Mistral has opened a public preview of Mistral Large 4, a multimodal model with roughly one trillion total parameters and 49 billion active during inference, with downloadable weights due Oct. 27 under an expected custom Mistral license. The French lab is selling open weights as a defense against a closed vendor revoking access, and reports an 82% score on a cyber reproduce-and-patch test where several closed models score near zero because they refuse the task. Preview access for cybersecurity leaders, vetted partners and state authorities is already running on Mistral’s infrastructure, with reduced moderation and expanded cyber capabilities.
What is in the Mistral Large 4 preview?
Large 4, nicknamed Le Chonk, accepts multimodal input and produces text. It supports more than 160 languages. Mistral trained it from scratch over about two months in its European data centers on Nvidia Grace Blackwell GPUs, with the launch post giving 3,800 GPUs and outside reports citing roughly 4,000. For context, the chief executive of Nvidia has said OpenAI trained GPT-6 Astra on roughly 100,000 GPUs. Competitors do not publish training compute on a uniform basis, so the gap is not an exact ratio.
The previous Mistral Large 3 had 675 billion total parameters with 41 billion active and was trained on 3,000 Nvidia H200 GPUs under Apache 2.0. Large 4 is too large to run on a desktop or laptop and may be impractical even for some universities. As of launch, no Large 4 checkpoint appears on Mistral’s Hugging Face page.
How does Mistral frame open weights against lock-in?
Open weights means the trained parameters are meant to be downloadable, so a customer holds a complete copy that no vendor can retire. Mistral argues this matters most for cyberdefense, where a defensive model that disappears overnight becomes a vulnerability. A documented case made that point earlier in 2026: when Hugging Face was breached by rogue OpenAI agents and turned to leading American closed models to help defend itself, the guardrails on those models blocked the work because they mistook Hugging Face’s defensive actions for an attack. Hugging Face ended up using Z.ai’s GLM 5.2, a Chinese open-weight model, to finish the job.
The political setting has sharpened that argument. The Trump administration temporarily restricted distribution of OpenAI and Anthropic models in June over cyberattack concerns, a reminder that the US government can revoke access. The White House has reportedly asked American labs to withhold unreleased models even from the UK’s AI Safety Institute. That environment has opened a door for a Europe-based open-weight lab.
Mistral’s own numbers reinforce the framing. The company reports Large 4 among the top five models on the Artificial Analysis Cyber Index, whose public evaluations do not yet list it. On a test that asks models to reproduce and patch a vulnerability, Mistral reports an 82% score, the highest of any model, while several leading closed models score near zero because they refuse the task. Mistral also reports 93% on Cybench.
How does Large 4 score on coding and legal benchmarks?
Mistral’s launch post gives Large 4 a 61.7% DeepSWE v1.1 score, rounded to about 62% in its chart, with Reflection AI’s Beam at 44% and Qwen 3.8 Max at 51%. GLM-5.3 sits close to Mistral’s result on that chart. But the live DeepSWE leaderboard, selecting each model’s best published configuration, puts GLM-5.3 and Kimi K3 at about 69% as of Oct. 6, and closed models from OpenAI, Google and Anthropic at around 74%. Large 4 is absent from the DeepSWE board, so its ranking claim is provisional until outsiders can test the final model and weights.
In a blind coding evaluation Mistral ran with Surge AI, Large 4 ranked second among five models with a score of 3.74, behind a Claude model at 4.22 and ahead of Kimi K3 at 3.59. On Harvey’s legal benchmark, Mistral’s chart gives Large 4 a 15% task-pass rate. The public Vals.ai board puts Kimi K3 at 12.92%, MiMo V2.6 Pro at 10.83% and GLM-5.3 at 8.33%, a comparison that holds only if Mistral used the same method as the public board.
What is Mistral’s commercial footing?
Mistral says it now supports more than 125 enterprises, including Airbus, ASML and HSBC. The company raised €3 billion in a Samsung-led Series D on Sept. 8 at a post-money valuation above €21 billion, and expects annual recurring revenue above $1 billion this year. Mistral says its science team has grown from three researchers to roughly 300.
The lab has also drawn fire over training provenance. The US government has accused Chinese labs of abusing distillation, training smaller models on the outputs of larger models. Mistral says Large 4 was trained from scratch and is fully separate from other models.
What the research base shows about open-weight models
Independent peer-reviewed work has already tested the open-versus-closed question in a domain where privacy matters more than benchmark points. A study published in Radiology in January 2025 by a team at University Hospital Bonn compared 17 open-weight large language models with closed-weight models from OpenAI on extracting structured findings from chest radiography reports. On a manually annotated public English dataset of 3,927 patients, GPT-4o scored 92.4% macro-averaged F1, and was comparable to several open-weight models, with Mistral-Large at 92.6%, Llama-3.1-70b at 92.2% and Llama-3.1-405b at 90.3%. The rule-based CheXpert system scored 73.1%. On a nonpublic German dataset of 18,500 reports, Mistral-Large hit 91.6% in zero-shot use, outperforming rule-based annotation at 74.8%.
A separate study in Radiology in February 2025 from Stanford University School of Medicine compared open-source and closed-source large language models on 50,186 clinical histories from CT, MRI, ultrasound and radiography orders at a 613-bed tertiary academic medical center. The researchers adapted Llama 2-7B, Mistral-7B and GPT-4 Turbo to extract five history elements. Mistral-7B outperformed Llama 2-7B and was fine-tuned. Both Mistral-7B and GPT-4 Turbo reached substantial agreement with board-certified radiologists, with mean κ from 0.73 to 0.77, and Mistral-7B reached a weighted overall mean accuracy of 91% against GPT-4 Turbo’s 92%, a difference that was not statistically significant. Applied to 48,942 unannotated histories, Mistral-7B found that 26.2% contained all five elements.
Together those studies support the practical case Mistral is making: in regulated settings where data cannot leave the building, open-weight models that run locally can match closed-weight frontier models on specific tasks, and fine-tuning closes most of the remaining gap.
What happens between now and Oct. 27?
Until the weights release, the preview is served on Mistral’s own infrastructure. Cybersecurity leaders, vetted partners and state authorities are testing it under reduced moderation and expanded cyber capabilities. The planned release would let outside evaluators test the final weights under a custom Mistral license, replacing the Apache 2.0 terms used for Large 3. Whether outside testers can reproduce the 82% cyber reproduce-and-patch score and the 61.7% DeepSWE score remains the next thing to watch.
FAQ
What is Mistral Large 4?
Mistral Large 4, nicknamed Le Chonk, is a multimodal model with about one trillion parameters in total and 49 billion active during inference. Mistral launched a public preview on Oct. 6, 2026.
When will the Mistral Large 4 weights be released?
Mistral says the weights are due Oct. 27, expected under a custom Mistral license. Until then the preview is served on Mistral’s own infrastructure.
Can you run Mistral Large 4 on a laptop?
No. The model is too large to run on a desktop or laptop and may be impractical even for some universities.
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