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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match“Le Chonk” is Mistral AI’s informal name for Mistral Large 4, a general-purpose multimodal AI model—not a cat-themed product. Mistral announced a public preview on October 6, 2026: users can access it through the Mistral Studio API, while the company says model weights are planned for release by the end of October.
What does “Le Chonk” refer to?
Mistral’s October 6 announcement calls the model “unofficially ML4, very officially: le Chonk.” Its formal name is Mistral Large 4. The company has not explained a specific cat-meme origin or identified who came up with the nickname, so the supported takeaway is simply that Mistral itself used the playful name.
Despite the meme-flavored nickname, Mistral describes Large 4 as a general-purpose, natively multimodal model. It is intended to work across text and other forms of input rather than serve as a cat-specific application.
Can you use Mistral Large 4 now?
API preview: available now, according to Mistral
Mistral says the public preview is available through Mistral Studio. The announcement lists API pricing of $1.36 per million input tokens and $4.18 per million output tokens. These are separate rates; input and generated output are billed at different prices. Availability and pricing here refer to Mistral’s October 6, 2026 announcement.
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Weights: planned for later in October
Mistral says it plans to release the model weights by the end of October 2026. The Hugging Face listing displayed an expected date of October 31, 2026, which is an ETA rather than confirmation that the weights have been released. The preview API and future weight release are distinct access routes: the first is the currently announced way to try the model, while the second is a stated plan.
Mistral says it is red-teaming the preview with cybersecurity leaders, vetted partners and state authorities before the planned weight release. The announcement does not establish final license terms or the hardware required to run the weights yourself, so those details should not be assumed from the “open weights” positioning.
What are the model’s specifications?
Mistral describes Large 4 as having one trillion total parameters, with 49 billion active parameters. The company says it was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European data centers, and that the preview is served on the same infrastructure. It also says training data spans more than 160 languages, including every official EU language. These are Mistral-stated specifications and infrastructure details.
The 3,800-GPU figure is Mistral’s number. An Axios report published October 6 gave a different figure of 4,000 GPUs; the counts should not be blended or presented as equivalent.
What does Mistral say the model is good at?
Mistral positions Large 4 for coding and agentic workflows, multimodal understanding, and work in areas including cybersecurity, finance, law, engineering and manufacturing. The company’s announcement reports results on selected evaluations, but those vendor-reported scores are not an independent audit and do not establish that the model is best overall.
| Evaluation | Mistral-reported result | What the figure describes |
|---|---|---|
| Vulnerability reproduction and patching test | 82% | Result on one test; not a general security success rate. |
| Cybench | 93% | Share of Cybench challenges reported as completed. |
| DeepSWE v1.1 | 61.7% | Score on the named software-engineering evaluation. |
| SWE-Atlas-QnA | 59.4% | Score on the named evaluation. |
| Terminal-Bench 4 | 28.3% | Score on the named terminal benchmark. |
| Combined Coding Agent Index | 49.8% | Mistral’s combined index result. |
| Dense 200 | 42% for Large 4; 41% for GPT-6 Astra | Comparison reported by Mistral on this benchmark only. |
The scores are most useful when read as results on named evaluations, not as a single measure of real-world quality. Mistral also reports a 3.74 average rating in a blind coding evaluation by Surge AI, where the preview ranked second among five tested models, and 1,393 Elo on AA-Briefcase. Those figures likewise reflect the contexts stated by Mistral.
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What should finance and business readers take from the launch?
For an organization considering the model, the immediate decision is whether API access in preview suits its needs; self-hosting is not yet a settled option based on the announcement alone. API usage has separate input and output token rates, while the future availability of weights may offer a different deployment path. The final terms, release timing and practical hosting requirements would need to be known before making a deployment or cost comparison.
Mistral presents open weights and self-deployment as ways for organizations to gain more control over customization and deployment. That is a stated positioning, not a substitute for checking the final license and technical requirements once published.
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Sources
- Mistral AI, “Introducing Mistral Large 4,” October 6, 2026
- Hugging Face model listing
- Axios, “Western AI labs challenge China’s open-model lead,” October 6, 2026
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
