Introducing Mistral Large 4

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Introducing Mistral Large 4

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Mistral

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https://mistral.ai/news/mistral-large-4/

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Le chonk Introducing Mistral Large 4 Back to Blog 14 min read October 6, 2026 By Mistral Share this post Copy url to clipboard Copied Le Chonk Today, we’re launching a public preview of Mistral Large 4. Unofficially ML4, very officially: le Chonk . ML4 pushes the frontier of open-weight performance. You can try the preview API today on Mistral Studio . Weights drop end of this month. Frontier performance ML4 is a 1 trillion-parameter natively multimodal model with 49 billion active parameters. It is our largest and most capable model to date, and it continues to improve rapidly as we refine it. The model demonstrates exceptional performance across coding, agentic workflows, and multimodal understanding. It already achieves performance competitive with the strongest open-source models globally, while significantly outperforming any open-weight model developed in the US or Europe. On critical enterprise workloads, including cybersecurity, finance and law, we find it to be state-of-the-art among open models. In some domains such as visual grounding, it goes further still, surpassing even frontier closed models. We will release the weights by the end of the month. Until then, we are red-teaming the model in real-world settings with cybersecurity leaders, vetted partners, and state authorities, who will access the same model with reduced moderation and expanded cyber capabilities. Coding - DeepSWE Coding - Terminal Bench 4.0 Cyber Agentic behaviour Finance Agent Harvey's Legal Agent Grounding Forged in Europe. Built for AI sovereignty. ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe. The public preview is served on that same infrastructure. It is a significant milestone in our long-term investment across infrastructure, research, and product development: state-of-the-art performance in critical verticals, delivered through open weights, designed to give customers control over their AI. This is particularly important in cybersecurity, where provider-level refusals can block legitimate vulnerability research and incident response, and where losing access to a capability mid-incident can itself become a critical security risk. ML4 pairs top-tier cyber performance with open weights and self-deployment, giving organizations both the capability and the autonomy to run advanced security work under their own policies. The model will be available across multiple regions worldwide, including a European deployment that Mistral operates end-to-end, independently of other digital service providers and under European law. Fun fact: a significant share of ML4’s training data was multilingual, spanning more than 160 languages, including every official language of the European Union. We’ve been working closely with leading enterprises across the world in finance, engineering, manufacturing, logistics, pharmaceuticals, science, shipping, public sector, and other mission-critical industries to train ML4. In fact, the model uses the same training, customization, and RL environment we offer our customers through Mistral Forge. Try it today There is still more to come. As we work toward releasing the weights, we will share further details on the model architecture, additional benchmarks, and our post-training methodology. This model will also serve as the foundation for a new generation of specialized and optimized Mistral models. In the meantime, we invite you to try the preview API and share your feedback with us on social media. Capabilities deep-dive Cybersecurity ML4 is one of the world's strongest AI models for cybersecurity. On the Artificial Analysis Cyber Index, an independent evaluation of how well AI models find and fix security flaws in real software, it ranks among the top five models globally and leads open-weight models developed outside China by a wide margin. On one of the index's tests, which asks a model to reproduce a real vulnerability in open-source software and then patch it, ML4 scores 82%, the highest of any model. It also solves 93% of the challenges in Cybench, a set of 40 exercises drawn from security competitions, one of the highest scores reported for an open-weight model. That top score reflects a practical advantage. Several leading closed models, including Claude Opus 5.5 and GPT-6 Astra, score near zero on the same test because they refuse to perform the task. Yet defending software often starts with proving that a flaw is real, exactly the kind of work safety filters in closed models can block. This matters even more as threat actors increasingly jailbreak those same models to support offensive cyber activity: defenders need systems that can match those capabilities without being constrained by the same refusals. ML4 can do that work, and its capabilities extend beyond what it was explicitly trained for: in internal testing, it proved useful for analysing malware, prioritising vulnerabilities, and writing detection rules. For organisations that need sovereign, auditable AI for security operations, it will be able to run on private cloud or on-premise. ML4 against the field : efficiently reasoning over diverse complex challenges Malware reverse-engineering: solving an out-of-distribution investigation task AA Cyber Index CyberGym-E2E Cybench Agentic coding ML4 excels across software engineering, repository understanding, and complex terminal workflows, scoring 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, and 28.3% on Terminal-Bench 4. Its combined Coding Agent Index score of 49.8% places it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max. DeepSWE 1.1 Terminal Bench 4.0 SWE Atlas QnA We also ran a blind human evaluation with Surge AI on coding quality: professional annotators rated model outputs on a 1–5 scale, with model identities hidden. ML4 Preview ranked second of five models (3.74), ahead of Kimi K3 (3.59), GLM-5.3 (3.60) and GLM-5.2 (3.40), and behind only Claude Opus 5 (4.22). Agentic Workflows ML4 runs general-purpose agents that gather information, use tools, and produce finished deliverables across complex workflows. On AutomationBench — 657 business workflows across apps like Gmail, Google Sheets, Slack, and Salesforce — it scores 59.9%, ahead of Kimi K3, MiMo-V2.6-Pro, and DeepSeek V4 Pro. It's just as strong on the professional deliverables that knowledge work actually produces: spreadsheets, slides, and PDFs. On AA-Briefcase, which evaluates long-horizon knowledge work, it reaches 1,393 Elo, ahead of DeepSeek V4 Pro. Multimodal ML4 is a step change in the ability of our models to understand images. It reasons powerfully across complex documents, charts, and natural images, and brings vision to the industries where perception is critical such as engineering, manufacturing, and earth observation. The model can further combine visual grounding with agentic capabilities: from inspecting gigapixel satellite imagery — helping disaster-response teams act when time counts — to analyzing engineering-drawings — zooming in, inspecting, and verifying until the answer is exact. In our demos above, ML4 grounds dense natural scenes, verifies mechanical parts in technical drawings, retrieves evidence from PDFs, and scans massive geospatial images for the hardest-to-find objects. On visual grounding particularly, we find ML4 to be one of the most capable models we tested, for instance surpassing GPT-6-Astra on Dense 200 (42% vs 41%). Dense 200 ChartQA Pro GDP.pdf Science and Math ML4 brings strong scientific capabilities, built by combining AI-driven methods with our researchers' expertise in mathematics, physics, and chemistry. It's highly proficient at agentic coding for scientific tasks such as data analysis, modeling, and simulating physical reality, which lets researchers focus on the questions rather than the plumbing. In benchmarks, ML4 is state of the art on SciCode-Verified among open-weight models.