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# Mistral releases weights for Le Chonk, a trillion-parameter open model aimed at cyber defenders
- URL: https://www.skimwire.com/mistral-large-4-open-weights-cybersecurity/
- Published: 2026-10-29T19:49:27.000Z
- Updated: 2026-10-29T19:49:27.000Z
- Description: Anyone can now download and self-host Mistral Large 4. Mistral says it leads open models at coding and cyber defense, and admits attack risks.
- Author: Jeremy Moon
- Tags: AI, Security

## The skim

- Mistral released the weights for Mistral Large 4, nicknamed "Le Chonk," on Thursday. It's a trillion-parameter model that anyone can download from Hugging Face and run on their own servers.
- Mistral is pitching it at cyber defenders. It says the model scores 51% on the Artificial Analysis Cyber Index and that in its tests it refused 96.6% of malicious cyber requests.
- Mistral also says it saw "a potential for uplift to conduct real attacks" during the preview and is tightening its guardrails. A more aggressive pen-testing version stays restricted to vetted partners.

## What Mistral released

The French lab first showed Mistral Large 4 in a public preview on Oct. 6\. On Thursday it [published the weights](https://mistral.ai/news/mistral-large-4-oss/?ref=skimwire.com) of an improved version, after what it says were three more weeks of reinforcement learning. According to [Hugging Face](https://huggingface.co/blog/mistral-large4?ref=skimwire.com), it's a mixture-of-experts model with about 1 trillion total parameters, roughly 49 billion of them active for each token, a 1 million-token context window, and text and image input.

The license is a modified MIT license. The model is free to use, modify, fine-tune and sell products on, with one catch: companies that offer it as a hosted model service need a separate commercial license from Mistral. Through Mistral's own API, it costs $0.70 per million input tokens and $2.10 per million output tokens. Microsoft Foundry is the first cloud to carry it, and Nvidia offers it as a NIM container.

On coding, Mistral says the model scores 48% on Artificial Analysis's Coding Agent Index, effectively tied with China's GLM-5.3, the top open model there, while using 47% fewer tokens per task. It reports 41% on Terminal-Bench 4.0, up 13 points from the preview. Hugging Face says the model also scored 82% on a vulnerability reproduce-and-patch test, and 93% on Cybench, a set of 40 challenges drawn from security competitions. These are the company's and its partners' numbers, not independent tests.

## Why it matters

The strongest AI models for security work have mostly stayed locked up. Google's [Gemini 4 Carbon](https://www.skimwire.com/google-gemini-4-carbon-cybersecurity-model/), announced Wednesday, is available only to selected defenders for now. Mistral is going the other way. "Access that can't be cut off by definition because the model is open-weighted," Pierre Stock, Mistral's VP of science, told [TechCrunch](https://techcrunch.com/2026/10/29/mistral-releases-weights-for-its-trillion-parameter-ai-model-targeting-cybersecurity/?ref=skimwire.com). For governments and banks that can't send code or logs to a U.S. API, that's the pitch. Booz Allen, CrowdStrike, Elastic and Europe's cybersecurity agency ENISA said they've been testing it, and Cisco and Cloudflare backed the open approach in Mistral's announcement.

The trade-off is that open weights can't be recalled. Anyone can download them, and a determined user can try to fine-tune the refusals away. Mistral is unusually direct about the risk. It says it monitored cyber-related use of the model during the preview, and "we see a potential for uplift to conduct real attacks." A specialized variant built for penetration testing and red-teaming goes only to vetted partners such as Orange Cyberdefense, under contract.

## What's next

Mistral says a full technical report is "coming soon," and that customers can tune Large 4 into smaller, task-specific variants for well-defined security jobs. Researchers can now test the security claims themselves. That includes how easily the guardrails come off, which may matter as much as the benchmark scores.

*Sources:* [*Mistral*](https://mistral.ai/news/mistral-large-4-oss/?ref=skimwire.com)*,* [*Hugging Face*](https://huggingface.co/blog/mistral-large4?ref=skimwire.com) *and* [*TechCrunch*](https://techcrunch.com/2026/10/29/mistral-releases-weights-for-its-trillion-parameter-ai-model-targeting-cybersecurity/?ref=skimwire.com)*.*