Mistral Large 4, Explained: Where Europe's 1-Trillion-Parameter Model Leads, and Where It Trails

What Mistral released

On October 6, 2026, Mistral released Mistral Large 4 in public preview, nicknamed "Le Chonk." It is a natively multimodal mixture-of-experts model with 1 trillion total parameters, of which 49 billion are active for any one token. Mistral trained it on 3,800 NVIDIA Grace Blackwell GPUs in European datacenters, on data spanning more than 160 languages, including every official EU language. It is live now in Le Chat and in Mistral Studio, the API console.

The weights are not out yet. Mistral says they follow at the end of October. Pierre Stock, Mistral's VP of science, told TechCrunch the delay is for safety testing: "we'll work with trusted partners and governments to make sure that the open source weights can be used to defend, but not to [perform] malicious attacks." Mistral has not yet said which licence the weights will carry.

Where Mistral says it leads

Mistral did not pitch Large 4 as the best general model. It pitched it as the best at specific jobs, and its benchmark tables are built that way:

• Security. 93% on Cybench's 40 capture-the-flag exercises, 82% on a vulnerability reproduction test, and a place in the top five of the AA Cyber Index, leading open-weight models outside China. It also refuses more cyber-offence requests than any open model Mistral measured.
• Finance and law. Ahead of GPT-6 Astra on vals.ai's Finance Agent v2 and on Harvey's legal agent benchmark, according to Mistral's post.
• Coding. 61.7% on DeepSWE v1.1, and second of five in a Surge AI human evaluation of coding, behind Claude Opus 5 (4.22 out of 5) and ahead of GLM-5.3 and Kimi K3.
• Vision. 42% on the Dense 200 visual grounding test against GPT-6 Astra's 41%, with demonstrations on satellite imagery and engineering drawings.

These are Mistral's numbers, chosen by Mistral. Most are domain benchmarks where a focused model can beat a general one, and that is a legitimate result. It is not the same as being a frontier model overall.

What the independent index says

The Artificial Analysis Intelligence Index, which runs every model through the same general evaluation suite, scored the preview at 38.4 as of October 7. That is a big step up for Mistral: Large 3 scores 9.3 on the same index. But it puts Large 4 behind the leading Chinese open models it was compared against, with GLM-5.3 at 44.8 and Kimi K3 at 43.6, and well behind the US frontier, where Claude Opus 5.5 leads at 57.6.

The two pictures do not contradict each other. Mistral has built a model that is very good at security, finance and legal work and middling as a generalist. If your work is one of those domains, Mistral's tables are the relevant ones, and worth testing yourself. If you need one model for everything, the general index is the better guide, and you can see where Large 4 sits on the leaderboard.

How it compares on price

Large 4 undercuts OpenAI's price-performance model by roughly a third on input and more than half on output. Reflection AI's Beam, announced the day before, is the other new Western open-weight model this month. It is smaller (501 billion parameters, 23 billion active), text only, and by Reflection's own tables level with GLM-5.2 rather than ahead of it.

Why "European" is part of the product

Mistral sells Large 4 as a third option between US closed models and Chinese open ones, and the details are built for that buyer. It was trained in Europe, its European deployment runs independently under European law, and it is designed for private-cloud and on-premise installs. For a bank, a ministry or a defence supplier that cannot send data to a US provider and will not run a Chinese model, that matters more than a few points on a general index.

Should you use it?

The listing is at Mistral Large 4, with the rest of the family on the Mistral models page. For the trade-offs of running open weights at all, see what open models cannot do and open-source vs proprietary LLMs. The launch is in the October 6 briefing, and Beam in the October 5 briefing.