Mistral Releases Large 4 Preview, Claiming a Cybersecurity Edge Over Chinese Rivals

Mistral Begins Reduced-Moderation Cyber Testing Before Large 4 Weights Release

Mistral is testing Large 4 with cybersecurity leaders, vetted partners and state authorities before releasing its downloadable weights, giving those testers reduced moderation and expanded cyber capabilities. In its October 6 announcement, the company makes two distinct claims: the model can reproduce real software vulnerabilities, yet refuses malicious cybersecurity requests more often than open-source models in its safety comparisons.

The most concrete cybersecurity result concerns a task that combines reproducing a real vulnerability in open-source software with patching it. Mistral reports an 82% score on that component of the Artificial Analysis Cyber Index, calling it the highest score of any model. That is one test, not an overall cybersecurity ranking.

Mistral says Claude Opus 5.5 and GPT-6 Astra score near zero on the same test because they refuse the task. Its argument is that defenders sometimes need to demonstrate a vulnerability before repairing it. A refusal can therefore prevent legitimate security work, rather than indicate an inability to solve the problem.

The company also reports that Large 4 solves 93% of Cybench’s 40 security-competition exercises. In internal testing, Mistral says it proved useful for analyzing malware, prioritizing vulnerabilities and writing detection rules. Those examples extend its security pitch beyond the vulnerability-reproduction benchmark, but remain company-reported results.

Mistral separately measures whether Large 4 rejects malicious requests. It says the model’s average refusal rate on cybersecurity prompts from JailbreakBench, StrongREJECT and AgentHarm is higher than all open-source models in its comparison. This is distinct from refusing the vulnerability-reproduction task: the safety claim concerns requests classified as malicious.

Another safety measure addresses indirect prompt injection—attempts to steer an AI through instructions it encounters in outside material. Mistral reports 93.3% attack resistance on Lakera’s B3 benchmark. The partner testing uses the same model with reduced moderation, so its access conditions should not be confused with the model’s general safety results.

For security teams, Mistral’s deployment argument is about who controls access and refusals. It says provider-level restrictions can interrupt legitimate research or incident response. Planned open weights and self-deployment would let organizations run security work under their own policies, with private-cloud and on-premises use also planned.

Available now: the public-preview API through Mistral Studio.

API pricing: Mistral lists $1.36 per million input tokens and $4.18 per million output tokens in its model documentation.

Downloadable weights: Mistral commits to release them by the end of October 2026; they are not part of today’s API availability.