I've been trying Mistral AI for quiet a while now and always only subscribed for a month to test and then stopped because it was so disappointing and not enough for my work.

I did it again a week ago and holy shit is it different this time. It is absolutely viable and even vibe cli was able to tackle all my problems all week in way that didnt feel worse than my Claude results. I also were able to try ML4 and its so capable even in this preview version. And all of that so cheap in comparison and also easily trackable, because its either virtually unlimited or for vibe code its a clear and large amount transparently expressed in Euros.

I really don't know, but its the first time I totally consider moving to Mistral as my main AI hosting service.

Good job, Mistral!

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/

Original Article: https://www.linkedin.com/pulse/build-submit-arthur-mensch-4yt5e/

An idea has gained ground in Europe: we should leave frontier AI models to the Americans and the Chinese. It is advocated by serious people, whose reasoning has its logic. A frontier model costs hundreds of millions of euros per generation, and soon billions. Better, they argue, to follow the internet playbook, where value was created at the application layer, and to focus our efforts on what Europe does well: its industrial data, its small and medium-sized businesses, its domain expertise. For everything else, they say, open models released by others should  be enough.

This is a fundamental mistake. It will end up costing Europe what remains of its place in the world and lock it into a regime of economic dependence unprecedented in the history of civilizations.

The Cloud and software value has gone elsewhere

Europe followed the internet playbook. We developed the applications and left the platforms to others. Twenty years later, the value has gone elsewhere. European companies pay more than €260 billion every year to American cloud and software providers. Europe has become the continent that consumes what others build.

Whoever controls the model sets the terms

AI is not the internet. The internet ran on open protocols that nobody owned. Closed AI models, by contrast, have an owner. What an application or an agent can do is decided by the model, and whoever controls the model sets the terms: they change the price, restrict uses, withdraw a version. Every decision made upstream is imposed on the entire chain. Those who build on top bear the consequences, with no say in the matter.

The risk is anything but theoretical. Last June, an American lab suspended access to its most advanced models. Access was restored. But the decision was not made in Europe, nor for European reasons. Nobody asked for our opinion.

A vital matter of security and progress

Some will reply that open models solve the problem. That is partly true. An open model can be downloaded, self-hosted, and audited. But without actively contributing to the ecosystem, you cannot choose what gets released, nor when, nor under what license the next version will ship. Dependence on others, even on their openness, is still dependence.

Building frontier AI systems is becoming a vital matter of security. Models are now taking on a central role in cyber defense and in protecting critical infrastructure. A state must be able to audit the systems it uses, adapt them to its needs, and be certain it will still have access to them tomorrow. With a model designed and controlled elsewhere, none of these three conditions is guaranteed over time.

The question is also becoming salient for fundamental research in Europe. Frontier models are now indispensable to scientific progress in every field of research. They are increasingly used exclusively by certain companies, which have every interest in guarding the research advantages they confer without sharing access. Using AI for AI research accelerates progress for us and for our competitors alike, which points to very significant threshold effects.

The trap of pitting research against deployment

Pitting frontier AI research against industrial AI deployment is therefore a trap. Of course, part of Europe's growth will come from AI tailored to industry. But that requires building a European AI industry, not accepting an AI industry in which we would be mere tenants. We see it with our customers: For the most demanding uses, mastering the model is not a luxury; it is a prerequisite. You do not learn to modify and use a model effectively simply by importing it.

What it takes to build frontier AI

Europe has the means to build these systems. Black Forest Labs and ElevenLabs, two European companies, produce the best models in their category. At Mistral, our researchers and engineers are based in Paris, London, Munich, Barcelona and Warsaw, as well as in the United States, where we export our products. With fewer resources, we train general-purpose reasoning models that now rank among the best open models, along with the world's best specialized models in several use categories.

Furthermore, we know exactly how to improve our performance. We are about to increase our compute capacity sixfold over the coming year, building on the confidence we have earned in our methods and operations. We can move faster with more capital, and we will raise it.

Breaking with pessimism

Closing the gap demands first and foremost a break with pessimism. We keep being told the game is over and that Europe has lost. That story suits those who are ahead. It does not need to be true to be effective: all it takes is for us to believe it. At Mistral, this story has been held up against us for three years, and for three years we have been proving it wrong.

If Europe resigns itself, AI will go on being built elsewhere, and we will only have access to second-rate export versions, in exchange for fees running into the trillions of euros. That will wipe out any prospect of growth or power. It could even prevent us from continuing to hold together as a society.

Build or surrender: for now, the choice is still ours.

​

As a European, I'm genuinely happy to see Mistral Large 4 Preview competing with some of the strongest open-weight models.

Considering it's still a Preview, I think the final version has a good chance of becoming the strongest Western open-weight model, potentially surpassing Meta's newly released Muse Spark Open (39 on Artificial Analysis).

Also, Le Chonk? +1 to whoever came up with that name. 😂🐈

I'd love to move as much of my AI workload as possible to Mistral. Not just coding, but Voxtral, OCR, Vibe, and more. Hosting GLM 5.3 was also a great move.

But I have a few criticisms.

💰 1. ML4 pricing

ML4 Preview currently scores 38 on the Artificial Analysis Intelligence Index.

Meanwhile:

- DeepSeek V4.1 Flash: 39

- Muse Spark Open: 39

- Qwen3.8: 40

- GLM 5.3 Flash: 42

- GLM 5.3: 45

ML4's full listed price is $1.36/$4.18 per million input/output tokens, once the 50% promotion ends.

GLM 5.3 Flash costs $0.15/$0.50. That's roughly 8–9 times cheaper per token, despite scoring higher.

Mistral itself hosts the full GLM 5.3 for $1.40/$4.40, almost exactly ML4's full price.

If the final ML4 genuinely approaches GLM 5.3-level performance, I'd happily pay that price.

But if it ends up competing mostly with Flash-tier models while costing 5–10 times more, I really don't see that working.

I want to support European AI, but that doesn't mean ignoring price and performance.

🤯 2. The model lineup is a mess

Seriously, what happened to the model families?

We have:

- Small 4: affordable, but increasingly behind the competition.

- Medium 3.5: interesting for specific agentic workloads, but expensive compared to newer alternatives.

- Large 4: finally competitive, but currently in Preview.

I understand that Medium 3.5 may have partly served as a stopgap after months without a major flagship release. Fair enough.

But Medium 3.5 costs $1.50/$7.50, MORE than ML4's full listed price! 😂

The naming, release cadence, overlapping generations, and addition of third-party models have made the lineup unnecessarily confusing.

Why not simplify Mistral's general-purpose models into TWO families?

- Small/Flash: fast, affordable, capable, regularly updated.

- Large: Mistral's strongest frontier model.

That's it. Keep specialized models like Voxtral and OCR separate, and continue hosting third-party models.

Wouldn't that be clearer for developers, businesses, and regular users?

⚡ 3. Where is a competitive Small/Flash model?

This isn't just about developers wanting cheap coding models.

Businesses need affordable models for massive, repetitive workloads: document processing, classification, extraction, customer support, automated workflows, and millions of daily API calls.

For these tasks, efficiency and price can matter more than frontier-level intelligence.

Small 4 was competitively priced, but it doesn't really compete with the latest GLM Flash, DeepSeek Flash, or GPT-6 Luna in overall capability.

DeepSeek V4.1 Flash is even temporarily FREE through OpenCode.

When will Mistral release a genuinely competitive Small/Flash successor?

And if that's not happening soon, why not at least host GLM 5.3 Flash or DeepSeek V4.1 Flash yourselves?

I'd much rather use them through European infrastructure.

🎛️ 4. Reasoning: seriously, just OFF or HIGH?

I checked the documentation, and apparently Small 4, Medium 3.5, and ML4 all primarily support "none" or "high" reasoning effort.

Why?

The general API schema lists additional levels, but they're not documented as supported by these models.

Meanwhile, even GLM 5.3 hosted by Mistral supports "low", "high", and "max".

Why can't Mistral's own models offer Low, Medium, High, and perhaps Extra High?

Not every task needs maximum reasoning, and switching it off entirely isn't always ideal. It affects latency, token usage, and costs.

And in Vibe (formerly Le Chat), we still only get Fast/Think, without granular control.

I really hope this isn't the long-term approach.

💻 5. Vibe Desktop. Any actual plans?

I'm developing my own open-source desktop client built on Mistral's Vibe engine.

But seriously, is an official Vibe Desktop coming or not? 😅

Windows, macOS, Linux?

I've seen references to desktop functionality, but is there an actual plan? Is it being developed, tested privately, or simply not a priority?

I'm not asking for release dates. Just some clarity.

Should we keep building our own clients, or is Mistral planning an official one?

📢 6. Communication is still frustrating

I appreciate Mistral's technical work, research, and releases.

But communication around product direction, pricing, model families, Vibe, and future features is often almost nonexistent.

Nobody's asking for daily announcements or endless marketing hype.

A simple "we're working on it", "not planned", or "this is our priority" would already help.

There are people investing time and money into your ecosystem, including developers building open-source projects around it.

We don't need marketing fluff. We need some direction.

P.S

I don't want this to sound like hate towards Mistral. Quite the opposite.

I'm genuinely happy that Europe has a company capable of competing at this level, and I want to move MORE of my workloads to Mistral, not fewer.

But I don't want to use Mistral only because it's European.

What do you think? Especially those of you using Mistral's APIs regularly.

The Mistral Large 4 blog (https://mistral.ai/news/mistral-large-4/) mentions a CTF Speedrun benchmark with 19 challenges. What is the dataset or benchmark used?

Settings -> Privacy -> Personalization -> Sensitive data in Knowledge

What Knowledge can save

Race or ethnic origin

Political opinions

Religious beliefs

Philosophical beliefs

Trade union membership

Genetic data

Biometric data

Health data

Medical or health details

Sex life or sexual orientation

Never saved

Government identifiers, such as Social Security, passport, national identity, or tax ID numbers

Criminal records

Financial account numbers

Banking information

Bank or payment details

Passwords

Secrets

Credentials

Tokens

Sensitive information about other people

Sérieux, j'essaie de comprendre, et je n'y arrive pas. Alors je pose mon raisonnement, dites-moi où je me trompe.

1. « Mistral est moins performant »

Oui, sur les benchmarks purs, les frontier d'OpenAI ou d'Anthropic sont devant — je ne le nierai pas. Mais je trouve que ce reproche mélange deux questions.

D'abord, pour qui ? À quel moment un particulier ou une PME a besoin d'un modèle qui a coûté des centaines de millions, voire des milliards, en entraînement… pour trier ses mails, rédiger un courrier ou résumer un PDF ? Pour l'immense majorité des usages grand public, la performance est saturée depuis longtemps. La course aux benchmarks, c'est un sport de labos, pas un critère d'achat pour monsieur et madame Tout-le-monde.

Ensuite, « moins performant » ne veut plus dire grand-chose côté pro. Le code qui sort de Vibe Code avec GLM-5.3 est propre — modèle chinois certes, mais hébergé et exécuté sur l'infrastructure européenne de Mistral. Et maintenant il y a ML4 : un MoE de 1 000 milliards de paramètres, entraîné dans leurs propres datacenters européens sur un cluster minuscule comparé à la concurrence, déjà au niveau des meilleurs open-weight hors de Chine sur pas mal de tâches métiers — alors qu'il est littéralement encore en entraînement. Les poids sortent à la fin du mois, et Mistral dit que c'est un socle : le modèle va continuer à progresser et servir de base à toute une génération de modèles dérivés. Juger Mistral sur un benchmark d'un modèle qui n'est pas fini, c'est un peu juger une cuisson à mi-cuisson.

2. « Mistral ne fournit pas de souveraineté »

Là, je comprends encore moins. Ils conçoivent leurs propres modèles, entraînés et hébergés en Europe, open-weight en plus — donc auditables et déployables chez soi. Et quand ils proposent en plus des modèles ouverts comme GLM sur leur infrastructure européenne, on les accuse de devenir des « installateurs de modèles chinois » ? Il faut choisir : soit la souveraineté, c'est la capacité de l'Europe à bâtir et opérer sa propre couche IA (modèles + hébergement + données sous juridiction européenne), et alors Mistral coche les cases ; soit c'est un fantasme d'autarcie totale qui n'existe nulle part — le hardware est américain partout, et aucun acteur au monde n'a de GPU « souverains » hors de la chaîne NVIDIA/TSMC/ASML. La souveraineté, c'est du management de risques, pas un drapeau collé sur un GPU.

3. « Vibe CLI est nul »

Je l'utilise régulièrement : il fait très bien le travail, et il est devenu encore plus réactif depuis les composants réécrits en Rust. Ce n'est pas parce qu'un outil n'a pas la hype de Claude Code qu'il est mauvais.

Pour être honnête, je ne prétends pas que Mistral soit parfait : sur le code agentique pur, ML4 reste encore derrière GLM-5.3 et les ténors du marché, et je comprends qu'un client pro parte sur du moins cher. Mais sur Reddit, j'ai surtout l'impression de lire un bashing automatique, un réflexe « français, donc forcément médiocre », évalué sur des snapshots qui datent d'avant chaque nouveau modèle. Entre un acteur européen qui progresse vite et l'abandon complet de la couche IA aux Américains et aux Chinois, mon choix est vite fait.

Voilà, cassez-moi gentiment dans les commentaires.

I just looked through the Model Cards of Mistral and found a Model called Timestral 1.0 which I thus far haven't heard about. It stated it is a Zero shot time series forecast Model but nothing more.

I checked on Google, Vibe and claude and they couldn't find anything.

The webpage seems to be down now in the meantime:

https://docs.mistral.ai/models/timestral-1-0

Does anyone have further info?

Bonjour à tous,

Je suis actuellement étudiant en première année de prépa intégrée en école d'ingénieur en informatique et j'utilise beaucoup l'IA pour m'aider à comprendre des exercices, me faire des QCM etc. J'ai utilisé pendant longtemps Mistral mais avec le modèle vieillissant j'en ai utilisé d'autres, notamment Grok pour les maths (il y a un an) et maintenant je profite de l'offre google AI plus gratuite pour les étudiants.

Mais après m'être renseigné, j'ai vu que Gemini flash 3.6 était globalement pas si bien que ça et qu'il était je cite "le pro des hallucinations" ce qui m'inquiète car je n'ai pas envie d'apprendre n'importe lol. Et maintenant j'hésite entre GLM dans Mistral Vibe et Gemini flash 3.6.

Qu'en pensez vous ?

Lequel serait le mieux adapté à mon utilisation (pour l'instant du code assez simple, des maths niveau terminale/bac +1, de la physique, de l'électronique tout ça tout ça) ?

J'ai regardé sur internet et apparemment GLM ne gère pas la reconnaissance d'image (que j'utilise pour envoyer des exercices, etc) mais ça semble marcher dans Vibe. Je me dis que les gens ici sauraient sans doute répondre !

Bonne soirée

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?

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.

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/

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!

So i got this Mistral pro sub yesterday and already 1/5th of monthly usage gone. At this rate, it means work 5 days a month, then not, without relying on other subscriptions. I do already have claude and codex subs, but codex sub i wish to get rid of soon. Would be nice to get a secondary, or maybe even third Mistral sub for claude to orchestrate, but for some reason Mistral does not want my money.

Yes yes i know PAYG does exist, but it costs A LOT compared to just getting some other sub instead. I mean 50€ for one day of usage if i were paying the API prices. At those prices paid 25 days a month, i could get multiple 200€ claude subs instead and have better model with tons and tons of more usage.

Also yes team plans do exist, but 2 team seats cost more than 3 pro subs, and would be too expensive to justify over Claude, even tho i would prefer EU company, and mistral 4 large seems good enough to do what i do, orchestrated by claude at least.

So apparently i will need to get more claude subs after i get rid of codex, even tho i would rather get 2 more mistral subs alongside my claude sub.

Please Mistral, would you take my money?

(Or at least adjust the usage from monthly to 5h + weekly and increasing overall monthly limit, because most would not use all weekly limits, and they would reset with smaller usage pools, compensating for those who do use all of it then having slightly more usage overall.)

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.

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!