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Introducing Mistral Large 4

▲ 522 points • 5 comments • by j-bu • 3d ago • HN discussion ↗

Pangram verdict · v3.3

We believe this text is mainly human-written, with some AI content.

12 %

AI likelihood · overall

Human
94% human-written 6% AI-generated
SEGMENTS · HUMAN 1 of 2
SEGMENTS · AI 1 of 2
WORD COUNT 665
PEAK AI % 77% · §2
Analyzed
Oct 6
backend: pangram/v3.3
Segments scanned
2 windows
avg 333 words each
Distribution
94 / 6%
human / AI fraction
Verdict
Human
Pangram v3.3

Article text · 665 words · 2 segments analyzed

Human AI-generated
§1 Human · 12%

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 performanceML4 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.DemosForged 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 todayThere 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-diveCybersecurityML4 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.

§2 AI · 77%

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.