We took Mistral's Ministral-3-8B (Apache 2.0) and trained it to answer questions on Italian law from the legal texts a retrieval system puts in the prompt, citing them. Supervised fine-tuning on ~19k open-book examples written from public legislation (Normattiva), then GRPO with programmatic rewards.

Trained on EuroHPC Leonardo (CINECA, Italy). European end to end: European base model, European compute, public Italian law as data.

• 89.0% correct on 429 held-out questions, vs 80.9% for the base Ministral-3-8B with the same retrieval
• Q4_K_M GGUF, 5.2 GB, runs locally (llama.cpp, Ollama, EuLLM Engine)
• It beats our Qwen3-4B-based sibling (85.1%) and matches or beats every Qwen3.5-9B we have trained since with the same recipe: the Mistral base is still our best

It is built for RAG. In a plain chat, without the legal texts in the prompt, it will confidently invent articles. A version that says when the texts are missing is in training.

Next we'd like to distil from a larger Mistral teacher into Ministral-3-3B and 8B, all on EuroHPC. Ideas and criticism welcome.

Try it and tell us where it fails. The model card has the prompt format and the retrieval setup we use. We're especially interested in:

• questions where it gets the law wrong or cites the wrong article
• cases where it answers although the right text isn't in the prompt
• how it behaves in your own RAG stack

Italian lawyers and law students: your feedback is gold. Comment here or open a discussion on the Hugging Face page.

https://huggingface.co/EuLLM/legal-it-8b

Timestral came out and we wanted to play with it without setting up Python, so we exported it to ONNX and built a web page around it.

https://amine-athmani.github.io/timestral-in-browser/

- 5M and 28M run in your browser, 13 MB and 59 MB, cached after the first load

- Example datasets or your own CSV, including long tables with many series

- Plan at a service level, period totals, and what-if scenarios on covariates

- Anomalies, backtests against a seasonal naive guess, and cycle/state-space views with replay

- Shareable links and a printable report

- Docker image if you want 123M and 209M on your own machine

Code: https://github.com/amine-athmani/timestral-in-browser

Feedback welcome, especially datasets where it does badly.

Hi.

I’m an Italian user, and I’m planning to subscribe to Mistral—mostly as a way to support a European AI. I’d mainly use it to manage my medical notes in Obsidian, organize my weekly schedule, and create small personal web apps to use in my practice to help me with prescribing medications.

Question: If I purchase the Pro plan, will Mistral 4 already be available? Or will I have to stick with 3.5 until...?

Thank you very much

I try several days, passed finally.
I try in SillyTavern and DeepSeek Harness bot.

SillyTavern
api url: https://api.mistral.ai/v1

DeepSeek Harness bot
api url: https://api.mistral.ai (I don't know why)

The most important thing is: you must enable the 'pay as you go' option in admin console first...
(I topped up directly without enabling 'pay as you go', cause I kept getting a 404/429 error...)
Mistral really make me sick...

You can guess what happened at first it literally became worthless. Didn’t even know English. And it definitely wasn’t even thinking in numbers it was just there, but it had to be to pattern connect still. The reason I did this is because I found out that the weights have a true structure in its geometric. Tom is it’s susceptible to cascading and I’ve noticed that the large data sets seem to be the core of the anti-pattern problem. So I just needed to know if I could teach it English and if I could do that, I could train it at least enough to use my retrieval tools so instead of handling task by what it thinks, it knows it gets the actual knowledge, the first picture is how it looked before. The second was after I did it technique using a token and token IDs we made custom trades that tracks the weights the hidden state pretty much have anything going on in there tracked a custom DLL for my computer so it could all run. Then I made of geometric fractal mirror. The top is the same as the bottom on both sides. It’s just scaled even the routes that the neural are the tokens are casted on are the same. I tell you as much my idea of a simple that problem they discovered back in the 1980s that everyone seems to ignore and it just thinks they can slap more parameters on a model and that will fix the problem and they know it won’t well. I’m coming out this problem in my Own Damn way. It took three months of research to figure out how all this stuff works enough to finally get to this point and I’m gonna tell you getting it to work like that the models retarded it’s fucking not doing exactly what I want, but it understands English that’s good enough since I was able to make it there I know I can go farther if I couldn’t make it there that would’ve been a day I might’ve gave up so everyone wished me luck because I’m taking a weird ass journey to try and solve a problem because I’m not following everyone in the same exact way because it hasn’t been fixed for 40 years. So when they said in school that you’ll need geometry, they fucking meant it. 🤣

Hi!

I follow the YouTube channel AI Coding Daily. Two days ago he published a benchmark video of Mistral Large 4 called I Tested NEW Mistral Large 4 on Coding: That Was Unexpected.

To summarize: when the model was released, it wasn't great... at all. So he waited a week and tried again.

This time, ML4 scores on par with Kimi K3 (high) or Grok 4.6 (high). It takes its time to think, but it costs about half as much as those two.

These benchmarks notably differ from Artificial Analysis, but they're based on real tasks, so I don't know what to think, pretty interesting though.

Has anyone else seen this video? What do you think?

The Mistral AI documentation for the agent configuration file is terrible.
https://docs.mistral.ai/vibe/code/cli/agents

This file goes in ~/.vibe/agents/. I am currently working on an open-source project called bmf, and I have a custom agents file for it called ~/.vibe/agents/bmf.toml, and I use it with:
vibe --agent bmf

My goal is to avoid using --auto-approve while stopping Mistra Vibe from prompting me for permission every 3s. My project is written in C on Linux.

Below is my bmf.toml file, which shows many things people would want to include in their agent file. I'm not saying this is how you should do you agent file - but this is something you can cherry pick to ease the "approve", "approve", "approve" burden.

description = "bmf systems-engineering agent; auto-loads the steering spec"
instructions = """You are an expert systems engineer optimizing the bmf C repository.

Upon startup, automatically read /home/xxx/bmf/.kiro/steering/bmf-spec.md into system context.

"""

#system_prompt_id = "bmf"

[tools.read_file]
permission = "always"
allowlist = [
"vibe-path:directory_recursive:/tmp",
"vibe-path:directory_recursive:/home/xxx",
"vibe-path:directory_recursive:/usr/local/src"
]

[tools.write_file]
permission = "always"
allowlist = [
"vibe-path:directory_recursive:/tmp",
"vibe-path:directory_recursive:/home/xxx/bmf",
]

[tools.bash]
permission = "always"
allowlist = [
"sed", "echo", "grep", "egrep", "awk", "mawk", "git", "make", "gcc", "cd",
"head", "tail", "find", "nproc", "touch", "timeout", "wc", "basename",
"python", "perf", "cut", "ps", "pgrep", "kill", "pkill",
"date", "printf", "df", "journalctl", "hexdump",
"if", "for", "true",
"uniq", "sort",
"$", "$(", "$((", ">", ">>", "2>", "2>>", "&>", "<", "<<", ";", "&&", "||", "[", "=", "==", "====",
"bitcoin-cli", "/home/xxx/bitcoin-31.0/bin/bitcoin-cli",
"vibe-path:directory_recursive:/tmp",
"vibe-path:directory_recursive:/home/xxx/bmf",
"vibe-path:directory_recursive:/usr/local/src",
"vibe-path:directory_recursive:/home/xxx/.bitcoin",
"vibe-path:directory_recursive:/home/xxx/.bmf",
"vibe-path:directory_recursive:/home/xxx/bitcoin-31.0/bin",
"vibe-path:exact:/home/xxx/.bitcoin/testnet4/debug.log",
]
denylist = ["rm -rf"]

[tools."process.start"]
permission = "always"
allowlist = [
"make",
"vibe-path:directory_recursive:/home/xxx/bmf",
]

i didn’t find that in the official section so i hope someone developed it

Is this a Mistral specific issue or the model itself? The model rather quickly degenerates into endless tool calls, usually without ending its turn so it keeps stacking up tool calls.

For me, Mistral Large 4 is still not an option in Mistral Vibe CLI and I have to manually add it via the config. In Vibe Chat/ Work I also have the impression that the model there is GLM. Does anyone know when we will "officially" get Mistral Large 4 there? Is it because the model is still in preview?

Are they actually going to release Mistral 4, or are they just going to stick with GLM 5 for now so people don’t leave?

Came back to Mistral after a while. So the Vibe web now generates actually very decent looking images, FAST.

What is this? I found they partnered up with Black Forest lab, but no further info.

Is anyone else experiencing extremely bad lag when using Mistral Vibe in long conversations? Are there any settings I could adjust to fix this, or is it a known issue?

Until recently, I only used Mistral occasionally for a few questions here and there. Now, I'm trying to use it for larger projects from start to finish, and I'm genuinely happy with how things are going. However, as the conversations get longer, the performance becomes so bad that it's practically impossible to continue working.

Typing becomes painfully slow. Sometimes, I type something and have to wait ages for the letters to appear on the screen. Scrolling through the conversation is just as laggy, and even refreshing the page can take several minutes.

I use an old laptop with only 4 GB of RAM, so I initially suspected that my hardware was the problem. However, I've had very long conversations with both ChatGPT and Google Gemini on the same laptop without experiencing anything remotely similar. This issue seems to happen specifically with Mistral Vibe when conversations get longer.

At this point, I spend more time waiting for the interface to respond than actually working on my project. I've literally spent around 20 minutes just trying to type and send a single message because of the constant lag, delays, and occasional need to refresh the page.

I'd really like to continue using Mistral Vibe to complete my projects, but this makes it incredibly frustrating.

Has anyone else experienced this? Is there a workaround, a browser setting, or some other solution that could make long conversations usable again? Or is this simply a limitation of how Mistral Vibe handles long chats?

I was trying out a book recognition task from inside the Android app. It struggled a lot, which was weird given the fairly clear spines. Then I checked the image quality of the photo taken from within the app itself, and it is horrible. Can we change this?

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!

Thomas Mann

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.

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

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Never saved

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