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Mistral releases Large 4 weights on Hugging Face
On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.
On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Termin
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Baseten releases 2B decision model joey-1
On October 29, 2026, Baseten's Base Labs released joey-1, a 2B-parameter decision model that directly outputs a single-token label and calibrated confidence. The model was post-trained from Qwen3.5-2B and shares the same data recipe and API as elephant-1 and mammoth-1, so switching requires only a one-line change. According to a Baseten engineering blog post, joey-1 has a P50 end-to-end latency of 20ms, roughly 2.5 times faster than elephant-1 (49ms), and is priced at $0.01 per million tokens, one-tenth that of mammoth-1. The model is now live on Baseten Model APIs with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments unlock a 262k context window and support fine-tuned weights. Unite.AI, citing the model card, reported that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among models with fewer than 4B parameters.
iced at $0.01 per million tokens, one-tenth that of mammoth-1. The model is now live on Baseten Model APIs with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments unlock a 262k context window and support fine-tuned weights. Unite.AI, citing the model card, reported that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among
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Mistral releases Large 4 weights on Hugging Face
On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.
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Hugging Face releases open-source DNA model Carbon-X
On October 23, 2026, Hugging Face's Thomas Wolf announced a collaboration with TIGEM, the University of Naples Federico II, and Zhongguancun Academy to train and release the open-source DNA model Carbon-X. The model can directly read raw DNA and interpret it in natural language, with open weights, and its training data excluded human and animal viruses. According to its release information, Carbon-X scored 67% on its own DNA benchmark, higher than Claude Opus 5.5's 61% and Gemini 4 Argon's 50%, and within less than one point of GPT-6.1 Sol while costing about 1,000 times less. On October 26, MarkTechPost reported that Carbon-X is a 14.8B-parameter decoder-only DNA language model based on Qwen3-14B, using a hybrid tokenizer with 5,515 newly added DNA tokens, supporting a 40,960-token context, with weights released under Apache 2.0 and runnable in bfloat16 on a single 80GB GPU. The report also stated that Hugging Face self-reported an average of 66.7% across 5 DNA benchmarks, with BioReason Coding reaching
On October 23, 2026, Hugging Face's Thomas Wolf announced a collaboration with TIGEM, the University of Naples Federico II, and Zhongguancun Academy to train and release the open-source DNA model Carbon-X. The model can directly read raw DNA and interpret it in natural language, with open weights, and its training data excluded human and animal viruses. According to its release information, Carbon-X scored 67% on its own DNA benchmark, higher than Claude Opus 5.5's 61% and Gemini 4 Argon's 50%,
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MiniMax open-sources M3.1 Flash model weights
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B total parameters and activates 23B per token, and uses MiniMax Sparse Attention and DSpark speculative decoding to improve inference efficiency; the weights are now available on Hugging Face and ModelScope, with API and MCode desktop entry points also provided. On the same day, Together AI, as a day-one launch partner, deployed the model to its AI Native Cloud production environment, so users do not need to manage GPUs themselves; according to Together AI, the model targets agentic tasks, codi
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Hugging Face releases DNA model Carbon-X
On October 23, 2026, Hugging Face's Thomas Wolf announced a collaboration with TIGEM, the University of Naples Federico II, and Zhongguancun Academy to train and release the open-source DNA model Carbon-X. The model can directly read raw DNA and interpret it in natural language, with open weights, and its training data excluded human and animal viruses. According to its release information, Carbon-X scored 67% on its own DNA benchmark, higher than Claude Opus 5.5's 61% and Gemini 4 Argon's 50%, and within less than one point of GPT-6.1 Sol while costing about 1,000 times less. On October 26, MarkTechPost reported that Carbon-X is a 14.8B-parameter decoder-only DNA language model based on Qwen3-14B, using a hybrid tokenizer with 5,515 newly added DNA tokens, supporting a 40,960-token context, with weights released under Apache 2.0 and runnable in bfloat16 on a single 80GB GPU. The report also stated that Hugging Face self-reported an average of 66.7% across 5 DNA benchmarks, with BioReason Coding reaching
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View story:Hugging Face releases open-source DNA model Carbon-X
Sarvam AI releases decision model Sarvam Prism
On November 3, 2026, Sarvam AI released Sarvam Prism, a System One decision model based on Sarvam 105B. According to the Sarvam AI Blog, the model achieved an average score of 71.8 with a median response time of 44 milliseconds on B200 GPUs, the highest score among the 25 decision models tested by Sarvam AI within 100 milliseconds. At confidence levels above 90%, the model's actual error rate was lower than expected: 2.2% versus 3.3% for English, and 4.4% versus 5.4% for Indic languages, and it kept its answers unchanged in a shuffled-options test of 2,000 questions. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API. There are currently no subsequent developments.
AI within 100 milliseconds. At confidence levels above 90%, the model's actual error rate was lower than expected: 2.2% versus 3.3% for English, and 4.4% versus 5.4% for Indic languages, and it kept its answers unchanged in a shuffled-options test of 2,000 questions. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API. There are currently no subsequent developments.
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SoMark open-sources the 1.3B document parsing model OXR-1.0
SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs a recursive self-improvement (RSI) loop, in which a general-purpose VLM judges parsing results and generates newly annotated pages, eliminating the need for manual annotation. QbitAI reports that the OXR main model open-sourced by the SoMark team has only 1.3B parameters and topped all three benchmark tests, with full parsing speed exceeding that of PaddleOCR-VL-1.6 and MinerU2.5-Pro, which were also evaluated in the same comparison. It covers 13 categories of document elements, including text, tables, formulas, chemical structure formulas, and code blocks, and can parse reading order and output layout coordinates.
SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs
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Liquid AI releases six open-weight 2B-class models in the LFM3 series
On October 28, 2026, Liquid AI released the LFM3 series of open-weight models, comprising six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, scores 38.42 versus 3.02 on WebArena-Verified, and LFM3-VL-2B's average score across 62 vision benchmarks rose from 57.6 to 71.4. All models are available for download on Hugging Face, along with single-GPU fine-tuning notebooks. A separate report the same day stated that the LFM3-1.7B text model outperforms its 2.6B predecessor, uses a 24-layer triple-hybrid architecture, and scores 41.29 on AutomationBench.
ng six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM
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Ant Ling Open-Sources Ling-3.1-flash-1019
On October 27, 2026, Ant Ling open-sourced Ling-3.1-flash-1019. According to Artificial Analysis, the model scored 44 on the Artificial Analysis Intelligence Index v4.3, higher than its predecessor's 41; it is a reasoning model with 560B total parameters and 25B activated parameters, supports a 1M token context window, is released under the MIT license, is available as open weights on Hugging Face, and can be accessed through InclusionAI's first-party API. QbitAI reported that this version was further trained and optimized on the basis of Ling-3.1-flash, and that the base model Ling-3.1-flash-base was also open-sourced. On October 30, Ant Ling announced that the model is being made available to more developers through Novita AI, stating that its reasoning capabilities and real-world agent performance are stronger.
s a reasoning model with 560B total parameters and 25B activated parameters, supports a 1M token context window, is released under the MIT license, is available as open weights on Hugging Face, and can be accessed through InclusionAI's first-party API. QbitAI reported that this version was further trained and optimized on the basis of Ling-3.1-flash, and that the base model Ling-3.1-flash-base was also open-sourced. On October 30, Ant Ling announced that the model is being made available to more
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Baidu releases open-source model Unlimited OCR 2
On October 27, 2026, Baidu released and open-sourced the document parsing model Unlimited OCR 2. According to Baidu's official introduction, the model uses a new SR-SWA mechanism to compress the attention interaction between text and vision into a fixed-size working reference, thereby parsing documents of almost arbitrary length within a limited context window; Baidu says that as documents grow longer, per-step decoding latency and memory usage remain nearly constant, and that it achieves SOTA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.
TA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.
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Tencent Hunyuan launches open-source interactive video world model Hy-Nomas
Tencent Hunyuan launched the open-source interactive video world model Hy-Nomas on October 24, 2026. According to official release information, the model supports interaction using text prompts and camera trajectories, and has long-horizon generation capability with world memory; it can be adapted to specialized domains such as robotics via lightweight LoRA, and has been optimized for inference on consumer-grade GPUs. The official release also provides website, GitHub, Hugging Face, and demo entry points. There are currently no subsequent development facts.
d model Hy-Nomas on October 24, 2026. According to official release information, the model supports interaction using text prompts and camera trajectories, and has long-horizon generation capability with world memory; it can be adapted to specialized domains such as robotics via lightweight LoRA, and has been optimized for inference on consumer-grade GPUs. The official release also provides website, GitHub, Hugging Face, and demo entry points. There are currently no subsequent development facts.
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Recursive releases open-weight local coding model Flint
On October 21, 2026, Recursive Superintelligence released Flint, an open-weight local coding model. According to related reports, Flint is a 40B mixture-of-experts model that activates approximately 2.5B parameters per token, is licensed under Apache 2.0, and its weights have been made available on Hugging Face; it was built from scratch in under 4 months by the automated research system Eureka Machine. On October 22, Hugging Face co-founder Thomas Wolf commented that Recursive researchers quantized the lm_head—which is usually recommended to be kept at high precision—to 4 bits, and the result differed from bf16 by only 0.7 percentage points. On the same day, OpenRouter launched Flint, saying it is aimed at coding agents, supports a 262K context, reasoning, and tool calling, and is free to use during the launch period; Unsloth released a Dynamic GGUF quantized version of Flint, saying it can run locally on devices with 24GB of memory. The model now offers multiple usage paths through open weights, hosting platforms, and quantized versions.
reports, Flint is a 40B mixture-of-experts model that activates approximately 2.5B parameters per token, is licensed under Apache 2.0, and its weights have been made available on Hugging Face; it was built from scratch in under 4 months by the automated research system Eureka Machine. On October 22, Hugging Face co-founder Thomas Wolf commented that Recursive researchers quantized the lm head—which is usually recommended to be kept at high precision—to 4 bits, and the result differed from bf16
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Baseten releases decision models mammoth-1 and elephant-1
Baseten's research team Base Labs has released two decision models, mammoth-1 (27B) and elephant-1 (9B), both fully fine-tuned from Qwen3.5, with model weights made public. According to Baseten Base Labs, mammoth-1 ranks first among 208 models on the Jev Decision Index 0.4 with a score of 71.24, leading the second-place Perplexity Decider v1.2 by 1.8 points; elephant-1 scores 62.65 on that leaderboard. Baseten's engineering blog states that elephant-1 is the highest-ranked 9B decision model, and on the Artificial Analysis Decision Index the two score 65.9 and 65.2 respectively; earlier reports said elephant-1 tied for first on that index with 65 points alongside d1-ultra. Decision models do not generate text; instead they return probability distributions over options, ratings, or yes/no questions, and a confidence threshold can determine whether to accept automatically or escalate to a human. Both models are open-sourced on Hugging Face and available for a one-week free trial on Baseten Model APIs, with zero data retention by default.
score 65.9 and 65.2 respectively; earlier reports said elephant-1 tied for first on that index with 65 points alongside d1-ultra. Decision models do not generate text; instead they return probability distributions over options, ratings, or yes/no questions, and a confidence threshold can determine whether to accept automatically or escalate to a human. Both models are open-sourced on Hugging Face and available for a one-week free trial on Baseten Model APIs, with zero data retention by default.
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Nvidia releases open-weight models for enterprise agents and PCs
On October 19, 2026, Nvidia released open-weight models for always-on agents and edge computing. According to TechCrunch AI, the Nemotron 3.5 Super VL is a 120-billion-parameter enterprise-grade agent model, which Nvidia says can achieve accuracy comparable to Nemotron 3 Ultra on instruction-following and tool-calling tasks; two lightweight Bolt models are designed to run locally on AI PCs equipped with RTX Spark chips and on DGX Spark workstations. The report said Super VL and Nemotron 3.5 Bolt 74B-A9B 24GB are already available, and the smallest Bolt E4B will launch later this month. On October 21, Nvidia announced an expansion of its open model portfolio covering language, robotics, biology and quantum computing, positioned around European sovereign intelligence; the portfolio includes Nemotron 3.5 Super VL and Nemotron 3.5 Bolt, among others. The report said Super VL is now available on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, and Bolt E4B 4GB is expected to be available on October 23.
ady available, and the smallest Bolt E4B will launch later this month. On October 21, Nvidia announced an expansion of its open model portfolio covering language, robotics, biology and quantum computing, positioned around European sovereign intelligence; the portfolio includes Nemotron 3.5 Super VL and Nemotron 3.5 Bolt, among others. The report said Super VL is now available on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, and Bolt E4B 4GB is expected to be available on October 23.
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Bria Releases 8B Open-Weight Image Model Fibo 2.0
On October 20, 2026, Bria released Fibo 2.0, an 8B-parameter open-weight image model. According to Artificial Analysis, the model ranks first among open-weight models on AA-Image-Editing v2.1 and is statistically tied for the lead with Qwen-Image-2.1 on AA-Image-T2I v2.1. Two sets of weights were released: Fibo 2.0 Dev for text-to-image, and the distilled Fibo 2.0 Edit Turbo for image editing. The model is released as open weights under a non-commercial license, and commercial use requires a separate agreement with Bria; the weights are available on Hugging Face and can be used via the Bria API and fal. Bria API pricing is $32 per 1k images. Information on the fal platform the same day shows that the model supports up to four input images, can perform background replacement, product repositioning and lighting adjustment, and is said to be trained only on licensed data.
o for image editing. The model is released as open weights under a non-commercial license, and commercial use requires a separate agreement with Bria; the weights are available on Hugging Face and can be used via the Bria API and fal. Bria API pricing is $32 per 1k images. Information on the fal platform the same day shows that the model supports up to four input images, can perform background replacement, product repositioning and lighting adjustment, and is said to be trained only on licensed
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NVIDIA Expands Nemotron Retriever Series and Releases Multiple Open Models
On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, releasing multiple open models covering embedding, reranking, and document parsing. The models introduced include Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and Nemotron 3.5 Parse with 631 million parameters, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100. Open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM and through Baseten, DeepInfra, and FriendliAI.
for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100. Open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM and through Baseten, DeepInfra, and FriendliAI.
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