super 120B A12B](/models/nvidia-nemotron-3-super-120b-a12b/) | NVIDIA | 2026-03-11 | Major Release | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [NVIDIA Nemotron 3 Super 120B A12B](/models/nvidia-nemotron-3-super-120b-a12b/) | NVIDIA | 2026-03-11 | Major Release | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Sarvam",
   "model": null,
   "release_date": "2026-03-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Sarvam 105B](/models/sarvam-105b/) | Sarvam | 2026-03-06 | Major Release | 128K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Sarvam 105B](/models/sarvam-105b/) | Sarvam | 2026-03-06 | Major Release | 128K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Sarvam",
   "model": null,
   "release_date": "2026-03-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Sarvam 30B](/models/sarvam-30b/) | Sarvam | 2026-03-06 | Major Release | 65K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Sarvam 30B](/models/sarvam-30b/) | Sarvam | 2026-03-06 | Major Release | 65K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 0.8B](/models/qwen3-5-0-8b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 0.8B](/models/qwen3-5-0-8b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 2B](/models/qwen3-5-2b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 2B](/models/qwen3-5-2b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 4B](/models/qwen3-5-4b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 4B](/models/qwen3-5-4b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-03-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 9B](/models/qwen3-5-9b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 9B](/models/qwen3-5-9b/) | Alibaba | 2026-03-02 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Liquid AI",
   "model": null,
   "release_date": "2026-02-25",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2 24B A2B](/models/lfm2-24b-a2b/) | Liquid AI | 2026-02-25 | Major Release | 32K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2 24B A2B](/models/lfm2-24b-a2b/) | Liquid AI | 2026-02-25 | Major Release | 32K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 122B A10B](/models/qwen3-5-122b-a10b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 122B A10B](/models/qwen3-5-122b-a10b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 27B](/models/qwen3-5-27b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 27B](/models/qwen3-5-27b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 35B A3B](/models/qwen3-5-35b-a3b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 35B A3B](/models/qwen3-5-35b-a3b/) | Alibaba | 2026-02-24 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Cohere",
   "model": null,
   "release_date": "2026-02-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Tiny Aya Global](/models/tiny-aya-global/) | Cohere | 2026-02-17 | Major Release | 8K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Tiny Aya Global](/models/tiny-aya-global/) | Cohere | 2026-02-17 | Major Release | 8K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 397B A17B](/models/qwen3-5-397b-a17b/) | Alibaba | 2026-02-16 | Major Release | 262K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3.5 397B A17B](/models/qwen3-5-397b-a17b/) | Alibaba | 2026-02-16 | Major Release | 262K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open-weight MoE with a 1M+ token context window and strong coding.",
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V3.2](/models/deepseek-deepseek-v3-2/) Open-weight MoE with a 1M+ token context window and strong coding. | DeepSeek | 2026-02-12 | Update | 1M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V3.2](/models/deepseek-deepseek-v3-2/) Open-weight MoE with a 1M+ token context window and strong coding. | DeepSeek | 2026-02-12 | Update | 1M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [DeepSeek V3.2](/models/deepseek-deepseek-v3-2/) Open-weight MoE with a 1M+ token context window and strong coding. | DeepSeek | 2026-02-12 | Update | 1M tokens | Open Source |"
   ]
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniMax-M2.5](/models/minimax-m2-5/) | MiniMax | 2026-02-12 | Major Release | 204K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [MiniMax-M2.5](/models/minimax-m2-5/) | MiniMax | 2026-02-12 | Major Release | 204K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Nanbeige",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nanbeige4.1-3B](/models/nanbeige4-1-3b/) | Nanbeige | 2026-02-11 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Nanbeige4.1-3B](/models/nanbeige4-1-3b/) | Nanbeige | 2026-02-11 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Z AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-5](/models/glm-5/) | Z AI | 2026-02-11 | Major Release | 200K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-5](/models/glm-5/) | Z AI | 2026-02-11 | Major Release | 200K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3 Coder Next](/models/qwen3-coder-next/) | Alibaba | 2026-02-03 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Qwen3 Coder Next](/models/qwen3-coder-next/) | Alibaba | 2026-02-03 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "StepFun",
   "model": null,
   "release_date": "2026-02-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Step 3.5 Flash](/models/step-3-5-flash-0202/) | StepFun | 2026-02-02 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Step 3.5 Flash](/models/step-3-5-flash-0202/) | StepFun | 2026-02-02 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "LongCat",
   "model": null,
   "release_date": "2026-01-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LongCat Flash Lite](/models/longcat-flash-lite/) | LongCat | 2026-01-28 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LongCat Flash Lite](/models/longcat-flash-lite/) | LongCat | 2026-01-28 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Kimi",
   "model": null,
   "release_date": "2026-01-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2.5](/models/kimi-k2-5/) | Kimi | 2026-01-27 | Major Release | 256K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2.5](/models/kimi-k2-5/) | Kimi | 2026-01-27 | Major Release | 256K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-01-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Moonshot's open-weight frontier MoE with strong agentic benchmarks.",
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2](/models/moonshot-ai-kimi-k2/) Moonshot's open-weight frontier MoE with strong agentic benchmarks. | Moonshot AI | 2026-01-20 | Major Release | 2M tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2](/models/moonshot-ai-kimi-k2/) Moonshot's open-weight frontier MoE with strong agentic benchmarks. | Moonshot AI | 2026-01-20 | Major Release | 2M tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Kimi K2](/models/moonshot-ai-kimi-k2/) Moonshot's open-weight frontier MoE with strong agentic benchmarks. | Moonshot AI | 2026-01-20 | Major Release | 2M tokens | Open Source |"
   ]
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Liquid AI",
   "model": null,
   "release_date": "2026-01-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2.5-1.2B-Thinking](/models/lfm2-5-1-2b-thinking/) | Liquid AI | 2026-01-20 | Major Release | 32K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2.5-1.2B-Thinking](/models/lfm2-5-1-2b-thinking/) | Liquid AI | 2026-01-20 | Major Release | 32K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "StepFun",
   "model": null,
   "release_date": "2026-01-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Step3 VL 10B](/models/step-3-vl-10b/) | StepFun | 2026-01-20 | Major Release | 65K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [Step3 VL 10B](/models/step-3-vl-10b/) | StepFun | 2026-01-20 | Major Release | 65K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Z AI",
   "model": null,
   "release_date": "2026-01-19",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-4.7-Flash](/models/glm-4-7-flash/) | Z AI | 2026-01-19 | Major Release | 200K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [GLM-4.7-Flash](/models/glm-4-7-flash/) | Z AI | 2026-01-19 | Major Release | 200K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://aiflashreport.com/models",
   "title": "All AI Models (120) \u2014 AI Flash Report",
   "published_at": null,
   "organization": "Liquid AI",
   "model": null,
   "release_date": "2026-01-05",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2.5-1.2B-Instruct](/models/lfm2-5-1-2b-instruct/) | Liquid AI | 2026-01-05 | Major Release | 32K tokens | Open Source |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# All AI models",
    "120 frontier model releases tracked. Filter by company, category, year, availability, or model type.",
    "| Model | Company | Released | Category | Context | Availability |",
    "| [LFM2.5-1.2B-Instruct](/models/lfm2-5-1-2b-instruct/) | Liquid AI | 2026-01-05 | Major Release | 32K tokens | Open Source |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://bitcoinethereumnews.com/tech/google-jumps-back-into-the-open-source-ai-race-with-gemma-4",
   "title": "Google Jumps Back Into the Open Source AI Race With Gemma 4",
   "published_at": "2026-04-02T19:49:36",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 globally for the 31B model; positions itself as the strongest American contender against DeepSeek, Qwen, and other Chinese leaders; built from the same world-class research as Gemini 3, bringing breakthrough intelligence for advanced reasoning and agentic workflows.",
   "organization_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "Google dropped Gemma 4, a family of open models under the Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "\u2014 Google (@Google) [April 2, 2026](https://twitter.com/Google/status/2039736220834480233?ref_src=twsrc%5Etfw)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Jumps Back Into the Open Source AI Race With Gemma 4",
    "The four-model lineup spans phones to data centers with the 31B model ranking #3 globally already.",
    "U.S. open-source AI gets a needed boost, as Gemma 4\u2014backed by DeepMind\u2014positions itself as the strongest American contender against DeepSeek, Qwen, and other Chinese leaders."
   ]
  },
  {
   "url": "https://swfte.com/zh/ai/models/zhipu-glm-5-2",
   "title": "GLM-5.2 \u2014 Pricing, Benchmarks & Specs | AI Model Directory",
   "published_at": "2026-08-04T22:03:15",
   "organization": "Z.ai (Zhipu AI)",
   "model": null,
   "release_date": "2026-06-12",
   "parameters": "744B MoE / ~40B active",
   "context_window": null,
   "license": null,
   "significance": "SWE-bench Pro 62.1%",
   "organization_evidence": [
    "# GLM-5.2",
    "Z.ai (Zhipu AI)open-sourceOpen Source",
    "GLM-5.2 is a open-source AI model by Z.ai (Zhipu AI), released on June 12, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2",
    "GLM-5.2 is a open-source AI model by Z.ai (Zhipu AI), released on June 12, 2026."
   ],
   "parameters_evidence": [
    "# GLM-5.2",
    "Z.ai's 13 Jun 2026 open-weight flagship, and the model that held the top open-weight slot until Kimi K3 landed a month later. 744B MoE / ~40B active, MIT-licensed weights on Hugging Face, 1M context, 131K max output."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2",
    "The headline result is SWE-bench Pro 62.1%, which beats GPT-5.5's 58.6%: an MIT-licensed model you can self-host outscoring a $5/$30 US flagship on agentic coding."
   ]
  },
  {
   "url": "https://wan27.org/blog/laguna-s-2-1",
   "title": "Laguna S 2.1 Released: Poolside Drops Open-Weight Coding Model That Beats Rivals 14x Its Size | Wan 2.7",
   "published_at": "2026-07-23T00:00:00",
   "organization": "Poolside",
   "model": null,
   "release_date": "2026-07-21",
   "parameters": "118B total parameters and 8 billion active parameters per token",
   "context_window": null,
   "license": null,
   "significance": "Scores 70.2% on Terminal-Bench 2.1 in thinking mode, outperforming DeepSeek V4 Pro Max and other large models while operating with high efficiency.",
   "organization_evidence": [
    "Laguna S 2.1 Released: Poolside Drops Open-Weight Coding Model That Beats Rivals 14x Its Size",
    "Poolside released Laguna S 2.1, a 118B MoE coding model with 8B active params. Scores 70.2% on Terminal-Bench 2.1, beats 1.6T rivals, free on OpenRouter."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Laguna S 2.1 Released: Poolside Drops Open-Weight Coding Model That Beats Rivals 14x Its Size",
    "On July 21, 2026, an open-weight coding model that activates only 8 billion parameters per token posted a higher Terminal-Bench 2.1 score than DeepSeek V4 Pro Max \u2014 a 1.6-trillion-parameter system with 49 billion active parameters per token."
   ],
   "parameters_evidence": [
    "Laguna S 2.1 Released: Poolside Drops Open-Weight Coding Model That Beats Rivals 14x Its Size",
    "Laguna S 2.1 is an open-weight **Mixture-of-Experts model with 118 billion total parameters and 8 billion active parameters per token**, designed for agentic coding and long-horizon software engineering work."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Laguna S 2.1 Released: Poolside Drops Open-Weight Coding Model That Beats Rivals 14x Its Size",
    "Poolside released Laguna S 2.1, a 118B MoE coding model with 8B active params. Scores 70.2% on Terminal-Bench 2.1, beats 1.6T rivals, free on OpenRouter."
   ]
  },
  {
   "url": "https://ainewshome.com/article/deepseek-v4-open-source-launch",
   "title": "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models",
   "published_at": "2026-04-26T18:44:53",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6-trillion-parameter (49 billion active)",
   "context_window": null,
   "license": null,
   "significance": "Tops the table on coding (LiveCodeBench: 93.5%, Codeforces rating: 3,206, SWE-Verified: 80.6%), with inference FLOPs at 27% and KV cache at 10% of DeepSeek V3.2.",
   "organization_evidence": [
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models",
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "parameters_evidence": [
    "**DeepSeek-V4-Pro** is a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion parameters activated per token.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On **coding**, V4-Pro tops the table.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ]
  },
  {
   "url": "https://ainewshome.com/article/deepseek-v4-open-source-launch",
   "title": "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models",
   "published_at": "2026-04-26T18:44:53",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6-trillion-parameter total / 49 billion active parameters",
   "context_window": "1 million tokens",
   "license": "MIT license",
   "significance": "Tops the coding benchmark table, matching or exceeding several closed frontier models on coding tasks while trailing by 3 to 6 months on knowledge and reasoning.",
   "organization_evidence": [
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "model_evidence": [
    "**DeepSeek-V4-Pro** is a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion parameters activated per token.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "release_date_evidence": [
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "parameters_evidence": [
    "**DeepSeek-V4-Pro** is a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion parameters activated per token.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "context_window_evidence": [
    "Both support a one-million-token context window.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "license_evidence": [
    "Both are released under the MIT license, with weights live on Hugging Face.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "significance_evidence": [
    "The headline benchmark numbers, as reported by DeepSeek, place V4-Pro ahead of Claude Opus 4.6, GPT-5.4, and Gemini-3.1-Pro on coding contests, within 0.2 points on SWE-Verified, and 3 to 6 months behind on knowledge and pure reasoning.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ]
  },
  {
   "url": "https://ainewshome.com/article/deepseek-v4-open-source-launch",
   "title": "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models",
   "published_at": "2026-04-26T18:44:53",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284-billion-parameter total / 13 billion active parameters",
   "context_window": "1 million tokens",
   "license": "MIT license",
   "significance": "A sibling model offering high efficiency and low pricing as part of the V4 series preview.",
   "organization_evidence": [
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "model_evidence": [
    "**DeepSeek-V4-Flash** is a 284-billion-parameter sibling with 13 billion activated.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "release_date_evidence": [
    "DeepSeek released the preview version of its V4 model series on April 24, 2026 \u2014 the same Friday that Google disclosed it would invest up to $40 billion in Anthropic.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "parameters_evidence": [
    "**DeepSeek-V4-Flash** is a 284-billion-parameter sibling with 13 billion activated.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "context_window_evidence": [
    "Both support a one-million-token context window.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ],
   "license_evidence": [
    "Both are released under the MIT license, with weights live on Hugging Face.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
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    "**DeepSeek-V4-Flash** is a 284-billion-parameter sibling with 13 billion activated.",
    "DeepSeek V4 ships with open weights and Huawei Ascend support, narrowing the gap with closed AI models"
   ]
  },
  {
   "url": "https://ai2.work/blog/alibaba-s-three-model-blitz-fuels-the-open-weight-ai-arms-race",
   "title": "Alibaba's Three-Model Blitz Fuels the Open-Weight AI Arms Race",
   "published_at": "2026-04-03T00:00:00",
   "organization": "Alibaba",
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   "organization_evidence": [
    "Alibaba's Three-Model Blitz Fuels the Open-Weight AI Arms Race",
    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "First came Qwen3.5 on February 16, 2026 \u2014 a 397-billion-parameter open-weight behemoth that processes text, images, and video natively, supports 201 languages, and runs at inference speeds that would have seemed implausible eighteen months ago."
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   "release_date_evidence": [
    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "First came Qwen3.5 on February 16, 2026 \u2014 a 397-billion-parameter open-weight behemoth that processes text, images, and video natively, supports 201 languages, and runs at inference speeds that would have seemed implausible eighteen months ago."
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    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "First came Qwen3.5 on February 16, 2026 \u2014 a 397-billion-parameter open-weight behemoth that processes text, images, and video natively, supports 201 languages, and runs at inference speeds that would have seemed implausible eighteen months ago."
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    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "First came Qwen3.5 on February 16, 2026 \u2014 a 397-billion-parameter open-weight behemoth that processes text, images, and video natively, supports 201 languages, and runs at inference speeds that would have seemed implausible eighteen months ago.",
    "Qwen3.5 (February 16, 2026) is the flagship open-weight entry \u2014 a 397B-A17B Mixture-of-Experts model that activates only 17 billion parameters per forward pass, making local deployment far more practical than the raw parameter count suggests."
   ]
  },
  {
   "url": "https://ai2.work/blog/alibaba-s-three-model-blitz-fuels-the-open-weight-ai-arms-race",
   "title": "Alibaba's Three-Model Blitz Fuels the Open-Weight AI Arms Race",
   "published_at": "2026-04-03T00:00:00",
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    "Alibaba's Three-Model Blitz Fuels the Open-Weight AI Arms Race",
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    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "Qwen3.5-Omni (March 30, 2026) goes further into the real-time interaction layer."
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    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "Qwen3.5-Omni (March 30, 2026) goes further into the real-time interaction layer.",
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    "Alibaba's Three-Model Blitz Fuels the Open-Weight AI Arms Race",
    "Then came Qwen3.5-Omni on March 30, a Thinker-Talker architecture capable of real-time speech, multi-hour audio comprehension, and video understanding that, by Alibaba's own benchmarks, surpasses Gemini 3.1 Pro in audio reasoning.",
    "Inside the Three-Model Blitz: What Each Release Actually Does",
    "Qwen3.5-Omni (March 30, 2026) goes further into the real-time interaction layer."
   ]
  },
  {
   "url": "https://datanorth.ai/news/mistral-medium-3-5-release",
   "title": "Mistral AI Releases Medium 3.5",
   "published_at": "2026-05-01T08:18:09",
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   "organization_evidence": [
    "# Mistral AI Releases Medium 3.5",
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   "release_date_evidence": [
    "# Mistral AI Releases Medium 3.5",
    "*Publication date: May 1, 2026*"
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   "parameters_evidence": [
    "# Mistral AI Releases Medium 3.5",
    "Mistral AI has released Mistral Medium 3.5, a 128-billion parameter dense language model with a 256,000-token context window and open weights under a modified MIT license."
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   "significance_evidence": [
    "# Mistral AI Releases Medium 3.5",
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  {
   "url": "https://prismnews.com/news/google-deepmind-launches-gemma-4-under-apache-20-opening",
   "title": "Google DeepMind Launches Gemma 4 Under Apache 2.0, Opening Commercial Use",
   "published_at": "2026-04-03T12:02:27",
   "organization": "Google DeepMind",
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   "release_date": "2026-04-02",
   "parameters": "Effective 2B (E2B) and Effective 4B (E4B), 26B, 31B",
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   "significance": "The 31B model ranks as the number three open model globally on the Arena AI text leaderboard, with the 26B MoE variant at number six.",
   "organization_evidence": [
    "That barrier fell on April 2 when Google DeepMind released Gemma 4, a family of four open-weight models under the Apache 2.0 license, stripping away the custom clauses, commercial-use carve-outs, and redistribution limits that had kept organizations on the sidelines."
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   "release_date_evidence": [
    "That barrier fell on April 2 when Google DeepMind released Gemma 4, a family of four open-weight models under the Apache 2.0 license, stripping away the custom clauses, commercial-use carve-outs, and redistribution limits that had kept organizations on the sidelines."
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   "parameters_evidence": [
    "Google released four variants to cover the full deployment spectrum: an Effective 2B (E2B) and Effective 4B (E4B) tuned for mobile and edge hardware, a 26B Mixture-of-Experts model for mid-range server workloads, and a 31B dense model for high-performance inference on developer workstations and cloud infrastructure."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "The 31B model ranks as the number three open model globally on the Arena AI text leaderboard, with the 26B MoE variant at number six."
   ]
  },
  {
   "url": "https://computing.news/2026-07-20-china-moonshot-ai-releases-open-source-model.html",
   "title": "China\u2019s Moonshot AI Releases Open-Source Kimi K3 Model, Challenging Anthropic\u2019s Opus",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-17",
   "parameters": null,
   "context_window": null,
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   "significance": "Comparable to Anthropic's Claude Opus in performance on internal benchmarks, ranked first on the Frontend code benchmark on Arena, and achieved scores within 2% of Claude Opus on major benchmarks including MMLU, HumanEval, and GSM8K.",
   "organization_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
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   "model_evidence": null,
   "release_date_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "The model is described by Moonshot AI as comparable to Anthropic\u2019s Claude Opus in performance, based on internal benchmarks.",
    "Moonshot AI\u2019s Kimi K3 now ranks first on the Frontend code benchmark on Arena, according to the report.",
    "The company stated that Kimi K3 achieved scores within 2% of Claude Opus on major benchmarks including MMLU, HumanEval, and GSM8K."
   ]
  },
  {
   "url": "https://wowhow.hashnode.dev/glm-5-1-zai-open-source-swe-bench-pro-developer-guide-2026",
   "title": "GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026)",
   "published_at": "2026-04-24T18:07:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
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   "significance": "First Open-Source Model to Top SWE-bench Pro",
   "organization_evidence": [
    "# GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026)",
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   "release_date_evidence": [
    "# GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026)",
    "On April 7, 2026, Z.ai dropped GLM-5.1 \u2014 a 754-billion-parameter open-weight model that, for nine days, held the top spot on SWE-bench Pro with a score of 58.4%, beating GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%)."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "# GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026)",
    "On April 7, 2026, Z.ai dropped GLM-5.1 \u2014 a 754-billion-parameter open-weight model that, for nine days, held the top spot on SWE-bench Pro with a score of 58.4%, beating GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%)."
   ]
  },
  {
   "url": "https://zdnet.com/article/ai-model-release-tracker",
   "title": "Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI",
   "published_at": "2026-08-11T13:00:00",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A go-to workhorse for high-volume agentic tasks, faster than similar models, customizable, and capable of running locally.",
   "organization_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3.5 Lightning",
    "Nvidia | Aug. 11, 2026"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3.5 Lightning",
    "Nvidia | Aug. 11, 2026"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3.5 Lightning",
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    "Nvidia framed its [new model](https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/), part of its existing Nemotron 3 family, as a go-to workhorse for \"high-volume\" agentic tasks."
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  {
   "url": "https://zdnet.com/article/ai-model-release-tracker",
   "title": "Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI",
   "published_at": "2026-08-11T13:00:00",
   "organization": "Moonshot",
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   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Largest open-source model on the market; topped Anthropic's Fable 5 on the Arena benchmark for front-end coding.",
   "organization_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Kimi K3",
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   "model_evidence": null,
   "release_date_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Kimi K3",
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    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Kimi K3",
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   "significance_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Kimi K3",
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    "At 2.8 trillion parameters, Kimi K3 is the largest open-source model on the market, designed for \"long-horizon coding, knowledge work, and reasoning,\" according to [Moonshot's announcement](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart).",
    "Most notably, Kimi K3 [topped Anthropic's Fable 5](https://x.com/arena/status/2077824029126504525?) on the Arena benchmark for front-end coding, which measures complex agentic coding tasks."
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  {
   "url": "https://zdnet.com/article/ai-model-release-tracker",
   "title": "Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI",
   "published_at": "2026-08-11T13:00:00",
   "organization": "Thinking Machines",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "First model from Thinking Machines, open-weight and generalist, scoring similarly to other open models on agentic coding and tool use, and slightly better on design and audio.",
   "organization_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Inkling",
    "Thinking Machines | July 15, 2026"
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   "release_date_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Inkling",
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   "context_window_evidence": null,
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    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Inkling",
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    "Inkling\n\nThinking Machines | July 15, 2026\n\n**What it does:** [Inkling](https://thinkingmachines.ai/news/introducing-inkling/) is the first model from Thinking Machines, a startup founded by several ex-OpenAI executives including Mira Murati."
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  },
  {
   "url": "https://zdnet.com/article/ai-model-release-tracker",
   "title": "Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI",
   "published_at": "2026-08-11T13:00:00",
   "organization": "Nvidia",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": "30B",
   "context_window": null,
   "license": null,
   "significance": "Provides agents with multimodal input, perceiving and reasoning across visual, audio, and textual inputs within a single shared perception-to-action loop.",
   "organization_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3 Nano Omni",
    "Nvidia | April 28, 2026"
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   "release_date_evidence": [
    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3 Nano Omni",
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    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3 Nano Omni",
    "Nvidia | April 28, 2026",
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   "context_window_evidence": null,
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    "Here are the biggest model releases of 2026 so far and what to know about them.",
    "## Nemotron 3 Nano Omni",
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  },
  {
   "url": "https://aichina.news/blog/allen-institute-for-ai-drops-olmo-2-32b-a-high-quality-rlvr-fine-sg1fdg",
   "title": "Allen Institute for AI Drops OLMo-2-32B: A High-Quality, RLVR-Fine-Tuned Model with Ascend NPU Support \u2014 AICHINA.news Blog",
   "published_at": "2026-07-21T14:14:58",
   "organization": "Allen Institute for AI",
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   "release_date": "2026-07-21",
   "parameters": "32-billion-parameter",
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   "significance": "achieves a commendable balance between strong reasoning capabilities and instruction-following precision, fine-tuned using Reinforcement Learning from Verifiable Rewards (RLVR) to enhance factual accuracy and curb hallucinations",
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    "Allen Institute for AI Drops OLMo-2-32B: A High-Quality, RLVR-Fine-Tuned Model with Ascend NPU Support",
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   "release_date_evidence": [
    "Allen Institute for AI Drops OLMo-2-32B: A High-Quality, RLVR-Fine-Tuned Model with Ascend NPU Support",
    "models/allenai July 21, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": [
    "Allen Institute for AI Drops OLMo-2-32B: A High-Quality, RLVR-Fine-Tuned Model with Ascend NPU Support",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Allen Institute for AI Drops OLMo-2-32B: A High-Quality, RLVR-Fine-Tuned Model with Ascend NPU Support",
    "Designed to offer a high-quality, open-source alternative for developers, this 32-billion-parameter Large Language Model (LLM) achieves a commendable balance between strong reasoning capabilities and instruction-following precision."
   ]
  },
  {
   "url": "https://codersera.com/blog/ornith-1-0-vs-qwen-3-7-coding-2026",
   "title": "Ornith 1.0 vs Qwen 3.7: Best Local Coding Model 2026",
   "published_at": "2026-06-30T05:11:23",
   "organization": "DeepReinforce",
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   "release_date": "2026-06-25",
   "parameters": "9B to 397B",
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   "significance": "Posts 82.4 on SWE-Bench Verified and 77.5 on Terminal-Bench 2.1 - enough to claim it surpasses Claude Opus 4.7.",
   "organization_evidence": [
    "# Ornith 1.0 vs Qwen 3.7: Best Local Coding Model in 2026?",
    "Ornith 1.0 (DeepReinforce, MIT-licensed, shipped 25 June 2026) is the one you actually run locally \u2014 open weights from 9B to 397B, post-trained on Qwen 3.5 and Gemma 4."
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   "release_date_evidence": [
    "# Ornith 1.0 vs Qwen 3.7: Best Local Coding Model in 2026?",
    "Ornith 1.0 (DeepReinforce, MIT-licensed, shipped 25 June 2026) is the one you actually run locally \u2014 open weights from 9B to 397B, post-trained on Qwen 3.5 and Gemma 4."
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   "parameters_evidence": [
    "# Ornith 1.0 vs Qwen 3.7: Best Local Coding Model in 2026?",
    "Ornith 1.0 (DeepReinforce, MIT-licensed, shipped 25 June 2026) is the one you actually run locally \u2014 open weights from 9B to 397B, post-trained on Qwen 3.5 and Gemma 4."
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   "significance_evidence": [
    "# Ornith 1.0 vs Qwen 3.7: Best Local Coding Model in 2026?",
    "On 25 June 2026, a relatively small lab called **DeepReinforce** dropped **Ornith 1.0** on Hugging Face: an MIT-licensed family of agentic coding models that, by its own numbers, posts **82.4 on SWE-Bench Verified** and **77.5 on Terminal-Bench 2.1** \u2014 enough to claim it surpasses Claude Opus 4.7."
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  },
  {
   "url": "https://ai-primer.com/engineer/stories/mistral-releases-medium-3-5-128b-256k-work-mode",
   "title": "Mistral releases Medium 3.5 with 128B weights, 256K context, and Work Mode",
   "published_at": "2026-04-29T00:00:00",
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   "significance": "flagship merged model for instruction following, reasoning, and coding with text and image input, text output, 256K context window, configurable reasoning effort, and remote coding agents in Vibe",
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    "release",
    "## Mistral Medium 3.5",
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  },
  {
   "url": "https://0to1log.com/en/news/2026-04-03-research-digest",
   "title": "Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s",
   "published_at": "2026-04-03T09:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": "2B, 4B, 26B, 31B",
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   "license": null,
   "significance": "Effective 2B (E2B), Effective 4B (E4B) for devices, plus 26B Mixture of Experts and 31B Dense, all under Apache 2.0. Gemma 31B debuts at No. 3 and 26B MoE at No. 6 on the Arena AI open-model leaderboard.",
   "organization_evidence": [
    "# Latest AI News",
    "# Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s",
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   "model_evidence": null,
   "release_date_evidence": [
    "# Latest AI News",
    "### Google Gemma 4 (Apache 2.0, edge-to-workstation lineup)",
    "[1](https://arstechnica.com/ai/2026/04/google-announces-gemma-4-open-ai-models-switches-to-apache-2-0-license/)"
   ],
   "parameters_evidence": [
    "# Latest AI News",
    "# Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s",
    "Google releases Gemma 4 in four sizes \u2014 Effective 2B (E2B), Effective 4B (E4B) for devices, plus 26B Mixture of Experts (MoE, a model type that activates only a subset of parameters per token) and 31B Dense \u2014 all under the permissive Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Latest AI News",
    "# Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s",
    "Google releases Gemma 4 in four sizes \u2014 Effective 2B (E2B), Effective 4B (E4B) for devices, plus 26B Mixture of Experts (MoE, a model type that activates only a subset of parameters per token) and 31B Dense \u2014 all under the permissive Apache 2.0 license.",
    "On the Arena AI open-model leaderboard, Gemma 31B debuts at No. 3 and the 26B MoE at No. 6, with Google claiming they outperform models up to 20\u00d7 larger on that benchmark."
   ]
  },
  {
   "url": "https://0to1log.com/en/news/2026-04-03-research-digest",
   "title": "Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s",
   "published_at": "2026-04-03T09:00:00",
   "organization": "NVIDIA",
   "model": "Nemotron 3 Super",
   "release_date": "2026",
   "parameters": "120-billion-parameter",
   "context_window": "1,000,000-token",
   "license": "permissive",
   "significance": "120-billion-parameter open-weight model with only 12B active parameters per token, featuring a native 1,000,000-token context window and hybrid architecture combining Mamba state-space layers and Transformer layers.",
   "organization_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "NVIDIA launches Nemotron 3 Super, a 120-billion-parameter open-weight model with only 12B active parameters per token."
   ],
   "model_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "NVIDIA launches Nemotron 3 Super, a 120-billion-parameter open-weight model with only 12B active parameters per token."
   ],
   "release_date_evidence": [
    "# Latest AI News",
    "# Google\u2019s Gemma 4 goes Apache 2.0, pushes local multimodal LLMs from phones to H100s"
   ],
   "parameters_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "NVIDIA launches Nemotron 3 Super, a 120-billion-parameter open-weight model with only 12B active parameters per token."
   ],
   "context_window_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "It offers a native 1,000,000-token context window to retain full workflow state, claims up to 5\u00d7 higher throughput and up to 2\u00d7 higher accuracy than its predecessor, and introduces a hybrid architecture: Mamba state-space layers for efficient long-context processing plus Transformer layers for precise recall."
   ],
   "license_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "Licensing is permissive but NVIDIA-specific, so enterprises should review terms."
   ],
   "significance_evidence": [
    "# Latest AI News",
    "### NVIDIA Nemotron 3 Super: 120B hybrid MoE for agent throughput",
    "NVIDIA launches Nemotron 3 Super, a 120-billion-parameter open-weight model with only 12B active parameters per token.",
    "It offers a native 1,000,000-token context window to retain full workflow state, claims up to 5\u00d7 higher throughput and up to 2\u00d7 higher accuracy than its predecessor, and introduces a hybrid architecture: Mamba state-space layers for efficient long-context processing plus Transformer layers for precise recall."
   ]
  },
  {
   "url": "https://innobu.com/en/articles/kimi-k3-open-weight-frontier-model-2026.html",
   "title": "Kimi K3: China's Open-Weight AI at Frontier Level",
   "published_at": "2026-07-17T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Rank 4 of 189 models, ahead of Claude Opus 4.8 and GPT-5.5, with 2.8 trillion parameters, a 1 million token context window, and native vision.",
   "organization_evidence": [
    "# Kimi K3: China's Open-Weight Model Reaches the Top Tier",
    "Moonshot AI delivers the largest open model ever at 2.8 trillion parameters, and ends the era of bargain pricing at the same time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3: China's Open-Weight Model Reaches the Top Tier",
    "The full weights are scheduled to follow by July 27, 2026 under a Modified MIT license that permits commercial use."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3: China's Open-Weight Model Reaches the Top Tier",
    "Moonshot AI released Kimi K3 on July 16, 2026: a mixture-of-experts model with 2.8 trillion parameters, a 1 million token context window and native vision.",
    "In the independent Artificial Analysis evaluation, K3 reaches rank 4 of 189 models, ahead of Claude Opus 4.8 and GPT-5.5."
   ]
  },
  {
   "url": "https://onthewire.ai/article/china-s-zhipu-shipped-a-frontier-scale-coding-model-with-the-weights-fully-open",
   "title": "China's Zhipu Shipped a Frontier-Scale Coding Model \u2014 With the Weights Fully Open",
   "published_at": "2026-06-17T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A frontier-scale mixture-of-experts coding model with ~743 billion total parameters and 1M-token context released with full weights live on Hugging Face under an MIT license.",
   "organization_evidence": [
    "# China's Zhipu Shipped a Frontier-Scale Coding Model \u2014 With the Weights Fully Open",
    "Zhipu (Z.ai) released GLM-5.2: a ~743-billion-parameter mixture-of-experts model, 1M-token context, MIT-licensed, weights live on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "17 June 2026",
    "# China's Zhipu Shipped a Frontier-Scale Coding Model \u2014 With the Weights Fully Open"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# China's Zhipu Shipped a Frontier-Scale Coding Model \u2014 With the Weights Fully Open",
    "Zhipu (Z.ai) released GLM-5.2: a ~743-billion-parameter mixture-of-experts model, 1M-token context, MIT-licensed, weights live on Hugging Face."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/ai2-molmomotion-3d-motion-forecasting-open-weight-robotics-video-builder-guide",
   "title": "AI2's MolmoMotion: Open 3D Motion Forecasting From Video \u2014 Robotics and Video Generation Builder Guide",
   "published_at": "2026-07-04T15:00:00",
   "organization": "The Allen Institute for AI (Ai2)",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": "4B",
   "context_window": null,
   "license": null,
   "significance": "MolmoMotion-AR achieved 0.109 meters average displacement error on PointMotionBench, outperforming all prior 3D motion forecasting methods in the comparison.",
   "organization_evidence": [
    "AI2's MolmoMotion: Open 3D Motion Forecasting From Video \u2014 Robotics and Video Generation Builder Guide",
    "The Allen Institute for AI (Ai2) released **MolmoMotion** on June 17, 2026 \u2014 an open-weight vision-language model that predicts where objects will move in 3D space before they move, given a short video clip, marked points on the target object, and a plain-language action instruction like \u201cPut the bowl on the table.\u201d"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI2's MolmoMotion: Open 3D Motion Forecasting From Video \u2014 Robotics and Video Generation Builder Guide",
    "The Allen Institute for AI (Ai2) released **MolmoMotion** on June 17, 2026 \u2014 an open-weight vision-language model that predicts where objects will move in 3D space before they move, given a short video clip, marked points on the target object, and a plain-language action instruction like \u201cPut the bowl on the table.\u201d"
   ],
   "parameters_evidence": [
    "MolmoMotion provides this at 4B parameter scale from a single video clip.",
    "Who This Is Actually For",
    "**Video generation builders** \u2014 current video generation models struggle with motion consistency. Injecting predicted 3D trajectories as conditioning signals (rather than 2D optical flow or pose estimates) gives the generator a physically grounded target."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Benchmark Numbers",
    "MolmoMotion-AR achieved **0.109 meters average displacement error** on PointMotionBench, outperforming all prior 3D motion forecasting methods in the comparison."
   ]
  },
  {
   "url": "https://ufukozen.com/blog/qwen-3-5-open-source-release",
   "title": "Qwen3.5 397B Release | Open Source Multimodal AI Agent",
   "published_at": "2026-02-17T00:00:00",
   "organization": "Qwen",
   "model": "Qwen3.5-397B-A17B",
   "release_date": null,
   "parameters": "397 Billion",
   "context_window": null,
   "license": "Open Weights",
   "significance": "State-of-the-art performance in code generation and logical deduction. Capable of understanding and interacting with Graphical User Interfaces and processing video input natively.",
   "organization_evidence": [
    "# Qwen3.5 is Here: The 397B Open Source Giant",
    "By releasing the weights for a model of this scale and capability, Qwen is ensuring that the most powerful AI tools remain accessible to developers and researchers worldwide."
   ],
   "model_evidence": [
    "# Qwen3.5 is Here: The 397B Open Source Giant",
    "- **Model:** Qwen3.5-397B-A17B (MoE)"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "# Qwen3.5 is Here: The 397B Open Source Giant",
    "- **Total Parameters:** 397 Billion"
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "# Qwen3.5 is Here: The 397B Open Source Giant",
    "- **License:** Open Weights"
   ],
   "significance_evidence": [
    "# Qwen3.5 is Here: The 397B Open Source Giant",
    "- **Coding & Reasoning:** State-of-the-art performance in code generation and logical deduction.",
    "- **GUI + Video:** Capable of understanding and interacting with Graphical User Interfaces and processing video input natively."
   ]
  },
  {
   "url": "https://theneuralfeed.com/article/mistral-ai-releases-medium-3-5-model-in-public-preview/cT0OsSRA",
   "title": "Mistral Medium 3.5 Launch: 128B Dense Model with 256k Context and Cloud Coding",
   "published_at": "2026-05-20T06:09:33",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-05-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It achieves 77.6% on SWE-Bench Verified and 91.4 on \u03c4\u00b3-Telecom for agentic capabilities.",
   "organization_evidence": [
    "# Mistral Medium 3.5 Launch: 128B Dense Model with 256k Context and Cloud Coding",
    "Releasebot / Mistral AI - WikipediaMay 20, 2026",
    "Mistral AI introduced Mistral Medium 3.5, a 128B dense flagship model available in public preview."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 Launch: 128B Dense Model with 256k Context and Cloud Coding",
    "Releasebot / Mistral AI - WikipediaMay 20, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 Launch: 128B Dense Model with 256k Context and Cloud Coding",
    "Mistral AI introduced Mistral Medium 3.5, a 128B dense flagship model available in public preview.",
    "It achieves 77.6% on SWE-Bench Verified and 91.4 on \u03c4\u00b3-Telecom for agentic capabilities."
   ]
  },
  {
   "url": "https://computertech.co/google-gemma-4-review",
   "title": "Google Gemma 4 Review 2026: Most Capable Open Model Family Built on Gemini 3 (Beats Models 20x Larger) - ComputerTech",
   "published_at": "2026-04-03T08:12:40",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "31B (Dense)",
   "context_window": null,
   "license": null,
   "significance": "Claimed the #3 spot on Arena AI's global leaderboard, outperforming models 20 times its size; 89.2% on AIME 2026 benchmarks",
   "organization_evidence": [
    "Google Gemma 4 Review 2026: Most Capable Open Model Family Built on Gemini 3 (Beats Models 20x Larger)",
    "On April 2, 2026, Google dropped a bombshell that shook the open-source AI community: Gemma 4, the first truly Apache 2.0 licensed model family built directly from Gemini 3 research."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Review 2026: Most Capable Open Model Family Built on Gemini 3 (Beats Models 20x Larger)",
    "On April 2, 2026, Google dropped a bombshell that shook the open-source AI community: Gemma 4, the first truly Apache 2.0 licensed model family built directly from Gemini 3 research."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Review 2026: Most Capable Open Model Family Built on Gemini 3 (Beats Models 20x Larger)",
    "Comprehensive Comparison Table",
    "| Parameters | 31B (Dense) | 400B (17B active) | 675B (41B active) | 27B (Dense) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Review 2026: Most Capable Open Model Family Built on Gemini 3 (Beats Models 20x Larger)",
    "The 31-billion parameter flagship model immediately claimed the #3 spot on Arena AI\u2019s global leaderboard, outperforming models 20 times its size."
   ]
  },
  {
   "url": "https://valueaddvc.com/pulse/moonshot-kimi-k3-open-weights-2026",
   "title": "Moonshot AI Drops Record 2.8 Trillion-Parameter Kimi K3",
   "published_at": "2026-07-26T23:30:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion-parameter",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight release to date",
   "organization_evidence": [
    "Moonshot AI released the complete open weights of Kimi K3 on Sunday night, roughly a day ahead of its own previously communicated July 27 target, making the 2.8 trillion-parameter model the largest open-weight release to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot AI released the complete open weights of Kimi K3 on Sunday night, roughly a day ahead of its own previously communicated July 27 target, making the 2.8 trillion-parameter model the largest open-weight release to date."
   ],
   "parameters_evidence": [
    "Kimi K3 is a 2.8 trillion-parameter Mixture-of-Experts model that activates only 104 billion parameters per token, ships under a permissive Modified MIT license, and supports a 1 million-token context window with native text, image and video input",
    "Moonshot AI released the complete open weights of Kimi K3 on Sunday night, roughly a day ahead of its own previously communicated July 27 target, making the 2.8 trillion-parameter model the largest open-weight release to date."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Moonshot AI released the complete open weights of Kimi K3 on Sunday night, roughly a day ahead of its own previously communicated July 27 target, making the 2.8 trillion-parameter model the largest open-weight release to date."
   ]
  },
  {
   "url": "https://stable-learn.com/en/glm-5-2-open-source-release",
   "title": "GLM-5.2 Goes Fully Open Today: 753B Parameters Beat GPT-5.5 at 1/6 the Cost",
   "published_at": "2026-06-17T10:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Beats GPT-5.5 on FrontierSWE at 1/6th the cost, first open-weight model to break 80% on Terminal-Bench 2.1, and ranks #2 globally in LMArena Coding Blind Test.",
   "organization_evidence": [
    "News 5 min read",
    "# GLM-5.2 Goes Fully Open Today: 753B Parameters Beat GPT-5.5 at 1/6 the Cost",
    "Z.ai officially open-sources GLM-5.2 today: 753B parameters, 1M-token context, MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Goes Fully Open Today: 753B Parameters Beat GPT-5.5 at 1/6 the Cost",
    "## Today, GLM-5.2 Goes Fully Open",
    "June 17, 2026: Z.ai (formerly Zhipu AI) officially open-sources its flagship model **GLM-5.2** under the MIT license with full weights available."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Goes Fully Open Today: 753B Parameters Beat GPT-5.5 at 1/6 the Cost",
    "## Performance: Beats GPT-5.5 on FrontierSWE",
    "**FrontierSWE** evaluates Agent performance on multi-hour to multi-day open-source technical projects. GLM-5.2 hits **74.4%**, surpassing GPT-5.5\u2019s 72.6%, with only a 0.7% gap to Claude Opus 4.8.",
    "**Terminal-Bench 2.1** scores **81.0**, making it the **first open-weight model to break 80% on this evaluation**.",
    "- **LMArena Coding Blind Test**: GLM-5.2 beats Claude Opus 4.7 and 4.8, ranking **#2 globally**"
   ]
  },
  {
   "url": "https://onlylabs.fyi/analysis/minimax",
   "title": "MiniMax analysis",
   "published_at": "2026-08-10T08:06:02",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-07-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Omni-modal video generation model \u2014 text/image/video/audio-to-video with native stereo sound, up to 15s at 2K resolution.",
   "organization_evidence": [
    "# MiniMax analysis",
    "- **MiniMax H3** (July 2026): Omni-modal video generation model \u2014 text/image/video/audio-to-video with native stereo sound, up to 15s at 2K resolution. Open-sourced on Hugging Face (35,295 downloads, 3,307 likes, ~33B params) and GitHub (298 stars) under a community license. Distributed in both original checkpoint and diffusers format P1P2E1E14W2W3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **H3 launch**: 35,295 Hugging Face downloads and 3,307 likes within days of release (July 28, 2026); GitHub repo accumulated 298 stars in its first week E1P1."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **MiniMax H3** (July 2026): Omni-modal video generation model \u2014 text/image/video/audio-to-video with native stereo sound, up to 15s at 2K resolution. Open-sourced on Hugging Face (35,295 downloads, 3,307 likes, ~33B params) and GitHub (298 stars) under a community license. Distributed in both original checkpoint and diffusers format P1P2E1E14W2W3."
   ]
  },
  {
   "url": "https://onlylabs.fyi/analysis/minimax",
   "title": "MiniMax analysis",
   "published_at": "2026-08-10T08:06:02",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Multimodal MoE model (image-text-to-text, ~427B params) with agent and coding tags.",
   "organization_evidence": [
    "# MiniMax analysis",
    "- **MiniMax M3** (June 2026): Multimodal MoE model (image-text-to-text, ~427B params) with agent and coding tags. Also released in MXFP8 quantized format (~440B params, 41,786 downloads) P6P7E5E15E35."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **MiniMax M3** (June 2026): Multimodal MoE model (image-text-to-text, ~427B params) with agent and coding tags. Also released in MXFP8 quantized format (~440B params, 41,786 downloads) P6P7E5E15E35."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **MiniMax M3** (June 2026): Multimodal MoE model (image-text-to-text, ~427B params) with agent and coding tags. Also released in MXFP8 quantized format (~440B params, 41,786 downloads) P6P7E5E15E35."
   ]
  },
  {
   "url": "https://onlylabs.fyi/analysis/minimax",
   "title": "MiniMax analysis",
   "published_at": "2026-08-10T08:06:02",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Text-generation model (~229B params, 892,617 downloads \u2014 the highest-download model in the M2 family)",
   "organization_evidence": [
    "# MiniMax analysis",
    "- **MiniMax M2.7** (April 2026): Text-generation model (~229B params, 892,617 downloads \u2014 the highest-download model in the M2 family) E7E49."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **MiniMax M2.7** (April 2026): Text-generation model (~229B params, 892,617 downloads \u2014 the highest-download model in the M2 family) E7E49."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **MiniMax M2.7** (April 2026): Text-generation model (~229B params, 892,617 downloads \u2014 the highest-download model in the M2 family) E7E49."
   ]
  },
  {
   "url": "https://onlylabs.fyi/analysis/minimax",
   "title": "MiniMax analysis",
   "published_at": "2026-08-10T08:06:02",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Text-generation model (~229B params, 702,344 downloads)",
   "organization_evidence": [
    "# MiniMax analysis",
    "- **MiniMax M2.5** (February 2026): Text-generation model (~229B params, 702,344 downloads) E3E44."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **MiniMax M2.5** (February 2026): Text-generation model (~229B params, 702,344 downloads) E3E44."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **MiniMax M2.5** (February 2026): Text-generation model (~229B params, 702,344 downloads) E3E44."
   ]
  },
  {
   "url": "https://braiviq.com/blog/alibaba-qwen-3-6-china-open-source-ai-2026-uk-multi-model",
   "title": "Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
   "published_at": "2026-05-05T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Qwen3.6-35B-A3B release on Hugging Face Hub and ModelScope",
   "organization_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "parameters_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "**Apr 16 2026** - Qwen3.6-35B-A3B release on Hugging Face Hub and ModelScope (Apache 2.0) \u00b7 **Apr 22 2026** - Qwen3.6-27B dense model release - beats larger MoE models on coding and reasoning \u00b7 **3** - Open-weights frontier-class model families released in April 2026: Llama 4, DeepSeek V4, Qwen 3.6 \u00b7 **Apache 2.0** - Licence under which the open Qwen 3.6 weights ship - fully commercial-use friendly"
   ]
  },
  {
   "url": "https://braiviq.com/blog/alibaba-qwen-3-6-china-open-source-ai-2026-uk-multi-model",
   "title": "Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
   "published_at": "2026-05-05T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "beats much larger MoE models on coding and reasoning at a fraction of the inference cost",
   "organization_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "parameters_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "April 2026 saw Alibaba ship Qwen 3.6 in three configurations: Qwen3.6-35B-A3B (April 16) and Qwen3.6-27B (April 22), both under the Apache 2.0 licence - alongside the proprietary Qwen3.5-Omni and Qwen3.6-Plus."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AI Development",
    "# Alibaba's Qwen 3.6 Just Made Open-Source AI A Three-Way Race - And It's Reshaping What 'Multi-Model' Means For UK Businesses",
    "The 27B dense model beats much larger MoE models on coding and reasoning at a fraction of the inference cost."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/the-open-source-llm-race-just-got-fiercer-why-llam-5191e884",
   "title": "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
   "published_at": "2026-07-06T12:24:48",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": "397 billion total parameters with 17 billion active",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts with 256 experts, native 262,144-token context window extending toward roughly one million tokens, native vision-language support, and coverage of an astonishing 201 languages.",
   "organization_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "As of July 2026, three families dominate conversations whenever engineers ask which open-weight model to build on: Meta's Llama 4, Alibaba's Qwen 3.5, and Mistral AI's Large 3.",
    "Alibaba's Qwen 3.5, rolled out in February 2026, tops out at a 397-billion-total and 17-billion-active mixture-of-experts (MoE) model built from 256 experts."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "Alibaba's Qwen 3.5, rolled out in February 2026, tops out at a 397-billion-total and 17-billion-active mixture-of-experts (MoE) model built from 256 experts."
   ],
   "parameters_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "Alibaba's Qwen 3.5, rolled out in February 2026, tops out at a 397-billion-total and 17-billion-active mixture-of-experts (MoE) model built from 256 experts."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "Alibaba's Qwen 3.5, rolled out in February 2026, tops out at a 397-billion-total and 17-billion-active mixture-of-experts (MoE) model built from 256 experts.",
    "It ships with a native 262,144-token context window that extends toward roughly one million tokens, native vision-language support, and coverage of an astonishing 201 languages."
   ]
  },
  {
   "url": "https://frontiernews.ai/news/article/the-open-source-llm-race-just-got-fiercer-why-llam-5191e884",
   "title": "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
   "published_at": "2026-07-06T12:24:48",
   "organization": "Mistral AI",
   "model": "Mistral Small 4",
   "release_date": "2026-03",
   "parameters": "119 billion total parameters with only 6 billion active parameters per token",
   "context_window": "256,000",
   "license": "Apache 2.0",
   "significance": "A remarkably lean 119-billion-total model that activates only 6 billion parameters per token.",
   "organization_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "As of July 2026, three families dominate conversations whenever engineers ask which open-weight model to build on: Meta's Llama 4, Alibaba's Qwen 3.5, and Mistral AI's Large 3.",
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "model_evidence": [
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "release_date_evidence": [
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "parameters_evidence": [
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "context_window_evidence": [
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "license_evidence": [
    "Key Specifications Across the Three Frontrunners",
    "- **Mistral Small 4:** 119 billion total parameters with only 6 billion active parameters per token, 256,000-token context window, multimodal, Apache 2.0 license, released March 2026."
   ],
   "significance_evidence": [
    "The Open-Source LLM Race Just Got Fiercer: Why Llama 4, Qwen 3.5, and Mistral Are Reshaping AI in 2026",
    "In March 2026, Mistral added Mistral Small 4, a remarkably lean 119-billion-total model that activates only 6 billion parameters per token."
   ]
  },
  {
   "url": "https://dev.to/bean_bean/google-gemma-4-review-2026-the-open-model-that-runs-locally-and-beats-closed-apis-2d99",
   "title": "Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
   "published_at": "2026-04-13T05:44:18",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, ~4B active of 26B, 31B Dense",
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 global open-source for 31B Dense and superior size-to-performance for 26B MoE, native function-calling and structured JSON, 256K context window on 26B/31B.",
   "organization_evidence": [
    "# Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "Google released Gemma 4 on **April 2, 2026**, under a fully permissive Apache 2.0 license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "Google released Gemma 4 on **April 2, 2026**, under a fully permissive Apache 2.0 license."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "ModelActive ParamsContextMultimodalBest ForHardware Floor **Gemma 4 E2B**2B128KText + Image + AudioMobile, IoT, edge devicesSmartphone / Raspberry Pi **Gemma 4 E4B**4B128KText + Image + AudioLaptop inference, quick prototypes8GB RAM MacBook M2+ **Gemma 4 26B MoE (A4B)**~4B active of 26B256KText + Image + VideoProduction APIs, agentic pipelines16-32GB unified memory **Gemma 4 31B Dense**31B256KText + Image + VideoMaximum quality, research, fine-tuning32GB+ (M3 Max / GPU cloud)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4 Review 2026: The Open Model That Runs Locally and Beats Closed APIs",
    "- 31B Dense l\u00e0 m\u00f4 h\u00ecnh open-source x\u1ebfp h\u1ea1ng #3 to\u00e0n c\u1ea7u; 26B MoE hi\u1ec7u su\u1ea5t v\u01b0\u1ee3t tr\u1ed9i so v\u1edbi k\u00edch th\u01b0\u1edbc",
    "On the Arena AI leaderboard it ranks **#6** among all open models; the 31B Dense sits at **#3**."
   ]
  },
  {
   "url": "https://presenc.ai/research/moonshot-kimi-model-lineage-2026",
   "title": "Moonshot Kimi Model Lineage 2026",
   "published_at": "2026-05-15T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K2.6",
   "release_date": "2026-04-20",
   "parameters": "1,000B (1T)",
   "context_window": "262,144 tokens",
   "license": "Open weights",
   "significance": "First open-weight model to beat GPT-5.4 on SWE-Bench Pro",
   "organization_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "Kimi K2.6 (April 2026) is the first open-weight model to beat GPT-5.4 (xhigh) on SWE-Bench Pro."
   ],
   "model_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "Kimi K2.6 (April 2026) is the first open-weight model to beat GPT-5.4 (xhigh) on SWE-Bench Pro."
   ],
   "release_date_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "## Kimi Release Timeline",
    "Kimi Release Timeline",
    "| Apr 20, 2026 | Kimi K2.6 GA | 1T MoE, 32B active, 262K context, 300-agent swarms |"
   ],
   "parameters_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "## Kimi K2.6 Specifications",
    "Kimi K2.6 Specifications",
    "| Total parameters | 1,000B (1T) |"
   ],
   "context_window_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "## Kimi K2.6 Specifications",
    "Kimi K2.6 Specifications",
    "| Context window | 262,144 tokens |"
   ],
   "license_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "## Kimi K2.6 Specifications",
    "Kimi K2.6 Specifications",
    "| License | Open weights |"
   ],
   "significance_evidence": [
    "## What this is",
    "Moonshot AI's Kimi line went from a long-context chatbot to the best-in-class open-weight coding model in 12 months.",
    "Kimi K2.6 (April 2026) is the first open-weight model to beat GPT-5.4 (xhigh) on SWE-Bench Pro."
   ]
  },
  {
   "url": "https://medium.com/@vinoth.lingam333/glm-5-2-the-744b-open-weight-model-with-a-1m-context-and-why-it-matters-for-builders-3d2e8065abb3",
   "title": "GLM-5.2: The 744B Open-Weight Model With a 1M Context \u2014 and Why It Matters for Builders",
   "published_at": "2026-06-20T00:00:54",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "It is the strongest open-source coding model available right now, and its Terminal-Bench score lands within a few points of Claude Opus 4.8.",
   "organization_evidence": [
    "GLM-5.2: The 744B Open-Weight Model With a 1M Context \u2014 and Why It Matters for Builders",
    "On June 13, 2026, Z.ai (Zhipu) shipped **GLM-5.2** \u2014 a 744-billion-parameter Mixture-of-Experts model with a genuine 1-million-token context window and MIT-licensed open weights."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "GLM-5.2: The 744B Open-Weight Model With a 1M Context \u2014 and Why It Matters for Builders",
    "On June 13, 2026, Z.ai (Zhipu) shipped **GLM-5.2** \u2014 a 744-billion-parameter Mixture-of-Experts model with a genuine 1-million-token context window and MIT-licensed open weights."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "GLM-5.2: The 744B Open-Weight Model With a 1M Context \u2014 and Why It Matters for Builders",
    "It is the strongest open-source coding model available right now, and its Terminal-Bench score lands within a few points of Claude Opus 4.8."
   ]
  },
  {
   "url": "https://chatforest.com/builders-log/minimax-m3-open-weight-frontier-msa-1m-context-agentic-coding-builder-guide",
   "title": "MiniMax M3: The First Open-Weight Frontier Coding Model with 1M Context \u2014 Builder Guide",
   "published_at": "2026-06-16T15:00:00",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": "428B",
   "context_window": null,
   "license": null,
   "significance": "the first open-weight model to simultaneously offer frontier-level coding performance, a 1-million-token context window, and native multimodal input in a single checkpoint",
   "organization_evidence": [
    "MiniMax M3: The First Open-Weight Frontier Coding Model with 1M Context \u2014 Builder Guide",
    "On June 1, 2026, MiniMax released **M3** \u2014 and made a claim that deserves scrutiny: this is the first open-weight model to simultaneously offer frontier-level coding performance, a 1-million-token context window, and native multimodal input in a single checkpoint."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 1, 2026, MiniMax released **M3** \u2014 and made a claim that deserves scrutiny: this is the first open-weight model to simultaneously offer frontier-level coding performance, a 1-million-token context window, and native multimodal input in a single checkpoint.",
    "MiniMax M3: The First Open-Weight Frontier Coding Model with 1M Context \u2014 Builder Guide"
   ],
   "parameters_evidence": [
    "MiniMax M3 is a 428B-parameter Mixture of Experts model (23B active at inference) that scores 59.0% on SWE-Bench Pro, supports a 1M-token context window via a novel sparse attention architecture, accepts image and video input natively, and is available as open weights \u2014 all for $0.30/M input tokens via API.",
    "MiniMax M3: The First Open-Weight Frontier Coding Model with 1M Context \u2014 Builder Guide",
    "## The One-Sentence Summary"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On June 1, 2026, MiniMax released **M3** \u2014 and made a claim that deserves scrutiny: this is the first open-weight model to simultaneously offer frontier-level coding performance, a 1-million-token context window, and native multimodal input in a single checkpoint.",
    "MiniMax M3: The First Open-Weight Frontier Coding Model with 1M Context \u2014 Builder Guide"
   ]
  },
  {
   "url": "https://ai-brainer.com/news/ibm-granite-4-1-open-language-models-512k-context-apache-2026-05-15",
   "title": "IBM Granite 4.1: Open Language Models With 512K Context Under Apache 2.0",
   "published_at": "2026-05-15T16:15:06",
   "organization": "IBM",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Benchmark results position Granite 4.1 competitively: the 8B model scores 92.49 on GSM8K (math), 87.20 on HumanEval (code), and 87.06 on IFEval (instruction following). For tool calling, the 30B model scores 73.68 on BFCL v3, proving its fitness for agentic applications.",
   "organization_evidence": [
    "# IBM Granite 4.1: Open Language Models With 512K Context Under Apache 2.0",
    "IBM releases Granite 4.1 \u2013 a family of dense language models in three sizes (3B, 8B, 30B), trained on 15 trillion tokens."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# IBM Granite 4.1: Open Language Models With 512K Context Under Apache 2.0",
    "IBM released the Granite 4.1 family on April 29, 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# IBM Granite 4.1: Open Language Models With 512K Context Under Apache 2.0",
    "Benchmark results position Granite 4.1 competitively: the 8B model scores 92.49 on GSM8K (math), 87.20 on HumanEval (code), and 87.06 on IFEval (instruction following)."
   ]
  },
  {
   "url": "https://aiweekly.co/alerts/allen-ais-tmax-trains-open-terminal-agent-to-27-accuracy",
   "title": "Allen AI's Tmax Trains Open Terminal Agent to 27% Accuracy",
   "published_at": "2026-06-23T06:36:04",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-06-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "strongest result among open-weights models under 10 billion parameters under official settings on Terminal-Bench 2.0",
   "organization_evidence": [
    "Allen AI's Tmax Trains Open Terminal Agent to 27% Accuracy",
    "A collaboration between the University of Washington and the Allen Institute for AI, [described on Hugging Face](https://huggingface.co/papers/2606.23321) and published June 22, introduces Tmax: a simplified reinforcement learning recipe that trains a 9B-parameter model to 27.2% on Terminal-Bench 2.0, which the authors describe as the strongest result among open-weights models under 10B parameters under official settings."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Allen AI's Tmax Trains Open Terminal Agent to 27% Accuracy",
    "A collaboration between the University of Washington and the Allen Institute for AI, [described on Hugging Face](https://huggingface.co/papers/2606.23321) and published June 22, introduces Tmax: a simplified reinforcement learning recipe that trains a 9B-parameter model to 27.2% on Terminal-Bench 2.0, which the authors describe as the strongest result among open-weights models under 10B parameters under official settings."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Allen AI's Tmax Trains Open Terminal Agent to 27% Accuracy",
    "TMax-9B scores 27.2% on Terminal-Bench 2.0, the strongest result among open-weights models under 10 billion parameters under official settings."
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03",
   "parameters": "119B/6B active",
   "context_window": null,
   "license": null,
   "significance": "Hybrid instruct/reason",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Mistral Small 4 | Mistral AI | 2026-03 | Apache 2.0 | 119B/6B active | 128K | Hybrid instruct/reason |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Mistral Small 4 | Mistral AI | 2026-03 | Apache 2.0 | 119B/6B active | 128K | Hybrid instruct/reason |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Mistral Small 4 | Mistral AI | 2026-03 | Apache 2.0 | 119B/6B active | 128K | Hybrid instruct/reason |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Mistral Small 4 | Mistral AI | 2026-03 | Apache 2.0 | 119B/6B active | 128K | Hybrid instruct/reason |"
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek-V4 Pro",
   "release_date": "2026-04",
   "parameters": "1.6T",
   "context_window": "256K",
   "license": "MIT",
   "significance": "1M context variant",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "model_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "context_window_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "license_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ],
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Pro | DeepSeek | 2026-04 | MIT | 1.6T | 256K | 1M context variant |"
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek-V4 Flash",
   "release_date": "2026-04",
   "parameters": "284B",
   "context_window": "256K",
   "license": "MIT",
   "significance": "Fast inference",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "model_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "context_window_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "license_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ],
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| DeepSeek-V4 Flash | DeepSeek | 2026-04 | MIT | 284B | 256K | Fast inference |"
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04",
   "parameters": "2B-31B",
   "context_window": "256K",
   "license": "Apache 2.0",
   "significance": "Native audio/video",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "model_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "context_window_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "license_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ],
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Gemma 4 | Google | 2026-04 | **Apache 2.0** | 2B-31B | 256K | Native audio/video |"
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "Z.ai",
   "model": "GLM-5.2",
   "release_date": "2026-06",
   "parameters": "744B/40B active",
   "context_window": "1M",
   "license": "MIT",
   "significance": "Coding breakout",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "model_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "context_window_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "license_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ],
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| GLM-5.2 | Z.ai | 2026-06 | MIT | 744B/40B active | **1M** | Coding breakout |"
   ]
  },
  {
   "url": "https://github.com/yoyo406/Open-sources-AI-models",
   "title": "GitHub - yoyo406/Open-sources-AI-models: List of open sources AI models\ud83d\ude0a\ud83d\ude4c.",
   "published_at": null,
   "organization": "Moonshot",
   "model": "Kimi K2.6",
   "release_date": "2026-04",
   "parameters": "1T/32B active",
   "context_window": "256K",
   "license": "Modified MIT",
   "significance": "Agent swarms",
   "organization_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "model_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "release_date_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "parameters_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "context_window_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "license_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ],
   "significance_evidence": [
    "# \ud83e\udd16 Awesome Open-Source AI Models \u2014 Complete Edition",
    "## 1. Large Language Models (General)",
    "| Model | Author | Release | License | Params | Context | Highlight |",
    "| Kimi K2.6 | Moonshot | 2026-04 | Modified MIT | 1T/32B active | 256K | Agent swarms |"
   ]
  },
  {
   "url": "https://medium.com/@ritukampani/qwen3-6-35b-a3b-is-here-and-it-can-actually-write-agents-not-just-code-a87b50ad1853",
   "title": "Qwen3.6\u201335B-A3B Is Here and It Can Actually Write Agents \u2014 Not Just Code",
   "published_at": "2026-04-18T10:06:01",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-18",
   "parameters": "35B total parameters, with 3.6 billion active at any given inference step",
   "context_window": null,
   "license": null,
   "significance": "Mixture-of-experts model with genuine agentic coding capabilities and built-in support for agentic workflows.",
   "organization_evidence": [
    "Qwen3.6\u201335B-A3B Is Here and It Can Actually Write Agents \u2014 Not Just Code",
    "*What makes this release different from the typical* ***open-source LLM drops, internet cheers, then moves on story*** *is the specific bet Alibaba\u2019s Qwen team is making."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen3.6\u201335B-A3B Is Here and It Can Actually Write Agents \u2014 Not Just Code",
    "8 min readApr 18, 2026"
   ],
   "parameters_evidence": [
    "Qwen3.6\u201335B-A3B Is Here and It Can Actually Write Agents",
    "The name is a bit of a mouthful but it unpacks cleanly: 35 billion total parameters, with 3.6 billion active at any given inference step."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen3.6\u201335B-A3B Is Here and It Can Actually Write Agents \u2014 Not Just Code",
    "Qwen has been riding that wave for a while now, and with the release of Qwen3.6\u201335B-A3B \u2014 an open-source mixture-of-experts model with genuine agentic coding capabilities \u2014 that cycle might accelerate dramatically."
   ]
  },
  {
   "url": "https://newstarget.com/2026-07-20-china-moonshot-ai-releases-open-source-model.html",
   "title": "China\u2019s Moonshot AI Releases Open-Source Kimi K3 Model, Challenging Anthropic\u2019s Opus",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-17",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "comparable to Anthropic's Claude Opus in performance, and ranks first on the Frontend code benchmark on Arena",
   "organization_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
   ],
   "parameters_evidence": [
    "Kimi K3 contains 2.8 trillion parameters, serving as a measure of the AI's scale and processing power, according to a report by the BBC. [8]"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The model is described by Moonshot AI as comparable to Anthropic's Claude Opus in performance, based on internal benchmarks.",
    "Moonshot AI's Kimi K3 now ranks first on the Frontend code benchmark on Arena, according to the report. [1]"
   ]
  },
  {
   "url": "https://dev.to/kaixintelligence/glms-2026-breakthrough-why-zhipu-ais-open-source-model-is-dominating-hacker-news-59ob",
   "title": "GLM's 2026 Breakthrough: Why Zhipu AI's Open-Source Model Is Dominating Hacker News",
   "published_at": "2026-08-14T09:00:47",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Long-context understanding with native 2M token context windows, hybrid reasoning, tool-use and agentic native design, efficient local inference, and open code weights allowing commercial use with minimal restrictions.",
   "organization_evidence": [
    "# GLM's 2026 Breakthrough: Why Zhipu AI's Open-Source Model Is Dominating Hacker News",
    "Every few years, a model emerges that reshapes the open-source AI landscape.",
    "In 2026, that model is GLM\u2014the latest iteration of Zhipu AI's General Language Model suite."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Why GLM Is Trending in 2026",
    "The Hacker News surge in 2026 wasn't coincidental. Zhipu AI dropped a new model release that combines several capabilities the community has been craving:",
    "- **Long-context understanding**: Native 2M token context windows, with an effective memory mechanism that avoids quadratic attention blowups using latent attention compression.",
    "- **Hybrid reasoning**: A dynamic system that switches between fast pattern-matched responses and deliberate step-by-step reasoning, depending on task complexity.",
    "- **Tool-use and agentic native design**: The model can call external APIs, write and execute code, and plan multi-step workflows without needing fragile, hand-crafted wrappers.",
    "- **Efficient local inference**: A 9-billion-parameter dense model can run on a single consumer GPU with quantization, while a 47-billion-parameter MoE model runs on a professional workstation.",
    "The release also arrived with a surprisingly permissive license. Code weights are fully open, and they allow commercial use with minimal restrictions."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/minimax-m3-open-weight-1m-context-multimodal-model-weights-released-june-2026",
   "title": "MiniMax M3 Open-Weight Model Lands With 1M Context and Native Multimodal",
   "published_at": "2026-06-15T18:53:42",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "pairs a 1M-token context window with native image and video understanding, strong coding ability, and MiniMax Sparse Attention (MSA) which cuts per-token compute at 1M context to roughly one-to-twentieth of the prior generation",
   "organization_evidence": [
    "MiniMax M3 Open-Weight Model Lands With 1M Context and Native Multimodal",
    "MiniMax published the open weights and technical report for M3, an open-weight model pairing a 1M-token context window with native image and video understanding.",
    "As of mid-June 2026, MiniMax has delivered exactly that."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Open-Weight Model Lands With 1M Context and Native Multimodal",
    "Dr. Nova Chen\u2605Jun 15, 2026\u26056 min read",
    "Following the initial M3 launch on June 1, the company has now published both the **open weights** and the full technical report (arXiv:2606.13392) for **MiniMax M3** on Hugging Face and GitHub."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Open-Weight Model Lands With 1M Context and Native Multimodal",
    "MiniMax published the open weights and technical report for M3, an open-weight model pairing a 1M-token context window with native image and video understanding.",
    "What makes M3 notable is the combination it brings together in a single open release: strong coding ability, a **one-million-token context window**, and native understanding of images and video.",
    "The headline engineering contribution is **MiniMax Sparse Attention (MSA)**, a sparse attention operator built specifically for million-token contexts."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/minimax-m3-open-weight-1m-context-multimodal-model-weights-released-june-2026",
   "title": "MiniMax M3 Open-Weight Model Lands With 1M Context and Native Multimodal",
   "published_at": "2026-06-15T18:53:42",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": "34B",
   "context_window": null,
   "license": null,
   "significance": "a 34B open reasoning model for robotaxis with 360-degree perception and a permissive OpenMDW license",
   "organization_evidence": [
    "NVIDIA Alpamayo 2 Super Ships an Open 34B AV Model",
    "NVIDIA released Alpamayo 2 Super for commercial use \u2014 a 34B open reasoning model for robotaxis with 360-degree perception and a permissive OpenMDW license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Alpamayo 2 Super Ships an Open 34B AV Model",
    "Dr. Nova Chen\u2605Aug 4, 2026\u26056 min read",
    "NVIDIA released Alpamayo 2 Super for commercial use \u2014 a 34B open reasoning model for robotaxis with 360-degree perception and a permissive OpenMDW license."
   ],
   "parameters_evidence": [
    "NVIDIA Alpamayo 2 Super Ships an Open 34B AV Model",
    "NVIDIA released Alpamayo 2 Super for commercial use \u2014 a 34B open reasoning model for robotaxis with 360-degree perception and a permissive OpenMDW license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Alpamayo 2 Super Ships an Open 34B AV Model",
    "NVIDIA released Alpamayo 2 Super for commercial use \u2014 a 34B open reasoning model for robotaxis with 360-degree perception and a permissive OpenMDW license."
   ]
  },
  {
   "url": "https://agentrq.com/blog/molmoweb-open-web-agent",
   "title": "MolmoWeb: The Open-Source Web Agent That Navigates by Screenshots Alone",
   "published_at": "2026-04-01T08:17:16",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-03-24",
   "parameters": "4B and 8B",
   "context_window": null,
   "license": null,
   "significance": "sets new open-weight state of the art on four major web benchmarks and matches OpenAI's o3 on WebVoyager",
   "organization_evidence": [
    "MolmoWeb: The Open-Source Web Agent That Navigates by Screenshots Alone",
    "**MolmoWeb**, released by Allen Institute for AI on March 24, 2026, takes a different approach: it looks at a screenshot of your browser, figures out what to do next, and clicks."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MolmoWeb: The Open-Source Web Agent That Navigates by Screenshots Alone",
    "**MolmoWeb**, released by Allen Institute for AI on March 24, 2026, takes a different approach: it looks at a screenshot of your browser, figures out what to do next, and clicks."
   ],
   "parameters_evidence": [
    "MolmoWeb: The Open-Source Web Agent That Navigates by Screenshots Alone",
    "The result is a fully open-weight web agent \u2014 4B and 8B sizes, Apache 2.0 license \u2014 that sets new open-weight state of the art on four major web benchmarks and matches OpenAI's o3 on WebVoyager."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MolmoWeb: The Open-Source Web Agent That Navigates by Screenshots Alone",
    "The result is a fully open-weight web agent \u2014 4B and 8B sizes, Apache 2.0 license \u2014 that sets new open-weight state of the art on four major web benchmarks and matches OpenAI's o3 on WebVoyager."
   ]
  },
  {
   "url": "https://llm-stats.com/models/qwen3.6-27b",
   "title": "Qwen3.6-27B Benchmarks, Pricing & Context Window",
   "published_at": null,
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-04-21",
   "parameters": "27.8 billion",
   "context_window": null,
   "license": null,
   "significance": null,
   "organization_evidence": [
    "## FAQ",
    "Common questions about Qwen3.6-27B.",
    "Qwen3.6-27B was released on April 21, 2026 by Qwen.",
    "Qwen3.6-27B was created by Qwen."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## FAQ",
    "Common questions about Qwen3.6-27B.",
    "Qwen3.6-27B was released on April 21, 2026 by Qwen."
   ],
   "parameters_evidence": [
    "## FAQ",
    "Common questions about Qwen3.6-27B.",
    "Qwen3.6-27B has 27.8 billion parameters."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": null
  },
  {
   "url": "https://machineherald.io/article/2026-03/22-mistral-launches-small-4-and-forge-at-gtc-2026-as-open-weight-models-close-the-gap-with-proprietary-ai",
   "title": "Mistral Launches Small 4 and Forge at GTC 2026 as Open-Weight Models Close the Gap With Proprietary AI",
   "published_at": "2026-03-22T09:23:20",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B",
   "context_window": null,
   "license": null,
   "significance": "Consolidates reasoning, multimodal understanding, and agentic coding into a single deployment, with 40 percent lower end-to-end latency and three times the throughput of Small 3, and outperforming OpenAI's GPT-oss-120b on LiveCodeBench.",
   "organization_evidence": [
    "# Mistral Launches Small 4 and Forge at GTC 2026 as Open-Weight Models Close the Gap With Proprietary AI",
    "Mistral AI used NVIDIA\u2019s GTC 2026 conference in San Jose to make two announcements that together signal a strategic inflection for Europe\u2019s most prominent AI company."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On March 16, the Paris-based startup released [Mistral Small 4](https://huggingface.co/mistralai/Mistral-Small-4), a 119-billion-parameter mixture-of-experts model licensed under Apache 2.0 that consolidates reasoning, multimodal understanding, and agentic coding into a single deployment.",
    "# Mistral Launches Small 4 and Forge at GTC 2026 as Open-Weight Models Close the Gap With Proprietary AI"
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    "Mistral releases Small 4, a 119B-parameter open-weight model under Apache 2.0, and Forge, an enterprise platform for training custom models on proprietary data, as open-weight AI closes to within three months of frontier systems.",
    "# Mistral Launches Small 4 and Forge at GTC 2026 as Open-Weight Models Close the Gap With Proprietary AI",
    "Small 4 employs a mixture-of-experts architecture with 128 experts, four of which activate per token, yielding roughly 6 billion active parameters per forward pass despite 119 billion total parameters."
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    "On March 16, the Paris-based startup released [Mistral Small 4](https://huggingface.co/mistralai/Mistral-Small-4), a 119-billion-parameter mixture-of-experts model licensed under Apache 2.0 that consolidates reasoning, multimodal understanding, and agentic coding into a single deployment.",
    "# Mistral Launches Small 4 and Forge at GTC 2026 as Open-Weight Models Close the Gap With Proprietary AI",
    "Mistral reports that Small 4 delivers 40 percent lower end-to-end latency and three times the throughput of Small 3 in optimized configurations.",
    "On LiveCodeBench, the model outperforms OpenAI\u2019s GPT-oss-120b while producing 20 percent shorter outputs, according to [VentureBeat](https://venturebeat.com/technology/mistrals-small-4-consolidates-reasoning-vision-and-coding-into-one-model-at)."
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  },
  {
   "url": "https://happycapyguide.com/blog/google-gemma-4-apache-open-source-ai-model-2026",
   "title": "Google Gemma 4 Review: Apache 2.0 Open-Source AI That Rivals Gemini 3",
   "published_at": "2026-04-08T07:41:23",
   "organization": "Google DeepMind",
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   "parameters": "E2B, E4B, 26B MoE, 31B Dense",
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   "release_date_evidence": [
    "Google Gemma 4 Review: Apache 2.0 Open-Source AI That Rivals Gemini 3",
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    "Google Gemma 4 Review: Apache 2.0 Open-Source AI That Rivals Gemini 3",
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    "Google Gemma 4 Review: Apache 2.0 Open-Source AI That Rivals Gemini 3",
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    "The 31B Dense model ranks #3 on the Arena AI open model leaderboard."
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  },
  {
   "url": "https://noqta.tn/en/news/kimi-k3-open-weights-huggingface-2026",
   "title": "Kimi K3 Open Weights Launch on Hugging Face: The World's Largest Open AI Model Arrives",
   "published_at": "2026-07-27T00:00:00",
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   "release_date_evidence": [
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   "parameters_evidence": [
    "Moonshot AI \u2014 Kimi K3 Blog",
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    "At 2.8 trillion parameters, K3 is the largest open-weight model ever published, and its arrival reshapes the economics and geopolitics of AI deployment for enterprises and developers worldwide."
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   "context_window_evidence": null,
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    "Moonshot AI \u2014 Kimi K3 Blog",
    "## Key Highlights",
    "- **#2 on the Vals AI Index** and **#1 in Frontend Code Arena** among all publicly evaluated models"
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  },
  {
   "url": "https://basedai.co/models/glm-5-2",
   "title": "GLM-5.2: specs, license, pricing, and how to run it \u2014 BasedAI",
   "published_at": null,
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   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matching GPT-5.5-tier coding at roughly one sixth of the cost",
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    "| Vendor | Zhipu AI (Z.ai) (China) |"
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   "release_date_evidence": [
    "Parameters",
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    "| Release date | 2026-06-13 |"
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    "Parameters",
    "Launch coverage framed GLM-5.2 as matching GPT-5.5-tier coding at roughly one sixth of the cost \u2014 continuing the GLM line's pattern (GLM-4.5, 4.6, 5, 5.1) of aggressively pricing against Claude and GPT for the coding-agent market."
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  },
  {
   "url": "https://creativeainews.com/articles/diffusiongemma-google-4x-faster-local-text-2026",
   "title": "DiffusionGemma: Google's Fast Open Text Model",
   "published_at": "2026-06-10T13:00:00",
   "organization": "Google DeepMind",
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   "release_date": "2026-06-10",
   "parameters": "26-billion-parameter",
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   "significance": "generates text up to 4x faster by denoising blocks of tokens in parallel, and it runs on a single RTX GPU",
   "organization_evidence": [
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    "DiffusionGemma: Google's 4x Faster Open Text Model"
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   "release_date_evidence": [
    "Google DeepMind released DiffusionGemma on June 10, 2026, an Apache 2.0 open model that generates text up to 4x faster by denoising blocks of tokens in parallel, and it runs on a single RTX GPU.",
    "DiffusionGemma: Google's 4x Faster Open Text Model"
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   "parameters_evidence": [
    "DiffusionGemma is a 26-billion-parameter mixture-of-experts model with 3.8 billion parameters active during inference.",
    "DiffusionGemma vs Fast Autoregressive Models",
    "DiffusionGemma: Google's 4x Faster Open Text Model"
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Google DeepMind released DiffusionGemma on June 10, 2026, an Apache 2.0 open model that generates text up to 4x faster by denoising blocks of tokens in parallel, and it runs on a single RTX GPU.",
    "DiffusionGemma: Google's 4x Faster Open Text Model"
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  },
  {
   "url": "https://moclaw.ai/blog/inkling-ai-thinking-machines",
   "title": "Inkling AI: Thinking Machines' First Open-Weight Model",
   "published_at": "2026-07-17T09:24:02",
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   "release_date": "2026-07-15",
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   "significance": "Matches Nemotron 3 Ultra on Terminal Bench 2.1 while spending roughly a third of the tokens.",
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   "context_window_evidence": null,
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    "Inkling AI: Thinking Machines' First Open-Weight Model",
    "The pitch is efficiency, not top scores: it matches [Nemotron 3 Ultra](https://www.nvidia.com) on Terminal Bench 2.1 while spending roughly a third of the tokens."
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  },
  {
   "url": "https://bestofai.io/models/llama-5",
   "title": "Llama 5 \u2014 Multimodal AI Model | Best of AI",
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   "organization": "Meta",
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   "release_date": "2026-07",
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   "significance": "Meta says it edges out Gemini 3.5 Pro and GPT-5.6 on several open benchmarks while still running efficiently thanks to the sparse expert routing.",
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   "release_date_evidence": [
    "# Llama 5",
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    "# Llama 5",
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    "# Llama 5",
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  },
  {
   "url": "http://ai-tldr.dev/releases/google-diffusion-gemma",
   "title": "Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in\u2026",
   "published_at": "2026-06-10T12:00:00",
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    "[DiffusionGemma](/models/diffusion-gemma/) is a new open-weight Gemma model from Google DeepMind that ships under Apache-2.0 on Hugging Face."
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   "release_date_evidence": [
    "Google DeepMind \u00b7 2026-06-10 \u00b7 major",
    "# Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in Parallel via Discrete Block Diffusion, Hits 1,000+ Tokens/Sec on H100 and 700+ on RTX 5090 While Posting 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6"
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    "# Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in Parallel via Discrete Block Diffusion, Hits 1,000+ Tokens/Sec on H100 and 700+ on RTX 5090 While Posting 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6",
    "Google opens a 26B-parameter Gemma that denoises 256 tokens at once instead of generating them one by one.",
    "Key specs",
    "| Total parameters | 25.2B |",
    "| Active params | 3.8B |",
    "It is a 25.2B mixture-of-experts with 3.8B parameters active per step, exposed through Transformers, [vLLM](/tools/vllm), [SGLang](/tools/sglang), and Docker Model Runner."
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    "# Google Ships DiffusionGemma \u2014 Apache-2.0 26B/3.8B-Active Mixture-of-Experts That Denoises 256 Tokens in Parallel via Discrete Block Diffusion, Hits 1,000+ Tokens/Sec on H100 and 700+ on RTX 5090 While Posting 77.6% MMLU Pro, 73.2% GPQA Diamond, and 69.1% LiveCodeBench v6",
    "Key specs",
    "| MMLU | 77.6% |",
    "| GPQA | 73.2% |",
    "| Live code bench v6 | 69.1% |"
   ]
  },
  {
   "url": "https://cntechpost.com/2026/04/08/zhipu-open-sources-flagship-glm-5-1-model-raises-prices-ai-race-heats-up",
   "title": "Zhipu open-sources flagship GLM-5.1 model and raises prices as AI race heats up",
   "published_at": "2026-04-08T06:19:05",
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   "significance": "GLM-5.1 is currently the only open-source model capable of eight hours of continuous work, and it performed exceptionally well in the SWE-bench Pro benchmark, which most closely resembles real-world software development, according to an announcement on Wednesday.",
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   "model_evidence": null,
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   "significance_evidence": [
    "Zhipu open-sources flagship GLM-5.1 model and raises prices as AI race heats up",
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    "GLM-5.1 is currently the only open-source model capable of eight hours of continuous work, and it performed exceptionally well in the SWE-bench Pro benchmark, which most closely resembles real-world software development, according to an announcement on Wednesday."
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  },
  {
   "url": "https://x.com/allen_ai/status/2067274059813982648",
   "title": "Ai2 (@allen_ai) on X",
   "published_at": "2026-06-17T15:51:58",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-06-17",
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   "license": null,
   "significance": "Weights downloadable and training data inspectable for MolmoMotion.",
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    "Everything is open\u2014download the MolmoMotion weights, inspect the training data, & customize for your applications."
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   "release_date_evidence": [
    "3:51 PM \u00b7 Jun 17, 2026",
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   "context_window_evidence": null,
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   "significance_evidence": [
    "Everything is open\u2014download the MolmoMotion weights, inspect the training data, & customize for your applications."
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  },
  {
   "url": "https://theaidude.net/blog/how-open-weights-models-like-deepseek-v4-flash-work-in-2026",
   "title": "Open Weights Explained: Running DeepSeek V4 Flash",
   "published_at": "2026-08-02T00:00:00",
   "organization": "DeepSeek",
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   "release_date": "2026-07-31",
   "parameters": "284 billion total parameters and 13 billion active",
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   "license": null,
   "significance": "Scored 50 on Artificial Analysis's Intelligence Index, well above the median of 25 across 101 same-class models.",
   "organization_evidence": [
    "[DeepSeek](/tools/deepseek) published V4-Flash-0731 on July 31, 2026 under an MIT license, and [Artificial Analysis](https://artificialanalysis.ai/models/deepseek-v4-flash) scored it 50 on its Intelligence Index, well above the median of 25 across the 101 same-class models it is compared against."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[DeepSeek](/tools/deepseek) published V4-Flash-0731 on July 31, 2026 under an MIT license, and [Artificial Analysis](https://artificialanalysis.ai/models/deepseek-v4-flash) scored it 50 on its Intelligence Index, well above the median of 25 across the 101 same-class models it is compared against."
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   "parameters_evidence": [
    "V4-Flash-0731 has 284 billion total parameters and 13 billion active."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "[DeepSeek](/tools/deepseek) published V4-Flash-0731 on July 31, 2026 under an MIT license, and [Artificial Analysis](https://artificialanalysis.ai/models/deepseek-v4-flash) scored it 50 on its Intelligence Index, well above the median of 25 across the 101 same-class models it is compared against."
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  },
  {
   "url": "https://aimodelsnavi.com/en/blog/qwen3-6-35b-a3b-open-source-agent",
   "title": "Alibaba Open-Sources Qwen3.6-35B-A3B: MoE Model with 3B Activated Parameters Achieves 51.5 on Terminal-Bench 2.0, Outperforming Predecessor in Agent Coding",
   "published_at": "2026-04-15T00:00:00",
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   "significance": "Significantly surpasses its predecessor, Qwen3.5-35B-A3B, in agent coding tasks, with Terminal-Bench 2.0 scores jumping from 40.5 to 51.5 and QwenWebBench Elo ratings rising from 978 to 1397, approaching or exceeding the previous dense flagship, Qwen3.5-27B.",
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   "model_evidence": null,
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   "significance_evidence": [
    "Benchmark",
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  },
  {
   "url": "https://news.lavx.hu/article/zuck-rekindles-open-weights-llama-drama-with-muse-glimmer",
   "title": "Zuck rekindles open weights Llama drama with Muse Glimmer",
   "published_at": "2026-08-10T20:32:40",
   "organization": "Meta",
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   "significance": "A 30-billion-parameter open weights model aimed at local inference, coding agents and tool use.",
   "organization_evidence": [
    "Zuck rekindles open weights Llama drama with Muse Glimmer",
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   "model_evidence": null,
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   "significance_evidence": [
    "Zuck rekindles open weights Llama drama with Muse Glimmer",
    "Meta has released Muse Glimmer, a 30-billion-parameter open weights model aimed at local inference, coding agents and tool use."
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  },
  {
   "url": "https://techfastforward.com/articles/mistral-leanstral-cuts-formal-verification-costs-95-percent",
   "title": "Mistral Leanstral Cuts Formal Verification Costs 95 Percent",
   "published_at": "2026-07-12T05:09:13",
   "organization": "Mistral",
   "model": null,
   "release_date": "2026-07-04",
   "parameters": "119-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Mistral Leanstral 1.5 solves 587 of 672 PutnamBench proofs at $4 per problem, 75x cheaper than proprietary tools, finding five previously unknown bugs in open-source code.",
   "organization_evidence": [
    "Model Release",
    "# Mistral Leanstral Cuts Formal Verification Costs 95 Percent",
    "On July 4, 2026, [Mistral released Leanstral 1.5](https://mistral.ai/news/leanstral-1-5/), a 119-billion-parameter mixture-of-experts model with 6.5 billion active parameters, built entirely for Lean 4, the formal proof assistant that expresses software correctness as mathematical theorems."
   ],
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   "release_date_evidence": [
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    "On July 4, 2026, [Mistral released Leanstral 1.5](https://mistral.ai/news/leanstral-1-5/), a 119-billion-parameter mixture-of-experts model with 6.5 billion active parameters, built entirely for Lean 4, the formal proof assistant that expresses software correctness as mathematical theorems."
   ],
   "parameters_evidence": [
    "Model Release",
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    "On July 4, 2026, [Mistral released Leanstral 1.5](https://mistral.ai/news/leanstral-1-5/), a 119-billion-parameter mixture-of-experts model with 6.5 billion active parameters, built entirely for Lean 4, the formal proof assistant that expresses software correctness as mathematical theorems."
   ],
   "context_window_evidence": null,
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   "significance_evidence": [
    "Model Release",
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    "Mistral Leanstral 1.5 solves 587 of 672 PutnamBench proofs at $4 per problem, 75x cheaper than proprietary tools, finding five previously unknown bugs in open-source code."
   ]
  },
  {
   "url": "https://pub.towardsai.net/gemma-3-was-dead-last-gemma-4-is-world-class-heres-everything-that-changed-73e4119225bc",
   "title": "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
   "published_at": "2026-04-11T21:19:18",
   "organization": "Google DeepMind",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "2.3B effective (5.1B with embeddings) for E2B; 4.5B effective (8B with embeddings) for E4B; 26B A4B (4B active); 31B dense",
   "context_window": "128K tokens for E2B and E4B, 256K for the larger models",
   "license": "Apache 2.0",
   "significance": "Rank third among open models on the Arena AI leaderboard (29th overall across all models) with an ELO of 1451, day-zero support across major inference runtimes, natively handles text, images, audio, and video across all sizes.",
   "organization_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "Google DeepMind released [Gemma 4 ](https://ai.google.dev/gemma/docs/core?hl=en)on April 2, 2026, and it landed differently than any previous Gemma release."
   ],
   "model_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "Google DeepMind released [Gemma 4 ](https://ai.google.dev/gemma/docs/core?hl=en)on April 2, 2026, and it landed differently than any previous Gemma release."
   ],
   "release_date_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "Google DeepMind released [Gemma 4 ](https://ai.google.dev/gemma/docs/core?hl=en)on April 2, 2026, and it landed differently than any previous Gemma release."
   ],
   "parameters_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "## Four Models, Two Deployment Tiers, One Family",
    "E2B is listed as \u201c2.3B effective (5.1B with embeddings)\u201d and E4B as \u201c4.5B effective (8B with embeddings).\u201d",
    "**2. Workstation tier: 26B A4B and 31B**",
    "The model has 25.2 billion total parameters, but only approximately 3.8 billion of them fire per token during inference \u2014 the rest sit in memory, available for routing, but unused for any given token."
   ],
   "context_window_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "## Four Models, Two Deployment Tiers, One Family",
    "Both E2B and E4B support text, image, and audio input.",
    "Both have a 128K token context window."
   ],
   "license_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "Google DeepMind released [Gemma 4 ](https://ai.google.dev/gemma/docs/core?hl=en)on April 2, 2026, and it landed differently than any previous Gemma release.",
    "Not because the benchmark numbers are impressive \u2014 they are \u2014 but because the model family finally covers the full deployment spectrum from a [Raspberry Pi](https://www.raspberrypi.com/) to an [NVIDIA H100](https://www.nvidia.com/en-eu/data-center/h100/), natively handles text, images, audio, and video across all sizes, ships under an Apache 2.0 license with no usage restrictions, and launched with day-zero support across every major inference runtime."
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   "significance_evidence": [
    "Gemma 3 Was Dead Last. Gemma 4 Is World-Class. Here\u2019s Everything That Changed.",
    "Google DeepMind released [Gemma 4 ](https://ai.google.dev/gemma/docs/core?hl=en)on April 2, 2026, and it landed differently than any previous Gemma release.",
    "Not because the benchmark numbers are impressive \u2014 they are \u2014 but because the model family finally covers the full deployment spectrum from a [Raspberry Pi](https://www.raspberrypi.com/) to an [NVIDIA H100](https://www.nvidia.com/en-eu/data-center/h100/), natively handles text, images, audio, and video across all sizes, ships under an Apache 2.0 license with no usage restrictions, and launched with day-zero support across every major inference runtime.",
    "The 31B dense model now ranks third among open models on the [Arena AI leaderboard](https://arena.ai/leaderboard/text) (29th overall across all models) with an ELO of 1451, and the 26B MoE variant achieves 1441 while activating only 3.8 billion parameters per token."
   ]
  },
  {
   "url": "https://aichina.news/blog/minimax-m2-7-ascend-flagos-a-new-open-source-champion-for-huawei-s-recyp3",
   "title": "MiniMax-M2.7-ascend-FlagOS: A New Open-Source Champion for Huawei's Ascend Ecosystem \u2014 AICHINA.news Blog",
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   "significance": "A general-purpose open-source large language model ported to run efficiently on Huawei's Ascend NPUs via the FlagOS platform.",
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   "release_date_evidence": [
    "MiniMax-M2.7-ascend-FlagOS: A New Open-Source Champion for Huawei's Ascend Ecosystem",
    "models/FlagRelease June 20, 2026 2 min read Auto-generated from Modelers"
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    "Unlike many of its competitors which are strictly GPU-centric, MiniMax-M2.7-ascend-FlagOS is architected to leverage the Ascend NPU architecture efficiently."
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  },
  {
   "url": "https://techflareai.com/llama-3-open-source-complete-guide-2026-3",
   "title": "Llama 3 Open Source \u2013 Everything You Need to Know",
   "published_at": "2026-02-14T20:38:00",
   "organization": "Meta",
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   "release_date": "2026",
   "parameters": "7B, 13B, 30B, 65B",
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   "significance": "Extended context windows of 8 k tokens, sparse Mixture\u2011of\u2011Experts (MoE) layers that cut inference latency by up to 30 % on NVIDIA A100 GPUs, and quantization\u2011ready training.",
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   "release_date_evidence": [
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    "What Is LLaMA 3? A Deep Dive into the Architecture",
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  {
   "url": "https://insiderllm.com/guides/mistral-mixtral-guide",
   "title": "Are Mistral Models Still Worth Running? Only Nemo 12B (Here's Why)",
   "published_at": "2026-02-03T00:00:00",
   "organization": "Mistral AI",
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   "release_date": "2026-04-29",
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   "release_date_evidence": [
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   "url": "https://techjacksolutions.com/ai-tools/gemma/what-is-gemma",
   "title": "What Is Google Gemma? #3 Open Model, Apache 2.0 (2026)",
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   "release_date_evidence": [
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   "context_window_evidence": null,
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   "url": "https://techjacksolutions.com/ai-tools/gemma/what-is-gemma",
   "title": "What Is Google Gemma? #3 Open Model, Apache 2.0 (2026)",
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   "model": "Gemma 4 E4B",
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   "license_evidence": [
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   "url": "https://techjacksolutions.com/ai-tools/gemma/what-is-gemma",
   "title": "What Is Google Gemma? #3 Open Model, Apache 2.0 (2026)",
   "published_at": "2026-05-28T13:32:40",
   "organization": "Google DeepMind",
   "model": "Gemma 4 26B MoE",
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   "organization_evidence": [
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   "parameters_evidence": [
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   "context_window_evidence": [
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   "license_evidence": [
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   "url": "https://techjacksolutions.com/ai-tools/gemma/what-is-gemma",
   "title": "What Is Google Gemma? #3 Open Model, Apache 2.0 (2026)",
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   "organization_evidence": [
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   "context_window_evidence": [
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   "license_evidence": [
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  },
  {
   "url": "https://cryptobriefing.com/kimi-k2-7-code-open-source-release",
   "title": "Kimi AI releases open-source K2.7 Code model with 1 trillion parameters on APIs and Hugging Face",
   "published_at": "2026-06-12T13:26:37",
   "organization": "Moonshot AI",
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   "release_date": "2026-06",
   "parameters": "1 trillion total parameters with 32 billion active parameters",
   "context_window": null,
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   "significance": "A 21.8% gain on Kimi Code Bench v2, an 11.0% improvement on Program Bench, and a 31.5% jump on MLS Bench Lite.",
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    "Moonshot AI just dropped Kimi-K2.7-Code, an open-source coding model that wants to make AI-assisted programming less wasteful and more capable."
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   "release_date_evidence": [
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   "parameters_evidence": [
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  },
  {
   "url": "https://heybeagle.com/blog/the-repo-that-finally-fit-inside-a-single-context-window",
   "title": "The Repo That Finally Fit Inside a Single Context Window",
   "published_at": "2026-06-19T20:01:27",
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  },
  {
   "url": "https://researchaudio.io/p/a-chinese-lab-shipped-a-new-attention-architecture-it-is-20-cheaper-at-1m-context-nobody-is-writing",
   "title": "A Chinese lab shipped a new attention architecture. It is 20\u00d7 cheaper at 1M context. Nobody is writing about it",
   "published_at": "2026-06-15T15:35:48",
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  {
   "url": "https://getyourdozai.blogspot.com/2026/06/the-2026-ai-model-release-race-every.html",
   "title": "The 2026 AI Model Release Race: Every Major LLM Launch You Need to Know",
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    "| Apr 24 | **DeepSeek V4-Pro/Flash** | DeepSeek | Open-weight MoE, 370B total params |"
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  },
  {
   "url": "https://gentic.news/ai-models",
   "title": "AI Model Comparison 2026 \u2014 Price, Context & Benchmarks",
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    "| Model | Input | Output | Context | SWE-bench \u25bc |",
    "| [DeepSeek V4 Pro](/ai-models/deepseek-v4-pro)openopen DeepSeek \u00b7 2026-04 | $0.43 | $0.87 | 1.05M | 80.6% |"
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    "| Model | Input | Output | Context | SWE-bench \u25bc |",
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   "published_at": "2026-04-23T08:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
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   "license": null,
   "significance": "First open-source model to top SWE-bench Pro, holding the top spot for nine days with a score of 58.4%, beating GPT-5.4 and Claude Opus 4.6.",
   "organization_evidence": [
    "GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026) | WOWHOW",
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    "GLM-5.1 is the first open-source model to top SWE-bench Pro with 58.4%, beating GPT-5.4. MIT-licensed, 754B params from Z.ai. Deploy via API, Ollama, or vLLM. Full guide."
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   "model_evidence": null,
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    "GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026) | WOWHOW",
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    "On April 7, 2026, Z.ai dropped GLM-5.1 \u2014 a 754-billion-parameter open-weight model that, for nine days, held the top spot on SWE-bench Pro with a score of 58.4%, beating GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%)."
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   "significance_evidence": [
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    "GLM-5.1 is the first open-source model to top SWE-bench Pro with 58.4%, beating GPT-5.4. MIT-licensed, 754B params from Z.ai. Deploy via API, Ollama, or vLLM. Full guide.",
    "On April 7, 2026, Z.ai dropped GLM-5.1 \u2014 a 754-billion-parameter open-weight model that, for nine days, held the top spot on SWE-bench Pro with a score of 58.4%, beating GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%)."
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  },
  {
   "url": "https://kingy.ai/ai-launches/ai-launch-tracker-minimax-m3-specs-benchmarks-a-chinese-open-weight-model-takes-aim-at-the-frontier",
   "title": "AI Launch Tracker \u2013 MiniMax M3 Specs & Benchmarks: A Chinese Open-Weight Model Takes Aim at the Frontier",
   "published_at": "2026-06-01T22:08:47",
   "organization": "MiniMax",
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   "release_date": "2026-05-31",
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   "significance": "First open model to fuse frontier-level coding and agentic performance, a 1-million-token context window, and native multimodality with computer operation; beats several proprietary models on major benchmarks like SWE-Bench Pro.",
   "organization_evidence": [
    "Shanghai-based [AI](https://Kingy.ai) startup MiniMax pushed its most ambitious model yet into the world.",
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   "release_date_evidence": [
    "On the evening of May 31, 2026 (Eastern time), Shanghai-based [AI](https://Kingy.ai) startup MiniMax pushed its most ambitious model yet into the world.",
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   "significance_evidence": [
    "The pitch is unusually bold for an open-weight release \u2014 [MiniMax claims M3](https://www.minimax.io/blog/minimax-m3) is the first open model to fuse three capabilities that, until now, only the closed frontier labs could offer simultaneously: frontier-level coding and agentic performance, a one-million-token context window, and native multimodality, complete with the ability to operate a desktop computer.",
    "MiniMax M3 is the most technically ambitious release in the company\u2019s history, and arguably a milestone for open-weight AI generally."
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  {
   "url": "https://edgen.tech/fr/news/post/mira-muratis-inkling-debuts-as-best-open-source-model-in-the-west",
   "title": "Mira Murati's Inkling debuts as best open-source model in the West",
   "published_at": "2026-07-26T14:42:40",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-26",
   "parameters": "975 billion",
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   "license": null,
   "significance": "It scores 74.1% on MCP Atlas for agentic tool use, nearly 30 points above Nvidia's Nemotron",
   "organization_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
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   "model_evidence": null,
   "release_date_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "## Rachel Kim](https://www.edgen.tech/fr/author/news/rachel-kim)\u00b7Jul 26 2026, 14:42"
   ],
   "parameters_evidence": [
    "Mira Murati's Inkling debuts as best open-source model in the West",
    "Mira Murati's Thinking Machines Lab released Inkling, a 975-billion-parameter open-source model that is the best Western-trained open-weight AI model \u2014 but its narrow strengths and heavy censorship limit its appeal to most developers."
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    "Mira Murati's Inkling debuts as best open-source model in the West",
    "- It scores 74.1% on MCP Atlas for agentic tool use, nearly 30 points above Nvidia's Nemotron"
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  },
  {
   "url": "https://aichina.news/blog/full-transparency-meets-ascend-introducing-the-olmo-3-1-32b-open-zk815c",
   "title": "Full Transparency Meets Ascend: Introducing the OLMo-3.1-32B Open Source Powerhouse \u2014 AICHINA.news Blog",
   "published_at": "2026-04-15T14:43:29",
   "organization": "Allen Institute for AI",
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   "release_date": "2026-04-15",
   "parameters": "32 billion",
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   "release_date_evidence": [
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    "models/allenai April 15, 2026 2 min read Auto-generated from Modelers"
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  },
  {
   "url": "https://aitraining2u.com/chinese-open-source-ai-models-2026.html",
   "title": "Chinese Open-Source AI Models in 2026: Kimi, Qwen, DeepSeek, GLM, Doubao & Hunyuan Compared",
   "published_at": "2026-07-23T00:00:00",
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  {
   "url": "https://aitraining2u.com/chinese-open-source-ai-models-2026.html",
   "title": "Chinese Open-Source AI Models in 2026: Kimi, Qwen, DeepSeek, GLM, Doubao & Hunyuan Compared",
   "published_at": "2026-07-23T00:00:00",
   "organization": "Alibaba",
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    "| Alibaba | Qwen 3.6 | 27B dense / 35B-A3B MoE (Max closed) | 256K\u21921M | Apache-2.0 | Multilingual + easy self-host |"
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   "model_evidence": null,
   "release_date_evidence": [
    "The 2026 landscape at a glance",
    "| Lab | Flagship (2026) | Architecture | Context | Licence | Best for |",
    "| Alibaba | Qwen 3.6 | 27B dense / 35B-A3B MoE (Max closed) | 256K\u21921M | Apache-2.0 | Multilingual + easy self-host |",
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   "significance_evidence": [
    "Alibaba \u2014 Qwen 3.6",
    "The 2026 **Qwen 3.6** series includes a dense 27B and a 35B-A3B MoE (only ~3B parameters active per token) that score at the frontier on agentic coding while fitting on modest hardware."
   ]
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  {
   "url": "https://aitraining2u.com/chinese-open-source-ai-models-2026.html",
   "title": "Chinese Open-Source AI Models in 2026: Kimi, Qwen, DeepSeek, GLM, Doubao & Hunyuan Compared",
   "published_at": "2026-07-23T00:00:00",
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    "| Lab | Flagship (2026) | Architecture | Context | Licence | Best for |",
    "| DeepSeek | V4 (pro / flash) | MoE, adaptive reasoning | 1M | Open weights (MIT-style) | Reasoning + cost efficiency |"
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  },
  {
   "url": "https://aitraining2u.com/chinese-open-source-ai-models-2026.html",
   "title": "Chinese Open-Source AI Models in 2026: Kimi, Qwen, DeepSeek, GLM, Doubao & Hunyuan Compared",
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  {
   "url": "https://aitraining2u.com/chinese-open-source-ai-models-2026.html",
   "title": "Chinese Open-Source AI Models in 2026: Kimi, Qwen, DeepSeek, GLM, Doubao & Hunyuan Compared",
   "published_at": "2026-07-23T00:00:00",
   "organization": "Tencent",
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   "significance": "Unusually strong on multimodal generation (image, 3D and video).",
   "organization_evidence": [
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    "| Tencent | Hunyuan Hy3 | 295B MoE / 21B active | 256K | Apache-2.0 | Multimodal (image/3D/video) |"
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   "release_date_evidence": [
    "Tencent \u2014 Hunyuan Hy3",
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  },
  {
   "url": "https://theneuralfeed.com/share/post/Ecrr4Oxx",
   "title": "Qwen shifts to closed source after firing lead researcher Junyang Lin",
   "published_at": "2026-06-22T07:20:20",
   "organization": "GLM",
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   "release_date": "2026-06",
   "parameters": null,
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   "license": null,
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   "organization_evidence": [
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   "release_date_evidence": [
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   "significance_evidence": [
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  {
   "url": "https://theneuralfeed.com/share/post/Ecrr4Oxx",
   "title": "Qwen shifts to closed source after firing lead researcher Junyang Lin",
   "published_at": "2026-06-22T07:20:20",
   "organization": "Kimi",
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   "release_date": "2026-06-12",
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   "release_date_evidence": [
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   "significance_evidence": [
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  {
   "url": "https://theneuralfeed.com/share/post/Ecrr4Oxx",
   "title": "Qwen shifts to closed source after firing lead researcher Junyang Lin",
   "published_at": "2026-06-22T07:20:20",
   "organization": "MiniMax",
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   "release_date": "2026-06-11",
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   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
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    "GLM released GLM-5.2 in June 2026, Kimi released Kimi-K2.7-Code on June 12, MiniMax released MiniMax-M3 on June 11, Step released Step-3.7-Flash on June 11, and DeepSeek released DeepSeek-V4-Pro/V4-Flash in April 2026."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen shifts to closed source after firing lead researcher Junyang Lin",
    "Deep Dive",
    "As of mid-2026, every other major Chinese AI lab has released an open-source model more recently than Qwen."
   ]
  },
  {
   "url": "https://theneuralfeed.com/share/post/Ecrr4Oxx",
   "title": "Qwen shifts to closed source after firing lead researcher Junyang Lin",
   "published_at": "2026-06-22T07:20:20",
   "organization": "Step",
   "model": null,
   "release_date": "2026-06-11",
   "parameters": null,
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   "license": null,
   "significance": "As of mid-2026, every other major Chinese AI lab has released an open-source model more recently than Qwen.",
   "organization_evidence": [
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    "GLM released GLM-5.2 in June 2026, Kimi released Kimi-K2.7-Code on June 12, MiniMax released MiniMax-M3 on June 11, Step released Step-3.7-Flash on June 11, and DeepSeek released DeepSeek-V4-Pro/V4-Flash in April 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Qwen shifts to closed source after firing lead researcher Junyang Lin",
    "Deep Dive",
    "GLM released GLM-5.2 in June 2026, Kimi released Kimi-K2.7-Code on June 12, MiniMax released MiniMax-M3 on June 11, Step released Step-3.7-Flash on June 11, and DeepSeek released DeepSeek-V4-Pro/V4-Flash in April 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen shifts to closed source after firing lead researcher Junyang Lin",
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    "As of mid-2026, every other major Chinese AI lab has released an open-source model more recently than Qwen."
   ]
  },
  {
   "url": "https://theneuralfeed.com/share/post/Ecrr4Oxx",
   "title": "Qwen shifts to closed source after firing lead researcher Junyang Lin",
   "published_at": "2026-06-22T07:20:20",
   "organization": "DeepSeek",
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   "release_date": "2026-04",
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   "release_date_evidence": [
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   "significance_evidence": [
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  },
  {
   "url": "https://singularity.kiwi/mistral-medium-3-5-remote-agents-open-weights",
   "title": "Mistral Medium 3.5 Launches with Remote Agents \u2014 and Open Weights",
   "published_at": "2026-04-30T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-29",
   "parameters": null,
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   "license": null,
   "significance": "Hits 77.6% on SWE-Bench Verified, ahead of Devstral 2 and Qwen3.5 397B, and features remote cloud coding agents.",
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    "Mistral AI just dropped something that should make the big US labs nervous."
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   "model_evidence": null,
   "release_date_evidence": [
    "The Paris-based company announced the release on April 29, positioning it squarely against the agentic AI wave that\u2019s dominated 2026."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Mistral AI just dropped something that should make the big US labs nervous.",
    "The model hits 77.6% on SWE-Bench Verified, ahead of Devstral 2 and Qwen3.5 397B.",
    "Mistral Medium 3.5 is the most credible open-weight challenger to the proprietary agent platforms yet."
   ]
  },
  {
   "url": "https://nextomoro.com/kimi-k2-5",
   "title": "Kimi K2.5",
   "published_at": "2026-05-15T05:12:02",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-01-29",
   "parameters": "1 trillion total parameters with 32 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "Kimi K2.5 sits in the leading tier of Chinese-origin open-weights frontier models alongside DeepSeek V4, Qwen 3.6, and GLM-5.1, with subsequent K2.6 micro-versions extending the family's reach into the second quarter of 2026.",
   "organization_evidence": [
    "# Kimi K2.5",
    "Kimi K2.5 is Moonshot AI's January 2026 open-weights multimodal flagship, a 1-trillion-parameter mixture-of-experts model with 32 billion active parameters, native image and video understanding, and a 256K context window under a modified MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K2.5",
    "- **Released:** January 29, 2026."
   ],
   "parameters_evidence": [
    "# Kimi K2.5",
    "- **Architecture:** Sparse mixture-of-experts. 1 trillion total parameters with 32 billion active per token. 384 total experts, 8 selected experts plus 1 shared expert per token, 61 layers (including one dense layer), Multi-head Latent Attention (MLA), 160,000-token vocabulary."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K2.5",
    "As of May 2026, Kimi K2.5 sits in the leading tier of Chinese-origin open-weights frontier models alongside [DeepSeek V4](https://nextomoro.com/deepseek-v4/), [Qwen 3.6](https://nextomoro.com/qwen-3-6/), and [GLM-5.1](https://nextomoro.com/glm-5-1/), with subsequent K2.6 micro-versions extending the family's reach into the second quarter of 2026."
   ]
  },
  {
   "url": "https://vettedconsumer.com/minimax-m3-the-first-open-weight-multimodal-frontier-model-and-the-license-catch",
   "title": "MiniMax M3: Open-Weight Multimodal Frontier Model",
   "published_at": "2026-06-25T16:16:47",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the first open-weight frontier model that natively reads text, images and video",
   "organization_evidence": [
    "Open Models",
    "MiniMax M3: The First Open-Weight Multimodal Frontier Model (and the License Catch)",
    "MiniMax just changed that."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Open Models",
    "MiniMax M3: The First Open-Weight Multimodal Frontier Model (and the License Catch)",
    "**MiniMax M3**, released June 1, 2026, is the first open-weight *frontier* model that's **natively multimodal**: it reads text, images and video in one model, carries a million-token context, and lands near the top of the open-weight leaderboards."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Models",
    "MiniMax M3: The First Open-Weight Multimodal Frontier Model (and the License Catch)",
    "MiniMax M3 is the first open-weight frontier model that natively reads text, images and video."
   ]
  },
  {
   "url": "https://actuia.com/index.php/en/news/while-france-debates-ai-sovereignty-portugal-delivered-its-own-for-eur7-million",
   "title": "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
   "published_at": "2026-07-03T08:46:53",
   "organization": "Portuguese government",
   "model": null,
   "release_date": "2026-07-01",
   "parameters": "9-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "the first open large language model (LLM) developed in European Portuguese",
   "organization_evidence": [
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, the Portuguese government officially unveiled **Am\u00e1lia**, which its creators describe as the first open large language model (LLM) developed in European Portuguese."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, Portugal officially unveiled Am\u00e1lia, its first open large language model in European Portuguese, funded with \u20ac7 million in public money."
   ],
   "parameters_evidence": [
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "The text core, a 9-billion-parameter model, was not trained from scratch: it builds on existing foundation models, including [EuroLLM-9B](https://huggingface.co/utter-project/EuroLLM-9B), an open European multilingual model, as well as GlorIA, an earlier Portuguese model."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "While France Debates AI Sovereignty, Portugal Delivered Its Own for \u20ac7 Million",
    "On July 1, 2026, the Portuguese government officially unveiled **Am\u00e1lia**, which its creators describe as the first open large language model (LLM) developed in European Portuguese."
   ]
  },
  {
   "url": "https://the-ledger.net/events/deepseek-v4-pro-release",
   "title": "DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips \u2014 The Ledger",
   "published_at": "2026-04-23T16:11:48",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "V4-Pro, with 1.6 trillion total parameters and 49 billion active per inference step, scored within 0.2 percentage points of Claude Opus 4.7 on SWE-bench Verified and outperformed it on LiveCodeBench, while offering API pricing at approximately one-seventh of comparable Western model rates.",
   "organization_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "A ledger entry in the models archive, dated 2026-04-24.",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "V4-Pro, with 1.6 trillion total parameters and 49 billion active per inference step, scored within 0.2 percentage points of Claude Opus 4.7 on SWE-bench Verified and outperformed it on LiveCodeBench, while offering API pricing at approximately one-seventh of comparable Western model rates."
   ]
  },
  {
   "url": "https://the-ledger.net/events/deepseek-v4-pro-release",
   "title": "DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips \u2014 The Ledger",
   "published_at": "2026-04-23T16:11:48",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion total parameters",
   "context_window": "one-million-token",
   "license": "MIT",
   "significance": "V4-Pro, with 1.6 trillion total parameters and 49 billion active per inference step, scored within 0.2 percentage points of Claude Opus 4.7 on SWE-bench Verified and outperformed it on LiveCodeBench, while offering API pricing at approximately one-seventh of comparable Western model rates.",
   "organization_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "model_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "release_date_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "A ledger entry in the models archive, dated 2026-04-24.",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "parameters_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "V4-Pro, with 1.6 trillion total parameters and 49 billion active per inference step, scored within 0.2 percentage points of Claude Opus 4.7 on SWE-bench Verified and outperformed it on LiveCodeBench, while offering API pricing at approximately one-seventh of comparable Western model rates."
   ],
   "context_window_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "Both support a one-million-token context window and are available as open weights under the MIT license \u2014 permitting unrestricted commercial use, modification, and redistribution without DeepSeek authorization."
   ],
   "license_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "significance_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "V4-Pro, with 1.6 trillion total parameters and 49 billion active per inference step, scored within 0.2 percentage points of Claude Opus 4.7 on SWE-bench Verified and outperformed it on LiveCodeBench, while offering API pricing at approximately one-seventh of comparable Western model rates."
   ]
  },
  {
   "url": "https://the-ledger.net/events/deepseek-v4-pro-release",
   "title": "DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips \u2014 The Ledger",
   "published_at": "2026-04-23T16:11:48",
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284 billion parameters",
   "context_window": "one-million-token",
   "license": "MIT",
   "significance": "DeepSeek-V4-Flash is a lighter variant at 284 billion parameters with 13 billion active per token.",
   "organization_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "model_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "release_date_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "A ledger entry in the models archive, dated 2026-04-24.",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "parameters_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "DeepSeek-V4-Flash is a lighter variant at 284 billion parameters with 13 billion active per token."
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   "context_window_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "Both support a one-million-token context window and are available as open weights under the MIT license \u2014 permitting unrestricted commercial use, modification, and redistribution without DeepSeek authorization."
   ],
   "license_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "On April 24, 2026 \u2014 one year after DeepSeek's V3 model destabilized AI market valuations across the United States \u2014 DeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash as open-weight production models under the MIT license."
   ],
   "significance_evidence": [
    "# DeepSeek Releases V4-Pro, Open-Weight Frontier Model Trained on Huawei Ascend Chips",
    "DeepSeek-V4-Flash is a lighter variant at 284 billion parameters with 13 billion active per token."
   ]
  },
  {
   "url": "https://explainx.ai/blog/qwen-agentworld-language-world-model-agents-2026",
   "title": "Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
   "published_at": "2026-06-24T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen-AgentWorld-35B-A3B",
   "release_date": "2026-06-23",
   "parameters": "35B-A3B",
   "context_window": "256K",
   "license": "open-source",
   "significance": "outperforms Claude Sonnet 4.6 at that size, scoring 56.39 on AgentWorldBench",
   "organization_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "Qwen-AgentWorld is Alibaba's native language world model that simulates seven agent environments \u2014 MCP, Search, Terminal, SWE, Web, OS, Android \u2014 within a single model."
   ],
   "model_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The 35B-A3B version (MoE, 3B active parameters, 256K context) is fully open-source on Hugging Face."
   ],
   "release_date_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The paper dropped June 23, 2026."
   ],
   "parameters_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The 35B-A3B version (MoE, 3B active parameters, 256K context) is fully open-source on Hugging Face."
   ],
   "context_window_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The 35B-A3B version (MoE, 3B active parameters, 256K context) is fully open-source on Hugging Face."
   ],
   "license_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The 35B-A3B version (MoE, 3B active parameters, 256K context) is fully open-source on Hugging Face."
   ],
   "significance_evidence": [
    "# Qwen-AgentWorld: The First Language World Model for General AI Agents (2026)",
    "The three-stage pipeline adds +8.66 points overall at 35B-A3B scale (47.73 \u2192 56.39), putting it above Claude Sonnet 4.6 (56.04) at that size."
   ]
  },
  {
   "url": "https://9to5google.com/2026/04/02/google-gemma-4",
   "title": "Google announces open Gemma 4 model with Apache 2.0 license",
   "published_at": "2026-04-02T16:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 goes from simple chat to complex logic and agentic workflows, with the company noting that it outcompetes models 20x its size.",
   "organization_evidence": [
    "Google announces open Gemma 4 model with Apache 2.0 license"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "![](https://9to5google.com/wp-content/uploads/sites/4/2026/04/Gemma-4-cover.jpg?quality=82&strip=all&w=1600)",
    "Google announces open Gemma 4 model with Apache 2.0 license"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google announces open Gemma 4 model with Apache 2.0 license",
    "Google says Gemma 4 goes from simple chat to \u201ccomplex logic and agentic workflows,\u201d with the company noting that it \u201coutcompetes models 20x its size.\u201d"
   ]
  },
  {
   "url": "https://jbiznews.com/moonshots-kimi-k3-weights-arrive-monday-the-largest-open-model-ever-released",
   "title": "Moonshot\u2019s Kimi K3 Weights Arrive Monday, the Largest Open Model Ever Released",
   "published_at": "2026-07-26T22:52:00",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-16",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "Ranked first in the Frontend Code category on Arena blind human preference platform with 1,679 points and ranked fourth of 189 models on the Artificial Analysis Intelligence Index.",
   "organization_evidence": [
    "**Beijing** \u2014 Moonshot AI is scheduled to publish the full weights of Kimi K3 on Monday, making a 2.8-trillion-parameter system freely downloadable and marking the point at which openly available models reach the same tier as the leading commercial products from American laboratories.",
    "Moonshot, the Beijing-based startup backed by Alibaba, released K3 on July 16, describing it as the largest open-source AI model in the world."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Moonshot, the Beijing-based startup backed by Alibaba, released K3 on July 16, describing it as the largest open-source AI model in the world."
   ],
   "parameters_evidence": [
    "**Beijing** \u2014 Moonshot AI is scheduled to publish the full weights of Kimi K3 on Monday, making a 2.8-trillion-parameter system freely downloadable and marking the point at which openly available models reach the same tier as the leading commercial products from American laboratories."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "On the Arena blind human preference platform, K3 took first place in the Frontend Code category with 1,679 points, ahead of Claude Fable 5 at 1,631, GPT-5.6 Sol at 1,618 and GLM-5.2 at 1,587 \u2014 a 17-place jump from K2.6.",
    "On the broader Artificial Analysis Intelligence Index, K3 scores 57 and ranks fourth of 189 models, level with Claude Opus 4.8 and GPT-5.5, behind Claude Fable 5 and GPT-5.6 Sol."
   ]
  },
  {
   "url": "https://lossless.group/keeping-up/glm-5-2-long-horizon-coding",
   "title": "GLM-5.2 \u2014 An Open-Weights Model Built for Long-Horizon Coding",
   "published_at": null,
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": "~753B",
   "context_window": null,
   "license": null,
   "significance": "Beats GPT-5.5 on multiple long-horizon coding benchmarks at roughly 1/6th the API cost, lands within a few points of Claude Opus 4.8, and is the first open-weights model to cross 80% on Terminal-Bench.",
   "organization_evidence": [
    "# GLM-5.2 \u2014 An Open-Weights Model Built for Long-Horizon Coding",
    "On **June 16, 2026**, Z.ai (formerly Zhipu AI) released **GLM-5.2**, a **~753B-parameter** open-weights model built specifically for **long-horizon agentic coding** \u2014 the kind of task that runs for hours, spans dozens of tool calls, and accumulates a long, messy trajectory the model has to keep coherent."
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   "model_evidence": null,
   "release_date_evidence": [
    "On **June 16, 2026**, Z.ai (formerly Zhipu AI) released **GLM-5.2**, a **~753B-parameter** open-weights model built specifically for **long-horizon agentic coding** \u2014 the kind of task that runs for hours, spans dozens of tool calls, and accumulates a long, messy trajectory the model has to keep coherent.",
    "# GLM-5.2 \u2014 An Open-Weights Model Built for Long-Horizon Coding"
   ],
   "parameters_evidence": [
    "On **June 16, 2026**, Z.ai (formerly Zhipu AI) released **GLM-5.2**, a **~753B-parameter** open-weights model built specifically for **long-horizon agentic coding** \u2014 the kind of task that runs for hours, spans dozens of tool calls, and accumulates a long, messy trajectory the model has to keep coherent.",
    "# GLM-5.2 \u2014 An Open-Weights Model Built for Long-Horizon Coding"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The headline that traveled fastest: GLM-5.2 **beats GPT-5.5 on multiple long-horizon coding benchmarks at roughly 1/6th the API cost**, and lands within a few points of Claude Opus 4.8 \u2014 while being something you can download and run on your own hardware.",
    "The shape of it: **GLM-5.2 edges out GPT-5.5 on the long-horizon coding benchmarks** (FrontierSWE, SWE-bench Pro, PostTrainBench, SWE-Marathon), **trails Opus 4.8 by small margins** (1% on FrontierSWE, 4 points on Terminal-Bench), and is **the first open-weights model to cross 80% on Terminal-Bench**.",
    "# GLM-5.2 \u2014 An Open-Weights Model Built for Long-Horizon Coding"
   ]
  },
  {
   "url": "https://docs.ainft.com/reference/minimax-m3",
   "title": "MiniMax M3",
   "published_at": null,
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax Sparse Attention (MSA) and open-source model combining frontier coding ability, million-token context, and native multimodality",
   "organization_evidence": [
    "Overview",
    "MiniMax M3 is the third-generation large language model from MiniMax, a Shanghai-based AI lab backed by Tencent, Alibaba, and miHoYo."
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   "release_date_evidence": [
    "Overview",
    "Released on June 1, 2026, M3 introduces MiniMax Sparse Attention (MSA), a sub-quadratic attention architecture that supports up to 1M token context with only 1/20th per-token compute of the previous generation."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Overview",
    "Released on June 1, 2026, M3 introduces MiniMax Sparse Attention (MSA), a sub-quadratic attention architecture that supports up to 1M token context with only 1/20th per-token compute of the previous generation.",
    "M3 is a natively multimodal model supporting image and video input, and is also the first domestic model to combine frontier coding ability, million-token context, and native multimodality \u2014 and an open-source model to do so."
   ]
  },
  {
   "url": "https://santageai.com/news/2026/04/25/deepseek-v4-open-source-release",
   "title": "DeepSeek Releases V4, an Open-Source Model Family Built for Million-Token Context",
   "published_at": "2026-04-25T00:00:00",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6 trillion parameters, 49 billion active",
   "context_window": null,
   "license": null,
   "significance": "V4 Pro leads all current open-source models in world knowledge, math, STEM, and coding tasks.",
   "organization_evidence": [
    "TLDR",
    "DeepSeek released V4 Pro (1.6 trillion parameters, 49 billion active) and V4 Flash (284 billion parameters, 13 billion active), both open-source."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## DeepSeek V4 ships two open models with million-token context",
    "DeepSeek, the Chinese AI lab that upended Silicon Valley a year ago with its cost-efficient approach to frontier AI, released its V4 model family on Friday, April 24, 2026."
   ],
   "parameters_evidence": [
    "TLDR",
    "DeepSeek released V4 Pro (1.6 trillion parameters, 49 billion active) and V4 Flash (284 billion parameters, 13 billion active), both open-source."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## DeepSeek V4 ships two open models with million-token context",
    "On benchmarks, V4 Pro leads all current open-source models in world knowledge, math, STEM, and coding tasks."
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  },
  {
   "url": "https://insiderllm.com/guides/qwen-models-guide",
   "title": "Best Qwen Models Ranked: Which to Run Locally (May 2026)",
   "published_at": "2026-02-02T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "SWE-bench 73.4, GPQA Diamond 86.0, AIME26 92.7, token-generation speed feels like a 3B dense model, native 262K context",
   "organization_evidence": [
    "Best Qwen Models Ranked: Which to Run Locally (May 2026)",
    "While everyone was talking about Llama and DeepSeek, Alibaba quietly built the best open-source model family in the world."
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   "model_evidence": null,
   "release_date_evidence": [
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   "url": "https://buildfastwithai.com/blogs/mistral-medium-3-5-review",
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   "url": "https://frankx.ai/blog/gemma-3-analysis-2026",
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    "| Benchmark | Gemma 3 27B | Gemma 4 31B | What it measures |",
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  {
   "url": "https://communistchina.news/2026-07-20-china-moonshot-ai-releases-open-source-model.html",
   "title": "China\u2019s Moonshot AI Releases Open-Source Kimi K3 Model, Challenging Anthropic\u2019s Opus",
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   "url": "https://wimes.org/articles/2026-06-14-glm-5-2-no-benchmarks-rollout",
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   "url": "https://mungomash.com/ai/deepseek/versions",
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   "parameters_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ]
  },
  {
   "url": "https://mungomash.com/ai/deepseek/versions",
   "title": "DeepSeek Versions",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Pro",
   "release_date": "2026-04-24",
   "parameters": "1.6T total / 49B active",
   "context_window": "1,000,000-token",
   "license": "MIT",
   "significance": "General availability \u2014 V4-Pro-0813, an agent-capability upgrade \u2014 August 13, 2026, with open weights the same day.",
   "organization_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ],
   "model_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ],
   "release_date_evidence": [
    "DeepSeek version table",
    "Current frontier flagship. 1.6T total / 49B active MoE. 1M-token context. Dual Thinking / Non-Thinking modes. General availability \u2014 V4-Pro-0813, an agent-capability upgrade \u2014 August 13, 2026, with open weights the same day. MIT-licensed."
   ],
   "parameters_evidence": [
    "DeepSeek version table",
    "Current frontier flagship. 1.6T total / 49B active MoE. 1M-token context. Dual Thinking / Non-Thinking modes. General availability \u2014 V4-Pro-0813, an agent-capability upgrade \u2014 August 13, 2026, with open weights the same day. MIT-licensed."
   ],
   "context_window_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ],
   "license_evidence": [
    "DeepSeek Versions",
    "DeepSeek's current frontier model is **DeepSeek-V4-Pro**, first released in preview on **April 24, 2026** and promoted to **general availability on August 13, 2026** as the **DeepSeek-V4-Pro-0813** build \u2014 **1.6 trillion total / 49B active parameters** in a Mixture-of-Experts architecture with a **1,000,000-token context window**, dual Thinking / Non-Thinking modes, and an MIT license."
   ],
   "significance_evidence": [
    "DeepSeek version table",
    "Current frontier flagship. 1.6T total / 49B active MoE. 1M-token context. Dual Thinking / Non-Thinking modes. General availability \u2014 V4-Pro-0813, an agent-capability upgrade \u2014 August 13, 2026, with open weights the same day. MIT-licensed."
   ]
  },
  {
   "url": "https://mungomash.com/ai/deepseek/versions",
   "title": "DeepSeek Versions",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek-V4-Flash",
   "release_date": "2026-04-24",
   "parameters": "284B total / 13B active",
   "context_window": "1,000,000-token",
   "license": "MIT",
   "significance": "Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace.",
   "organization_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "model_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "release_date_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "parameters_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "context_window_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "license_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ],
   "significance_evidence": [
    "DeepSeek version table",
    "Fast / cheap V4 companion. 284B total / 13B active MoE. Same 1M context and dual-mode hybrid-attention architecture as V4-Pro. Official API release \u2014 V4-Flash-0731, an agent-capability upgrade \u2014 July 31, 2026, with open weights on HuggingFace. MIT-licensed."
   ]
  },
  {
   "url": "https://mungomash.com/ai/deepseek/versions",
   "title": "DeepSeek Versions",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek-OCR-2",
   "release_date": "2026-01-27",
   "parameters": "~3B",
   "context_window": "False",
   "license": "Apache 2.0",
   "significance": "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d).",
   "organization_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ],
   "model_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ],
   "release_date_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ],
   "parameters_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ],
   "significance_evidence": [
    "DeepSeek version table",
    "Second-generation document-OCR model. ~3B parameters, second-gen DeepEncoder (\u201cVisual Causal Flow\u201d). Apache 2.0-licensed, following DeepSeek-Math-V2 off the MIT default."
   ]
  },
  {
   "url": "https://gigazine.net/gsc_news/en/20260423-qwen-3-6-27b",
   "title": "The 'Qwen3.6-27B,' a system running on a local PC with performance approaching that of the Claude Opus 4.5, has been released and is available for anyone to download.",
   "published_at": "2026-04-23T02:40:00",
   "organization": "Qwen",
   "model": null,
   "release_date": "2026-04-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B across all major coding benchmarks",
   "organization_evidence": [
    "The 'Qwen3.6-27B,' a system running on a local PC with performance approaching that of the Claude Opus 4.5, has been released and is available for anyone to download.",
    "Qwen (Tongyi Lab), an AI research team at the Chinese AI company Alibaba, has released ' **Qwen3.6-27B** ,' a multimodal AI model with 27 billion parameters."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "The 'Qwen3.6-27B,' a system running on a local PC with performance approaching that of the Claude Opus 4.5, has been released and is available for anyone to download.",
    "[![](https://i.gzn.jp/img/2026/04/23/qwen-3-6-27b/00_m.jpg)](https://i.gzn.jp/img/2026/04/23/qwen-3-6-27b/00.jpg)"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The 'Qwen3.6-27B,' a system running on a local PC with performance approaching that of the Claude Opus 4.5, has been released and is available for anyone to download.",
    "Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B across all major coding benchmarks."
   ]
  },
  {
   "url": "https://ceevis.com/google-gemma-4-open-weight-moe-architecture",
   "title": "Google Gemma 4: Inside the Open-Weight MoE Model Rewiring Edge AI",
   "published_at": "2026-08-05T21:10:59",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04",
   "parameters": "2.3B (E2B), 4.5B (E4B), 12B, 31B dense, and 26B total parameters (3.8B activated per token, labeled 26B-A4B)",
   "context_window": null,
   "license": null,
   "significance": "Replaced custom license with Apache 2.0; introduced a unified, encoder-free 12B design and a 26B-A4B Mixture-of-Experts variant.",
   "organization_evidence": [
    "## Key Takeaways",
    "- Gemma 4 is Google DeepMind\u2019s fourth-generation open-weight model family, shipping four sizes \u2014 2.3B (E2B), 4.5B (E4B), 12B, and 31B dense \u2014 plus a sparse Mixture-of-Experts variant (26B total parameters, 3.8B activated per token, labeled 26B-A4B)."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## What Gemma 4 Actually Is",
    "Open-weight AI has spent the past two years chasing a single, stubborn benchmark: can a model small enough to run on a laptop or phone do work that used to require a cloud API call? Google\u2019s Gemma 4, released in April 2026, is the most credible attempt yet from a major lab to close that gap \u2014 not because of one headline number, but because of a set of architectural choices that specifically target the memory and latency constraints of consumer hardware rather than data-center GPUs."
   ],
   "parameters_evidence": [
    "## Key Takeaways",
    "- Gemma 4 is Google DeepMind\u2019s fourth-generation open-weight model family, shipping four sizes \u2014 2.3B (E2B), 4.5B (E4B), 12B, and 31B dense \u2014 plus a sparse Mixture-of-Experts variant (26B total parameters, 3.8B activated per token, labeled 26B-A4B)."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Key Takeaways",
    "- Google switched Gemma\u2019s licensing from its earlier custom terms to Apache 2.0, removing commercial-use friction that had limited enterprise adoption of prior releases.",
    "- The 12B variant uses a new unified, encoder-free design that ingests raw audio and image patches directly into the language model, rather than routing through separate vision/audio encoders \u2014 a structural change from earlier Gemma generations."
   ]
  },
  {
   "url": "https://earlyterms.com/term/kimi-k2-6",
   "title": "Kimi K2.6 is Moonshot AI's April 20, 2026 open-weight flagship \u2014 a 1T-parameter Mixture-of-Experts model (32B",
   "published_at": "2026-04-20T12:48:32",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-04-20",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "58.6 on SWE-Bench Pro and 54.0 on HLE-with-tools, narrowly topping GPT-5.4 and Claude Opus 4.6 on both.",
   "organization_evidence": [
    "# Kimi K2.6",
    "[Kimi K2.6](https://www.kimi.com/blog/kimi-k2-6) is Moonshot AI's April 20, 2026 open-weight flagship \u2014 a 1T-parameter Mixture-of-Experts model (32B active, 384 experts, 256K context, native multimodal) released under a [Modified MIT License](https://huggingface.co/moonshotai/Kimi-K2.6) on Hugging Face and via the Kimi API."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K2.6",
    "[Kimi K2.6](https://www.kimi.com/blog/kimi-k2-6) is Moonshot AI's April 20, 2026 open-weight flagship \u2014 a 1T-parameter Mixture-of-Experts model (32B active, 384 experts, 256K context, native multimodal) released under a [Modified MIT License](https://huggingface.co/moonshotai/Kimi-K2.6) on Hugging Face and via the Kimi API."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K2.6",
    "K2.6 graduates Moonshot's `Code Preview` branch into a general-availability model built for long-horizon agentic coding: 12+ hour autonomous runs, 4,000 coordinated tool calls, and an `Agent Swarm` architecture of up to 300 parallel sub-agents. It posts 58.6 on SWE-Bench Pro and 54.0 on HLE-with-tools, narrowly topping GPT-5.4 and Claude Opus 4.6 on both."
   ]
  },
  {
   "url": "https://aifoss.dev/blog/glm-5-2-review-self-hosted-guide-2026",
   "title": "GLM-5.2 Review 2026: MIT 744B Coding Model, Real VRAM Math",
   "published_at": "2026-07-08T00:00:00",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744B",
   "context_window": null,
   "license": null,
   "significance": "Scored 62.1% on SWE-bench Pro \u2014 ahead of GPT-5.5 (58.6%) at roughly one-sixth the API cost.",
   "organization_evidence": [
    "# GLM-5.2 Review 2026: MIT 744B Coding Model, Real VRAM Math",
    "**TL;DR**: GLM-5.2 is Z.ai\u2019s 744B MIT-licensed MoE coding model, released June 13, 2026."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 Review 2026: MIT 744B Coding Model, Real VRAM Math",
    "**TL;DR**: GLM-5.2 is Z.ai\u2019s 744B MIT-licensed MoE coding model, released June 13, 2026.",
    "Z.ai (formerly Zhipu AI) shipped GLM-5.2 on June 13, 2026, with the open weights on Hugging Face under `zai-org/GLM-5.2` and a standalone pay-per-token API following on June 16."
   ],
   "parameters_evidence": [
    "# GLM-5.2 Review 2026: MIT 744B Coding Model, Real VRAM Math",
    "**TL;DR**: GLM-5.2 is Z.ai\u2019s 744B MIT-licensed MoE coding model, released June 13, 2026.",
    "- **Parameters**: 744B total / ~40B active per forward pass (Mixture-of-Experts)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Review 2026: MIT 744B Coding Model, Real VRAM Math",
    "It scored 62.1% on SWE-bench Pro \u2014 ahead of GPT-5.5 (58.6%) at roughly one-sixth the API cost."
   ]
  },
  {
   "url": "https://minimax-ai.chat/models/minimax-m3",
   "title": "MiniMax M3: API Pricing, Benchmarks & 1M Context",
   "published_at": "2026-06-30T14:00:23",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Supports a one-million-token context window with native text, image, and video input, achieves strong performance on coding and agentic benchmarks such as SWE-bench Pro (59.0%), Terminal-Bench 2.1 (66.0%), SWE-fficiency (34.8%), KernelBench Hard (28.8%), and MCP Atlas (74.2%), and features MiniMax Sparse Attention for improved prefill and decoding efficiency.",
   "organization_evidence": [
    "MiniMax M3 is an [open-weight, multimodal Mixture-of-Experts model](https://www.minimax.io/models/text/m3) built for coding, tool use, long-running agents, and large-context workloads.",
    "MiniMax M3 at a Glance",
    "MiniMax"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "[Released on June 1, 2026](https://www.minimax.io/blog/minimax-m3), it has approximately 428 billion total parameters, activates about 23 billion parameters per token, accepts text, images, and video, and offers an API context window of up to one million tokens.",
    "MiniMax M3 at a Glance",
    "| Release date | June 1, 2026 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 is an [open-weight, multimodal Mixture-of-Experts model](https://www.minimax.io/models/text/m3) built for coding, tool use, long-running agents, and large-context workloads.",
    "MiniMax M3 at a Glance",
    "MiniMax reports strong performance across coding and agentic benchmarks."
   ]
  },
  {
   "url": "https://machineherald.io/article/2026-08/12-nvidia-releases-nemotron-35-lightning-a-30-billion-parameter-open-model-built-for-high-volume-ai-agent-execution",
   "title": "Nvidia Releases Nemotron 3.5 Lightning, a 30-Billion-Parameter Open Model Built for High-Volume AI Agent Execution",
   "published_at": "2026-08-12T10:55:07",
   "organization": "Nvidia",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Nemotron 3.5 Lightning is an open 30-billion-parameter mixture-of-experts model with 3 billion active parameters, purpose-built for the execution layer of AI agents, delivering up to four times the output speed and 30% faster agentic task completion compared with other models in its weight class, and reaching 86% accuracy while completing 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy.",
   "organization_evidence": [
    "Nvidia Releases Nemotron 3.5 Lightning, a 30-Billion-Parameter Open Model Built for High-Volume AI Agent Execution"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Nvidia Releases Nemotron 3.5 Lightning, a 30-Billion-Parameter Open Model Built for High-Volume AI Agent Execution"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04",
   "parameters": "1600B",
   "context_window": null,
   "license": null,
   "significance": "DeepSeek's April 2026 frontier flagship. 1.6T total / 49B active MoE with hybrid Compressed Sparse Attention + Heavily Compressed Attention.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "1600B"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship. 1.6T total / 49B active MoE with hybrid Compressed Sparse Attention + Heavily Compressed Attention."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Pro",
   "release_date": "2026-04",
   "parameters": "1600B",
   "context_window": "1024K",
   "license": "COMMERCIAL OK",
   "significance": "DeepSeek's April 2026 frontier flagship. 1.6T total / 49B active MoE with hybrid Compressed Sparse Attention + Heavily Compressed Attention.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek V4 Pro (1.6T MoE)"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "1600B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b71024K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b71024K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship. 1.6T total / 49B active MoE with hybrid Compressed Sparse Attention + Heavily Compressed Attention."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek V4 Flash",
   "release_date": null,
   "parameters": "284B",
   "context_window": "1024K",
   "license": "COMMERCIAL OK",
   "significance": "The cost-efficient sibling of V4-Pro. 284B total / 13B active MoE, same hybrid CSA+HCA attention, same 1M context. The MoE active-param rati",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's April 2026 frontier flagship."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek V4 Flash (284B MoE)"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "284B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b71024K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b71024K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "The cost-efficient sibling of V4-Pro. 284B total / 13B active MoE, same hybrid CSA+HCA attention, same 1M context. The MoE active-param rati"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": "DeepSeek V4",
   "release_date": "2026-03",
   "parameters": "745B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "DeepSeek's spring 2026 frontier MoE. 745B total / 38B active. The current open-weight benchmark leader on coding + math; closes the gap with",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's spring 2026 frontier MoE."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek V4"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's spring 2026 frontier MoE."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "745B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 DEEPSEEK",
    "DeepSeek's spring 2026 frontier MoE. 745B total / 38B active. The current open-weight benchmark leader on coding + math; closes the gap with"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Alibaba",
   "model": "Qwen 3.5 235B-A17B",
   "release_date": "2026-05",
   "parameters": "397B",
   "context_window": "256K",
   "license": "COMMERCIAL OK",
   "significance": "Alibaba's May 2026 flagship. 397B total / 17B active MoE with hybrid thinking-mode toggle inherited from Qwen 3. Strongest open scientific r",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "Alibaba's May 2026 flagship."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "Qwen 3.5 235B-A17B (MoE)"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "Alibaba's May 2026 flagship."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "397B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "COMMERCIAL OK\u00b7256K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "COMMERCIAL OK\u00b7256K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 QWEN",
    "Alibaba's May 2026 flagship. 397B total / 17B active MoE with hybrid thinking-mode toggle inherited from Qwen 3. Strongest open scientific r"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Meta",
   "model": "Llama 4 Scout",
   "release_date": "2026",
   "parameters": "109B",
   "context_window": "9766K",
   "license": "COMMERCIAL OK",
   "significance": "Meta's 2026 flagship MoE model. 109B total parameters with only 17B active per forward pass and a record 10-million-token context window \u2014 u",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Llama 4 Scout"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "109B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b79766K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b79766K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model. 109B total parameters with only 17B active per forward pass and a record 10-million-token context window \u2014 u"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Meta",
   "model": "Llama 4 Maverick",
   "release_date": "2026",
   "parameters": "400B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Meta's high-end Llama 4 sibling \u2014 128 experts MoE built for performance over efficiency. Multilingual strength is its standout. Effectively",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's high-end Llama 4 sibling \u2014 128 experts MoE built for performance over efficiency."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Llama 4 Maverick"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "400B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7977K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7977K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's high-end Llama 4 sibling \u2014 128 experts MoE built for performance over efficiency. Multilingual strength is its standout. Effectively"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Meta",
   "model": "Llama 4 405B",
   "release_date": "2026",
   "parameters": "405B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Meta's dense flagship in the Llama 4 line. 405B params; comparable footprint to Llama 3.1 405B with the Llama 4 reasoning improvements.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's dense flagship in the Llama 4 line."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Llama 4 405B"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "405B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's dense flagship in the Llama 4 line. 405B params; comparable footprint to Llama 3.1 405B with the Llama 4 reasoning improvements."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Meta",
   "model": "Llama 4 70B",
   "release_date": "2026",
   "parameters": "70B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Llama 4 dense at 70B. Drop-in successor to Llama 3.3 70B; same hardware envelope, better on reasoning benchmarks.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Llama 4 70B"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Meta's 2026 flagship MoE model."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "70B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 LLAMA",
    "Llama 4 dense at 70B. Drop-in successor to Llama 3.3 70B; same hardware envelope, better on reasoning benchmarks."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4 31B Dense",
   "release_date": null,
   "parameters": "31B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Google's flagship dense Gemma 4. Beats some 400B-class proprietary models on benchmarks. Targets the 24GB single-GPU sweet spot.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Google's flagship dense Gemma 4."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Gemma 4 31B Dense"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "31B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Google's flagship dense Gemma 4. Beats some 400B-class proprietary models on benchmarks. Targets the 24GB single-GPU sweet spot."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4 26B MoE",
   "release_date": null,
   "parameters": "26B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "MoE variant of Gemma 4. Faster per-token than the 31B dense at similar quality on most tasks.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Google's flagship dense Gemma 4."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Gemma 4 26B MoE"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "26B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "MoE variant of Gemma 4. Faster per-token than the 31B dense at similar quality on most tasks."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4 E4B",
   "release_date": null,
   "parameters": "4B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Edge-class Gemma 4. The 'Effective 4B' branding signals it punches above its parameter count via training-data quality.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Google's flagship dense Gemma 4."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Gemma 4 E4B (Effective 4B)"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "4B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Edge-class Gemma 4. The 'Effective 4B' branding signals it punches above its parameter count via training-data quality."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Google",
   "model": "Gemma 4 E2B",
   "release_date": null,
   "parameters": "2B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "Smallest Gemma 4. Designed for phones and Raspberry-Pi-class hardware.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Google's flagship dense Gemma 4."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Gemma 4 E2B (Effective 2B)"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "2B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "COMMERCIAL OK\u00b7MULTIMODAL\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GEMMA",
    "Smallest Gemma 4. Designed for phones and Raspberry-Pi-class hardware."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Mistral",
   "model": "Mistral Medium 3.5",
   "release_date": "2026-04",
   "parameters": "675B",
   "context_window": "256K",
   "license": "RESTRICTED",
   "significance": "Mistral's April 2026 frontier MoE. 675B total / 41B active. Strong European-multilingual lineage carries through; the new release competes h",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "Mistral's April 2026 frontier MoE."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "Mistral Medium 3.5 (675B MoE)"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "Mistral's April 2026 frontier MoE."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "675B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "RESTRICTED\u00b7256K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "RESTRICTED\u00b7256K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 MISTRAL",
    "Mistral's April 2026 frontier MoE. 675B total / 41B active. Strong European-multilingual lineage carries through; the new release competes h"
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "Zhipu",
   "model": "GLM-5",
   "release_date": "2026",
   "parameters": "200B",
   "context_window": "195K",
   "license": "COMMERCIAL OK",
   "significance": "Zhipu's GLM-5 currently leads the Open LLM Leaderboard 2026. Strong reasoning and bilingual EN/ZH capability.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "Zhipu's GLM-5 currently leads the Open LLM Leaderboard 2026."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "GLM-5"
   ],
   "release_date_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "Zhipu's GLM-5 currently leads the Open LLM Leaderboard 2026."
   ],
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "200B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "COMMERCIAL OK\u00b7195K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "COMMERCIAL OK\u00b7195K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 OTHER",
    "Zhipu's GLM-5 currently leads the Open LLM Leaderboard 2026. Strong reasoning and bilingual EN/ZH capability."
   ]
  },
  {
   "url": "https://runlocalai.co/models",
   "title": "Open-weight model directory | RunLocalAI",
   "published_at": null,
   "organization": "IBM",
   "model": "Granite 3.3 8B",
   "release_date": null,
   "parameters": "8B",
   "context_window": "128K",
   "license": "COMMERCIAL OK",
   "significance": "IBM Granite 3.3. Iterative refresh of 3.2 \u2014 same architecture; improved instruction following and tool-call reliability. Apache 2.0.",
   "organization_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "IBM Granite 3.3."
   ],
   "model_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "Granite 3.3 8B"
   ],
   "release_date_evidence": null,
   "parameters_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "8B"
   ],
   "context_window_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "license_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "COMMERCIAL OK\u00b7128K CTX"
   ],
   "significance_evidence": [
    "BLK \u00b7 MODEL REGISTRY",
    "Open-weight models",
    "FAM \u00b7 GRANITE",
    "IBM Granite 3.3. Iterative refresh of 3.2 \u2014 same architecture; improved instruction following and tool-call reliability. Apache 2.0."
   ]
  },
  {
   "url": "https://nextomoro.com/alibaba-qwen-damo",
   "title": "Alibaba Qwen / DAMO",
   "published_at": "2026-04-27T14:40:45",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "Dense open-weight model outperforming 397B MoE on agentic coding benchmarks",
   "organization_evidence": [
    "# Alibaba Qwen / DAMO"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "parameters_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MarkTechPost: Alibaba Qwen Team Releases Qwen3.6-27B",
    "[MarkTechPost: Alibaba Qwen Team Releases Qwen3.6-27B](https://www.marktechpost.com/2026/04/22/alibaba-qwen-team-releases-qwen3-6-27b-a-dense-open-weight-model-outperforming-397b-moe-on-agentic-coding-benchmarks/?ref=nextomoro.com). April 2026 open-weights release coverage.",
    "Qwen3.6-27B, the open-weights companion, has been characterized in industry coverage as outperforming substantially larger open-weights mixture-of-experts models on agentic coding benchmarks, with reports of dense-model leadership against 397B-parameter MoE competitors."
   ]
  },
  {
   "url": "https://nextomoro.com/alibaba-qwen-damo",
   "title": "Alibaba Qwen / DAMO",
   "published_at": "2026-04-27T14:40:45",
   "organization": "Alibaba",
   "model": "Qwen3.6-27B",
   "release_date": "2026-04",
   "parameters": "27B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Dense open-weight model outperforming 397B MoE on agentic coding benchmarks",
   "organization_evidence": [
    "# Alibaba Qwen / DAMO"
   ],
   "model_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "release_date_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "parameters_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "significance_evidence": [
    "MarkTechPost: Alibaba Qwen Team Releases Qwen3.6-27B",
    "[MarkTechPost: Alibaba Qwen Team Releases Qwen3.6-27B](https://www.marktechpost.com/2026/04/22/alibaba-qwen-team-releases-qwen3-6-27b-a-dense-open-weight-model-outperforming-397b-moe-on-agentic-coding-benchmarks/?ref=nextomoro.com). April 2026 open-weights release coverage.",
    "Qwen3.6-27B, the open-weights companion, has been characterized in industry coverage as outperforming substantially larger open-weights mixture-of-experts models on agentic coding benchmarks, with reports of dense-model leadership against 397B-parameter MoE competitors."
   ]
  },
  {
   "url": "https://nextomoro.com/alibaba-qwen-damo",
   "title": "Alibaba Qwen / DAMO",
   "published_at": "2026-04-27T14:40:45",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
   "release_date": "2026-04",
   "parameters": "35B-A3B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "Mixture-of-experts variant positioned for community fine-tuning and on-premises deployment",
   "organization_evidence": [
    "# Alibaba Qwen / DAMO"
   ],
   "model_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "release_date_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "parameters_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ],
   "significance_evidence": [
    "Models and products",
    "**Qwen3.6-27B and Qwen3.6-35B-A3B.** April 2026 open-weights releases under Apache 2.0. Dense and mixture-of-experts variants positioned for community fine-tuning and on-premises deployment."
   ]
  },
  {
   "url": "https://nextomoro.com/alibaba-qwen-damo",
   "title": "Alibaba Qwen / DAMO",
   "published_at": "2026-04-27T14:40:45",
   "organization": "Alibaba",
   "model": "Qwen3.5-Omni",
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Provides native cross-modal interaction across image, video, audio, and text in a single model",
   "organization_evidence": [
    "# Alibaba Qwen / DAMO"
   ],
   "model_evidence": [
    "Models and products",
    "**Qwen3.5 and Qwen3.5-Omni.** February to April 2026 releases. Qwen3.5-Omni provides native cross-modal interaction across image, video, audio, and text in a single model."
   ],
   "release_date_evidence": [
    "Models and products",
    "**Qwen3.5 and Qwen3.5-Omni.** February to April 2026 releases. Qwen3.5-Omni provides native cross-modal interaction across image, video, audio, and text in a single model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Models and products",
    "**Qwen3.5 and Qwen3.5-Omni.** February to April 2026 releases. Qwen3.5-Omni provides native cross-modal interaction across image, video, audio, and text in a single model."
   ]
  },
  {
   "url": "https://the-agent-report.com/2026/08/meta-muse-glimmer-open-weight-local-agent-model",
   "title": "Meta Drops Muse Glimmer: A 30B Open-Weight Agent Model That Runs on Your Laptop",
   "published_at": "2026-08-11T06:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-08-10",
   "parameters": "~29.6B",
   "context_window": null,
   "license": null,
   "significance": "scoring 35 on the Artificial Analysis Intelligence Index \u2014 21 points above Llama 4 Maverick. The model leads its size class on agentic benchmarks (MCP Atlas: 75.5 vs Gemma4-31B\u2019s 54.2, SWE-Bench Pro: 51.2 vs Gemma\u2019s 36.9) but trails Qwen3.6-27B on knowledge work (GDPVal-AA: 953 vs 1,141) with an 82% hallucination rate.",
   "organization_evidence": [
    "Meta Drops Muse Glimmer: A 30B Open-Weight Agent Model That Runs on Your Laptop",
    "**TL;DR** \u2014 Meta released Muse Glimmer on August 10, 2026: a 30-billion-parameter open-weight AI model (Apache 2.0) purpose-built for agentic tasks that runs on a single consumer GPU."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "**TL;DR** \u2014 Meta released Muse Glimmer on August 10, 2026: a 30-billion-parameter open-weight AI model (Apache 2.0) purpose-built for agentic tasks that runs on a single consumer GPU.",
    "Meta Drops Muse Glimmer: A 30B Open-Weight Agent Model That Runs on Your Laptop"
   ],
   "parameters_evidence": [
    "Key specs at a glance:",
    "| Parameters | ~29.6B (dense, including 1.8B vision encoder) |",
    "Meta Drops Muse Glimmer: A 30B Open-Weight Agent Model That Runs on Your Laptop",
    "**TL;DR** \u2014 Meta released Muse Glimmer on August 10, 2026: a 30-billion-parameter open-weight AI model (Apache 2.0) purpose-built for agentic tasks that runs on a single consumer GPU."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meta Drops Muse Glimmer: A 30B Open-Weight Agent Model That Runs on Your Laptop",
    "**TL;DR** \u2014 Meta released Muse Glimmer on August 10, 2026: a 30-billion-parameter open-weight AI model (Apache 2.0) purpose-built for agentic tasks that runs on a single consumer GPU. It\u2019s Meta\u2019s first open-weight release since Llama 4 (16 months ago), scoring 35 on the Artificial Analysis Intelligence Index \u2014 21 points above Llama 4 Maverick. The model leads its size class on agentic benchmarks (MCP Atlas: 75.5 vs Gemma4-31B\u2019s 54.2, SWE-Bench Pro: 51.2 vs Gemma\u2019s 36.9) but trails Qwen3.6-27B on knowledge work (GDPVal-AA: 953 vs 1,141) with an 82% hallucination rate."
   ]
  },
  {
   "url": "https://winbuzzer.com/2026/04/03/google-releases-gemma-4-open-models-under-apache-20-license-xcxwbn",
   "title": "Google Releases Gemma 4 Open Models Under Apache 2.0 License",
   "published_at": "2026-04-03T10:11:00",
   "organization": "Google",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "The 31B model scored 89.2% on AIME 2026 and 86.4% on agentic tau2-bench, up from 20.8% and 6.6% in the previous Gemma 3 generation.",
   "organization_evidence": [
    "# Google Releases Gemma 4 Open Models Under Apache 2.0 License",
    "Google has released Gemma 4, a family of four open-weight AI models under Apache 2.0, with edge-to-workstation variants built on Gemini 3 technology."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Releases Gemma 4 Open Models Under Apache 2.0 License",
    "- Performance Leap:The 31B model scored 89.2% on AIME 2026 and 86.4% on agentic tau2-bench, up from 20.8% and 6.6% in the previous Gemma 3 generation."
   ]
  },
  {
   "url": "https://sdd.sh/2026/07/kimi-k3-open-weights-license-surprise",
   "title": "Kimi K3's Weights Are Open. Its License Isn't What Anyone Reported.",
   "published_at": "2026-07-28T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion total parameters, 104 billion active per token",
   "context_window": null,
   "license": null,
   "significance": "2.8 trillion total parameters, 104 billion active per token, competitive framing against frontier proprietary models",
   "organization_evidence": [
    "Kimi K3's Weights Are Open. Its License Isn't What Anyone Reported.",
    "Moonshot AI published the full weights for Kimi K3 on July 27 \u2014 a day ahead of its own July 27 target window closing, and by most measures the largest open-weight model release to date."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Kimi K3's Weights Are Open. Its License Isn't What Anyone Reported.",
    "Moonshot AI published the full weights for Kimi K3 on July 27 \u2014 a day ahead of its own July 27 target window closing, and by most measures the largest open-weight model release to date."
   ],
   "parameters_evidence": [
    "Kimi K3's Weights Are Open. Its License Isn't What Anyone Reported.",
    "2.8 trillion total parameters, 104 billion active per token, a 1M-token context window, and day-zero inference support from both vLLM and SGLang."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Kimi K3's Weights Are Open. Its License Isn't What Anyone Reported.",
    "2.8 trillion total parameters, 104 billion active per token, a 1M-token context window, and day-zero inference support from both vLLM and SGLang."
   ]
  },
  {
   "url": "https://thecontext.dev/en/news/2026-06-16-glm-5-2-long-horizon",
   "title": "GLM-5.2 Ships Its Weights: Open Models Have Made the Frontier a Quarterly Refresh",
   "published_at": "2026-06-16T00:00:00",
   "organization": "Zhipu",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "74.4 on FrontierSWE, 34.3 on PostTrainBench, 13.0 on SWE-Marathon, Terminal-Bench 2.1 81.0, SWE-bench Pro 62.1",
   "organization_evidence": [
    "# GLM-5.2 Ships Its Weights: Open Models Have Made the Frontier a Quarterly Refresh",
    "Zhipu released GLM-5.2 weights under MIT, with a 1M context, a long-horizon focus, and a tunable thinking budget."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5.2 Ships Its Weights: Open Models Have Made the Frontier a Quarterly Refresh",
    "On FrontierSWE, which measures whether an agent can finish open-ended engineering projects on the scale of hours to tens of hours, it reports 74.4, trailing Opus 4.8 at 75.1 by about a point and edging past GPT-5.5 at 72.6.",
    "On PostTrainBench, where each agent gets one H100 and is judged by how much it can improve small models through post-training, it reports 34.3, beating Opus 4.7 and GPT-5.5 and ranking second only to Opus 4.8 at 37.2.",
    "On SWE-Marathon, covering compilers, kernel optimization, and production-grade services, it reports 13.0 against Opus 4.8\u2019s 26.0, a 2x gap.",
    "On standard coding boards the jump is sharper: Terminal-Bench 2.1 goes from GLM-5.1\u2019s 63.5 to 81.0, SWE-bench Pro from 58.4 to 62.1."
   ]
  },
  {
   "url": "https://dev.to/breachprotocol/thinking-machines-releases-inkling-now-the-top-ranked-us-open-weights-model-1ead",
   "title": "Thinking Machines releases Inkling, now the top-ranked US open-weights model",
   "published_at": "2026-07-16T02:58:53",
   "organization": "Thinking Machines Lab",
   "model": null,
   "release_date": "2026-07-15",
   "parameters": "975-billion-parameter",
   "context_window": null,
   "license": null,
   "significance": "Inkling debuts at 41 on the Artificial Analysis Intelligence Index, the top score for any US open-weights model",
   "organization_evidence": [
    "Thinking Machines Lab has released Inkling, a 975-billion-parameter open-weights model published under the permissive Apache 2.0 license.",
    "Thinking Machines Lab, the research company founded by former OpenAI CTO Mira Murati."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "- **When:** Released July 15, 2026."
   ],
   "parameters_evidence": [
    "Thinking Machines Lab has released Inkling, a 975-billion-parameter open-weights model published under the permissive Apache 2.0 license."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **The headline number:** Inkling debuts at 41 on the Artificial Analysis Intelligence Index, the top score for any US open-weights model, with Nemotron 3 Ultra second at 38."
   ]
  },
  {
   "url": "https://news.lavx.hu/article/qwen-releases-2-4-trillion-parameter-open-model-matching-frontier-closed-systems-on-coding-benchmarks",
   "title": "Qwen releases 2.4 trillion parameter open model, matching frontier closed systems on coding benchmarks",
   "published_at": "2026-08-12T16:18:04",
   "organization": "Alibaba",
   "model": null,
   "release_date": null,
   "parameters": "2.4 trillion total parameters (95 billion active)",
   "context_window": null,
   "license": null,
   "significance": "outperforms several proprietary systems on software engineering tasks",
   "organization_evidence": [
    "Qwen releases 2.4 trillion parameter open model, matching frontier closed systems on coding benchmarks",
    "Alibaba's Qwen team open-sourced Qwen3.8-2.4T-A95B, a mixture-of-experts model that activates 95 billion parameters per inference and outperforms several proprietary systems on software engineering tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "Qwen releases 2.4 trillion parameter open model, matching frontier closed systems on coding benchmarks",
    "Alibaba's Qwen team open-sourced Qwen3.8-2.4T-A95B, a mixture-of-experts model that activates 95 billion parameters per inference and outperforms several proprietary systems on software engineering tasks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Qwen releases 2.4 trillion parameter open model, matching frontier closed systems on coding benchmarks",
    "Alibaba's Qwen team open-sourced Qwen3.8-2.4T-A95B, a mixture-of-experts model that activates 95 billion parameters per inference and outperforms several proprietary systems on software engineering tasks."
   ]
  },
  {
   "url": "https://ai.google.dev/gemma?authuser=7&hl=it",
   "title": "Gemma \u2014 Google DeepMind",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-06",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "A unified, encoder-free multimodal model",
   "organization_evidence": [
    "Large Language Models (LLMs), such as Gemma, may sometimes provide inaccurate or offensive content that doesn\u2019t represent Google\u2019s views."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "June 2026",
    "Slide 1 of 14",
    "# Gemma",
    "Our most capable open models"
   ],
   "parameters_evidence": [
    "### Introducing Gemma 4 12B",
    "Slide 1 of 14",
    "# Gemma",
    "Our most capable open models"
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "A unified, encoder-free multimodal model",
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    "# Gemma",
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  },
  {
   "url": "https://ai.google.dev/gemma?authuser=7&hl=it",
   "title": "Gemma \u2014 Google DeepMind",
   "published_at": null,
   "organization": "Google",
   "model": null,
   "release_date": "2026-01",
   "parameters": "4B",
   "context_window": null,
   "license": null,
   "significance": "High-dimensional medical imaging interpretation in a compute-efficient model from Health AI Developer Foundations",
   "organization_evidence": [
    "Large Language Models (LLMs), such as Gemma, may sometimes provide inaccurate or offensive content that doesn\u2019t represent Google\u2019s views."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "January 2026",
    "Slide 1 of 14",
    "# Gemma",
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   "parameters_evidence": [
    "### Introducing MedGemma 1.5 4B",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
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  },
  {
   "url": "https://docs.apiyi.com/en/news/kimi-k3-launch",
   "title": "Kimi K3 Launch: 2.8T-Param Open-Source Flagship",
   "published_at": "2026-07-17T17:15:29",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Largest open-source model ever with 2.8 trillion total parameters (MoE)",
   "organization_evidence": [
    "On July 16, 2026 (UTC+8), Moonshot AI officially released its next-generation flagship model **Kimi K3**.",
    "Background"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On July 16, 2026 (UTC+8), Moonshot AI officially released its next-generation flagship model **Kimi K3**.",
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   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "- **Largest open-source model ever**: 2.8 trillion total parameters (MoE), the first open model in the 3-trillion-parameter class; full weights promised by July 27, 2026 (UTC+8)",
    "Key Takeaways"
   ]
  },
  {
   "url": "https://dataconomy.com/2026/06/18/glm-5-2-open-source-model-rivaling-gpt-5-5-coding-benchmarks",
   "title": "Z.ai Launches GLM-5.2 Open Model With Strong Coding Performance - Dataconomy",
   "published_at": "2026-06-18T13:04:29",
   "organization": "Z.ai",
   "model": null,
   "release_date": "2026-06-16",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Outperforms OpenAI\u2019s GPT-5.5 on several long-horizon coding benchmarks, scores 62.1 on SWE-bench Pro, and scores 81.0 on Terminal-Bench 2.1 (surpassing 80% as the first open-weights model to do so).",
   "organization_evidence": [
    "Chinese AI startup Z.ai, formerly known as Zhipu AI, released the GLM-5.2 open-weights model on June 16, featuring a 753-billion-parameter system."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Chinese AI startup Z.ai, formerly known as Zhipu AI, released the GLM-5.2 open-weights model on June 16, featuring a 753-billion-parameter system."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "The model outperforms OpenAI\u2019s GPT-5.5 on several long-horizon coding benchmarks and costs roughly one-sixth as much to operate via API.",
    "GLM-5.2, which is available under an MIT open-source license on [Hugging Face](https://huggingface.co/zai-org/GLM-5.2), scored 62.1 on SWE-bench Pro, compared to GPT-5.5\u2019s score of 58.6.",
    "Additionally, it achieved a score of 81.0 on Terminal-Bench 2.1, becoming the first open-weights model to surpass 80% on that benchmark."
   ]
  },
  {
   "url": "https://runaihome.com/blog/minimax-m3-local-ai-vram-hardware-guide-2026",
   "title": "MiniMax M3 Local AI Hardware Guide 2026: The 428B Open-Weight Model You (Probably) Can't Run at Home",
   "published_at": "2026-06-16T00:00:00",
   "organization": "MiniMax",
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   "release_date": "2026-06-01",
   "parameters": "428B",
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   "significance": "Scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, 74.2% on MCP Atlas, and 83.5 on BrowseComp \u2014 the last figure beating Claude Opus 4.7\u2019s 79.3 on autonomous browsing.",
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    "MiniMax released M3 on June 1, 2026."
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    "MiniMax M3 Local AI Hardware Guide 2026: The 428B Open-Weight Model You (Probably) Can't Run at Home",
    "MiniMax released M3 on June 1, 2026."
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   "parameters_evidence": [
    "MiniMax M3 Local AI Hardware Guide 2026: The 428B Open-Weight Model You (Probably) Can't Run at Home",
    "MiniMax released M3 on June 1, 2026.",
    "It\u2019s an open-weight Mixture-of-Experts model with roughly **428B total parameters and ~23B activated per token**, spread across 256 fine-grained experts."
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   "context_window_evidence": null,
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    "MiniMax M3 Local AI Hardware Guide 2026: The 428B Open-Weight Model You (Probably) Can't Run at Home",
    "On benchmarks, M3 punches at the frontier.",
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   ]
  },
  {
   "url": "https://tensorfeed.ai/originals/meituan-longcat-2-owl-alpha-openrouter",
   "title": "Owl Alpha Was Meituan All Along. LongCat-2.0 Open-Sourced Today at 1.6T, Zero Nvidia, and It Has Been Number One on OpenRouter For Two Months.",
   "published_at": "2026-06-30T00:00:00",
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   "release_date": "2026-06-30",
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   "significance": "SWE-bench Pro of 59.5, number one on OpenRouter",
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    "Open Frontier \u00b7 Model Release",
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   ]
  },
  {
   "url": "https://tritonite.io/stories/deepseek-v4-sovereign-stack-2026",
   "title": "DeepSeek V4 on Huawei Ascend: The Sovereign AI Stack Just Got Real",
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  {
   "url": "https://tritonite.io/stories/deepseek-v4-sovereign-stack-2026",
   "title": "DeepSeek V4 on Huawei Ascend: The Sovereign AI Stack Just Got Real",
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  {
   "url": "https://tritonite.io/stories/deepseek-v4-sovereign-stack-2026",
   "title": "DeepSeek V4 on Huawei Ascend: The Sovereign AI Stack Just Got Real",
   "published_at": "2026-07-22T00:00:00",
   "organization": "DeepSeek",
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  },
  {
   "url": "https://vantaige.io/blog/mistral-medium-3-5-vibe-self-host-4-gpus-2026",
   "title": "Mistral Medium 3.5 Self Host: 77.6% SWE-Bench on 4 GPUs (2026)",
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   "organization_evidence": [
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   "release_date_evidence": [
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  },
  {
   "url": "https://officechai.com/ai/google-releases-gemma-4-open-models-calls-them-best-in-world-in-their-category",
   "title": "Google Releases Gemma 4 Open Models, Calls Them \"Best In World\" In Their Category",
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   "parameters_evidence": [
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  },
  {
   "url": "https://warp2search.net/story/moonshot-ai-releases-kimi-k3-first-openweight-28t-frontier-model-on-hugging-face",
   "title": "Moonshot AI Releases Kimi K3: First Open-Weight 2.8T Frontier Model on Hugging Face",
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  },
  {
   "url": "https://superteams.ai/glossary/glm-5-2",
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   "release_date_evidence": [
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  },
  {
   "url": "https://archyde.com/minimax-m3-high-performance-low-cost-open-weights-ai-model",
   "title": "MiniMax M3: High-Performance, Low-Cost Open-Weights AI Model \u2013 Archyde",
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    "M3\u2019s 200B parameter model, with 1M context, achieves similar performance at 1/20th the cost, demonstrating that efficiency gains can rival sheer scale."
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    "On June 1, 2026, Chinese startup MiniMax disrupted the AI landscape with its M3 large language model, which achieves frontier-tier coding and agentic performance at a fraction of the cost of U.S. Proprietary systems.",
    "The model\u2019s 1-million-token context window, native multimodality, and upcoming open-source release under an unspecified license challenge the entrenched cost-performance paradigm of closed AI ecosystems."
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   "license_evidence": [
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    "On June 1, 2026, Chinese startup MiniMax disrupted the AI landscape with its M3 large language model, which achieves frontier-tier coding and agentic performance at a fraction of the cost of U.S. Proprietary systems.",
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  },
  {
   "url": "https://shadowfetch.com/news-room/ibm-granite-4-1-open-language-models",
   "title": "IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
   "published_at": "2026-08-05T17:21:03",
   "organization": "IBM",
   "model": "Granite 4.1",
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   "significance": "pairs 3B, 8B, and 30B dense checkpoints with a post-training pipeline that IBM says sharpens instruction following and tool use across 12 languages",
   "organization_evidence": [
    "Models",
    "# IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
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   "model_evidence": [
    "Models",
    "# IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
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   "release_date_evidence": [
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    "# IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
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    "Models",
    "# IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
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   "license_evidence": [
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   "significance_evidence": [
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    "# IBM ships Granite 4.1, a multilingual open-model family built around stronger tool calling",
    "Released April 29, 2026 under Apache 2.0, the Granite 4.1 generation pairs 3B, 8B, and 30B dense checkpoints with a post-training pipeline that IBM says sharpens instruction following and tool use across 12 languages."
   ]
  },
  {
   "url": "https://metirai.com/blog/summer-2026-frontier-model-release-flood-multi-model",
   "title": "Summer 2026's Frontier AI Model Release Flood, Explained",
   "published_at": "2026-07-26T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion",
   "context_window": null,
   "license": null,
   "significance": "largest open-weight release published to date",
   "organization_evidence": [
    "Summer 2026's Frontier AI Model Release Flood, Explained",
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   "model_evidence": null,
   "release_date_evidence": [
    "Summer 2026's Frontier AI Model Release Flood, Explained",
    "Kimi K3's full weights followed on July 27, the largest open-weight release published to date."
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   "parameters_evidence": [
    "Summer 2026's Frontier AI Model Release Flood, Explained",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Summer 2026's Frontier AI Model Release Flood, Explained",
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  },
  {
   "url": "https://precisionaiacademy.com/blog/news-2026-04-14-3",
   "title": "Google Gemma 4: A 31B Open Model That Beats Models 10x Its Size",
   "published_at": "2026-04-14T00:00:00",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A 31-billion-parameter dense model, released under the Apache 2.0 license, that runs on a single consumer GPU and outperforms models with 10 times more parameters on major benchmarks.",
   "organization_evidence": [
    "**Google released Gemma 4 in early April 2026,** and the developer community is still absorbing what this model family means.",
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    "The headline numbers tell most of the story: a 31-billion-parameter dense model, released under the Apache 2.0 license, that runs on a single consumer GPU and outperforms models with 10 times more parameters on major benchmarks."
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   "url": "https://ailearningguides.com/kimi-k3-open-weight-moe-2026",
   "title": "Kimi K3 in 2026: Open-Weight 2.8T MoE Rivals GPT-5.6",
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   "release_date_evidence": [
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    "# Kimi K3 in 2026: Open-Weight 2.8T MoE Rivals GPT-5.6",
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   "url": "https://glm-5.org",
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   "url": "https://happycapyguide.com/blog/minimax-m2-7-open-source-coding-model-2026",
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   "url": "https://nexu.io/blog/qwen-3-6-35b-a3b-open-source-moe",
   "title": "Qwen 3.6-35B-A3B Is Open: 3B Active Params, 73.4% SWE-bench, Drops Into nexu Tonight",
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   "url": "https://smartchunks.com/meta-llama-4-scout-maverick-llamacon-2026",
   "title": "Meta Drops Two Llama 4 Models As Microsoft Reveals A Shocking Stat | Smart Chunks",
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   "url": "https://smartchunks.com/meta-llama-4-scout-maverick-llamacon-2026",
   "title": "Meta Drops Two Llama 4 Models As Microsoft Reveals A Shocking Stat | Smart Chunks",
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   "url": "https://thekodelab.com/en/posts/google-gemma-4",
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   "release_date_evidence": [
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   "url": "https://groundtruth.day/news/moonshot-ships-kimi-k3-weights-2-8-trillion-parameters.html",
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   "url": "https://felloai.com/ja/best-open-source-ai-models",
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   "url": "https://felloai.com/ja/best-open-source-ai-models",
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   "url": "https://felloai.com/ja/best-open-source-ai-models",
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  {
   "url": "https://felloai.com/ja/best-open-source-ai-models",
   "title": "Best Open Source AI Models in 2026, Ranked and Compared",
   "published_at": "2026-06-21T15:16:24",
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  {
   "url": "https://felloai.com/ja/best-open-source-ai-models",
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  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
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   "release_date": "2026-08-27",
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   "release_date_evidence": [
    "## [August 2026](/releases/2026-08) 6",
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   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "Alibaba released open weights for Qwen3.8-Flash-Next: 125B parameters plus a 51B N-gram embedding table with only 6B active, trained at roughly 1/9 the cost of Qwen3.7-Plus while self-reporting DeepSWE 58.7 and SWE-bench Pro 62.5."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-08",
   "parameters": "27B",
   "context_window": null,
   "license": null,
   "significance": "scoring 52 on the Artificial Analysis Intelligence Index \u2014 the same as GPT-5.6 Luna at max reasoning \u2014 and 51 on the Agentic index",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "Alibaba's overnight community darling: a 27B-parameter Apache 2.0 model scoring 52 on the Artificial Analysis Intelligence Index \u2014 the same as GPT-5.6 Luna at max reasoning \u2014 and 51 on the Agentic index."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## [August 2026](/releases/2026-08) 6",
    "Alibaba's Qwen team is the highest-velocity open-weights lab on the timeline \u2014 almost every entry is a model drop, from the Qwen 2.5 and Qwen 3 language-model lines to Wan video and Z-Image generation models."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.8-27B",
    "Alibaba's overnight community darling: a 27B-parameter Apache 2.0 model scoring 52 on the Artificial Analysis Intelligence Index \u2014 the same as GPT-5.6 Luna at max reasoning \u2014 and 51 on the Agentic index."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "Alibaba's overnight community darling: a 27B-parameter Apache 2.0 model scoring 52 on the Artificial Analysis Intelligence Index \u2014 the same as GPT-5.6 Luna at max reasoning \u2014 and 51 on the Agentic index."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Wan-Animate-2",
   "release_date": "2026-08",
   "parameters": "14B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "It wins over 70% of blind preference comparisons.",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Wan-Animate-2",
    "Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Wan-Animate-2",
    "Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0."
   ],
   "release_date_evidence": [
    "## [August 2026](/releases/2026-08) 6",
    "Alibaba's Qwen team is the highest-velocity open-weights lab on the timeline \u2014 almost every entry is a model drop, from the Qwen 2.5 and Qwen 3 language-model lines to Wan video and Z-Image generation models."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Wan-Animate-2",
    "Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Wan-Animate-2",
    "Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0."
   ],
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Wan-Animate-2",
    "Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0. It wins over 70% of blind preference comparisons."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.6-27B",
   "release_date": "2026-04",
   "parameters": "27B",
   "context_window": null,
   "license": "Apache-2.0",
   "significance": "beats Alibaba's own 400B flagship on every major coding benchmark",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.6-27B",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.6-27B",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ],
   "release_date_evidence": [
    "## [April 2026](/releases/2026-04) 7",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.6-27B",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.6-27B",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ],
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.6-27B",
    "Alibaba shipped Qwen3.6-27B, a dense 27B-parameter model under Apache 2.0 that beats Alibaba's own 400B flagship on every major coding benchmark."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen 3.6-35B-A3B",
   "release_date": "2026-04",
   "parameters": "35B MoE with only 3B active parameters",
   "context_window": "262K extensible to 1M",
   "license": "Apache 2.0",
   "significance": "scores 73.4% on SWE-bench Verified",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ],
   "release_date_evidence": [
    "## [April 2026](/releases/2026-04) 7",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ],
   "context_window_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "It is natively multimodal with 262K context extensible to 1M, and the crew called it the strongest mid-size LLM on nearly all benchmarks, putting to rest doubts about Qwen's open-source commitment after Junyang Ling's departure."
   ],
   "license_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ],
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.6-35B-A3B",
    "Alibaba Qwen open-sourced Qwen 3.6-35B-A3B under Apache 2.0 the same morning Opus 4.7 dropped: a 35B MoE with only 3B active parameters that scores 73.4% on SWE-bench Verified, rivaling models 10x its size."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen 3.5",
   "release_date": "2026-02",
   "parameters": "35B / 3B active",
   "context_window": null,
   "license": null,
   "significance": "outperforms their previous 235B flagship",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.5",
    "Alibaba released the Qwen 3.5 family of open-weight models, headlined by Qwen3.5-35B-A3B, a 35B model with only 3B active parameters that outperforms their previous 235B flagship."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.5",
    "Alibaba released the Qwen 3.5 family of open-weight models, headlined by Qwen3.5-35B-A3B, a 35B model with only 3B active parameters that outperforms their previous 235B flagship."
   ],
   "release_date_evidence": [
    "## [February 2026](/releases/2026-02) 4",
    "Alibaba released the Qwen 3.5 family of open-weight models, headlined by Qwen3.5-35B-A3B, a 35B model with only 3B active parameters that outperforms their previous 235B flagship."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.5",
    "Alibaba released the Qwen 3.5 family of open-weight models, headlined by Qwen3.5-35B-A3B, a 35B model with only 3B active parameters that outperforms their previous 235B flagship."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen 3.5",
    "Alibaba released the Qwen 3.5 family of open-weight models, headlined by Qwen3.5-35B-A3B, a 35B model with only 3B active parameters that outperforms their previous 235B flagship."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.5-397B-A17B",
   "release_date": "2026-02",
   "parameters": "397B total parameters, just 17B active",
   "context_window": "262K native context extendable to 1M",
   "license": null,
   "significance": "scoring 52.5% on Terminal Bench, third place among open-source models",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.5-397B-A17B",
    "Alibaba released Qwen3.5-397B-A17B, billed as the first open-weight native multimodal MoE model, with 397B total parameters, just 17B active, 512 experts, and 262K native context extendable to 1M."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.5-397B-A17B",
    "Alibaba released Qwen3.5-397B-A17B, billed as the first open-weight native multimodal MoE model, with 397B total parameters, just 17B active, 512 experts, and 262K native context extendable to 1M."
   ],
   "release_date_evidence": [
    "## [February 2026](/releases/2026-02) 4",
    "Alibaba released Qwen3.5-397B-A17B, billed as the first open-weight native multimodal MoE model, with 397B total parameters, just 17B active, 512 experts, and 262K native context extendable to 1M."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.5-397B-A17B",
    "Alibaba released Qwen3.5-397B-A17B, billed as the first open-weight native multimodal MoE model, with 397B total parameters, just 17B active, 512 experts, and 262K native context extendable to 1M."
   ],
   "context_window_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.5-397B-A17B",
    "Alibaba released Qwen3.5-397B-A17B, billed as the first open-weight native multimodal MoE model, with 397B total parameters, just 17B active, 512 experts, and 262K native context extendable to 1M."
   ],
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3.5-397B-A17B",
    "It delivers 8.6-19x faster inference than Qwen3-Max and continues Qwen's strength in multilingual and medical tasks, scoring 52.5% on Terminal Bench, third place among open-source models."
   ]
  },
  {
   "url": "https://thursdai.news/companies/alibaba-qwen",
   "title": "Alibaba (Qwen) AI Releases: Qwen3.8-Max, Qwen 3 & 40 More \u2014 ThursdAI",
   "published_at": "2026-08-27T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3-Coder-Next",
   "release_date": "2026-02",
   "parameters": "80B MoE coding agent model with only 3B active parameters",
   "context_window": null,
   "license": null,
   "significance": "scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro",
   "organization_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3-Coder-Next",
    "Alibaba's Qwen3-Coder-Next is an 80B MoE coding agent model with only 3B active parameters that scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro."
   ],
   "model_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3-Coder-Next",
    "Alibaba's Qwen3-Coder-Next is an 80B MoE coding agent model with only 3B active parameters that scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro."
   ],
   "release_date_evidence": [
    "## [February 2026](/releases/2026-02) 4",
    "Alibaba's Qwen3-Coder-Next is an 80B MoE coding agent model with only 3B active parameters that scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro."
   ],
   "parameters_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3-Coder-Next",
    "Alibaba's Qwen3-Coder-Next is an 80B MoE coding agent model with only 3B active parameters that scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Alibaba (Qwen) AI Releases: Qwen3.8-Flash-Next, HappyShrimp 1.0 & Qwen3.8-27B",
    "### Qwen3-Coder-Next",
    "Alibaba's Qwen3-Coder-Next is an 80B MoE coding agent model with only 3B active parameters that scores 70.6% on SWE-Bench Verified and 44% on the much harder SWE-Bench Pro."
   ]
  },
  {
   "url": "https://neuronad.com/llama-vs-mistral",
   "title": "Llama vs Mistral (2026): Meta vs France in the Open-Source AI Race",
   "published_at": "2026-04-13T21:58:19",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-03-16",
   "parameters": "119B total, 6B active",
   "context_window": null,
   "license": null,
   "significance": "unifies three previously separate product lines into a single 119B-parameter MoE model: Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding), with 40% reduction in end-to-end completion time",
   "organization_evidence": [
    "Mistral AI \u2014 The European Challenger",
    "Neuronad Deep Dive \u2014 Open-Source AI Models",
    "Then came Mistral Small 4 in March 2026 \u2014 a 119B MoE model unifying reasoning, vision, and coding with only 6B active parameters per token."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Neuronad Deep Dive \u2014 Open-Source AI Models",
    "### Mistral Small 4: Three Models in One",
    "Released March 16, 2026, Mistral Small 4 is perhaps the most elegant model in the open-source landscape."
   ],
   "parameters_evidence": [
    "Neuronad Deep Dive \u2014 Open-Source AI Models",
    "Complete Model\nComparison",
    "| Efficient (Medium) | Llama 4 Scout (109B total, 17B active, 16 experts) | Mistral Small 4 (119B total, 6B active, 128 experts) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Neuronad Deep Dive \u2014 Open-Source AI Models",
    "### Mistral Small 4: Three Models in One",
    "Released March 16, 2026, Mistral Small 4 is perhaps the most elegant model in the open-source landscape.",
    "It unifies three previously separate product lines into a single 119B-parameter MoE model: *Magistral* (reasoning), *Pixtral* (multimodal vision), and *Devstral* (agentic coding).",
    "Compared to Mistral Small 3, the new model delivers a 40% reduction in end-to-end completion time in latency-optimized setups, and handles 3x more requests per second in throughput-optimized configurations."
   ]
  },
  {
   "url": "https://pivotnews.ai/five/story/mistral-medium-3-5-mistral-ai",
   "title": "Mistral Medium 3.5 - Mistral AI",
   "published_at": "2026-08-11T00:39:47",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-04-28",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "frontier-class multimodal model optimized for agentic and coding use cases",
   "organization_evidence": [
    "# Mistral Medium 3.5 - Mistral AI",
    "Mistral Medium 3.5 - Mistral AI | Mistral Docs",
    "Mistral Medium 3.5 Our frontier-class multimodal model optimized for agentic and coding use cases. Released as open weights under a Modified MIT license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral Medium 3.5 - Mistral AI",
    "April 28, 2026 Blog GA Modified MIT v 26.04",
    "Mistral Medium 3.5 Our frontier-class multimodal model optimized for agentic and coding use cases. Released as open weights under a Modified MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral Medium 3.5 - Mistral AI",
    "Our frontier-class multimodal model optimized for agentic and coding use cases. Released as open weights under a Modified MIT license."
   ]
  },
  {
   "url": "https://en.poc.hk/gemma4-12b",
   "title": "Google Gemma 4 12B: Encoder-Free Multimodal Model Redefines Laptop-Grade AI",
   "published_at": null,
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "the first encoder-free multimodal open-weight model running on 16GB VRAM",
   "organization_evidence": [
    "# Google Gemma 4 12B: Encoder-Free Multimodal Model Redefines Laptop-Grade AI",
    "Google DeepMind releases Gemma 4 12B \u2014 the first encoder-free multimodal open-weight model running on 16GB VRAM",
    "June 3, 2026 \u2014 Google DeepMind officially released Gemma 4 12B, a mid-tier open-weight multimodal model positioned between the edge-oriented E4B (Effective 4B) and the high-end 26B MoE."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google Gemma 4 12B: Encoder-Free Multimodal Model Redefines Laptop-Grade AI",
    "June 3, 2026 \u2014 Google DeepMind officially released Gemma 4 12B, a mid-tier open-weight multimodal model positioned between the edge-oriented E4B (Effective 4B) and the high-end 26B MoE."
   ],
   "parameters_evidence": [
    "# Google Gemma 4 12B: Encoder-Free Multimodal Model Redefines Laptop-Grade AI",
    "Google DeepMind releases Gemma 4 12B \u2014 the first encoder-free multimodal open-weight model running on 16GB VRAM",
    "Key specifications at a glance:",
    "- **Parameters**: 12B (dense architecture)"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google Gemma 4 12B: Encoder-Free Multimodal Model Redefines Laptop-Grade AI",
    "Google DeepMind releases Gemma 4 12B \u2014 the first encoder-free multimodal open-weight model running on 16GB VRAM"
   ]
  },
  {
   "url": "https://polprog.pl/en/learning/kimi-k3-open-weights",
   "title": "Moonshot AI publishes the Kimi K3 weights: 2.8 trillion parameters, licence, requirements and benchmarks",
   "published_at": "2026-07-27T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": "2.8 trillion total parameters",
   "context_window": null,
   "license": null,
   "significance": "Natively multimodal Mixture-of-Experts model with 2.8 trillion total parameters, 104 billion parameters activated while generating each token, 896 experts, a context window of 1,048,576 tokens, support for text, images and video, and approximately 2.5x improvement in overall scaling efficiency compared with Kimi K2.",
   "organization_evidence": [
    "# Moonshot AI publishes the Kimi K3 weights: 2.8 trillion parameters, licence, requirements and benchmarks",
    "On 27 July 2026, Moonshot AI published the full <strong>Kimi K3</strong> model weights, a code repository, its own licence and a technical report."
   ],
   "model_evidence": null,
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   ]
  },
  {
   "url": "https://tpsreport.news/news/qwen3-8-max-2-4-trillion-parameters",
   "title": "Qwen3.8-Max: Alibaba's 2.4T Parameter Open-Weight AI Model",
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   "url": "https://tpsreport.news/news/qwen3-8-max-2-4-trillion-parameters",
   "title": "Qwen3.8-Max: Alibaba's 2.4T Parameter Open-Weight AI Model",
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  {
   "url": "https://youtu.be/LkbREGlNX8Y",
   "title": "The AI Model Nobody's Talking About \u2014 Mistral 3.5 & The Sovereignty Question",
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   "organization_evidence": [
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   ]
  },
  {
   "url": "https://api.daily.dev/r/WnpUD5tZF",
   "title": "Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities",
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  },
  {
   "url": "https://felloai.com/fr/kimi-k2-6-is-here-the-open-source-ai-model-tying-gpt-5-5-on-coding",
   "title": "Kimi K2.6 Is Here: The Open-Source AI Model Tying GPT-5.5 on Coding",
   "published_at": "2026-04-28T18:33:37",
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    "With **1 trillion total parameters**, **32 billion active per token**, and a **256K-token context window**, K2.6 is now the **[#1 open-weight model](https://artificialanalysis.ai/models/kimi-k2-6)** on the Artificial Analysis Intelligence Index.",
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   ]
  },
  {
   "url": "https://forkast.news/zhipu-doubles-down-on-open-access-as-beijing-considers-closing-the-door",
   "title": "Zhipu Doubles Down on Open Access as Beijing Considers Closing the Door",
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   "organization_evidence": [
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   ]
  },
  {
   "url": "https://theagenttimes.com/articles/minimax-m3-delivers-open-weight-model-with-million-token-con-e4cdc990",
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   "organization_evidence": [
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   ]
  },
  {
   "url": "https://ownyourmind.ai/journal/open-frontier-catches-up",
   "title": "Three Bets on Open Weights",
   "published_at": "2026-04-26T00:00:00",
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   "organization_evidence": [
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   "model_evidence": null,
   "release_date_evidence": [
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   "context_window_evidence": null,
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   ]
  },
  {
   "url": "https://ownyourmind.ai/journal/open-frontier-catches-up",
   "title": "Three Bets on Open Weights",
   "published_at": "2026-04-26T00:00:00",
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   "model": "DeepSeek V4",
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   "significance": "real efficiency step with Hybrid Attention and Manifold-Constrained Hyper-Connections",
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  },
  {
   "url": "https://ownyourmind.ai/journal/open-frontier-catches-up",
   "title": "Three Bets on Open Weights",
   "published_at": "2026-04-26T00:00:00",
   "organization": "Alibaba",
   "model": "Qwen3.6-35B-A3B",
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   "organization_evidence": [
    "Qwen 3.6: The Bet That Doesn\u2019t Match The Pattern",
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   "context_window_evidence": [
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  },
  {
   "url": "https://developersdigest.tech/blog/llama-4-developers-guide",
   "title": "Llama 4: The Complete Developer's Guide to Meta's Open Source Models",
   "published_at": "2026-03-26T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "2026-05-24",
   "parameters": "17B Active / 109B Total",
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   "license": null,
   "significance": "Brings mixture-of-experts (MoE) architecture to the open-source mainstream with a 10 million token context window.",
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   ],
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   "release_date_evidence": [
    "**Last updated:** May 24, 2026. Verify model availability, hardware requirements, and licensing terms against the official Meta documentation before production deployment."
   ],
   "parameters_evidence": [
    "### Scout (17B Active / 109B Total)#",
    "Scout is the workhorse."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Llama 4 is the biggest leap yet - not because it is the best model on every benchmark, but because it brings mixture-of-experts (MoE) architecture to the open-source mainstream, delivering dramatically better performance per dollar of compute.",
    "The standout feature is the [context window](/glossary#context-window): 10 million tokens."
   ]
  },
  {
   "url": "https://developersdigest.tech/blog/llama-4-developers-guide",
   "title": "Llama 4: The Complete Developer's Guide to Meta's Open Source Models",
   "published_at": "2026-03-26T00:00:00",
   "organization": "Meta",
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   "release_date": "2026-05-24",
   "parameters": "17B Active / 400B Total",
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   "release_date_evidence": [
    "**Last updated:** May 24, 2026. Verify model availability, hardware requirements, and licensing terms against the official Meta documentation before production deployment."
   ],
   "parameters_evidence": [
    "### Maverick (17B Active / 400B Total)#",
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   ],
   "context_window_evidence": null,
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   "significance_evidence": [
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   ]
  },
  {
   "url": "https://udit.co/blog/mistral-forge-platform-small-4-open-source-billion-arr",
   "title": "Mistral launches Forge and Small 4: the open-source AI challenger targeting $1 billion ARR",
   "published_at": "2026-03-23T00:00:00",
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   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Released under Apache 2.0, Mistral Small 4 is a hybrid model designed for general conversation, software development, and multi-step reasoning, reportedly matching or exceeding competing models in its parameter class on benchmarks like MMLU, HumanEval, and mathematical reasoning.",
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   ],
   "model_evidence": null,
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Mistral Small 4: hybrid model under Apache 2.0",
    "Released alongside Forge, Mistral Small 4 is the latest iteration in Mistral's flagship small model line, and it arrives with a specification sheet that punches well above the weight class implied by its name.",
    "The model is designed as a true hybrid across three capability domains: general conversation, software development, and multi-step reasoning \u2014 a combination that has historically required choosing between specialized models optimized for different tasks.",
    "On benchmark performance, Mistral Small 4 reportedly matches or exceeds competing models in its parameter class across standard evaluations including MMLU, HumanEval, and mathematical reasoning benchmarks."
   ]
  },
  {
   "url": "https://dev.to/docdavkitty/kimi-k3-moonshot-ai-drops-a-28-trillion-parameter-open-model-and-it-codes-designs-chips-and-48c9",
   "title": "Kimi K3: Moonshot AI Drops a 2.8-Trillion-Parameter Open Model \u2014 and It Codes, Designs Chips, and Edits Video",
   "published_at": "2026-07-17T12:05:01",
   "organization": "Moonshot AI",
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   "release_date": "2026-07-16",
   "parameters": "2.8-trillion-parameter",
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   "significance": "largest open-weight model ever announced, competitive with GPT-5.5 and Claude Opus 4.8 on most benchmarks, and #1 (preliminary) on the Arena WebDev leaderboard",
   "organization_evidence": [
    "# Kimi K3: Moonshot AI Drops a 2.8-Trillion-Parameter Open Model \u2014 and It Codes, Designs Chips, and Edits Video",
    "Late on July 16, Chinese AI lab Moonshot AI published a blog post announcing Kimi K3 \u2014 a 2.8-trillion-parameter sparse Mixture-of-Experts model that instantly became the largest open-weight model in existence, roughly 1.75\u00d7 the size of DeepSeek V4 Pro's 1.6T parameters."
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   "release_date_evidence": [
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   "parameters_evidence": [
    "# Kimi K3: Moonshot AI Drops a 2.8-Trillion-Parameter Open Model \u2014 and It Codes, Designs Chips, and Edits Video",
    "Late on July 16, Chinese AI lab Moonshot AI published a blog post announcing Kimi K3 \u2014 a 2.8-trillion-parameter sparse Mixture-of-Experts model that instantly became the largest open-weight model in existence, roughly 1.75\u00d7 the size of DeepSeek V4 Pro's 1.6T parameters."
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   "context_window_evidence": null,
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   "significance_evidence": [
    "# Kimi K3: Moonshot AI Drops a 2.8-Trillion-Parameter Open Model \u2014 and It Codes, Designs Chips, and Edits Video",
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   ]
  },
  {
   "url": "http://digitalmatters.me/artificial-intelligence-ai/what-is-minimax-m3",
   "title": "What Is MiniMax M3? The Open-Weight 1M-Context Frontier Model, Explained",
   "published_at": "2026-07-28T18:30:00",
   "organization": "MiniMax",
   "model": "MiniMax M3",
   "release_date": "2026-05-31",
   "parameters": "230 billion to 430 billion",
   "context_window": "1,048,576 tokens",
   "license": "MiniMax Community License",
   "significance": "59.0% on SWE-Bench Pro",
   "organization_evidence": [
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    "# What Is MiniMax M3? The Open-Weight 1M-Context Frontier Model, Explained",
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   ],
   "model_evidence": [
    "Artificial Intelligence (AI)",
    "# What Is MiniMax M3? The Open-Weight 1M-Context Frontier Model, Explained",
    "**MiniMax M3** is an open-weight large language model from the Chinese AI lab MiniMax, released at the end of May 2026 with a context window of roughly one million tokens."
   ],
   "release_date_evidence": [
    "Artificial Intelligence (AI)",
    "# What Is MiniMax M3? The Open-Weight 1M-Context Frontier Model, Explained",
    "**MiniMax M3** is an open-weight large language model from the Chinese AI lab MiniMax, released at the end of May 2026 with a context window of roughly one million tokens."
   ],
   "parameters_evidence": [
    "Artificial Intelligence (AI)",
    "What MiniMax M3 actually is",
    "Independent trackers list M3\u2019s total size anywhere from roughly 230 billion to 430 billion parameters, with an active set of only about 10 billion to 23 billion parameters per token."
   ],
   "context_window_evidence": [
    "Artificial Intelligence (AI)",
    "# What Is MiniMax M3? The Open-Weight 1M-Context Frontier Model, Explained",
    "**MiniMax M3** is an open-weight large language model from the Chinese AI lab MiniMax, released at the end of May 2026 with a context window of roughly one million tokens."
   ],
   "license_evidence": [
    "Artificial Intelligence (AI)",
    "Open-weight is not the same as open-source",
    "MiniMax released M3 under its own MiniMax Community License."
   ],
   "significance_evidence": [
    "Artificial Intelligence (AI)",
    "What the benchmarks say, and who ran them",
    "MiniMax\u2019s headline claim is a score of 59.0% on SWE-Bench Pro, a software-engineering benchmark, which the company says edges out both GPT-5.5 and Gemini 3.1 Pro."
   ]
  },
  {
   "url": "https://braindetox.kr/en/posts/longcat_2_open_model_2026.html",
   "title": "LongCat-2.0: A 1.6-Trillion-Parameter Open Model Trained Without Nvidia (2026)",
   "published_at": "2026-07-03T00:00:00",
   "organization": "Meituan",
   "model": null,
   "release_date": "2026-06-30",
   "parameters": "1.6 trillion parameters",
   "context_window": null,
   "license": null,
   "significance": "Scored 59.5 on SWE-bench Pro, edging out GPT-5.5's 58.6 and Gemini 3.1 Pro's 54.2.",
   "organization_evidence": [
    "LongCat-2.0: A 1.6-Trillion-Parameter Open Model Trained Without Nvidia",
    "On June 30, 2026, Chinese food-delivery and local-services giant **Meituan** released a large language model called **LongCat-2.0**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "On June 30, 2026, Chinese food-delivery and local-services giant **Meituan** released a large language model called **LongCat-2.0**."
   ],
   "parameters_evidence": [
    "LongCat-2.0: A 1.6-Trillion-Parameter Open Model Trained Without Nvidia",
    "According to public materials, LongCat-2.0 is a **1.6-trillion-parameter** Mixture-of-Experts (MoE) model, of which only about **33 to 56 billion parameters (roughly 48 billion on average)** actually activate per token."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "4. How Good Is the Performance, Really",
    "Per Meituan's disclosures, LongCat-2.0 scored **59.5** on **SWE-bench Pro**, a benchmark built around resolving real GitHub issues across full repositories."
   ]
  },
  {
   "url": "https://aichina.news/blog/scaling-open-intelligence-allenais-tulu-3-405b-behemoth-lands-on-lejo8h",
   "title": "Scaling Open Intelligence: AllenAI\u2019s Tulu 3 405B Behemoth Lands on Modelers.cn \u2014 AICHINA.news Blog",
   "published_at": "2026-04-11T16:04:21",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": "2026-04-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Rivaling proprietary models such as GPT-4o in benchmarks and real-world tasks, with open-source alignment and Ascend optimisation.",
   "organization_evidence": [
    "Scaling Open Intelligence: AllenAI\u2019s Tulu 3 405B Behemoth Lands on Modelers.cn",
    "By combining Meta\u2019s most powerful base model with the Allen Institute for AI\u2019s (AI2) transparent Tulu 3 alignment pipeline, this model offers a rare level of insight into how a world-class instruction-follower is built and refined."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Scaling Open Intelligence: AllenAI\u2019s Tulu 3 405B Behemoth Lands on Modelers.cn",
    "models/allenai April 11, 2026 2 min read Auto-generated from Modelers"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Scaling Open Intelligence: AllenAI\u2019s Tulu 3 405B Behemoth Lands on Modelers.cn",
    "- **World-class performance:** Rivaling proprietary models such as GPT-4o in benchmarks and real-world tasks.",
    "- **Open-source alignment:** Uses the Tulu 3 recipe for unparalleled transparency and reproducibility.",
    "- **Ascend Optimisation:** Tailored for the Huawei Ascend hardware ecosystem to ensure peak efficiency."
   ]
  },
  {
   "url": "https://bitsminds.com/news/mistral-shieldstral-3b-policy-adaptive-guard-2026",
   "title": "Mistral's Shieldstral: A 3B Guard That Reads Your Policy",
   "published_at": "2026-08-05T08:49:16",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-08-04",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Mistral reports an average F1 of 84.9% across text-safety benchmarks, matching open guard models up to seven times larger, and 83.8% on multimodal safety \u2014 a new state of the art in its evaluation, ahead of OmniGuard-7B's 77.6%.",
   "organization_evidence": [
    "# Mistral's Shieldstral: A 3B Guard That Reads Your Policy",
    "Mistral AI has released [Shieldstral](https://mistral.ai/news/shieldstral/), a 3-billion-parameter multimodal safety classifier that flips the standard guard-model design on its head: instead of shipping with a fixed set of harm categories baked in during training, it reads the moderation policy as a plain-language prompt at inference time."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Mistral's Shieldstral: A 3B Guard That Reads Your Policy",
    "The model landed on August 4 as open weights under the Apache 2.0 license on [Hugging Face](https://huggingface.co/mistralai/Shieldstral-1.0-3B) \u2014 Mistral's third moderation model, but the first it has released openly after two hosted-API predecessors."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Mistral's Shieldstral: A 3B Guard That Reads Your Policy",
    "Mistral reports an average F1 of 84.9% across text-safety benchmarks, matching open guard models up to seven times larger, and 83.8% on multimodal safety \u2014 a new state of the art in its evaluation, ahead of OmniGuard-7B's 77.6%."
   ]
  },
  {
   "url": "https://thequantumdispatch.com/articles/google-gemma-4-12b-multimodal-open-model-runs-on-16gb-laptop-june-2026",
   "title": "Google's Gemma 4 12B Brings Multimodal AI to a 16GB Laptop",
   "published_at": "2026-06-09T22:20:20",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "Gemma 4 12B is a unified multimodal open model that reads images and audio, runs on a 16GB laptop, and includes 256K context windows across more than 140 languages.",
   "organization_evidence": [
    "# Google's Gemma 4 12B Brings Multimodal AI to a 16GB Laptop",
    "Google DeepMind released Gemma 4 12B on June 3, 2026 \u2014 an open multimodal model that reads images and audio and runs on a 16GB laptop, free under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Google's Gemma 4 12B Brings Multimodal AI to a 16GB Laptop",
    "Google DeepMind released Gemma 4 12B on June 3, 2026 \u2014 an open multimodal model that reads images and audio and runs on a 16GB laptop, free under Apache 2.0."
   ],
   "parameters_evidence": [
    "# Google's Gemma 4 12B Brings Multimodal AI to a 16GB Laptop",
    "Google DeepMind released Gemma 4 12B on June 3, 2026 \u2014 an open multimodal model that reads images and audio and runs on a 16GB laptop, free under Apache 2.0."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Google's Gemma 4 12B Brings Multimodal AI to a 16GB Laptop",
    "Google DeepMind released Gemma 4 12B on June 3, 2026 \u2014 an open multimodal model that reads images and audio and runs on a 16GB laptop, free under Apache 2.0.",
    "The most interesting design choice in Gemma 4 12B is its **unified multimodal architecture**, which processes images and audio without separate encoders.",
    "Gemma 4 models carry context windows up to 256K tokens and are fluent across more than 140 languages, with native vision throughout the lineup and native audio on the smaller variants."
   ]
  },
  {
   "url": "https://sdd.sh/2026/07/kimi-k3-2-8-trillion-open-weight-coding",
   "title": "Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
   "published_at": "2026-07-19T00:00:00",
   "organization": "Moonshot AI",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1-million-token",
   "license": "Modified MIT-style license",
   "significance": "The largest open-weight AI model ever released just beat Claude Fable 5 on a benchmark Anthropic\u2019s flagship model used to lead.",
   "organization_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "Moonshot AI, the Chinese lab behind the Kimi model family this blog has tracked through K2.6 and K2.7-Code, announced **Kimi K3** on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model the company calls its \u201cmost capable to date\u201d and bills as the first \u201copen 3T-class model.\u201d"
   ],
   "model_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "Moonshot AI, the Chinese lab behind the Kimi model family this blog has tracked through K2.6 and K2.7-Code, announced **Kimi K3** on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model the company calls its \u201cmost capable to date\u201d and bills as the first \u201copen 3T-class model.\u201d"
   ],
   "release_date_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "Moonshot AI, the Chinese lab behind the Kimi model family this blog has tracked through K2.6 and K2.7-Code, announced **Kimi K3** on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model the company calls its \u201cmost capable to date\u201d and bills as the first \u201copen 3T-class model.\u201d It\u2019s live via Moonshot\u2019s website and API now; open weights follow on **July 27** under the Modified MIT-style license Moonshot has used for its last several Kimi releases."
   ],
   "parameters_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "Moonshot AI, the Chinese lab behind the Kimi model family this blog has tracked through K2.6 and K2.7-Code, announced **Kimi K3** on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model the company calls its \u201cmost capable to date\u201d and bills as the first \u201copen 3T-class model.\u201d"
   ],
   "context_window_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "K3 is a sparse MoE architecture with 16 of 896 experts activated per token, a native **1-million-token context window**, and support for at least one elevated reasoning-effort mode (\u201cmax\u201d)."
   ],
   "license_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "Moonshot AI, the Chinese lab behind the Kimi model family this blog has tracked through K2.6 and K2.7-Code, announced **Kimi K3** on July 16 \u2014 a 2.8-trillion-parameter mixture-of-experts model the company calls its \u201cmost capable to date\u201d and bills as the first \u201copen 3T-class model.\u201d It\u2019s live via Moonshot\u2019s website and API now; open weights follow on **July 27** under the Modified MIT-style license Moonshot has used for its last several Kimi releases."
   ],
   "significance_evidence": [
    "# Kimi K3: A 2.8-Trillion-Parameter Open-Weight Model Just Beat Fable 5 on Terminal-Bench",
    "The largest open-weight AI model ever released just beat Claude Fable 5 on a benchmark Anthropic\u2019s flagship model used to lead."
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/zhipuai",
   "title": "Zhipu AI Model List 2026: All 14 Models",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Open flagship GLM for long-horizon coding agents and million-token context work",
   "organization_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.2 zhipuai/glm-5.2 Open flagship GLM for long-horizon coding agents and million-token context work | 1000K | Reasoning Tools Open | 2026-06-13 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.2 zhipuai/glm-5.2 Open flagship GLM for long-horizon coding agents and million-token context work | 1000K | Reasoning Tools Open | 2026-06-13 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.2 zhipuai/glm-5.2 Open flagship GLM for long-horizon coding agents and million-token context work | 1000K | Reasoning Tools Open | 2026-06-13 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/zhipuai",
   "title": "Zhipu AI Model List 2026: All 14 Models",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Strong GLM coding model for agentic engineering, terminals, and repository generation",
   "organization_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.1 zhipuai/glm-5.1 Strong GLM coding model for agentic engineering, terminals, and repository generation | 200K | Reasoning Tools Open | 2026-04-07 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.1 zhipuai/glm-5.1 Strong GLM coding model for agentic engineering, terminals, and repository generation | 200K | Reasoning Tools Open | 2026-04-07 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5.1 zhipuai/glm-5.1 Strong GLM coding model for agentic engineering, terminals, and repository generation | 200K | Reasoning Tools Open | 2026-04-07 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/zhipuai",
   "title": "Zhipu AI Model List 2026: All 14 Models",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "General GLM flagship for coding, analysis, and tool-heavy engineering workflows",
   "organization_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5 zhipuai/glm-5 General GLM flagship for coding, analysis, and tool-heavy engineering workflows | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5 zhipuai/glm-5 General GLM flagship for coding, analysis, and tool-heavy engineering workflows | 204K | Reasoning Tools Open | 2026-02-12 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-5 zhipuai/glm-5 General GLM flagship for coding, analysis, and tool-heavy engineering workflows | 204K | Reasoning Tools Open | 2026-02-12 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/zhipuai",
   "title": "Zhipu AI Model List 2026: All 14 Models",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-01-19",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Budget GLM lane for fast coding help, routing, and everyday automation",
   "organization_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-Flash zhipuai/glm-4.7-flash Budget GLM lane for fast coding help, routing, and everyday automation | 200K | Reasoning Tools Open | 2026-01-19 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-Flash zhipuai/glm-4.7-flash Budget GLM lane for fast coding help, routing, and everyday automation | 200K | Reasoning Tools Open | 2026-01-19 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-Flash zhipuai/glm-4.7-flash Budget GLM lane for fast coding help, routing, and everyday automation | 200K | Reasoning Tools Open | 2026-01-19 |"
   ]
  },
  {
   "url": "https://dreamprompting.com/all-models/zhipuai",
   "title": "Zhipu AI Model List 2026: All 14 Models",
   "published_at": null,
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-01-19",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Efficient GLM model for fast reasoning, coding, and agent workflows",
   "organization_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-FlashX zhipuai/glm-4.7-flashx Efficient GLM model for fast reasoning, coding, and agent workflows | 200K | Reasoning Tools Open | 2026-01-19 |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-FlashX zhipuai/glm-4.7-flashx Efficient GLM model for fast reasoning, coding, and agent workflows | 200K | Reasoning Tools Open | 2026-01-19 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Zhipu AI model list",
    "All **14** notable Zhipu AI models tracked in 2026, out of **279** across every lab. Sorted newest first, with context length and capabilities. Data from the open models.dev database.",
    "| Model | Context | Capabilities | Released |",
    "| GLM-4.7-FlashX zhipuai/glm-4.7-flashx Efficient GLM model for fast reasoning, coding, and agent workflows | 200K | Reasoning Tools Open | 2026-01-19 |"
   ]
  },
  {
   "url": "https://magicshot.ai/news/minimax-m3-open-weight-model",
   "title": "MiniMax M3 Lands: Open-Weight Model Hits GPT-5.5 Coding Scores",
   "published_at": "2026-06-01T11:18:27",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Hits GPT-5.5 coding scores; 59.0% on SWE-Bench Pro; 83.5 on BrowseComp; 66.0% on Terminal-Bench 2.1; 74.2% on MCP Atlas.",
   "organization_evidence": [
    "MiniMax M3 Lands: Open-Weight Model Hits GPT-5.5 Coding Scores",
    "MiniMax just shipped M3."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax M3 Lands: Open-Weight Model Hits GPT-5.5 Coding Scores",
    "Published: June 1, 2026",
    "The Tencent and Alibaba backed lab dropped M3 on June 1, 2026, ending a long stretch where every open-weight model had to pick two of those three capabilities."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M3 Lands: Open-Weight Model Hits GPT-5.5 Coding Scores",
    "On SWE-Bench Pro, the industry\u2019s nastiest real-world coding benchmark, M3 hits 59.0%."
   ]
  },
  {
   "url": "https://udit.co/blog/ai2-olmo-hybrid-7b-data-efficiency-breakthrough",
   "title": "AI2's OLMo Hybrid Achieves 2\u00d7 Data Efficiency \u2014 Open-Source AI Just Got Smarter",
   "published_at": "2026-03-07T00:00:00",
   "organization": "Allen Institute for AI",
   "model": null,
   "release_date": null,
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "matching Llama 3.1 8B benchmarks with 49% fewer training tokens \u2014 a breakthrough for open-source model efficiency",
   "organization_evidence": [
    "# AI2's OLMo Hybrid Achieves 2\u00d7 Data Efficiency \u2014 Open-Source AI Just Got Smarter",
    "Allen Institute for AI releases OLMo Hybrid 7B, matching Llama 3.1 8B benchmarks with 49% fewer training tokens \u2014 a breakthrough for open-source model efficiency."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# AI2's OLMo Hybrid Achieves 2\u00d7 Data Efficiency \u2014 Open-Source AI Just Got Smarter",
    "Allen Institute for AI releases OLMo Hybrid 7B, matching Llama 3.1 8B benchmarks with 49% fewer training tokens \u2014 a breakthrough for open-source model efficiency."
   ]
  },
  {
   "url": "https://thevibefather.com/blog/july-2026-ai-model-roundup",
   "title": "Every Major AI Model That Shipped in July 2026 \u2014 And Which Ones Matter",
   "published_at": "2026-07-25T01:18:27",
   "organization": "Moonshot",
   "model": "Kimi K3",
   "release_date": "2026-07-27",
   "parameters": "2.8-trillion-parameter",
   "context_window": "1M",
   "license": "Modified MIT",
   "significance": "Largest open-weight model published, frontier-adjacent coding",
   "organization_evidence": [
    "Unveiled July 16 at the World AI Conference in Shanghai and available through Moonshot's API since, with full weights publishing July 27 under a Modified MIT license.",
    "## Kimi K3 \u2014 the one that changes the open landscape"
   ],
   "model_evidence": [
    "## Kimi K3 \u2014 the one that changes the open landscape",
    "Unveiled July 16 at the World AI Conference in Shanghai and available through Moonshot's API since, with full weights publishing July 27 under a Modified MIT license."
   ],
   "release_date_evidence": [
    "Unveiled July 16 at the World AI Conference in Shanghai and available through Moonshot's API since, with full weights publishing July 27 under a Modified MIT license.",
    "## Kimi K3 \u2014 the one that changes the open landscape"
   ],
   "parameters_evidence": [
    "It is a 2.8-trillion-parameter multimodal mixture-of-experts model with a 1M token context window \u2014 the largest open-weight model released to date.",
    "## Kimi K3 \u2014 the one that changes the open landscape"
   ],
   "context_window_evidence": [
    "Unveiled July 16 at the World AI Conference in Shanghai and available through Moonshot's API since, with full weights publishing July 27 under a Modified MIT license.",
    "It is a 2.8-trillion-parameter multimodal mixture-of-experts model with a 1M token context window \u2014 the largest open-weight model released to date.",
    "## Kimi K3 \u2014 the one that changes the open landscape"
   ],
   "license_evidence": [
    "Unveiled July 16 at the World AI Conference in Shanghai and available through Moonshot's API since, with full weights publishing July 27 under a Modified MIT license.",
    "## Kimi K3 \u2014 the one that changes the open landscape"
   ],
   "significance_evidence": [
    "| **Kimi K3** | Jul 16 (weights Jul 27) | Open, Modified MIT | $3 / $15 | Largest open-weight model published, frontier-adjacent coding |",
    "## The month at a glance",
    "The month at a glance"
   ]
  },
  {
   "url": "https://buff.ly/9V3Em7I",
   "title": "Qwen3.6-35B-A3B Complete Review: Alibaba's Open-Source Coding Model That Beats Frontier Giants",
   "published_at": "2026-04-17T11:01:11",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-16",
   "parameters": "35B",
   "context_window": null,
   "license": null,
   "significance": "Outperforms dense 27B-param models and directly competes with frontier models on coding benchmarks, scoring 51.5 on Terminal-Bench 2.0 and 73.4 on SWE-bench Verified",
   "organization_evidence": [
    "Qwen3.6-35B-A3B Complete Review: Alibaba's Open-Source Coding Model That Beats Frontier Giants",
    "**Qwen3.6-35B-A3B** is the latest open-weight model from Alibaba's Qwen team, officially released on **April 16, 2026**."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "What Is Qwen3.6-35B-A3B?",
    "Released **April 16, 2026** under the **Apache 2.0 license**, freely available on Hugging Face, Ollama, and Unsloth (GGUF format)"
   ],
   "parameters_evidence": [
    "What Is Qwen3.6-35B-A3B?",
    "**Qwen3.6-35B-A3B** is Alibaba's latest open-source sparse Mixture-of-Experts (MoE) model with **35B total parameters** and only **3B active parameters per token**, making it incredibly efficient for local deployment"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "What Is Qwen3.6-35B-A3B?",
    "- **Outperforms** dense 27B-param models and directly competes with frontier models on coding benchmarks, scoring **51.5 on Terminal-Bench 2.0** and **73.4 on SWE-bench Verified**"
   ]
  },
  {
   "url": "http://ai-tldr.dev/models/leanstral-1-5",
   "title": "Leanstral 1.5: Mistral's Open Lean 4 Proof Model | AI/TLDR",
   "published_at": "2026-07-02T00:00:00",
   "organization": "Mistral AI",
   "model": null,
   "release_date": "2026-07-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Saturates miniF2F (100%), solves 587 of 672 PutnamBench problems, and reports 87% on FATE-H and 34% on FATE-X.",
   "organization_evidence": [
    "# Leanstral 1.5",
    "Leanstral 1.5 is an open-weight code agent model from Mistral AI, released on 2 July 2026 and specialized for Lean 4 formal proof engineering."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Leanstral 1.5",
    "Leanstral 1.5 is an open-weight code agent model from Mistral AI, released on 2 July 2026 and specialized for Lean 4 formal proof engineering.",
    "| Released | 2026-07-02 |"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Overview",
    "On the standard formal-mathematics benchmarks, Leanstral 1.5 saturates miniF2F (100%), solves 587 of 672 PutnamBench problems, and reports 87% on FATE-H and 34% on FATE-X."
   ]
  },
  {
   "url": "https://www-buildfastwithai-com.translate.goog/blogs/gemma-4-12b-guide?_x_tr_hl=vi&_x_tr_pto=tc&_x_tr_sl=en&_x_tr_tl=vi",
   "title": "Gemma 4 12B: Specs, Benchmarks & How to Run It Locally",
   "published_at": "2026-06-04T09:17:50",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "An open multimodal model that runs text, image, audio, and video natively on a laptop with 16 GB of RAM, nearly matching the twice-as-large 26B model across benchmarks while clearly beating the older Gemma 3 27B.",
   "organization_evidence": [
    "Gemma 4 12B: Specs, Benchmarks & How to Run It Locally",
    "Gemma 4 12B: Specs, Benchmarks, and How It Beats Bigger Models",
    "On June 3, 2026, Google DeepMind released **Gemma 4 12B**, an open multimodal model that runs text, image, audio, and video natively on a laptop with 16 GB of RAM \u2014 and, by Google's own account, nearly matches the twice-as-large 26B model across benchmarks while clearly beating the older Gemma 3 27B."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Gemma 4 12B: Specs, Benchmarks & How to Run It Locally",
    "Gemma 4 12B: Specs, Benchmarks, and How It Beats Bigger Models",
    "On June 3, 2026, Google DeepMind released **Gemma 4 12B**, an open multimodal model that runs text, image, audio, and video natively on a laptop with 16 GB of RAM \u2014 and, by Google's own account, nearly matches the twice-as-large 26B model across benchmarks while clearly beating the older Gemma 3 27B."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 12B: Specs, Benchmarks & How to Run It Locally",
    "Gemma 4 12B: Specs, Benchmarks, and How It Beats Bigger Models",
    "On June 3, 2026, Google DeepMind released **Gemma 4 12B**, an open multimodal model that runs text, image, audio, and video natively on a laptop with 16 GB of RAM \u2014 and, by Google's own account, nearly matches the twice-as-large 26B model across benchmarks while clearly beating the older Gemma 3 27B."
   ]
  },
  {
   "url": "https://blockport.io/latest-news/moonshot-kimi-k3-2-8t-open-weight-shifts-load-to-memory",
   "title": "Moonshot Kimi K3: 2.8T open-weight model shifts load to memory",
   "published_at": "2026-07-20T09:33:02",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "K3 is the largest open-weight model released to date.",
   "organization_evidence": [
    "# Moonshot Kimi K3: 2.8T open-weight model shifts load to memory",
    "Moonshot AI released Kimi K3, a 2.8 trillion-parameter open-weight model in mid-July that cuts per-token compute via mixture-of-experts and 4-bit quantization while raising memory demands."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Moonshot Kimi K3: 2.8T open-weight model shifts load to memory",
    "Moonshot AI announced Kimi K3 in mid-July and published the model weights on July 27."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Moonshot Kimi K3: 2.8T open-weight model shifts load to memory",
    "At 2.8 trillion parameters, K3 is the largest open-weight model released to date."
   ]
  },
  {
   "url": "https://nxcode.io/resources/news/glm-5-open-source-744b-model-complete-guide-2026",
   "title": "GLM-5 July 2026 Guide: GLM-5.2 Pricing, Use Cases and Caveats",
   "published_at": "2026-02-13T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "50.4% on Humanity's Last Exam (beating Claude Opus 4.5) and achieving the industry's lowest hallucination rate",
   "organization_evidence": [
    "## GLM-5 Complete Guide: China's 744B Open-Source Model That Rivals GPT-5.2",
    "**February 13, 2026** \u2014 Zhipu AI just released **GLM-5**, and the AI world is paying attention."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## The China AI Spring Festival Wave",
    "| **GLM-5** | Zhipu AI | Feb 11 | 744B open-source, lowest hallucination |",
    "**February 13, 2026** \u2014 Zhipu AI just released **GLM-5**, and the AI world is paying attention."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## GLM-5 Complete Guide: China's 744B Open-Source Model That Rivals GPT-5.2",
    "**February 13, 2026** \u2014 Zhipu AI just released **GLM-5**, and the AI world is paying attention. A **744-billion-parameter open-source model**, trained entirely on **Huawei Ascend chips** without a single NVIDIA GPU, scoring **50.4% on Humanity's Last Exam** (beating Claude Opus 4.5) and achieving the **industry's lowest hallucination rate**\u2014all under an **MIT license**."
   ]
  },
  {
   "url": "https://awesomeagents.ai/models/minimax-m2-5",
   "title": "MiniMax M2.5",
   "published_at": "2026-02-24T20:13:21",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-02-12",
   "parameters": "230B total / 10B active",
   "context_window": null,
   "license": null,
   "significance": "Best-in-class on SWE-Bench Verified (80.2%), matching Claude Opus 4.6 within 0.6 percentage points, and strongest cost-performance ratio among frontier-class models.",
   "organization_evidence": [
    "MiniMax M2.5 is the latest language model from Shanghai-based MiniMax, a company that IPO'd on the Hong Kong Stock Exchange in January 2026 at a valuation passing HK$90 billion.",
    "MiniMax M2.5 is a 230B MoE model (10B active) that scores 80.2% on SWE-Bench Verified while costing 1/10th to 1/20th of frontier competitors like Claude Opus 4.6 and GPT-5.2.",
    "| Provider | MiniMax (Shanghai, China) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Released on February 12, 2026, M2.5 is a Mixture-of-Experts model with 230 billion total parameters but only 10 billion active during inference - a design that delivers frontier-level coding and agentic performance at a fraction of the cost of proprietary competitors.",
    "Key Specifications",
    "| Release Date | February 12, 2026 |"
   ],
   "parameters_evidence": [
    "MiniMax M2.5 is a 230B MoE model (10B active) that scores 80.2% on SWE-Bench Verified while costing 1/10th to 1/20th of frontier competitors like Claude Opus 4.6 and GPT-5.2.",
    "Released on February 12, 2026, M2.5 is a Mixture-of-Experts model with 230 billion total parameters but only 10 billion active during inference - a design that delivers frontier-level coding and agentic performance at a fraction of the cost of proprietary competitors.",
    "Key Specifications",
    "| Parameters | 230B total / 10B active (Mixture-of-Experts) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax M2.5 is a 230B MoE model (10B active) that scores 80.2% on SWE-Bench Verified while costing 1/10th to 1/20th of frontier competitors like Claude Opus 4.6 and GPT-5.2.",
    "- Best-in-class on SWE-Bench Verified (80.2%), matching [Claude Opus 4.6](/models/claude-opus-4-6/) within 0.6 percentage points",
    "- Costs 1/10th to 1/20th of Opus, Gemini 3 Pro, and GPT-5 on output pricing - the strongest cost-performance ratio of any frontier-class model"
   ]
  },
  {
   "url": "https://shaam.blog/articles/deepseek-v4-guide-2026",
   "title": "DeepSeek V4: The 1.6T Open-Weight Model That Closes the Frontier Gap at 1/30th the Cost",
   "published_at": "2026-07-21T13:07:51",
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T",
   "context_window": null,
   "license": null,
   "significance": "DeepSeek V4 is the most capable open-weight AI model available in 2026, and it costs less per token than a coffee refills budget.",
   "organization_evidence": [
    "DeepSeek V4: The 1.6T Open-Weight Model That Closes the Frontier Gap at 1/30th the Cost",
    "DeepSeek V4 is a family of Mixture-of-Experts (MoE) language models released by Chinese AI lab DeepSeek on April 24, 2026, as a preview ([DeepSeek release notes](https://api-docs.deepseek.com/news/news260424))."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "DeepSeek V4 is a family of Mixture-of-Experts (MoE) language models released by Chinese AI lab DeepSeek on April 24, 2026, as a preview ([DeepSeek release notes](https://api-docs.deepseek.com/news/news260424)).",
    "DeepSeek V4: The 1.6T Open-Weight Model That Closes the Frontier Gap at 1/30th the Cost"
   ],
   "parameters_evidence": [
    "DeepSeek V4: The 1.6T Open-Weight Model That Closes the Frontier Gap at 1/30th the Cost",
    "DeepSeek V4 ships 1.6T parameters, 1M-token context, and near-frontier coding scores under MIT license \u2014 at roughly 1/30th of GPT-5.5's price.",
    "DeepSeek V4 is a family of Mixture-of-Experts (MoE) language models released by Chinese AI lab DeepSeek on April 24, 2026, as a preview ([DeepSeek release notes](https://api-docs.deepseek.com/news/news260424))."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "DeepSeek V4: The 1.6T Open-Weight Model That Closes the Frontier Gap at 1/30th the Cost",
    "DeepSeek V4 is the most capable open-weight AI model available in 2026, and it costs less per token than a coffee refills budget."
   ]
  },
  {
   "url": "https://dev.to/alifar/mistral-releases-shieldstral-a-3b-open-weight-model-for-on-device-content-safety-280p",
   "title": "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
   "published_at": "2026-08-05T12:52:10",
   "organization": "Mistral AI",
   "model": "Shieldstral",
   "release_date": "2026-08-04",
   "parameters": "3B",
   "context_window": null,
   "license": "Apache 2.0",
   "significance": "A 3B-parameter open-weight safety classifier designed to moderate text and images on-device, supporting policy-adaptive moderation where operators provide safety policy as a natural-language instruction at inference time.",
   "organization_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "Mistral AI has released **Shieldstral**, a 3B-parameter open-weight safety classifier designed to moderate text and images on-device."
   ],
   "model_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "Mistral AI has released **Shieldstral**, a 3B-parameter open-weight safety classifier designed to moderate text and images on-device."
   ],
   "release_date_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "Announced on August 4, 2026, the model is built on Mistral's Ministral-3B base and is intended to let organizations evaluate content against their own natural-language policies without retraining a separate moderation model for every policy revision."
   ],
   "parameters_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "Mistral AI has released **Shieldstral**, a 3B-parameter open-weight safety classifier designed to moderate text and images on-device."
   ],
   "context_window_evidence": null,
   "license_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "According to [Mistral's official Shieldstral announcement](https://mistral.ai/news/shieldstral), the model can run on a single 16GB NVIDIA GPU, and its weights are available under the Apache 2.0 license."
   ],
   "significance_evidence": [
    "Mistral Releases Shieldstral, a 3B Open-Weight Model for On-Device Content Safety",
    "Mistral AI has released **Shieldstral**, a 3B-parameter open-weight safety classifier designed to moderate text and images on-device."
   ]
  },
  {
   "url": "https://teqvolt.com/ai-news/gemma-4-12b-google-encoder-free-multimodal-laptop-model",
   "title": "Gemma 4 12B: Google's Encoder-Free Multimodal Laptop Model",
   "published_at": "2026-06-09T06:13:49",
   "organization": "Google",
   "model": null,
   "release_date": "2026-06-03",
   "parameters": "12B",
   "context_window": null,
   "license": null,
   "significance": "A multimodal model that ingests text, images, and audio, runs on a laptop with 16GB of memory, and throws out the encoder stack that nearly every other vision-language model still leans on.",
   "organization_evidence": [
    "Google released Gemma 4 12B on June 3, 2026, a multimodal open model with an encoder-free architecture that feeds vision and audio directly into the LLM backbone.",
    "# Gemma 4 12B: Google's Encoder-Free Multimodal Laptop Model"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google released Gemma 4 12B on June 3, 2026, a multimodal open model with an encoder-free architecture that feeds vision and audio directly into the LLM backbone.",
    "# Gemma 4 12B: Google's Encoder-Free Multimodal Laptop Model"
   ],
   "parameters_evidence": [
    "Google released Gemma 4 12B on June 3, 2026, a multimodal open model with an encoder-free architecture that feeds vision and audio directly into the LLM backbone.",
    "# Gemma 4 12B: Google's Encoder-Free Multimodal Laptop Model"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "**Gemma 4 12B**, released on June 3, 2026, is the most pointed version of that argument yet: a multimodal model that ingests text, images, and audio, runs on a laptop with 16GB of memory, and throws out the encoder stack that nearly every other vision-language model still leans on.",
    "# Gemma 4 12B: Google's Encoder-Free Multimodal Laptop Model"
   ]
  },
  {
   "url": "https://groundy.com/articles/zhipu-open-sources-glm-5-2-under-mit-while-anthropic-tightens-model-access",
   "title": "Zhipu Open-Sources GLM-5.2 Under MIT While Anthropic Tightens Model Access",
   "published_at": "2026-06-16T23:07:53",
   "organization": "Zhipu",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "744-billion-parameter mixture-of-experts model with 40 billion parameters active per token",
   "context_window": null,
   "license": null,
   "significance": "77.8% SWE-bench Verified score (for the GLM-5 base model published in February 2026) / matches the overall performance of Claude Opus 4.6",
   "organization_evidence": [
    "# Zhipu Open-Sources GLM-5.2 Under MIT While Anthropic Tightens Model Access",
    "Zhipu shipped GLM-5.2 with a 1M-token context window the day after the US ordered Anthropic to cut foreign access, with MIT-licensed weights promised within a week."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Thirty hours apart, two moves redrew the line on who can run a frontier model.",
    "On June 12, 2026, the US federal government ordered Anthropic to disable Fable 5 and Mythos 5 for all foreign nationals, inside and outside the United States, including Anthropic\u2019s own employees; the stated rationale was an alleged method to jailbreak Fable 5, which Anthropic [called](https://pasqualepillitteri.it/en/news/4948/glm-5-2-fully-open-frontier-ai-us-block) a \u201cmisunderstanding\u201d exposing only minor known vulnerabilities.",
    "The next day, Zhipu released GLM-5.2 under its Z.ai brand, with a 1M-token context window, Claude Code integration out of the box, and MIT-licensed open weights promised within the week."
   ],
   "parameters_evidence": [
    "Z.ai\u2019s published specs",
    "According to [Z.ai\u2019s published specs](https://www.aimadetools.com/blog/glm-5-2-complete-guide), GLM-5.2 is a 744-billion-parameter mixture-of-experts model with 40 billion parameters active per token, built on DeepSeek Sparse Attention and trained on 28.5 trillion tokens."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Zhipu AI Open Platform",
    "Zhipu\u2019s own platform page goes further, [claiming](https://open.bigmodel.cn/) its flagship foundation model \u201cmatches the overall performance of Claude Opus 4.6\u201d with enhancements on long-horizon tasks."
   ]
  },
  {
   "url": "https://basedai.co/compare/minimax-m2-vs-deepseek",
   "title": "MiniMax-M2 vs DeepSeek (2026): specs, pricing, and verdict \u2014 BasedAI",
   "published_at": null,
   "organization": "DeepSeek",
   "model": null,
   "release_date": "2026-04-24",
   "parameters": "1.6T (49B active)",
   "context_window": null,
   "license": null,
   "significance": "1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license \u2014 no usage thresholds, no acceptable-use gate.",
   "organization_evidence": [
    "## MiniMax-M2 vs DeepSeek at a glance",
    "| Vendor | MiniMax (China) | DeepSeek (China) |"
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## MiniMax-M2 vs DeepSeek at a glance",
    "| Released | 2025-10-27 | 2026-04-24 |",
    "DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier."
   ],
   "parameters_evidence": [
    "## MiniMax-M2 vs DeepSeek at a glance",
    "| Parameters | 230B (10B active) | 1.6T (49B active) |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## About DeepSeek V4",
    "It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license \u2014 no usage thresholds, no acceptable-use gate."
   ]
  },
  {
   "url": "https://cometapi.com/google-releases-gemma-4-open-source-model",
   "title": "Google Gemma 4: The Complete Guide to Google's Open-Source AI Model (2026) - CometAPI",
   "published_at": "2026-04-06T15:52:49",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "Dense + PLE",
   "context_window": null,
   "license": null,
   "significance": "multimodal capabilities (text + image inputs across all sizes, plus audio on edge models), native support for advanced reasoning and agentic workflows, long context windows up to 256K tokens, and optimization for everything from smartphones and Raspberry Pi to high-end GPUs",
   "organization_evidence": [
    "Google DeepMind officially released **Gemma 4** on April 2, 2026, marking a major milestone in open-source AI."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google DeepMind officially released **Gemma 4** on April 2, 2026, marking a major milestone in open-source AI."
   ],
   "parameters_evidence": [
    "| Model | Architecture | Total Params | Active Params (MoE) | Effective Params | Context Length | Modalities | Target Hardware |",
    "| Gemma 4 E2B | Dense + PLE | ~5.1B (incl. embeddings) | N/A | 2.3B | 128K | Text, Image, Audio | Smartphones, Raspberry Pi, edge IoT |",
    "Google released Gemma 4 in four carefully optimized sizes, each balancing performance, efficiency, and deployment scenarios."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Gemma 4 stands out for its multimodal capabilities (text + image inputs across all sizes, plus audio on edge models), native support for advanced reasoning and agentic workflows, long context windows up to 256K tokens, and optimization for everything from smartphones and Raspberry Pi to high-end GPUs.",
    "Google DeepMind officially released **Gemma 4** on April 2, 2026, marking a major milestone in open-source AI."
   ]
  },
  {
   "url": "https://reading.sh/glm-5-chinas-745b-parameter-open-source-model-that-leaked-before-it-launched-b2cfbafe99ef",
   "title": "GLM-5: China\u2019s 745B parameter open-source model that leaked before it launched",
   "published_at": "2026-02-12T02:02:53",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-02-11",
   "parameters": "745 billion",
   "context_window": null,
   "license": null,
   "significance": "Sits alongside Claude Opus 4.5 and Gemini 3 Pro on key benchmarks, with frontier-scale intelligence and agentic engineering capabilities.",
   "organization_evidence": [
    "# GLM-5: China\u2019s 745B parameter open-source model that leaked before it launched",
    "## Zhipu AI just dropped a frontier-class model trained on Huawei chips. Here\u2019s what makes it different from everything else.",
    "On February 11, Zhipu AI made it official."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5: China\u2019s 745B parameter open-source model that leaked before it launched",
    "On February 11, Zhipu AI made it official."
   ],
   "parameters_evidence": [
    "# GLM-5: China\u2019s 745B parameter open-source model that leaked before it launched",
    "On February 11, Zhipu AI made it official.",
    "It\u2019s a 745 billion parameter mixture-of-experts system, MIT licensed, trained entirely on Huawei Ascend chips, and priced at one dollar per million input tokens."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# GLM-5: China\u2019s 745B parameter open-source model that leaked before it launched",
    "It sits alongside Claude Opus 4.5 and Gemini 3 Pro on key benchmarks."
   ]
  },
  {
   "url": "https://marktechpost.com/2026/04/12/minimax-just-open-sourced-minimax-m2-7-a-self-evolving-agent-model-that-scores-56-22-on-swe-pro-and-57-0-on-terminal-bench-2",
   "title": "MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2",
   "published_at": "2026-04-12T09:20:15",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-03-18",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2",
   "organization_evidence": [
    "MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2",
    "MiniMax has officially open-sourced MiniMax M2.7, making the model weights publicly available on Hugging Face."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2",
    "Originally announced on March 18, 2026, MiniMax M2.7 is the MiniMax\u2019s most capable open-source model to date \u2014 and its first model to actively participate in its own development cycle, a meaningful shift in how large language models are built and iterated."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2"
   ]
  },
  {
   "url": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-a-30b-moe-built-for-agent-grunt-work",
   "title": "NVIDIA Nemotron 3.5 Lightning: A 30B MoE Built for Agent Grunt Work",
   "published_at": "2026-08-11T00:00:00",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-08-11",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Designed for high-volume agentic tasks, claiming roughly 4x throughput versus similarly-sized Qwen models and 30-35% faster performance than Qwen 3.6 MoE on the 10,000-task Pinch Bench.",
   "organization_evidence": [
    "NVIDIA Nemotron 3.5 Lightning: A 30B MoE Built for Agent Grunt Work",
    "NVIDIA released Nemotron 3.5 Lightning, a 30B-A3B open mixture-of-experts language model designed for high-volume agentic tasks, claiming roughly 4x throughput versus similarly-sized Qwen models and 30-35% faster performance than Qwen 3.6 MoE on the 10,000-task Pinch Bench."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "NVIDIA Nemotron 3.5 Lightning: A 30B MoE Built for Agent Grunt Work",
    "read8 min views13 publishedAug 11, 2026"
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "NVIDIA Nemotron 3.5 Lightning: A 30B MoE Built for Agent Grunt Work",
    "NVIDIA released Nemotron 3.5 Lightning, a 30B-A3B open mixture-of-experts language model designed for high-volume agentic tasks, claiming roughly 4x throughput versus similarly-sized Qwen models and 30-35% faster performance than Qwen 3.6 MoE on the 10,000-task Pinch Bench."
   ]
  },
  {
   "url": "https://llms3.com/node/qwen3",
   "title": "Qwen3 | LLMS3",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-04-22",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "dense 27B outperforms 397B MoE on coding. SWE-bench Verified 77.2% (vs Qwen3.5-397B-A17B's 76.2%), SWE-bench Pro 53.5% (vs 50.9%), Terminal-Bench 2.0 59.3% (vs 52.5%).",
   "organization_evidence": [
    "Model Class",
    "# Qwen3",
    "Alibaba's open-weight large language model family launched April 2025 and evolved through multiple 2026 releases."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Class",
    "# Qwen3",
    "- **Qwen3.6-27B (April 22, 2026) \u2014 dense 27B outperforms 397B MoE on coding.** SWE-bench Verified 77.2% (vs Qwen3.5-397B-A17B's 76.2%), SWE-bench Pro 53.5% (vs 50.9%), Terminal-Bench 2.0 59.3% (vs 52.5%). Hybrid Gated DeltaNet linear attention + traditional self-attention. Per [MarkTechPost \u2014 Qwen3.6-27B release](https://www.marktechpost.com/2026/04/22/alibaba-qwen-team-releases-qwen3-6-27b-a-dense-open-weight-model-outperforming-397b-moe-on-agentic-coding-benchmarks/)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Class",
    "# Qwen3",
    "- **Qwen3.6-27B (April 22, 2026) \u2014 dense 27B outperforms 397B MoE on coding.** SWE-bench Verified 77.2% (vs Qwen3.5-397B-A17B's 76.2%), SWE-bench Pro 53.5% (vs 50.9%), Terminal-Bench 2.0 59.3% (vs 52.5%). Hybrid Gated DeltaNet linear attention + traditional self-attention. Per [MarkTechPost \u2014 Qwen3.6-27B release](https://www.marktechpost.com/2026/04/22/alibaba-qwen-team-releases-qwen3-6-27b-a-dense-open-weight-model-outperforming-397b-moe-on-agentic-coding-benchmarks/)."
   ]
  },
  {
   "url": "https://llms3.com/node/qwen3",
   "title": "Qwen3 | LLMS3",
   "published_at": null,
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "397B/17B-active MoE, native vision, 201 languages. Beats Alibaba's own larger trillion-parameter model at a fraction of the cost.",
   "organization_evidence": [
    "Model Class",
    "# Qwen3",
    "Alibaba's open-weight large language model family launched April 2025 and evolved through multiple 2026 releases."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Model Class",
    "# Qwen3",
    "- **Qwen 3.5 (February 2026) \u2014 397B/17B-active MoE, native vision, 201 languages.** Beats Alibaba's own larger trillion-parameter model at a fraction of the cost. Per [VentureBeat \u2014 Qwen 3.5 beats trillion-parameter](https://venturebeat.com/technology/alibabas-qwen-3-5-397b-a17-beats-its-larger-trillion-parameter-model-at-a)."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Model Class",
    "# Qwen3",
    "- **Qwen 3.5 (February 2026) \u2014 397B/17B-active MoE, native vision, 201 languages.** Beats Alibaba's own larger trillion-parameter model at a fraction of the cost. Per [VentureBeat \u2014 Qwen 3.5 beats trillion-parameter](https://venturebeat.com/technology/alibabas-qwen-3-5-397b-a17-beats-its-larger-trillion-parameter-model-at-a)."
   ]
  },
  {
   "url": "https://publixly.com/articles/meta-llama-4-open-source-ai-revolution-2026",
   "title": "Meta Llama 4: The Open-Source AI That's Rewriting the Rules | Publixly",
   "published_at": "2026-03-29T00:00:00",
   "organization": "Meta",
   "model": null,
   "release_date": "early 2026",
   "parameters": "8B, 70B, and 405B",
   "context_window": null,
   "license": null,
   "significance": "Multimodal by default, Mixture of Experts (MoE) architecture, 128K token context window, and better instruction following.",
   "organization_evidence": [
    "# Meta Llama 4: The Open-Source AI That's Rewriting the Rules",
    "Llama 4 is Meta's fourth-generation large language model, released in early 2026 under an open-weights license."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Meta Llama 4: The Open-Source AI That's Rewriting the Rules",
    "Llama 4 is Meta's fourth-generation large language model, released in early 2026 under an open-weights license."
   ],
   "parameters_evidence": [
    "# Meta Llama 4: The Open-Source AI That's Rewriting the Rules",
    "Like its predecessors, it comes in multiple sizes: a compact 8B parameter version for edge devices, a 70B model for serious workloads, and a 405B \"Maverick\" variant that competes directly with frontier closed models."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta Llama 4: The Open-Source AI That's Rewriting the Rules",
    "The biggest upgrades from Llama 3:",
    "- **Multimodal by default** \u2014 Llama 4 sees images, reads documents, and processes audio natively, not as an add-on",
    "- **Mixture of Experts (MoE) architecture** \u2014 only activates relevant parts of the model per task, making it faster and cheaper to run",
    "- **128K token context window** \u2014 handles long documents, codebases, and extended conversations",
    "- **Better instruction following** \u2014 more reliable at doing what you actually ask"
   ]
  },
  {
   "url": "https://theplanettools.ai/blog/google-gemma-4-open-source-apache-2-release",
   "title": "Google Gemma 4 Released: Open-Source Frontier LLM Under Apache 2.0 Challenges Llama 4 and Qwen",
   "published_at": "2026-04-04T16:47:23",
   "organization": "Google",
   "model": null,
   "release_date": "2026-04-02",
   "parameters": "2B-31B",
   "context_window": null,
   "license": null,
   "significance": "31B ranks #3 open model globally. 89.2% AIME 2026, 256K context, native multimodal.",
   "organization_evidence": [
    "Google Gemma 4 Released: Open-Source Frontier LLM Under Apache 2.0 Challenges Llama 4 and Qwen",
    "Google Gemma 4 is a family of four open-source language models (E2B, E4B, 26B MoE, 31B Dense) released April 2, 2026, under Apache 2.0."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Google Gemma 4 Released: Open-Source Frontier LLM Under Apache 2.0 Challenges Llama 4 and Qwen",
    "Google Gemma 4 is a family of four open-source language models (E2B, E4B, 26B MoE, 31B Dense) released April 2, 2026, under Apache 2.0."
   ],
   "parameters_evidence": [
    "Google Gemma 4 Released: Open-Source Frontier LLM Under Apache 2.0 Challenges Llama 4 and Qwen",
    "Gemma 4 is Google DeepMind's open-source LLM family (2B-31B params). Apache 2.0 license. 31B ranks #3 open model globally. 89.2% AIME 2026, 256K context, native multimodal. Free."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Google Gemma 4 Released: Open-Source Frontier LLM Under Apache 2.0 Challenges Llama 4 and Qwen",
    "Gemma 4 is Google DeepMind's open-source LLM family (2B-31B params). Apache 2.0 license. 31B ranks #3 open model globally. 89.2% AIME 2026, 256K context, native multimodal. Free."
   ]
  },
  {
   "url": "https://blockchain-council.org/ai/meet-kimi-k3",
   "title": "Meet KIMI K3",
   "published_at": "2026-07-20T07:00:07",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-27",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "the most powerful open-weight large language model ever released",
   "organization_evidence": [
    "Meet KIMI K3",
    "On July 16, 2026, Moonshot AI launched **KIMI K3**, the most powerful open-weight large language model ever released."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Meet KIMI K3",
    "**KIMI K3** is not merely a parameter count story.",
    "It introduces a new architecture built on Kimi Delta Attention, a native vision and multimodal capability, an always-on reasoning mode called thinking mode, a one-million-token context window, and an open-weight release scheduled for July 27, 2026, under a Modified MIT license."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Meet KIMI K3",
    "On July 16, 2026, Moonshot AI launched **KIMI K3**, the most powerful open-weight large language model ever released."
   ]
  },
  {
   "url": "https://medium.com/the-ai-studio/glm-5-1-just-beat-gpt-5-4-and-claude-opus-and-its-free-and-open-source-049312ed1836",
   "title": "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
   "published_at": "2026-04-12T09:05:18",
   "organization": "Z.ai",
   "model": "GLM-5.1",
   "release_date": "2026-04-07",
   "parameters": null,
   "context_window": null,
   "license": "Free Open Source",
   "significance": "outscored GPT-5.4 and Claude Opus 4.6 on one of the most trusted coding tests in the field",
   "organization_evidence": [
    "Glm-5.1 AI | Artificial Intelligence | Free Open Source Model",
    "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
    "The model is called GLM-5.1.",
    "It was built by Z.ai, a Chinese AI lab formerly known as Zhipu AI, which spun out of Tsinghua University."
   ],
   "model_evidence": [
    "Glm-5.1 AI | Artificial Intelligence | Free Open Source Model",
    "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
    "The model is called GLM-5.1."
   ],
   "release_date_evidence": [
    "Glm-5.1 AI | Artificial Intelligence | Free Open Source Model",
    "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
    "It was released on April 7, 2026, its code is publicly available for anyone to download, and the company behind it built the whole thing without a single American chip."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": [
    "Glm-5.1 AI | Artificial Intelligence | Free Open Source Model",
    "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
    "A free AI model from China just outscored GPT-5.4 and Claude Opus 4.6 on one of the most trusted coding tests in the field."
   ],
   "significance_evidence": [
    "Glm-5.1 AI | Artificial Intelligence | Free Open Source Model",
    "GLM-5.1 Just Beat GPT-5.4 and Claude Opus (And It\u2019s Free and Open-Source)",
    "A free AI model from China just outscored GPT-5.4 and Claude Opus 4.6 on one of the most trusted coding tests in the field."
   ]
  },
  {
   "url": "https://news.cocoloop.cn/en/2026/06/minimax-m3-sparse-attention",
   "title": "MiniMax M3 brings sparse attention, supports million-token context",
   "published_at": "2026-06-02T11:21:39",
   "organization": "MiniMax",
   "model": "M3",
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": "1-million-token",
   "license": "open-weight",
   "significance": "SWE-Bench Pro: 59.0% (surpasses GPT-5.5, Gemini 3.1 Pro, close to Opus 4.7)",
   "organization_evidence": [
    "On June 1, MiniMax released the M3 model.",
    "In a nutshell: an open-weight model that packs a 1-million-token context, cutting-edge coding capabilities, and native multimodality."
   ],
   "model_evidence": [
    "On June 1, MiniMax released the M3 model.",
    "In a nutshell: an open-weight model that packs a 1-million-token context, cutting-edge coding capabilities, and native multimodality."
   ],
   "release_date_evidence": [
    "On June 1, MiniMax released the M3 model."
   ],
   "parameters_evidence": null,
   "context_window_evidence": [
    "On June 1, MiniMax released the M3 model.",
    "In a nutshell: an open-weight model that packs a 1-million-token context, cutting-edge coding capabilities, and native multimodality."
   ],
   "license_evidence": [
    "On June 1, MiniMax released the M3 model.",
    "In a nutshell: an open-weight model that packs a 1-million-token context, cutting-edge coding capabilities, and native multimodality."
   ],
   "significance_evidence": [
    "Coding is M3's main focus. MiniMax released these scores:",
    "- SWE-Bench Pro: 59.0% (surpasses GPT-5.5, Gemini 3.1 Pro, close to Opus 4.7)"
   ]
  },
  {
   "url": "https://ai-beat.github.io/news/2026/06/qwen-agentworld-language-world-models",
   "title": "Simulate the Terminal, Train the Agent \u00b7 AI Beat",
   "published_at": "2026-06-24T07:00:00",
   "organization": "Alibaba",
   "model": null,
   "release_date": "2026-06-23",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "A language world model whose output is the next environment state and observation across seven digital environments, used to train downstream RL policies more cheaply and improve agent benchmark performance.",
   "organization_evidence": [
    "# Simulate the Terminal, Train the Agent",
    "\u00b7 AI Beat Desk",
    "[Qwen-AgentWorld](https://arxiv.org/abs/2606.24597), released by Alibaba\u2019s Qwen team on June 23, makes this explicit."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Simulate the Terminal, Train the Agent",
    "\u00b7 AI Beat Desk",
    "[Qwen-AgentWorld](https://arxiv.org/abs/2606.24597), released by Alibaba\u2019s Qwen team on June 23, makes this explicit."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Simulate the Terminal, Train the Agent",
    "\u00b7 AI Beat Desk",
    "The result is a language world model \u2014 a model whose output is the next environment state and observation, rather than a helpful response.",
    "More practically interesting is what the models are supposed to *enable*: they demonstrate two downstream applications \u2014 using the world model as a standalone simulator to train downstream RL policies more cheaply, and using it as a warm-start foundation model whose pre-trained dynamics knowledge improves performance across seven agent benchmarks when fine-tuned."
   ]
  },
  {
   "url": "https://aiincider.com/google-gemma-4-open-models-apache",
   "title": "Google Gemma 4: The Most Capable Open AI Models You Can Run Locally",
   "published_at": "2026-04-06T16:36:22",
   "organization": "Google",
   "model": "Gemma 4",
   "release_date": "2026-04-02",
   "parameters": "2B, 4B, 26B, and 31B",
   "context_window": "256K-token",
   "license": "Apache 2.0",
   "significance": "Native multimodal capabilities, support for over 140 languages, up to 4x faster than predecessors, uses up to 60% less battery, agentic workflows, complex multi-step reasoning, and offline code generation.",
   "organization_evidence": [
    "Google just handed the open-source AI community its most powerful toolkit yet."
   ],
   "model_evidence": [
    "On April 2, 2026, the company released **Gemma 4** \u2014 a family of four open models built directly from the same research that powers Gemini 3, and for the first time in the Gemmaverse, licensed under the fully permissive **Apache 2.0** license."
   ],
   "release_date_evidence": [
    "On April 2, 2026, the company released **Gemma 4** \u2014 a family of four open models built directly from the same research that powers Gemini 3, and for the first time in the Gemmaverse, licensed under the fully permissive **Apache 2.0** license."
   ],
   "parameters_evidence": [
    "The **2B and 4B \u201cEffective\u201d models** are engineered for on-device deployment \u2014 running fully offline on phones, Raspberry Pi boards, and devices like the NVIDIA Jetson Orin Nano.",
    "The **26B Mixture-of-Experts (MoE)** and **31B Dense** variants are aimed at more powerful machines and enterprise workloads."
   ],
   "context_window_evidence": [
    "Key upgrades across the family include a **256K-token context window**, native multimodal capabilities (text, vision, and audio), and support for over 140 languages."
   ],
   "license_evidence": [
    "On April 2, 2026, the company released **Gemma 4** \u2014 a family of four open models built directly from the same research that powers Gemini 3, and for the first time in the Gemmaverse, licensed under the fully permissive **Apache 2.0** license."
   ],
   "significance_evidence": [
    "Key upgrades across the family include a **256K-token context window**, native multimodal capabilities (text, vision, and audio), and support for over 140 languages."
   ]
  },
  {
   "url": "https://informationtechnology.news/2026-07-20-china-moonshot-ai-releases-open-source-model.html",
   "title": "China\u2019s Moonshot AI Releases Open-Source Kimi K3 Model, Challenging Anthropic\u2019s Opus",
   "published_at": "2026-07-20T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07-17",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Comparable to Anthropic\u2019s Claude Opus in performance on internal benchmarks; ranks first on the Frontend code benchmark on Arena; achieved scores within 2% of Claude Opus on major benchmarks including MMLU, HumanEval, and GSM8K.",
   "organization_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Chinese AI startup Moonshot AI released its Kimi K3 large language model as an open-source model on July 17, 2026, according to the company.",
    "The model is described by Moonshot AI as comparable to Anthropic\u2019s Claude Opus in performance, based on internal benchmarks.",
    "Moonshot AI\u2019s Kimi K3 now ranks first on the Frontend code benchmark on Arena, according to the report.",
    "The company stated that Kimi K3 achieved scores within 2% of Claude Opus on major benchmarks including MMLU, HumanEval, and GSM8K."
   ]
  },
  {
   "url": "https://agentguides.dev/reviews/glm-5-2-review",
   "title": "GLM-5.2 Review \u2014 Zhipu's Open-Weight Coding Flagship",
   "published_at": "2026-06-21T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "SWE-bench Pro 62.1 (vs GPT-5.5 58.6)",
   "organization_evidence": [
    "# GLM-5.2 Review \u2014 Zhipu's Open-Weight Coding Flagship",
    "On **June 13, 2026**, Zhipu AI (Z.ai) released [GLM-5.2](https://z.ai), its open-weight coding flagship, and followed it on June 17 with a benchmark card that did something the Chinese open tier rarely does on day one: it put up cross-vendor coding numbers and **beat GPT-5.5 on every one of them**."
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   "release_date_evidence": [
    "# GLM-5.2 Review \u2014 Zhipu's Open-Weight Coding Flagship",
    "On **June 13, 2026**, Zhipu AI (Z.ai) released [GLM-5.2](https://z.ai), its open-weight coding flagship, and followed it on June 17 with a benchmark card that did something the Chinese open tier rarely does on day one: it put up cross-vendor coding numbers and **beat GPT-5.5 on every one of them**."
   ],
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   "significance_evidence": [
    "TL;DR verdict",
    "| **Headline number** | SWE-bench Pro 62.1 (vs GPT-5.5 58.6) |"
   ]
  },
  {
   "url": "https://heybeagle.com/blog/the-engineer-who-watched-a-model-tune-its-own-gpu-kernel",
   "title": "The Engineer Who Watched a Model Tune Its Own GPU Kernel",
   "published_at": "2026-06-03T04:02:04",
   "organization": "MiniMax",
   "model": null,
   "release_date": "2026-06-01",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "MiniMax M3 just shipped as the first open-weight model combining frontier coding, a one-million-token context window, and native multimodality.",
   "organization_evidence": [
    "The Engineer Who Watched a Model Tune Its Own GPU Kernel",
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   "release_date_evidence": [
    "The Engineer Who Watched a Model Tune Its Own GPU Kernel",
    "MiniMax M3 launched June 1, 2026 as the first open-weight model combining frontier coding, a one-million-token context window, and native multimodal input."
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   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "The Engineer Who Watched a Model Tune Its Own GPU Kernel",
    "MiniMax M3 just shipped as the first open-weight model combining frontier coding, a one-million-token context window, and native multimodality."
   ]
  },
  {
   "url": "https://hpcwire.com/aiwire/2026/06/12/nvidia-accelerates-google-deepminds-diffusiongemma-for-local-ai",
   "title": "Covering Scientific & Technical AI",
   "published_at": "2026-06-12T20:19:01",
   "organization": "Google DeepMind",
   "model": null,
   "release_date": "2026-06-12",
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   "context_window": null,
   "license": null,
   "significance": "Denoising up to 256 tokens per step, DiffusionGemma generates text in parallel up to 4x faster than an equivalent autoregressive model in the single-user regime.",
   "organization_evidence": [
    "## Off the Wire Press Releases",
    "## NVIDIA Accelerates Google DeepMind\u2019s DiffusionGemma for Local AI",
    "June 12, 2026 \u2014 This week, Google DeepMind released DiffusionGemma \u2014 an experimental open model built for exceptionally fast text generation."
   ],
   "model_evidence": null,
   "release_date_evidence": [
    "## Off the Wire Press Releases",
    "## NVIDIA Accelerates Google DeepMind\u2019s DiffusionGemma for Local AI",
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   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "## Off the Wire Press Releases",
    "## NVIDIA Accelerates Google DeepMind\u2019s DiffusionGemma for Local AI",
    "- **Parallel generation**: DiffusionGemma denoises up to 256 tokens per step instead of predicting one at a time.",
    "- **Up to 4x faster performance**: The boost means fast text generation, where single-user generation usually stalls \u2014 on local hardware."
   ]
  },
  {
   "url": "https://computingforgeeks.com/open-source-llm-comparison",
   "title": "Open Source LLM Comparison Table (2026) | ComputingForGeeks",
   "published_at": "2026-03-28T07:53:41",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": null,
   "parameters": "2.8T",
   "context_window": null,
   "license": null,
   "significance": "1M tokens",
   "organization_evidence": [
    "This reference covers every major open-source and open-weight large language model, with verified benchmark scores, license terms, hardware requirements, and hands-on performance data from real self-hosting tests.",
    "## Master Comparison Table",
    "| **Kimi K3** | Moonshot AI | 2.8T | 104B | MoE (896 experts) | 1M tokens | Text + Image + Video | Kimi K3 (custom) | Jul 2026 |"
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "This reference covers every major open-source and open-weight large language model, with verified benchmark scores, license terms, hardware requirements, and hands-on performance data from real self-hosting tests.",
    "## Master Comparison Table",
    "| **Kimi K3** | Moonshot AI | 2.8T | 104B | MoE (896 experts) | 1M tokens | Text + Image + Video | Kimi K3 (custom) | Jul 2026 |"
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "This reference covers every major open-source and open-weight large language model, with verified benchmark scores, license terms, hardware requirements, and hands-on performance data from real self-hosting tests.",
    "## Master Comparison Table",
    "| **Kimi K3** | Moonshot AI | 2.8T | 104B | MoE (896 experts) | 1M tokens | Text + Image + Video | Kimi K3 (custom) | Jul 2026 |"
   ]
  },
  {
   "url": "https://diaryofatoken.com/en/article/meta-llama-4-reasoning",
   "title": "Meta releases Llama 4 Reasoning, reasoning-specialized variant",
   "published_at": "2026-03-01T00:00:00",
   "organization": "Meta",
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   "release_date": "2026-03",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "performance comparable to or exceeding proprietary reasoning models on standard math tests (MATH 500, AMC/AIME) and on GPQA, the expert-level science questions test",
   "organization_evidence": [
    "# Meta releases Llama 4 Reasoning, reasoning-specialized variant",
    "Meta published Llama 4 Reasoning in March 2026, a specialized variant of the Llama 4 family trained with emphasis on chain-of-thought reasoning for complex analytical tasks: mathematics, logic, programming, and multi-step problem solving [1]."
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   "model_evidence": null,
   "release_date_evidence": [
    "# Meta releases Llama 4 Reasoning, reasoning-specialized variant",
    "Meta published Llama 4 Reasoning in March 2026, a specialized variant of the Llama 4 family trained with emphasis on chain-of-thought reasoning for complex analytical tasks: mathematics, logic, programming, and multi-step problem solving [1]."
   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "# Meta releases Llama 4 Reasoning, reasoning-specialized variant",
    "Benchmarks published by Meta show performance comparable to or exceeding proprietary reasoning models on standard math tests (MATH 500, AMC/AIME) and on GPQA, the expert-level science questions test."
   ]
  },
  {
   "url": "https://aimenta.ai/news/mistral-small-31-apache-2-open-weights-apac-enterprise-2026",
   "title": "Mistral AI Releases Mistral Small 3.1 Open-Weights Under Apache 2.0 for APAC Enterprise Self-Hosting",
   "published_at": "2026-04-26T00:00:00",
   "organization": "Mistral AI",
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   "release_date": null,
   "parameters": "22B",
   "context_window": null,
   "license": null,
   "significance": "outperforming GPT-4o Mini on APAC coding and bilingual Chinese-English reasoning benchmarks at 4x lower self-hosting inference cost",
   "organization_evidence": [
    "AIMenta editorial take",
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   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "AIMenta editorial take",
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AIMenta editorial take",
    "Mistral AI releases Mistral Small 3.1 as fully open-weights under Apache 2.0 \u2014 a 22B parameter model outperforming GPT-4o Mini on APAC coding and bilingual Chinese-English reasoning benchmarks at 4x lower self-hosting inference cost."
   ]
  },
  {
   "url": "https://aimenta.ai/news/mistral-small-31-apache-2-open-weights-apac-enterprise-2026",
   "title": "Mistral AI Releases Mistral Small 3.1 Open-Weights Under Apache 2.0 for APAC Enterprise Self-Hosting",
   "published_at": "2026-04-26T00:00:00",
   "organization": "ByteDance",
   "model": null,
   "release_date": null,
   "parameters": "7B and 32B",
   "context_window": null,
   "license": null,
   "significance": "superior performance versus Llama 3.1 on Asian-language reasoning, document understanding, and code generation tasks",
   "organization_evidence": [
    "AIMenta editorial take",
    "ByteDance releases Doubao-1.5 open-source model family under Apache 2.0 licence \u2014 7B and 32B parameter variants trained with comprehensive Japanese, Korean, Mandarin Chinese, and Indonesian multilingual data, with APAC enterprise benchmark results showing superior performance versus Llama 3.1 on Asian-language reasoning, document understanding, and code generation tasks."
   ],
   "model_evidence": null,
   "release_date_evidence": null,
   "parameters_evidence": [
    "AIMenta editorial take",
    "ByteDance releases Doubao-1.5 open-source model family under Apache 2.0 licence \u2014 7B and 32B parameter variants trained with comprehensive Japanese, Korean, Mandarin Chinese, and Indonesian multilingual data, with APAC enterprise benchmark results showing superior performance versus Llama 3.1 on Asian-language reasoning, document understanding, and code generation tasks."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "AIMenta editorial take",
    "ByteDance releases Doubao-1.5 open-source model family under Apache 2.0 licence \u2014 7B and 32B parameter variants trained with comprehensive Japanese, Korean, Mandarin Chinese, and Indonesian multilingual data, with APAC enterprise benchmark results showing superior performance versus Llama 3.1 on Asian-language reasoning, document understanding, and code generation tasks."
   ]
  },
  {
   "url": "https://medium.com/@pranavakailash/what-is-google-gemma-complete-history-and-guide-2026-3491553ec50d",
   "title": "What Is Google Gemma? Complete History and Guide (2026)",
   "published_at": "2026-04-13T09:31:01",
   "organization": "Google",
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   "release_date": "2026-04-02",
   "parameters": null,
   "context_window": null,
   "license": null,
   "significance": "Ranked #3 on the Arena AI text leaderboard with an MMLU-Pro score of 85.2% and an AIME 2026 score of 89.2%, under an Apache 2.0 license.",
   "organization_evidence": [
    "From a 2B model you could run on your laptop to 400 million downloads and Apache 2.0 and here\u2019s the full Gemma story.",
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   "release_date_evidence": [
    "Two years of community complaints, GitHub issues, and legal team rejections and finally on April 2, 2026 Gemma 4 launched under Apache 2.0.",
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   ],
   "parameters_evidence": null,
   "context_window_evidence": null,
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   "significance_evidence": [
    "**Gemma 4 31B (Dense):** Full dense transformer, 256K context window. Ranked #3 on the Arena AI text leaderboard.",
    "On MMLU-Pro, the 31B scored 85.2%.",
    "On AIME 2026 math competition problems: 89.2%.",
    "From a 2B model you could run on your laptop to 400 million downloads and Apache 2.0 and here\u2019s the full Gemma story."
   ]
  },
  {
   "url": "https://arjunjaggi.com/blog/kimi-k3-open-source-ai-acceleration",
   "title": "Kimi K3 Technical Report: Architecture, Benchmarks and Open Weights \u2014 Moonshot AI (Jul 2026)",
   "published_at": "2026-07-23T00:00:00",
   "organization": "Moonshot AI",
   "model": null,
   "release_date": "2026-07",
   "parameters": null,
   "context_window": null,
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   "significance": "Stronger reasoning, deeper tool use, and improved long-context handling. The model is immediately downloaded and benchmarked by the global community and sets new reference points for open-source agentic capability.",
   "organization_evidence": [
    "# Kimi K3 and the Open-Source AI Inflection: What Every Leader Needs to Understand",
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   ],
   "model_evidence": null,
   "release_date_evidence": [
    "# Kimi K3 and the Open-Source AI Inflection: What Every Leader Needs to Understand",
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   "license_evidence": null,
   "significance_evidence": [
    "# Kimi K3 and the Open-Source AI Inflection: What Every Leader Needs to Understand",
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    "The Open-Source Frontier: Key Releases"
   ]
  },
  {
   "url": "https://aijustdropped.com/model/glm-5-2",
   "title": "GLM-5.2 review",
   "published_at": "2026-06-13T00:00:00",
   "organization": "Zhipu AI",
   "model": null,
   "release_date": "2026-06-13",
   "parameters": "753B",
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   "license": null,
   "significance": "Claims to edge past GPT-5.5 on coding benchmarks with 62.1 on SWE-bench Pro and 99.2 on AIME 2026.",
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    "# GLM-5.2 review",
    "GLM-5.2, which Zhipu AI released on June 13, 2026 under its international Z.ai brand, is the clearest sign yet that the gap is closing where it matters most \u2014 coding \u2014 and it does it with MIT-licensed weights you can actually download."
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   "model_evidence": null,
   "release_date_evidence": [
    "# GLM-5.2 review",
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   "parameters_evidence": [
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   "significance_evidence": [
    "# GLM-5.2 review",
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    "The pitch is specific. It's a 753-billion-parameter mixture-of-experts model with roughly 40B parameters active per token, a genuinely usable 1-million-token context window, and benchmark numbers that Zhipu says nose ahead of the closed leaders: 62.1 on SWE-bench Pro against the ~58.6 it attributes to GPT-5.5, and 99.2 on AIME 2026."
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  },
  {
   "url": "https://opensourceforu.com/2026/07/minimax-bags-2b-doubles-down-on-open-source",
   "title": "MiniMax Bags $2B, Doubles Down On Open Source - Open Source For You",
   "published_at": "2026-07-13T08:30:54",
   "organization": "MiniMax",
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   "release_date": "2026-06",
   "parameters": "427 billion",
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   "significance": "delivers prefill performance nine times faster and decode performance 15 times faster than its previous flagship",
   "organization_evidence": [
    "MiniMax Bags $2B, Doubles Down On Open Source",
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   "model_evidence": null,
   "release_date_evidence": [
    "MiniMax Bags $2B, Doubles Down On Open Source",
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   "parameters_evidence": [
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    "The company\u2019s latest open-source flagship, MiniMax-M3, launched in June with 427 billion parameters and a one million-token context window."
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   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "MiniMax Bags $2B, Doubles Down On Open Source",
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  },
  {
   "url": "https://aiproductivity.ai/news/nvidia-diffusiongemma-26b-nvfp4-local-model",
   "title": "NVIDIA DiffusionGemma 26B: Run Locally with NVFP4",
   "published_at": "2026-06-11T03:28:47",
   "organization": "NVIDIA",
   "model": null,
   "release_date": "2026-06-11",
   "parameters": "26B",
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   "license": null,
   "significance": "A diffusion language model compressed with NVFP4 quantization for local inference on NVIDIA hardware.",
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    "NVIDIA published [nvidia/diffusiongemma-26B-A4B-it-NVFP4](https://huggingface.co/nvidia/diffusiongemma-26B-A4B-it-NVFP4) on Hugging Face on June 11, an instruction-tuned language model compressed for local inference on NVIDIA hardware."
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   "release_date_evidence": [
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    "NVIDIA published [nvidia/diffusiongemma-26B-A4B-it-NVFP4](https://huggingface.co/nvidia/diffusiongemma-26B-A4B-it-NVFP4) on Hugging Face on June 11, an instruction-tuned language model compressed for local inference on NVIDIA hardware."
   ],
   "parameters_evidence": [
    "Open Source",
    "NVIDIA Releases Quantized DiffusionGemma 26B on Hugging Face",
    "The \"26B\" refers to total parameters, but this is a mixture-of-experts model, meaning only a subset of the network activates per query - specifically 4 billion active parameters at any given moment."
   ],
   "context_window_evidence": null,
   "license_evidence": null,
   "significance_evidence": [
    "Open Source",
    "NVIDIA Releases Quantized DiffusionGemma 26B on Hugging Face",
    "NVIDIA published [nvidia/diffusiongemma-26B-A4B-it-NVFP4](https://huggingface.co/nvidia/diffusiongemma-26B-A4B-it-NVFP4) on Hugging Face on June 11, an instruction-tuned language model compressed for local inference on NVIDIA hardware."
   ]
  }
 ],
 "notes": [
  "Merged 10 queries (2000 total) \u2192 1913 unique results",
  "Search result set: r701f9a0e854 (1913 rows), reference it as FROM r701f9a0e854 in follow-up queries",
  "WARNING: semantic extraction failed on 1 of 1913 rows \u2014 extracted values are null on those rows. First error: timed_out after 90.0s"
 ]
}