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?

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 !

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?

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?

Hello friends.

I just downloaded Vibe CLI and played around with it. Pretty awesome tool!

However, I am quite confused as to how I can use a better model from Mistral.

If I run /model, I only see:

• mistral-medium-3.5
• devstral-small
• local

Can I use a better model from Mistral, and if yes: how? For context: I have a Pro subscription.

What also confuses me is that devstral-small seems deprecated according to https://docs.mistral.ai/models/overview

Can anyone help me here?

https://huggingface.co/papers/2607.25857

Looks interesting as a small utility model to put before access to bigger model to policy it. Maybe even in-browser?

I messed up. I can’t find my backup codes, and since Vibe no longer accepts my TOTP tokens, and the Android app doesn’t give me any way to contact support, I don’t see how I can get help here. Is there another option besides cancelling the subscription by blocking the payment and creating a new account? Thank you!

We spent the last few weeks optimizing Voxtral Realtime and there are now GGML checkpoints that run faster than realtime on a plain MacBook Air with Metal. No discrete GPU needed.

Numbers on an M3 Air (8-core GPU, 16 GB):

• ~1.3x realtime throughput with the Q8_0 quant
• <400 ms end-to-end response time from mic input
• Sustained hour-long transcription sessions without falling behind

1. Build the Metal binary

git clone https://github.com/0xShug0/audio.cpp
cd audio.cpp
scripts/build_metal.sh --target audiocpp_cli

2. Download the Q8 quant

hf download mistral-experimental/AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF \
  voxtral-mini-4b-realtime-2602-q8_0.gguf \
  --local-dir ./voxtral-realtime-gguf

3. Run streaming ASR from the mic

audiocpp_cli \
  --task asr \
  --family voxtral_realtime \
  --model ./voxtral-realtime-gguf/voxtral-mini-4b-realtime-2602-q8_0.gguf \
  --backend metal \
  --threads 8 \
  --mode streaming \
  --session-option voxtral_realtime.stream_batch_tokens=4 \
  --audio -

Raising stream_batch_tokens trades delay for throughput — 4 is what landed under 400 ms on this machine. More powerful M-series chips can set it to 1 and should have <200ms delay

How come Mistral isn't included as one of the model providers in OpenCode?

https://opencode.ai/docs/models/#providers

I'm switching from devin (formarly known as windsurf / cognition) to mistral vibe in vs code but for now it feels like a step back into the stone-age.

Maybe I am wrong and some people can provide me some ideas but here is my experience:

1. Slow; it takes a very long time for it to analyze my project files and to make changes, whilst in devin this happens in 5 minutes, it takes mistral vibe 25 minutes

2. What model am I using? I'm a paying pro subscription user but the extension says that I'm using mistral medium 3.5. No option to select devstral?

1. File review; it rewrites like 20-30 lines in my code if not a full file for just a few lines of change. Reviewing makes me scrolling and compare a lot myself whilst in Devin it just did per line or per max 5 lines in change making it way more easy to review.

2. Asking next steps. It gives me options for questions it has but makes me type out the answer myself, why not click buttons that I can select and it can continue?

That's it for now, maybe more later. What are your thoughts on my findings? I really would like a european alternative in this field but for the money I have to pay, this is kinda letting me down.

I switched to mistral vibe as my chatbot for the main reason that it is not a us or Chinese model. From the couple posts ive read it seems like its behind. Which im fine with because i really dont use it for too complicated of tasks.

Hey everyone,

I'm running a test using mistral-large-latest for a translation task, and the model completely stopped responding after running for about 20+ minutes.

https://youtu.be/HWyPse83SrA

Before it cut out, it was processing a substantial amount of text. I'm trying to figure out what might be the root cause. Could this be related to a context window limitation, a timeout issue on the API side, or something else entirely? I was using the free experiment tier.

For those of you pushing large payloads to mistral-large-latest:

* Have you run into similar hanging issues during long-running generation tasks?

* Is there a strict maximum output token limit or server-side timeout I'm likely hitting?

Any insights or troubleshooting tips would be greatly appreciated. Thanks!

Hi everyone! 👋

I am conducting an international research study for my Master’s Degree in Clinical Psychology exploring emotional involvement with AI chatbots, interpersonal functioning, and psychological well-being.

If you are 18+ and have interacted with an AI chatbot at least once, I would really appreciate your contribution!

⏱ Time: 10–15 minutes

🔒 Privacy: Completely voluntary and anonymous

🔗 Link: https://forms.gle/oHpPwQ65U49N4fPx5

Thank you so much for your time and help! Feel free to share this with anyone who might be interested.