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/

Mistral Large 4 (le Chonk) was trained on 3,800 NVIDIA Grace Blackwell GPUs, and as many people have pointed out, this is not much. For example, xAI boasts about having 200,000 GPUs.

Which made me wonder: does Mistral not have more compute capacity? Since information is a bit sparse, I decided to compile a list of data centers with primary sources.

The short answer is yes: Mistral has at least >4x the capacity to used to train ML4 right now in Bruyères-le-Châtel, and they are building three more data centers. In fact in July 2026 we learned they were selling capacity to Microsoft.

So why did they not use all of it for le Chonk? We can only speculate. Maybe they decided to sell compute until they finished hiring more researchers and now they're stuck with a contract for a few more months. In any case, they have the potential to greatly improve their models over the next year.

Here's what I found, in case someone else finds it useful

Bruyères-le-Châtel

Mistral's current main data center for training, already operational. The building is owned by Éclairion, a separate company, while Mistral owns the GPUs. It has a planned capacity of 120MW (targeting Q4 2027), with a first phase of 60 MW (that's the entire data center, we don't know if Mistral is the only client)

Éclairion website: https://eclairion.com/en/sites/bruyeres-le-chatel

February 2025: Mistral CEO plans to raise more funds to back AI data center (Bloomberg interview)

• https://www.bloomberg.com/news/articles/2025-02-10/mistral-ceo-plans-to-raise-more-funds-to-back-ai-data-center (paywalled)

Jun 2025 (GTC Paris): Nvidia reports Mistral is planning an "AI cloud" powered by 18,000 Grace Blackwell chips (they don't explicitly mention a location)

• https://blogs.nvidia.com/blog/france-sovereign-ai-infrastructure/
• https://nvidianews.nvidia.com/news/europe-ai-infrastructure
• https://mistral.ai/news/mistral-compute/

March 2026: 830M€ raised to buy 13,800 GB300 GPUs for a data center near Paris, bringing its total capacity to 44 MW. It's ambiguous if this a "new" data center or just an expansion.

• https://www.reuters.com/business/finance/frances-mistral-raises-830-million-debt-ai-data-centre-build-up-2026-03-30/
• https://www.cnbc.com/2026/03/30/mistral-ai-paris-data-center-cluster-debt-financing.html
• https://techcrunch.com/2026/03/30/mistral-ai-raises-830m-in-debt-to-set-up-a-data-center-near-paris/

June 2026: Nvidia announces Mistral's first deployment is already operational with 18,000 NVIDIA GB200s

• https://blogs.nvidia.com/blog/france-advances-europes-ai-future/

Fouju (Campus AI)

A venture between Mistral, Bpifrance, MGX and Nvidia to establish "Europe's largest AI Campus" (which I assume just means more data centers), located in the Paris region and with a target capacity of 1.4 GW. Expected to be operational by 2028.

May 2025: announced

• https://www.polytechnique.edu/en/press-room/press-releases/mgx-bpifrance-mistral-ai-and-nvidia-launch-joint-venture-build-europes-largest-ai-campus-france

June 2026: expansion announced with imminent selection of a second site, doubling the initial investment

• https://blogs.nvidia.com/blog/france-advances-europes-ai-future/
• https://www.mgx.ae/news-insights/bpifrance-mistral-and-mgx-expand-campus-ai-across-france-deliver-3-gw-network-ai
• https://www.datacenterdynamics.com/en/news/brookfield-ups-french-data-center-investment-by-10-billion/

Borlänge, Sweden

Built by EcoDataCenter with a 1.2B€ investment from Mistral, scheduled to open in 2027. The site claims "up to 600MW total available power" but the actual capacity might be smaller.

EcoDataCenter website: https://ecodatacenter.tech/data-center/ecodatacenter2

February 2026: announced

• https://ecodatacenter.tech/press/mistral-ai-and-ecodatacenter-partner-to-build-ai-focused-data-center-in-sweden-3431886
• https://www.cnbc.com/2026/02/11/mistral-ai-infrastructure-sweden.html

Les Ulis

10MW data center for inference, scheduled to open in Q3 2026.

May 2026: Announced

• https://mistral.ai/news/ai-now-summit-2026/

I'd love to set up some scheduled tasks that run periodically and notify me of interesting events (for example: check a number of software repos and notify me if releases contain changes relevant to me). However there doesn't seem to be a builtin way to be notified. The vibe app doesn't do anything when a scheduled task triggers.

I know I can build something with a connector to email or ntfy.sh, but I would first like to hear how others solve this.

Nella nostra piattaforma abbiamo un agente che guida l'utente attraverso alcuni workflow seguendolo nei passaggi più complessi ed automatizzando alcuni task. Come agente coordinatore siamo appena passati da medium 3.5 a large4.

Io sono entusiasta, più veloce, più preciso nelle chiamate ai tool e meno costoso.

Abbiamo dovuto un po' aggiustare il tiro perché inizialmente era troppo simpatico aggiungendo emoticon ovunque 😂 in generale comunque sono davvero felice di poter usare un modello eu!

Voi cosa avete notato nel confronto con modelli precedenti nei vostri usi? C'è qualcosa a cui prestare attenzione?

Depuis plus d'un an, j'utilise l'IA avant tout pour relire le tapuscrit de mon roman (pas écrire ni suggérer des idées, je lui interdis ça explicitement) et faire quelques recherches. J'ai commencé avec ChatGPT car je ne connaissais que ça, puis les polémiques en début d'année m'en ont un peu dégoûté, ainsi qu'un ton global qui me semblait bien trop infantilisant et flatteur. J'ai donc essayé Claude car j'en entendais beaucoup de bien. Je trouve que c'est effectivement une IA parfaite pour ce genre de tâches, c'est vraiment super.

Mais en tant que Français, je suis attaché à la souveraineté européenne et je culpabilisais beaucoup de financer et contribuer à la prospérité des IA américaines. J'ai brièvement essayé Mistral il y a quelques mois pour me sentir plus aligné sur mes valeurs, mais c'était vraiment très nul pour l'analyse de documents, avec un nombre d'hallucinations vraiment abusé. Je suis donc repassé à Claude tout en checkant régulièrement s'il y avait du nouveau du côté de Mistral.

Il y a quelques jours, j'ai vu que Mistral faisait la promotion de son nouveau modèle Large 4, lui permettant de combler une partie de son retard. J'ai donc refait le switch pour voir et, ok il y a encore beaucoup de défauts, quelques hallucinations, mais je vois clairement une amélioration majeure par rapport à mon premier essai. Maintenant, Mistral peut faire une analyse de mon PDF de 160 pages sans incohérence majeure, et me livrer des réflexions d'une qualité que je trouve comparable à celles de Claude. Franchement, chapeau. Il y a encore un peu de retard à rattraper, mais, pour l'utilisation que j'en fais, cela me paraît le bon moment pour soutenir cette IA européenne, qui a franchi un cap majeur et a besoin de notre soutien pour continuer de se développer.

Je ne pense pas que Mistral dépassera un jour les IA américaines et chinoises qui ont des moyens incomparables et ne s'embarrassent pas de nos questionnements éthiques (que je chéris, qui font qu'il fait bon vivre en Europe). Mais franchement, ça fait le taf, et si ça peut nous permettre de réduire notre dépendance à une puissance impérialiste qui nous méprise, alors il faut foncer.

Hey everyone!

I recently got invited to interview for a Forward Deployed Engineer Intern position at Mistral AI in Paris.

The interview process includes:

• Initial screening with a senior Applied AI team member
• Python Code Review (30-45 min)
• Vibe Coding (60 min)
• System Design (45 min)
• Technical Manager Interview (45 min)
• Culture Fit (30 min)

I was wondering if anyone here has gone through this process, either for the FDE internship or a similar Applied AI role at Mistral.

I'd especially love to know:

• What was the initial screening like? Was it mostly behavioral, or were there technical questions too?
• What kind of code did you have to review in the Python round?
• What exactly does the Vibe Coding round involve? Are AI coding assistants allowed?
• What kind of problems came up during System Design?
• How difficult were the technical rounds, and what would you recommend focusing on?

I'd really appreciate any insights or preparation tips from people who've been through it.

Thanks in advance!

Been using Mistral's Vibe CLI since the December launch and honestly the Python/Textual TUI was starting to get on my nerves. Slow startup, typing latency, and scrolling through a long session felt like wading through mud.

Turns out Mistral has been quietly shipping a full Rust rewrite of the interface. It's built on ratatui, talks to the same Python app-server over JSON-RPC, and you enable it with one env var:

VIBE_CLI=rust vibe

The difference is immediate. Startup is instant, typing feels native, and scrolling through long transcripts with big diffs no longer stutters. They're calling it "the new Vibe TUI" in the banner now.

Context: Vibe launched in December 2025 as a Python + Textual app. Around v2.25 (September 2026) they started shipping the Rust client as an experimental opt-in, and the October releases (2.26.x) made it the main focus, faster startup, typing, rendering of long sessions. The Python TUI is still the default, but the writing is on the wall.

Thanks god. The Python/Textual version was laggy as hell. That's a good decision from the team to ditch it.

Large 4 has dropped speed. Now it’s 2-3 slower than it was on first days. I have to switch back to glm because 4 is unbearably slow. Anybody else notice this?

I hope they fix these issues when trial/discounted period is over.

Hi people,

I left Le Chat just before the rebranding, moving to Claude. I did it because for my usage, it was simply terribly better. I still follow this subreddit because my heart still belongs to Mistral, and I'd like also my money. Can you help me to understand if for my usage it could be comparable to Claude and if maybe I'm missing some hack and I may underestimate its potential?

My use is the following: I do research in academia, dealing with forests, ecological modelling, spatial data and climate change. I'm not an expert in informatic, I originally used only R (before AI), now I also use javascript for some pretty complex ecological models. I use claude for the following reasons: when I do analysis, I run my models and I plan in-silico experiments, I need something that can keep in mind a lot of informations (about laws, practices, ecological notions, land planning instruments). At the same time, this something need go be able to open csv, sqlites, js, R scripts, rasters etc, to help me keeping all these files interconnected in a coherent way. Considering I'm not expert in coding but I'm only a poor environmental scientist that uses a lot his computer, I also need something helping me keeping the code very cleaned and checking consistency of different scripts that must run at the same time.

As for now I use projects in Claude, specialized in specific sectors of my research work.

Do you think I could do this with Mistral?

Sorry if I'm saying trivial things, maybe I just underevaluated the model

Thank you in advance!!

Is it just me or did Mistral Vibe (free and pro) decline in quality?
Lately a lot of the conversations I have with Vibe, lack context, it always focusses only on the last message and forgets or ignores the broader picture of the entire conversation.

Also recently I noticed how Vibe always focusses on playing Devils Advocate. It tries to find the slightest wrong wordings, making this the main point of the response, debunking theories. Even in philosophical topics, instead of playing with the theory, it falls back to institutional academic bs instead of exploring ideas.

Lately it feels more of like I am chatting with a extremely pesimistic and negative contraire opponent lawyer instead of having a "buddy" to chat with and explore topics.

It is frustrating how negative and nitpicking and hairsplitting Vibe has become. it falls into logical fallacies and overall just feels like it's current job is to take the exact opposit side of the discussion instead of freely exploring. This is a pretty new behaviour, maybe a week or two or three. And its seriously annoying.

It also ignores memories/instructions like formation or language preferences and recently started to put, even more than before, non latin characters in chats, switches languages mid sentence etc.

Is it just me? Or does anyone has the same problems?
And how can I fix it on my end? Is there any temporary fix to put into my memory for Vibe to work as it used to?

I loved Mistral Vibe, but right now it's nothing more than a generator for denial and depression rather than a open minded explorative tool.

Hey everyone! Looking for advice from people who've used Mistral models in production. I'm building a high volume text classification and JSON extraction pipeline, looking for the best balance of accuracy and API cost. My use case: • 50k to 100k messages monthly per customer. • Classify messages as relevant or irrelevant. • Extract product names, quantities, units and instructions into JSON. • Informal English, Hindi and Hinglish, including Romanized Hindi. • Distinguish orders from enquiries, corrections, cancellations and unrelated messages. • Low false negative rates and accurate quantities are essential. I tested Ministral 3 8B on 50 labeled examples: • Classification: 49/50 • Exact extraction: 35/47 • Quantities: 74/80 • Units: 71/80 • Average tokens: 1,069 input, 238 output • Average API cost: $0.000196 per request My rule based parser actually outperformed 8B on straightforward examples, so I'm looking for a significant accuracy improvement. My questions:

1. Is Ministral 3 14B a meaningful upgrade, or should I go straight to Mistral Small?
2. Which model handles strict JSON, multilingual instructions and numeric quantities most reliably?
3. Any prompting techniques or settings that improve accuracy without substantially increasing cost?
4. Does combining deterministic rules with a larger model for ambiguous messages work well in practice?
5. At 50k to 100k requests monthly, which model would you deploy if accuracy and cost both mattered?

I'd especially appreciate recommendations based on real deployments or benchmarks. I'm planning to compare models using the same labeled dataset. Thanks!

Hey everyone.

I'm currently using Mistral within the Hermes harness and I wanted to keep track on API costs.

I (zai glm 5.3 mostly) coded a plugin for Hermes wich gives me the actual cost of any session (compression safe).
It was a lot of trial and error, but I'm finally happy with it and want to share to anyone who is interested:

- https://github.com/AndiAtom/hermes-usage-history-rpc (the Gateway/Dashboard part)
- https://github.com/AndiAtom/hermes-token-stats (the Hermes Desktop part)

The RPC collects session stats out of Hermes state.db and saves them to it's own db.
The Client(and the Dashboard part) look like this:

Currently only Mistral API and OpenRouter costs are included.

Feel free to give it a try. For testing you can just install the gateway part and look at the Web Dashboard page.
Let me know if I'm missing smth huge. I don't usually develop stuff like this.

If anyone more capable than me wants to fork and fix/adjust -> Go for it :)
UI language is german for now. But will be changed to english in the next version. Maybe with a toggle and more languages. We'll see if anyone would be interested at all.

I use Mistral Vibe with sub-agents, and I got tired of scrolling logs to find out who was doing what. So I built a small tool that turns a session into a live call: each agent is a character at its desk, the lead gives orders, sub-agents join the call, and you see the real commands with their output. At the end you get a recap: commands, errors, tokens, files changed.

It reads Vibe sessions (2.26, both engines), next to Claude Code and Codex ones if you use those too.

What it is

• Free and open source (MIT), no account
• It only reads the session files Vibe already writes in ~/.vibe. Nothing is sent anywhere, and it costs no tokens
• Runs as a VS Code extension, from a terminal, or as a desktop app

Try it (Node 18+)

npx @double_geste/agent-standup --open

Links

• Site: agent-standup.com
• Code: github.com/Atoll92/agent-standup

It is a side project, not affiliated with Mistral. Feedback is welcome, especially from people who run several sub-agents at once.

Gave same prompt (see below) to ChatGPT’s web interface (pro-mode) and ministral 3 3b running on my secondhand phone (via pocketpal/llama.cpp). They spend roughly the same amount of time on their answers. Only the later gave the right answer. Or it gave a valid approximation stating the limitations. Chatgpt was simply wrong.

So: when should we expect ministral 4?

Prompt: Two large seas are connected through a narrow passage with width b. The first sea has a higher water level than the other. The sea floor has constant height. Water flows through the narrow passage from heigh to low water level. We assume that the first sea is so large and the width so small that the water level in the first and upstream sea is constant. The second and downstream sea is also large, but of finite size. The water level changes slowly. The flow is inviscid. What is the flow rate through the passage? How does the water level in the second sea change through time. The flow is subcritical.

proper desktop app any time soon? want to switch to EU product, but Claude seems unbeatable at the moment.

Per chi sta usando Mistral come aiuto alla programmazione, come si sta trovando? Io lo trovo molto base e continua a fare errori anche con prompt molto chiari. Non so se aumentando il piano il margine di errore si riduce, ho il premium base

Hello,

I'd like to use the Vibe CLI more, but I'm disappointed by the support it's currently receiving. For example:

• Its not possible to cycle back to the `plan` mode with `Shift-Tab`, see this Github issue that has been opened 2 weeks ago
• The `smart-approve` agent mode is not reference on the Vibe cli doc even though it was added at least on the 2.25.1 version

Is the Mistral team still actively supporting the VibeCli?

I am a free lance developer doing all sorts of work, from complex automation tools to my own SaaS-business. I recently bought Claude Max (99 USD) and I mean, it is great tbh, but also it is American. I really vouch for the use of European options (Using UpCloud for hosting my projects, Sweego for email etc) and I thought let's give Mistral a run. Just signed up for Mistral Pro and will use it for one of my projects this week. Will evaluate corrections needed as that is my main weak-spot, ie the amount of times I have to ask it to redo things because of low quality output. Let me know if you are also evaluating, or if you want some insights. Happy coding!