Mistral’s Le Chonk is now a top open model for coding, ranking alongside Codex GPT-6.1 Terra

Mistral has released the weights for Large 4, along with some impressive Artificial Analysis scores.

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October 29, 2026, 10:00 am ET

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ZDNET’s key takeaways

As planned, Mistral has released the weights for its ML4 model.

ML4 sets a new bar for Western open models.

Open models are increasingly competitive with proprietary models.

French AI lab Mistral shipped its latest model, Mistral Large 4 (ML4), nicknamed “Le Chonk” for its trillion-parameter size, in preview on Oct. 6. As promised, the lab has released the model’s weights, alongside some new benchmark scores that show Western open models gaining ground over their Chinese peers.

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After three weeks of testing with a select group of partners, Mistral’s model sets a new standard for Western-made open models, ranking at the top of Artificial Analysis’ Coding Agent Index among open models globally. The model also comes with three reasoning modes now (low, medium, and high), as is common for heftier models to give users more options based on their level of task complexity.

ML4’s score on the agentic coding benchmark Terminal-Bench 4.0 jumped to 41% from its preview version’s 28% to 41%, beating GLM-5.3 by one small but important point as the best model for coding agents. As a reminder, benchmark scores themselves should be taken with a grain of salt, especially considering how many models cheat.

Still, that improvement in performance is meaningful for just a few weeks of extra training. As proprietary AI remains expensive to use, economical models that don’t sacrifice performance are eye-catching. ML4 “rivals top open models such as GLM-5.3 on the Artificial Analysis Coding Agent Index while using 47% less tokens per task on average,” Mistral said.

When Mistral released ML4 in preview earlier this month, it touted how the model prioritizes cyber defense capabilities, advertising customizable control and data sovereignty. The company has spent the last few weeks working with developers and leaders to assess the model’s cybersecurity capabilities, a practice that’s becoming commonplace for proprietary American labs like OpenAI, Google, and Anthropic as concerns about model security mounts.

Similarly to Anthropic’s Project Glasswing and OpenAI’s rollout of Astra, Mistral’s initial testing partners got access to a less guardrailed version of ML4 with “expanded cybersecurity capabilities.”

At least by some measure, ML4’s promises seem to have come true: Le Chonk scored just above DeepSeek V4.1 Flash on the CyberGym benchmark for defensive cybersecurity, which measures how well models find “real memory-safety vulnerabilities in C/C++ open-source projects, reproducing them with proof-of-concept inputs, and patching them without breaking the projects’ tests.”

Recent AI security incidents have somewhat pitted open and proprietary models against each other. Initially framed as less safe because of their malleability, open models are viewed as a new option for cyber defense after proprietary models from Anthropic and OpenAI proved just as risky.

Mistral said ML4 is built for security that stays under user control — unlike proprietary models, which frontier labs can technically rescind access to at any time.

“The cyber defense capabilities will enable enterprises and governments to defend themselves against threat actors that are jailbreaking closed models to perform cyberattacks,” Mistral co-founder Guillaume Lample said during a briefing for the Oct. 6 preview release.

Following the Hugging Face breach, Mistral was one of many companies that signed Nvidia’s Open Secure AI Alliance, a cross-industry partnership that argues open models are crucial to democratizing defenses against increasingly common AI security incidents.

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By Nvidia’s logic, and its Alliance that aims to democratize AI security tools, the race is between Mistral and other open models to achieve state-of-the-art security prowess.

Training a competitive model in a compute shortage is no small task for a trimmer lab like Mistral, which doesn’t have the same resources as a pre-IPO giant like Anthropic.

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“ML4 was trained from scratch on 4,000 Nvidia Grace Blackwell GPUs over two months, deployed in Mistral’s own data centers in Europe,” the company said. For context, Nvidia CEO Jensen Huang said on X that OpenAI trained GPT-6 Astra on roughly 100,000 GPUs.

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