# Mistral Large 4 Mistral Large 4 (ML4, or "le Chonk" as [[Mistral AI]] calls it in its own announcement) is Mistral's new flagship: a 1 trillion parameter, natively multimodal [[AI Mixture of Experts (MoE)|mixture-of-experts]] model. The public preview opened on 6 October 2026 through the Mistral API and [[La Plateforme|Mistral Studio]]. The [[AI Open Weight Models|open weights]] are promised for the end of the month; the Hugging Face page counts down to 31 October 2026. Why should you care? Because the previous flagship, [[Mistral Large 3]], scored 9 on the [[Artificial Analysis]] index. ML4 scores 38. That jump is why [[Simon Willison]] opened his post with "Mistral are back in the game." ## Key facts | | [[Mistral Large 3]] | **Mistral Large 4** | |---|---|---| | Released | 2 December 2025 (GA) | 6 October 2026 (public preview) | | Total parameters | 675B | **1.05T**, plus a 1.6B vision encoder | | Active per token | 41B | **49B** (52B counting embeddings and output layers) | | [[Context Window\|Context]] | 256k | **1M** | | Input / output | text, image / text | text, image / text, reasoning | | Training hardware | 3,000 NVIDIA H200 | 3,800 NVIDIA Grace Blackwell, in Mistral's own European datacenters | | API price (per M tokens) | $0.50 in / $1.50 out | $0.68 in / $0.07 cached / $2.09 out (sale price; regular $1.36 / $0.14 / $4.18) | | Weights license | [[Apache 2.0 License\|Apache 2.0]] | not announced yet | The API names are `mistral-large-4` and `mistral-large-4-0`. [[AI Reasoning Models|Reasoning]] has exactly two levels through the API: `none` and `high`. About that active parameter count: Simon writes 49B, while Mistral's announcement and docs say 52B. The Hugging Face page settles it. 49B parameters are active per token, and 52B once you add the embeddings and output layers. ML4 is the first model on the roadmap funded by Mistral's €3 billion Series D, and Mistral says it will be the base for a new generation of specialized Mistral models. ## What Mistral claims These are Mistral's own numbers. Read them as marketing until independent evals catch up: - **Agentic coding**: 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, 28.3% on Terminal-Bench 4. Its Coding Agent Index of 49.8% puts it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max - **Blind human eval on coding** (Surge AI, 1 to 5 scale): 3.74, second of five models, ahead of [[Kimi K3]] (3.59) and GLM-5.3 (3.60), behind [[Claude Opus 5]] (4.22) - **Agentic workflows**: 59.9% on AutomationBench, 657 business workflows across Gmail, Google Sheets, Slack and Salesforce - **Vision**: 42% on Dense 200 against 41% for [[GPT-6 Astra]]. Visual grounding is the one area where Mistral claims to beat closed frontier models - **Cybersecurity**: top five on the Artificial Analysis Cyber Index, 93% on Cybench, and 82% on a reproduce-then-patch test (the best score of any model) - **Safety**: it resists 93.3% of attacks on Lakera's B3 benchmark for [[Prompt injection|indirect prompt injection]], and refuses malicious cyber prompts more often than the other open models Mistral tested - **Languages**: more than 160 in the training data, including every official EU language The cyber angle is the interesting one. On that 82% test, [[Claude Opus 5.5]] and GPT-6 Astra score near zero because they refuse the task. Mistral's argument: defenders often have to prove a flaw is real before they can patch it, and a model you run yourself, under your own policies, won't refuse halfway through an incident. Until the weights ship, Mistral is [[AI Red Teaming|red-teaming]] ML4 with security firms, vetted partners and state authorities, who get a version with "reduced moderation and expanded cyber capabilities". ## Simon Willison's take Simon's post is short, and the title does half the work. "Le chonk" is Mistral's own unofficial nickname for the model; he simply kept it. His points: - **Mistral are back in the game.** ML4 scores 38 on Artificial Analysis, "just behind [[DeepSeek V4.1 Flash|DeepSeek 4.1 Flash]], which is a 552B model". Mistral Large 3 scored 9 and, in his words, "drew this terrible pelican" - **The pelicans got better.** His usual test (an SVG of a pelican riding a bicycle) looked better at `high` reasoning, which oddly used fewer output tokens (2,717) than `none` (3,275). On [[Hacker News]] he added that the setting "didn't seem to make any real difference" beyond a tiny bit of thinking trace, and that it's the best pelican he's seen from any Mistral model - **Six months behind, and that's good news.** "It's certainly not a Fable-class model, but it's great to see Mistral put out a model that's back to being maybe about 6 months behind the frontier." Fable being Anthropic's [[Claude Fable 5.1]] That last sentence is the fairest one-line summary of ML4. Nobody serious claims ML4 beats the [[AI Frontier Model|frontier]]. What changed is that Europe has a model in the race again. ## Reception The [[Hacker News]] thread passed 2,000 points, and the top comments fall into two camps: - **Relief.** People are glad to see Mistral "still in the game even after some big stumbles with Large 3", and hope it becomes "the European go-to". Others liked the vision and cyber numbers enough to call it a possible daily driver - **Skepticism.** One commenter compared the mood to "people cheering for the last kid crossing the finish line": US labs keep pushing the frontier, Chinese labs keep getting closer than expected, and Mistral moves slower Both camps care about the [[Sovereign AI]] part. ML4 is trained in the EU and served in the EU, and Mistral runs a European deployment end to end, under European law and (in its words) independently of other digital service providers. ## What I take from it - **The sovereignty argument finally has a strong model behind it.** Until now, "use a European model" meant accepting a big quality gap. Six months behind the frontier at $0.68 / $2.09 per million tokens is a trade a lot of European companies will take, especially once they can self-host - **The weights matter more than the preview.** Yet another API from a European provider is nice. A 1T model you can run on your own infrastructure, for security work that US models refuse, is a different offer. Check the license when it lands: Large 3 was Apache 2.0, and Mistral hasn't said anything about ML4 yet - **Most of the benchmarks are Mistral's.** The Artificial Analysis score of 38 is the independent number, and it puts ML4 just behind a DeepSeek model half its size - **Expect the model to move.** Mistral says the reinforcement learning run behind the preview is still going and shows "no signs of saturation". What you test today may not be what ships at the end of the month ## References - Simon Willison, "Introducing Mistral Large 4: Le chonk" (6 October 2026): https://simonwillison.net/2026/Oct/6/le-chonk/ - Mistral announcement: https://mistral.ai/news/mistral-large-4/ - Model card (Mistral docs): https://docs.mistral.ai/models/mistral-large-4-0 - Hugging Face upcoming-release page: https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B - Artificial Analysis model page: https://artificialanalysis.ai/models/mistral-large-4 - Hacker News discussion: https://news.ycombinator.com/item?id=49977979 ## Related - [[Mistral AI]] - [[Mistral Large 3]] - [[Mistral Compute]] - [[La Plateforme]] - [[Sovereign AI]] - [[Apertus]] - [[AI Open Weight Models]] - [[AI Mixture of Experts (MoE)]] - [[AI Reasoning Models]] - [[AI Frontier Model]] - [[AI Red Teaming]] - [[Artificial Analysis]] - [[DeepSeek V4.1 Flash]] - [[Kimi K3]] - [[Claude Fable 5.1]] - [[GPT-6 Astra]] - [[Simon Willison]] - [[Hacker News]] - [[Large Language Models (LLMs)]]