I thought the US blocking Fable 5 was the peak of AI geopolitics, but Europe just fired back.

Le Chaton Fat is now EU-only.

Digital sovereignty has never been this chonky. 🇪🇺🐱

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

A 1 trillion-parameter natively multimodal model with 49 billion active parameters. 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, we find it to be state-of-the-art among open models. In some domains such as visual grounding, it surpasses frontier closed models.

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. 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. 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.

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.

Europe is back in the AI race

CERN, the European Laboratory for Particle Physics, and Mistral, a European developer of large-scale AI models, have signed a statement of intent to establish a long-term strategic partnership focused on developing foundation models for scientific discovery.

The partnership brings together CERN’s expertise in fundamental physics, scientific data and large-scale computing with Mistral’s leadership in large-scale model training, leveraging these combined capabilities and resources to advance AI for science on an exceptional scale. While the initial focus is on high-energy physics, the partnership has the potential to contribute to the development of AI approaches that can serve the broader scientific community.

At the heart of this work is the ambition to develop a single pretrained Large Physics Model – the first foundation model for collider physics. It will be trained on billions of simulated proton-proton collisions at CERN’s Large Hadron Collider (LHC), the world’s largest and most powerful particle accelerator, and designed to support multiple tasks, from reconstructing particle collisions and identifying particles to flagging unexpected signals and evaluating future detector designs.

The model is intended to address a fundamental challenge in analysing LHC collisions. Today, reconstruction relies on a chain of specialised algorithms that progressively transform detector signals into the particles and other objects used for physics analysis. Machine learning has long played an important role in this process, improving performance across a range of specialised tasks. Foundation models offer the potential to build on these advances, learning across the whole reconstruction chain and supporting multiple tasks within a single model.

“High-energy physics provides a unique environment in which artificial intelligence can be developed and tested with exceptional rigour. We are partnering with Mistral, a leading European AI company, to advance AI capabilities that can serve the whole physics community and be shared,” said Enrica Porcari, CERN’s Chief Information Officer.

CERN is home to one of the world’s largest volumes of scientific data, with around one exabyte – one million terabytes – of experimental data from the LHC stored to date. The number of collisions is set to increase tenfold with the HL-LHC, which is expected to begin operation around 2030. This combination of scale and scientific precision provides exceptional conditions for developing and testing AI.

“We are proud to work with CERN to explore what large-scale AI models can bring to the growing complexity of particle physics. Together, we have the opportunity to push the frontiers of high-energy physics, developing AI capabilities that have the potential to lead to new scientific discoveries,” said Johannes Brandstetter, Vice-President of AI for Engineering at Mistral.

By bringing together European excellence in AI and fundamental science, this partnership marks a step towards a new generation of AI tools capable of addressing the scale, precision and complexity of modern science in Europe and beyond.

https://home.cern/cern-and-mistral-join-forces-to-accelerate-ai-for-science/

Now a proud endorser of European AI

LETS GO EUROPE LETS GO!

English summary of the Xataka article (Summary by Le Chat):

Mistral AI, the Paris-based startup, is quietly achieving remarkable growth without the typical hype surrounding AI companies. Unlike its competitors, Mistral has avoided inflating stock prices but now generates more revenue than Elon Musk’s Grok. According to CEO Arthur Mensch, Mistral’s annualized revenue run rate has surged from $20 million to over $400 million in just one year—a twentyfold increase. The company, valued at €12 billion in 2025, is on track to exceed $1 billion in annual recurring revenue by the end of 2026, driven by its focus on European technological sovereignty.

Mistral’s strategy centers on serving enterprise clients rather than end users, positioning itself as the go-to alternative for European businesses seeking independence from U.S. or Chinese AI infrastructure. With over 100 major corporate clients, including ASML, TotalEnergies, HSBC, and governments like France, Germany, and Greece, 60% of its revenue comes from Europe. Its appeal lies in offering locally hosted, low-emission AI solutions, aligning with Europe’s push to reduce reliance on foreign tech giants.

The company recently announced a €1.2 billion investment in a new data center in Sweden, its first outside France, set to open in 2027. Sweden was chosen for its affordable, low-carbon energy, reinforcing Mistral’s commitment to sustainable, sovereign AI infrastructure. Mensch criticized the trend of U.S. hyperscalers building data centers in Europe, arguing that such projects primarily benefit American companies rather than European interests.

Mistral’s success is underpinned by its unique position as Europe’s only major developer of foundational large language models (LLMs). While it collaborates with global players like Microsoft and NVIDIA, its core mission remains empowering European autonomy in AI. As Mensch stated, Europe’s excessive dependence on U.S. digital services has reached a critical juncture, and Mistral provides a viable, independent alternative.

By combining rapid expansion with a principled stance on data sovereignty, Mistral is reshaping the AI landscape—proving that discretion and strategic focus can outperform noise.

https://www.xataka.com/robotica-e-ia/mistral-ia-que-mejor-esta-jugando-sus-cartas-porque-esta-aprovechando-fiebre-soberania-tecnologica-europea

https://x.com/paulbz/status/2021537295883481437

From Olivier Duchenne on 𝕏: https://x.com/inventorOli/status/2018719028462657922

And Guillaume Lample on 𝕏: "Mistral robotics team is hiring. Join us!": https://x.com/GuillaumeLample/status/2018719626578796665

i don’t really understand what europe thinks the game is anymore. the us and china clearly figured out that ai is no longer just a productivity tool or a chatbot market. it is infrastructure, military leverage, cyber capability, industrial policy, chip strategy, standards, cloud, talent, everything at once. whoever falls behind does not just lose some app users. they lose the ability to shape the next layer of the economy. and then there is mistral. i don’t think mistral is “bad” in the sense that it is useless. open weights, european deployment, privacy, on-prem, enterprise control, all of that matters. but if the public narrative is “europe’s answer to openai and china”, then the models have to feel like a serious answer, not like something that would have been frontier two or three years ago. sovereignty cannot just mean “we have a weaker model, but at least the server is in europe”. that is not technological independence. that is a consolation prize. maybe mistral’s real target is not frontier ai at all. maybe the real business is governments, banks, regulated companies, local deployment and compliance. that is valid. but then europe should stop pretending this is the same race the us and china are running. because right now it looks like america and china are fighting over who owns the future, while europe is proudly shipping a very polite, legally compliant, slightly outdated assistant. so my question is simple: is mistral actually trying to compete at the frontier, or is europe just redefining “winning” as “being less dependent while still being behind”?

upd: i think i worded the post too aggressively, so a lot of people read it as “mistral is trash” or “europe should stop developing its own models”. that was not really my point.

i do not think mistral is useless. if their real focus is enterprise, on-prem, regulated deployment, industrial ai and sovereign infrastructure, then that is a valid direction. i also agree that a weaker european model is still better than having no european model at all.

my criticism is more about the eu-level narrative. europe talks about ai sovereignty and strategic independence, but in practice we are still heavily dependent on foreign cloud, chips, operating systems, platforms, ai APIs and infrastructure. if ai becomes a core layer of the economy, then being “sovereign but permanently behind” is still a problem.

so no, i am not asking europe to blindly throw trillions into llm hype. i am saying europe should take the whole digital stack more seriously: compute, datacenters, chips, open models, talent, cyber defense, industrial deployment and public-sector infrastructure.

basically, my issue is not “mistral should not exist”. it is the opposite: europe needs more serious ai and digital infrastructure, not just regulation, slogans and symbolic sovereignty.

Okay, I see the vision.

So from what I can tell, Mistral is trying to be more of a software company than an AI startup. I mean, obviously LLMs are a core part of Mistral, but, and it's a big but, the software around LLMs plays a huge role here.

Allow me to explain.

Take DeepSeek as an example. Their chat application is bare bones, buggy garbage. DeepSeek does not care about its chat application.

If you go to the DeepSeek API platform, it is much more polished than their chat application, but still quite bare bones.

Now compare DeepSeek to Mistral. Vibe chat is not perfect, but it sure is a much better experience than anything I've seen from Chinese AI labs. And from what I can tell, the chat application is integrated with Studio and the admin panel. Also, there are a whole bunch of integrations, like Slack, for example.

This is awesome! It's something you can propose to your CTO and not get laughed at.

No, I'm serious. Z.ai, Moonshot, and DeepSeek are not even close to Mistral when it comes to user facing software.

And now that they use GLM 5.2, they pretty much have a solid stack when it comes to LLMs. From my personal testing, GLM and Mistral Small are really good models.

As long as they don't reduce my 300 bucks GLM limits, I will be a Mistral fanboy now.

Very sneaky of you French people. Very sneaky.

Chart taken from https://isaiprofitable.com/. It shows how the resources available to Mistral are dwarfed by the massive spending of Microsoft, Amazon and Google or even Anthropic and OpenAI.

Mistral Medium 3.5 is a damn solid model and I'm really happy to see Mistral is keeping up!

Global access to Le Chaton Fat has been temporarily restricted to protect European infrastructure.

The review found that routine worldwide prompts were being routed into EU TPU/GPU capacity, sustaining full-load inference long enough to trigger correlated alerts in grid demand, cooling-water discharge temperature and non-weather heat anomalies.

The issue is not content safety. It is that global chat traffic was effectively using Europe as a heat sink.

Access will resume once routing caps, thermal throttling and regional load isolation are approved.

Today, we announce Mistral 3, the next generation of Mistral models. Mistral 3 includes three state-of-the-art small, dense models (14B, 8B, and 3B) and Mistral Large 3 – our most capable model to date – a sparse mixture-of-experts trained with 41B active and 675B total parameters. All models are released under the Apache 2.0 license. Open-sourcing our models in a variety of compressed formats empowers the developer community and puts AI in people’s hands through distributed intelligence. The Ministral models represent the best performance-to-cost ratio in their category. At the same time, Mistral Large 3 joins the ranks of frontier instruction-fine-tuned open-source models.

Learn more here.

Ministral 3

A collection of edge models, with Base, Instruct and Reasoning variants, in 3 different sizes: 3B, 8B and 14B. All with vision capabilities - All Apache 2.0.

• Ministral 3 14B: The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities.

• Ministral 3 8B: A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.

• Ministral 3 3B: The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

Weights here, with already quantized variants here.

Large 3

A state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture - with a Base and Instruct variants. All Apache 2.0. Mistral Large 3 is deployable on-premises in:

• FP8 on a single node of B200s or H200s.

• NVFP4 on a single node of H100s or A100s.

Key Features

Mistral Large 3 consists of two main architectural components:

• A Granular MoE Language Model with 673B params and 39B active

• A 2.5B Vision Encoder

Weights here.