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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.
AI-generated from public reports
Latest development · Report
Anthropic Files for IPO as AI Infrastructure Spending and Market Concerns Run in ParallelDevelopments
2 confirmed developments
- Source:Mistral AI
Unsloth releases 1-bit GGUF, enabling Le Chonk to run locally
Source:Unsloth
Reports and discussion timeline
Browse reports and discussion by time. Their evidence levels remain distinct.
Oct 30
SiliconANGLE
Anthropic Files for IPO as AI Infrastructure Spending and Market Concerns Run in Parallel
Anthropic filed for an IPO this week, with the filing listing multiple risks including the massive scale of compute procurement, doubts about the sustainability of customer AI spending, and catastrophic risks. In the same period, Meta announced it will issue $95 billion in non-voting shares to continue building AI infrastructure, while Alphabet's stock fell more than 12% due to weak performance in its search and advertising businesses, wiping out over $500 billion in market value. The article also mentions that SpaceXAI is watching the IPO's progress because it supplies a large amount of compute to Anthropic.
heise online
heise Weekly: Mistral Open-Sources ML4, Cohere Secures Investment from Schwarz Group, Google Restricts Gemini…
On Thursday, Mistral released the weights of its largest model, Mistral Large 4 (ML4). The model has a total of one trillion parameters, activates only 49 billion per token, and can run on four GPUs. Mistral says it outperforms Kimi K3 in coding and cybersecurity. Cohere completed its largest funding round to date, led by Schwarz Group, which owns Lidl and Kaufland, with participation from the Canadian government and Nvidia. Google launched Gemini Carbon four weeks after Gemini Argon, but at the White House's request it is open only to U.S. intelligence agencies and select partners. In addition, an Anthropic experiment involving 50 statisticians showed that in 86% of cases experts adopted the option given by an AI agent, and about half treated the final recommendation as their own judgment.
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Cailian Press
Mistral Releases Large 4 Open-Weight Model, Focused on Coding and Cyber Defense
French company Mistral AI has released the Large 4 model (ML4), featuring advanced coding and cyber defense capabilities; enterprises can download the open-weight version and deploy it to run on their own infrastructure. The model has a total of 1.05 trillion parameters, with approximately 52 billion parameters actually activated per task execution, and was trained in a European data center using 3,800 NVIDIA Grace Blackwell GPUs. Mistral claims it is comparable to other leading open models on coding tasks and emphasizes that open source helps prevent frontier AI technology from concentrating in the hands of a few players.
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Testing Catalog
October 30 AI Daily Brief: Mistral Large 4 Tops Open-Source Rankings, Google Shuts Down Jules
Mistral released the open-weight model Mistral Large 4, taking the top spot among open-source models on FrontierCode. Google shut down Jules and merged its features into Antigravity, which launched a plugin marketplace including Jira, Notion, Stripe, and Figma. OpenAI doubled the usage quotas for Pro and Business Premium, while Claude's mobile app added multi-account login, CarPlay, and 24 languages.
Latent Space
Latent Space announces AIE CODE coding agent conference curation themes
Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.
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English text is being prepared.Selected
Mistral AI Opens the Mistral Large 4 Model Weights: 1T Parameters, 49 Billion Activated Parameters
On October 29 local time, Mistral AI officially opened the model weights of Mistral Large 4 (codename le Chonk). This is a natively multimodal model with 1 trillion total parameters and 49 billion activated parameters, trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own data center in Europe. The company says it scores 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but consumes 47% fewer Tokens per task on average. The model is released under a modified MIT license: personal, academic, fine-tuning, and conventional commercial use are free, while cloud providers offering MaaS resale need to obtain a separate commercial license; online API pricing is $0.70 per million input Tokens and $2.10 per million output Tokens.
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CNETSelected
Mistral Releases Flagship Model Mistral Large 4 and Opens Weight Downloads
Mistral AI has released its new flagship frontier model, Mistral Large 4, codenamed Le Chonk, making it available for download after ending its public preview. The model supports text and image processing, with a focus on strengthening defensive cybersecurity, agentic capabilities, and geospatial and grounding capabilities, and is said to achieve state-of-the-art results on agentic benchmarks such as SWE-bench and Terminal Bench. The model was trained on more than 160 languages and is available via Mistral Studio, the Mistral API, OpenRouter, Hugging Face open weights, Vibe Code, and Nvidia-hosted endpoints.
TechCrunch AISelected
Mistral Releases Weights for Trillion-Parameter Open Model Mistral Large 4, Focused on Cybersecurity
Mistral has released the weights for its trillion-parameter open-source model Mistral Large 4 (ML4, nicknamed Le Chonk), which enterprises and government agencies can download and deploy themselves; the weights are already available on Hugging Face and via its API. Mistral says the model's performance is comparable to that of the open-source model GLM-5.3, but it consumes 47% fewer tokens per task, and it ranks first among open-source models on Artificial Analysis's Coding Agent Index. Pierre Stock, Mistral's Vice President of Science, said that open weights mean organizations can deploy locally in their own environments to identify, coordinate, and respond to major security incidents in real time.
Oct 29
The Decoder
Mistral Releases Mistral Large 4 Weights: Coding Ability Leads Open-Source Models but Overall Trails Chinese Models
Mistral has released the weights of Mistral Large 4 on Hugging Face. The model, nicknamed Le Chonk, has approximately 1 trillion parameters with 52 billion activated per step, and supports text and image inputs. On Terminal-Bench 4.0, its score rose from 28% in the preview version to 41%, slightly above GLM-5.3's 40%, but on the new version of the Artificial Analysis Intelligence Index it scored only 26 points, below the 32 points of DeepSeek V4.1 Flash and GLM-5.3. API pricing is $0.70 per million input tokens and $2.10 per million output tokens, under a modified MIT license; using it as a commercial cloud service requires separate authorization.
SiliconANGLESelected
Mistral Opens the Weights of Mistral Large 4, a 1-Trillion-Parameter Natively Multimodal Model
Mistral AI announced that it is opening the weights of its frontier model Mistral Large 4 (codename Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardware and behind their firewalls, and the model offers three reasoning modes: low, medium, and high.
Numerama
Mistral Large 4 weights go live on Hugging Face, coding capability rises to Terminal-Bench 41%
On October 29, Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face; previously, on October 6, only paid API access was offered. The model has 1 trillion total parameters within a 100-billion-scale size, adopts a MoE architecture, and activates 49 billion parameters per token (52 billion including input and output layers), with 128 experts in total and 4 plus 1 shared expert enabled per token. After three weeks of reinforcement learning, its Terminal-Bench 4.0 score rose from 28% to 41%, and it scores 48% on the Artificial Analysis coding index, on par with GLM-5.3 while using an average of 47% fewer tokens per task; API pricing has been reduced to $0.70 per million input tokens and $2.10 per million output tokens, and the new license permits commercial use, but reselling model services requires a separate agreement.
Numerama
Mistral Large 4 open-weight release: improved coding and safety capabilities, API price halved
Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Bench 4.0 score from 28% to 41%; the Artificial Analysis coding index is 48%, on par with GLM-5.3 while using an average of 47% fewer tokens per task. API pricing drops to $0.70 per million input tokens and $2.10 per million output tokens; the new Mistral Ecosystem Open-Weight License permits commercial use, but reselling model services requires a separate agreement, and the full version requires 8 Nvidia B200 or B300 GPUs.
Artificial AnalysisSelected
Mistral Large 4 Opens Its Weights, Becoming the Top Non-Chinese Open-Source Model
Mistral AI updated and opened the weights of its flagship model Mistral Large 4, with 1.05T total parameters and 52B activated parameters. It scores 26 on the Artificial Analysis Intelligence Index v5, 1 point higher than the Preview version, and leads the US open-source model Muse Spark Open (25) by 1 point. The model outputs 82K tokens per task, 39% fewer than the Preview's 135K, and its hallucination rate on AA-Omniscience v1.1 dropped from 41% to 28%, though accuracy remains 26%. Pricing is $0.70/$2.10 per 1M input/output tokens, about 50% lower than the Preview, under the MEOW license; operating a Model-as-a-Service business requires a separate commercial license.
The New Stack
Mistral Open-Sources Mistral Large 4 Weights, Leading GLM-5.3 by 1 Point on Coding Benchmark
Mistral released the weights of its frontier open-source model Mistral Large 4 (ML4) on Hugging Face on Thursday; the model had only been available in preview three weeks earlier. Mistral calls ML4 "the world's best open-source agentic coding model," scoring 41% on Terminal-Bench 4.0, slightly above GLM-5.3's 40%, and says it uses an average of 47% fewer tokens per task than GLM-5.3; it scored 79% on the CyberGym E2E defensive cybersecurity test, compared with 29% for GLM-5.3. Mistral Vice President of Science Pierre Stock called ML4 merely a "stepping stone" and confirmed that smaller versions will be released in the coming months, one of which can run on a laptop.
Testing Catalog
Mistral releases Mistral Large 4 (Le Chonk) weights on Hugging Face
Mistral released the model weights of the new version of Le Chonk (Mistral Large 4) on Hugging Face, targeting agentic coding and cybersecurity scenarios. Mistral claims that the model ranks first among open-source models on Cognition's FrontierCode benchmark and sets a new benchmark for Pareto efficiency on Artificial Analysis, while also improving inference efficiency and reducing inference costs. The model is also claimed to achieve SOTA in cybersecurity defense and has been tested by vendors such as CrowdStrike, Cloudflare, Rubrik, and Zscaler; users can customize and self-deploy it with Mistral Forge.
UnslothFirst-partySelected
Unsloth Releases 1-bit GGUF, Mistral Le Chonk Can Run Locally on Mac Studio
Unsloth has released a 1-bit GGUF quantized version of Mistral Le Chonk, enabling this 1-trillion-parameter model to run locally on a Mac Studio, and claims that it still retains 91% top-1 accuracy after quantization. Unsloth also provided the GGUF download link and usage guide, and called Le Chonk the strongest Western open-source model currently available.
Mistral AI · XSelected
Mistral releases new Le Chonk weights, focusing on agentic coding and cybersecurity
Mistral AI has released the model weights for the new version of Le Chonk, claiming it ranks first among open models on Cognition's FrontierCode and sets a new benchmark on Artificial Analysis's Pareto efficiency. The model is described as reaching SOTA in cybersecurity defense and has been tested by vendors including CrowdStrike, Cloudflare, Rubrik, and Zscaler. The weights are now available for download on Hugging Face and can be customized and self-deployed with Mistral Forge.
t3n
Mistral Large 4 weights go live on Hugging Face; the company says it leads on CyberGym-E2E and…
After a three-week preview, Mistral AI has released the weights of Mistral Large 4 on Hugging Face. This multimodal model was trained on 3,800 Nvidia accelerators and has a parameter count of one trillion, and it is currently still difficult to run on a gaming PC or a single GPU. Mistral says it leads GLM-5.3 and Kimi K3 on the cybersecurity benchmark CyberGym-E2E, and is on par with GLM-5.3 on the agentic coding benchmark Terminal Bench 4.0, but there is no independent evaluation yet; preview pricing is $1.4 per million input tokens and $4.2 per million output tokens.
heise online
Mistral releases Large 4 and opens the model weights, with improved coding capabilities
Mistral has released the model weights of its flagship model Large 4 (ML4), which was previously available only as an API preview. After three weeks of testing by customers and cybersecurity partners and further training with reinforcement learning, ML4's success rate on Terminal-Bench 4.0 rose from 28% to 41%, and two new reasoning effort levels, Low and Medium, were added. Mistral research lead Pierre Stock said that at equivalent capability the new version uses about 50% fewer tokens than the preview; the weights became available for download on Hugging Face at 19:00 and are also offered through the Mistral API and the coding assistant Vibe.
Mistral AIFirst-partySelected
Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)
Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It 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-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.
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