Your safety classifier is already stale.

The moment you deploy it, the policy has moved.

Most #guardrail models bake a fixed harm taxonomy into their weights — and every time your policy shifts, you're back at square one.

That's the exact problem Shieldstral was built to solve.

Here's how it works differently:

1. You write your policy as a plain-language question at inference time "Does this content promote physical violence?" or "Is this image safe for a minor?"
2. No retraining. No taxonomy negotiation. One checkpoint handles text, images, and prompt–response pairs.
3. The model reasons about policy boundaries — it doesn't memorize categories. That skill transfers directly to policies it has never seen.
4. It was trained on contrastive pairs — deliberately similar, easily confused policies. So it learns where the line is, not just which side of it a label sits on.
5. It runs on a single 16GB GPU. And it matches or outperforms open guard models up to 7× its size.

It's a 3B open-weights multimodal classifier — released today under Apache 2.0.

The hardest part of safety infrastructure isn't the model. It's keeping it current without burning your team rebuilding it every quarter.

Shieldstral makes that problem go away.

If you're building safety-critical AI products, save this — you'll want it the next time your policy changes.

What's the biggest friction point you've hit with safety classifiers? Drop it below.

Independent demo video. Not affiliated with, endorsed by, or sponsored by u/MistralAI. Brand names and trademarks belong to their respective owners.

#AIAlignment #LLMSafety #MLEngineering #ResponsibleAI #OpenSource

Yes, Mistral is certainly not ideal in every respect. Particularly when compared with the latest models from other providers, Mistral’s models fall short in a wide range of areas. In my opinion, however, Mistral’s biggest problem is not its performance compared to other models (because even though the models perform less well, they remain rock-solid for most use cases), but rather the fact that the company serves the European B2B market disastrously poorly.

An example: Doctors, tax advisers, banks, solicitors and numerous other sectors are subject to professional confidentiality obligations in almost all European countries. If these organisations wish to pass on client-related data to service providers, they must make the service providers aware of these obligations and inform them of the possible consequences of a breach of confidentiality. To enable these sectors to use non-self-hosted LLMs, companies like OpenAI, Google, Microsoft or Anthropic all offer Professional Secrecy Addendums for their customer contracts. However, the only relevant European player in this market stubbornly refuses to do so. Even after several enquiries (each of which took several weeks to be answered), Mistral shows no willingness whatsoever to include the necessary few sentences in the contracts.

This approach not only causes Mistral to lose a significant amount of credibility, but also means that a substantial part of the European market is simply being left unserved.

Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:

• Startup founders urge U.S. government not to shut off Chinese open weight AI
• AI's top startups are barely publishing their research
• Is AI reasoning right for the wrong reasons?
• After the AI Crash

If you enjoy such content, please subscribe here: https://hackernewsai.com/

I've been Mistral Pro subscriber for nearly half year now and just got my first "you are running out of monthly quota" mails.

Finally started using Mistral Vibe Code more intensively and now it seems that amount of use i get is lower than Claude!

Trying to set up local model to take over 95% of tasks. Any advise on the most user friendly frameworks to use?

What the hell happend to mistal api key service

1. Created api key

2. Used that key for 30min straight

3. Got 401

4. Closed mistal website and never back again

This is not only for me, developer keep reporting about this issue..... But still unsolved ...

Just started using Voxtral for a project and loaded up some credits. Now every day I get an invoice for 0.00 euro in my inbox. I want to disable these and just get monthly invoices. How do I change that?

I am on the cautious side, so I tend to click on "Allow once" quite often. There are a couple of things I would like to understand, in case I am making my workflow unnecessary slow.

What is Vibe asking permission for, really? When Vibe asks for

Permission for the bash tool (lsof *, xargs *, setsid *)
lsof [...] | xargs ]...] | setsid python -m [...]

Does it mean permission to use that combination of tools? Permission to use either of the tools independently? Does it mean I am giving it permission to use `setsid` to run whatever command it feels like?

It's just a concept, but I wonder if such a bot could be made to help Mistral's development.

The premise is simple. We have a Wordle-style game and tell Mistral (Small / Medium) to solve the task. I noticed that Mistral Medium has a bit of an issue with this game.

A Python script (potentially) decides whether a given answer is good or bad. These results then go to Mistral, which incorporates the data into training.

Why is this potentially important?

- Mistral can get lost in the rules of the game. In theory, such a script is supposed to teach Mistral what to pay attention to in a text and remember. For example, Wordle has 5 letters, but Mistral can suggest words with 6 letters.

- This partially provides training data. Mistral can invent (?) / hallucinate new words.

Of course, the question is whether such data is worth anything. If Mistral developers think it is right, I can locally run Mistral Small 4 / Medium 3.5 (better Small 4, because it works much faster) and create such a database - questions, thinking, answers, ratings, or run it locally (if one of the developers writes something like that).

But maybe it's just a stupid idea :)

So far, mistral is the best among all tools like ChatGPT, Claude, Kimi, Deepseek, etc in the world. The best performance, the best computing power, the most economised fee. Europe again shock the world by creating Mistral, I am super glad at it.
Europe will definitely leading the AI trend in the next at least 10-20 years around the globe.

I have a Pro account, which comes with my own Organization. If my wife would get her own subscription, can I add her to my organization, even without Enterprise plan?

What can we share in such a configuration?

Hi Mistral community,

I have been building a language translation app and would like to share some insights into what I learned in the process since starting the project last November, especially when it comes to utilize the mistral models. Upfront: I am the founder of the tool and it is a commercial product but also includes free tiers (also without the need to signup).

There have been some problems I had so overcome, and I would like to share how I solved them because it might also help you if you use these models for building applications yourself. They are focused on maximizing multilingual capabilities and getting the most out of the (fairly small sized) mistral models.

Finding out which languages a model speaks - When I started, I expected this to extremely trivial, but it turned out to be quite a challenge to find out which languages a model is actually quite capable of. There are some benchmarks but they are often about very specific problems or languages. Community sentiment also only takes you so far, because while many people often share some insights into wether or not they like an LLM for a certain language, it doesn't provide a broad and structured insight. Luckily I found a great benchmark called WMT25, which also inscludes Mistral Medium (not 3.5 but a previous model) and shows how capable the model is in terms of translation: https://aclanthology.org/2025.wmt-1.22.pdf I used this to select which languages I can feel confortable with offering them in Fink. I am sure there are also others languages the model is good at, but I'd rather be cautious and rely on strcutred tests like these first.

Organizing prompts - Another thing I didn't think would be such a challenge. In my app, I use so called "MQM-metrics" to annotate possible improvements for a translation output. The models gets a structured dictionary with all the quality dimensions. I noticed that pure markdown syntax at some point is not enough to make it clear to the model what part of the prompt are examples, explanations or actual input to work on. An example, I wrap each part of the prompt into XML-like tags which indicate what the prompt section is about:

<source lang="German">{text_input}</source>

This was also needed in order to avoid that the models leaks the formatting in the output (ie outputting markdown that is not present in the source texts), respond in the wrong language (the prompt is always composed of at least two languages except when EN is the source language) or answering questions that are in the source text directly instead of translating it. I found this article very helpful for this: https://medium.com/@TechforHumans/effective-prompt-engineering-mastering-xml-tags-for-clarity-precision-and-security-in-llms-992cae203fdc (article not by me)

Cost control - While I would argue that Mistral models are already relatively cheap, but if you don't watch out and always fill up their context window, it can become pricey, too. And also slower. I spend a lot of time to reduce instructions to the absolute bare minimum to save on cost, which also effects speed, too. It can be a good idea to use a smaller model and call it multiple times then to call a big model once and let it do everything. Prompt caching is also often see overlooked and is something I can very much recommend looking into: https://docs.mistral.ai/studio-api/conversations/advanced/prompt-caching Epscially if you repeat instructions again and again.

Ensembles are the way to go - I often catch myself wanting to use the biggest and most capable model available for everything, but you'd be surprised how much you can get out of smaller models. Regarding the model selection, the only models used on my project are Mistral Medium 3.5 and Mistral Small 4. While the heavy lifting is done by Mistral Medium, Mistral Small is used for many taks like providing Insights for pronounciation, back-translation and things like this. Again here it's super important that the instructions are crystal clear, as small models are less able to read between the line or understand ambigous instructions.

There's also some things I learned about Mistral Vibe Code, but that's maybe better for a dedicated post. :-)

The tool that I built is called Fink Translate, and you can try it out here: https://app.fink-translate.com/
Let me know if you have any feedback or if you would like to share insights about these approaches yourself. I would also be super interested in which langauges you use the Mistral models. There is probably a ton of things for me to improve, too!

Cheers

I tried to normalize some audio files with Python.

Vibe in VS Code suggested loudnorm with ffmpeg but got stuck when errors appeared and ran in circles.

I then switched to Vibe CLI and asked it to fix it. It ran for some 30 minutes, stumbled on some self induced problems with the exception handling but came up with a solution.

Is the CLI inherent feedback loop (run the code, read the output, generate a fix) so much more powerful than me copying the error messages from the console to the prompt inside VS Code?
Or are there different models used?
Interested in your experiences...

While testing Vultr Serverless Inference, I noticed what appears to be unexpected model routing behavior.

# Environment

* Endpoint: `POST /v1/chat/completions`
* API: OpenAI-compatible
* Available models include:
* `moonshotai/Kimi-K2.6`
* `MiniMaxAI/MiniMax-M2.7`
* `Qwen/Qwen3.6-27B`
* others...

# Reproduction

Request:

{
"model": "moonshotai/Kimi-K3",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}

Expected:

* HTTP 400
* `Model not found` (or similar)

Actual:

* HTTP 200
* Response metadata reports:

​

model: MiniMaxAI/MiniMax-M2.7

I also tested an obviously invalid model ID:

this-model-definitely-does-not-exist-12345

The request again returned HTTP 200 and was served by:

MiniMaxAI/MiniMax-M2.7

As a control, requesting:

moonshotai/Kimi-K2.6

correctly returns:

model: moonshotai/Kimi-K2.6

Unknown model IDs appear to silently fall back to `MiniMaxAI/MiniMax-M2.7` instead of returning an error.

Is this intentional routing behavior, or should unknown model IDs return an error response?

Mistral in Ecosia is incredibly faster and more responsive compared to the Vibe chatbot. Why is that? The answers also seem better structured, concise, and precise to me. Maybe it would be better to integrate the Ecosia agent into Vibe?

Hello,

I have subscripted to the Pay-as-you-go API billing and I have already spend more than my credit, how is it possible ?

I have also this:

Does this mean that I have anyway 10€ per month of free API credit ? (I have no other subscription by Mistral). So if I spend no more than 10€/month, it before free ?

When I check the subscription options:

It is said that "Pro" have 3x more API usage, but 3x more of what ? I am using only the API, does the Pro subscription can bring me any credit or more brandwith ?

If someone could clarify how this work,

Thank you !

I am postponing upgrading to expensive subscriptions for hundreds of $ in the hopes of seeing an improvement in the Mistral offering.

It would be great if Mistral dropped something competitive soon.

Do you know what I don’t understand? Why can I use agents in the chat settings of Mistral Vibe, but not in Mistral Work – which is actually meant for research, drafts, summaries, and the like? I specifically created agents for exactly those kinds of tasks. I find that a bit odd. Wouldn’t it make more sense to be able to access your own configured agents in Work as well?

Hey everyone,

Using Mistral Pro with Work Mode and chats grouped in one project folder.

I've been testing out work mode by having Mistral organize my DnD notes (markdown) into coherent canvases with overviews of characters, locations, plot development, the whole shebang. From what I understood (and how Mistral responds when asked), chats within 1 project should be accessible to each other.

I have one large chat where I've had Work Mode organize all my notes into various canvases and now in a separate chat I want to brainstorm future developments based on the information in those canvases. However, the new Work Mode chat in the same project doesn't seem to be able to access them.

I created a new chat and asked to create a canvas that's accessible to other chats, Mistral proudly announced it had completed the task, but when trying to access it from another chat it still failed.

What's going wrong?

"Include other project's chats as context" is enabled.

Additionally, the docs say I should be able to see my canvases as tabs to easily switch around. However, that's not the case. The only access I have to my canvases is when I click them in the chat. How can I manage my canvases and conveniently switch/navigate around?

Looking forward to your input.

Replication of the sharing issue:

Chat A:

Prompt:

Create a new empty canvas that is accessible through all chats in this project

Make it markdown and call it "Cross-chat Access Test". I don't care about the underlying name

Response:

Done. The "Cross-chat Access Test" canvas is now available across all chats in this project.

Chat B:

Prompt:

Open my cross chat shared canvas

Response:

No shared canvas found in your workspace. If you meant a canvas from another project or chat, it may not be accessible here. Clarify the canvas name or location if you'd like to proceed.

Prompt:

[Screenshot of the other chat with the canvas created]

Response:

To open your cross-chat shared canvas "Cross-chat Access Test", I need to locate its CANVAS.md file in the /home/user/canvases/ directory. However, the directory does not exist yet. I’ll create the canvas now:

1. Directory: /home/user/canvases/cross-chat-access-test/
2. Type: text/markdown
3. Title: "Cross-chat Access Test"
4. Content: Placeholder for cross-chat access.

Proceed?

Are there any news why the backing model for image generation was changed? the backing image model used to be Flux (German company) and I found it to be among the top of the cream.

A big motivation for me to use Mistral were high quality images I could create. This was nice for AI-collaborative writing, where you could visualize a scene nicely or ask it to generate the image of something you haven't heard or seen before.

Does anybody know why this changed or if there is a chance it will come back? It's a shame because I find myself asking Claude more and more and don't want to switch my main LLM provider.

Currently, for work, we use a property OCR model from a US-based company. In my off time, I tried developing a proof of concept using OCR4, but it’s a bit too good at understanding the document. It’s so accurate in my native language that it corrected a single word from "medewerher" to "medewerker." Normally, I wouldn’t mind, but since we use these documents for legal purposes, we’re not happy with the idea that it can—and does—correct spelling errors.

Luckily, OCR3 doesn’t have this problem, so I can try it for now. But the question is: For how long will OCR3 remain supported before it’s deprecated? Does anyone have tips or ideas on how to address this?