Engineers working with a large open-weight model

Mistral has released the full weights of Mistral Large 4, the trillion-parameter flagship it nicknames Le Chonk, turning one of Europe’s most capable models into something anyone can download, run, and build on.

The release, announced October 29, follows a public preview that began October 6. Mistral says the shipped weights reflect three more weeks of reinforcement learning and customer testing since that preview — and that the extra training shows up most clearly in coding.

What was released

Large 4 is a natively multimodal mixture-of-experts model with roughly 1 trillion total parameters, about 49–52 billion of them active per token, and a 1-million-token context window, according to Mistral and its Hugging Face model card. The weights are listed as Mistral-Large-4-1T-A52B and can be deployed with vLLM or SGLang, while a quantized NVFP4 checkpoint targets Blackwell GPUs. Mistral also ships it through its own API and Studio, OpenRouter, NVIDIA-hosted endpoints, and Microsoft Foundry, which is the first cloud to carry it.

The license is where the “open” claim gets its asterisk. The weights sit under Mistral’s Ecosystem Open-Weight License, a modified MIT license: free to use, modify, fine-tune, and commercialize — with one condition. Businesses that operate a Model-as-a-Service offering must obtain a separate commercial license from Mistral first. Self-hosters, fine-tuners, and builders of end products face no such obligation, though the announcement does not spell out exactly where a SaaS wrapper ends and a Model-as-a-Service offering begins.

Hosted pricing also moved with the release. Through Mistral’s API, Large 4 now costs $0.70 per million input tokens, $0.07 per million cached input tokens, and $2.10 per million output tokens — roughly half the preview pricing.

What Mistral claims it can do

All of the performance figures below are Mistral’s own, and independent validation will take time:

  • FrontierCode 2.0 (Cognition’s private coding benchmark): 19.7%, which Mistral says leads all open-weight models.
  • Terminal-Bench 4.0 (command-line coding tasks): 41%, up about 13 points from the preview, with output tokens down 30%.
  • Artificial Analysis Coding Agent Index: 48%, effectively tied with China’s GLM-5.3 while using 47% fewer tokens per task.
  • Cyber defense: 51% on the Artificial Analysis Cyber Index, a 96.6% refusal rate on malicious cyber requests, and 97.1% resistance on the B3 agent-security benchmark, up from 93.3% in preview.

That cyber pitch is deliberate. Mistral is positioning Large 4 first and foremost as a defender’s tool — a model that finds vulnerabilities, reproduces them, and patches them. It reports 82% on a vulnerability reproduce-and-patch test and 93% on Cybench, a set of 40 challenges drawn from security competitions.

At the same time, Mistral is candid that the capability cuts both ways. It says it saw “a potential for uplift to conduct real attacks” during the preview and is tightening guardrails accordingly. A more aggressive penetration-testing variant stays restricted to vetted partners such as Orange Cyberdefense, under contract. Open weights, unlike an API, cannot be recalled — once the checkpoint is downloaded, Mistral cannot take it back — which is precisely why the restricted variant exists.

Why it matters

Large 4 lands in a week when the frontier’s center of gravity keeps shifting. Google’s Gemini 4 Carbon, announced a day earlier, is being rolled out only to vetted cyber defenders first. Mistral has taken the opposite route with its flagship: publish the weights and let defenders — and everyone else — run it themselves.

The practical question is whether the open-weight tier can now do serious agentic coding work without a closed lab’s API. On Mistral’s numbers, Large 4 closes much of the gap on coding efficiency, even if its overall intelligence score still trails the top closed models. What matters for most teams is narrower: can it complete long, messy terminal and coding tasks reliably, and at what token cost? The 47%-fewer-tokens claim, if it holds up in independent testing, is the number that changes budgets.

For Europe, the release is also a statement of strategy. Mistral, the continent’s leading frontier lab, is betting that openness — with a commercial carve-out for hosted-model resellers — is its edge against both American closed labs and Chinese open-weight rivals. Le Chonk is heavy, expensive to run at full size, and still awaiting its full technical report. But as of this week, the weights are out, and the open-weight frontier has a new reference point.

Sources: Skimwire — “Mistral Large 4 open weights: specs, license, cyber claims” (Oct 29, 2026); Unite.AI — “Mistral’s 1-Trillion-Parameter Le Chonk Checkpoint Lands on Hugging Face” (Oct 29, 2026); AI Weekly — “Mistral Opens 1T ‘Le Chonk’ Large 4 Under Modified MIT License” (Oct 29, 2026); DEV — “AI Daily Digest October 30, 2026” (Oct 30, 2026). All benchmark figures are Mistral’s own and await independent verification.