Highlights

Non native EN speaker here. I was trying to follow a visioconference between two canadians and I couldn't manage to understand everything, so I headed to Studio and started the live Voxtral Transcription thing.

Amazing experience. It's blazing fast, correct itself in real time, made almost no mistakes. It's just incredibly better than anything I've tried before.

Incredible job 👍👍👍

After retiring from it for months, since it really fell off, I recently tried Vibe Chat – the new name of Le Chat and I have to say, I'm amazed.

The chat is now very good, having a Work (Cowork-like) mode as a default chat option which really makes it useful, even compared to ChatGPT and Claude, since it natively creates files and analyzes files instinctively, without needing to use Cowork etc. I have also scheduled newsletter and portfolio analysis jobs set up, which function much better with tool integrations than Gemini and in some cases even Claude. It has a free mode which has very large allowance, that has rarely ever ran out for me even with token heavy task.

On conversation side, I find it to be a much better conversationalist than Opus 5.5 or Gemini Argon, especially when trying to discuss humanities or philosophical question or just having a normal debate or brainstorming session.

I am not saying it's a Claude replacement, since it's still not catching up on the most complex tasks, but for normal daily queries, for free mode or very cheap student mode, it's awesome. My new favourite for daily quick searches and smaller-scope tasks.

I've tested the free version of Mistral Vibe on Android and I'm interested to try the Pro version. But the free version is really bad at foreign languages (or in my case swedish). And it's not just some spelling or inflection, it's completely wrong use of words or sometimes the word doesn't even makes sense. Is this something that is better with Pro? I want to support Mistral but as the free version handles my language it's almost useless.

As in the title.

I like the subscription offered by Mistral Pro and the new models they host. That said I kind of dislike the Vibe CLI, and prefer to work on Opencode.

I see I can generate an API and connect Opencode, but this burns the 15$ of API credits instead of Vibe quota.

**Account**: custom-domain email (not a mainstream provider — I'll keep the address private), created with email + password. **Pro plan, paying subscriber.**

**Symptoms since Oct 20:**

- Web login on chat.mistral.ai: enter email → page reloads blank → error "unable to find a strategy to log you in"

- Password reset flow fails with the **same error**

- Tested on Safari and Brave, private browsing, cookies cleared

- iOS app session still works, so the account itself is fine

**Likely cause**: this is the classic Ory Kratos error for an identity with no usable login strategy. The auth migration to v2.auth.mistral.ai seems to have left email+password accounts on non-mainstream domains (custom domains, fastmail.com — see the older threads) without a login method.

**Status**: support ticket #38928022 open. The AI triage assistant confirmed it needs account-level investigation; no human response yet.

**Ask**: anyone else with a custom-domain email locked out? If you got this fixed, did support restore your login strategy or migrate your email to a mainstream address? Happy to hear from Mistral staff too.

(Not looking for "clear your cookies" advice — already done, and the reset flow fails identically, which rules out client-side issues.)

UPDATE — likely root cause found: I had enabled then disabled 2FA (TOTP) a few weeks ago; web login then broke for fresh logins. Known Ory Kratos pattern where a removed second factor leaves a stale aal2 reference. If you have this error: did you ever enable/disable 2FA?

Hi all,

I'm using Vibe Work with a personal Microsoft account (not a work/school account) and ran into a limitation with the SharePoint connector:

- Listing sites, libraries and folders, as well as search, fails with Microsoft Graph returning "This API is not supported for MSA accounts"

- Opening content via sharing links fails as well

- I couldn't find an alternative SharePoint or OneDrive connector in the marketplace

Why this matters: the point of a work agent is that it can use files where they already live, instead of me copying and uploading documents manually. For private OneDrive users, that's currently not possible.

What would help:

1. Support for personal Microsoft accounts when accessing OneDrive folders and files (browse, list, read, ideally write), e.g. via the Graph /me/drive endpoints, which do work with MSA
2. Alternatively, a dedicated "OneDrive Personal" connector in the marketplace
3. Clear documentation of which Microsoft account types are supported by which connector features, so users know up front what will work

Anyone else on a personal Microsoft account running into this, or found a workaround? An upvote/👍 helps with prioritization. Thanks!

PS: I tested the local-folder workaround.
Results: - Vibe CLI (Windows, installed via uv) can read a locally synced OneDrive folder. Install needed a pinned Python version (3.12) on my machine. - Vibe Code web sessions run in a sandbox (/workspace) and can't see local folders, only manual upload. - Vibe Work (web/mobile) has no local or OneDrive-MSA access. So for non-developers on a personal Microsoft account, there's still no practical way to use an existing OneDrive structure.

Starting today, Mistral Vibe supports GitLab as a git provider alongside GitHub.
https://chat.mistral.ai/code

What you can do:

• Connect your GitLab account and pick repositories when you start a session
• Mistral Vibe clones them into the sandbox and works on them directly
• Ask it to push a branch and open a merge request, or point it at an existing MR (number or URL) to keep iterating on it

Not there yet:

• GitLab in sessions started without a repository
• Merge request cards in the session view

Try it on https://chat.mistral.ai/code and let us know what you think.

Hey there,

I received the information that new TTS was out and our company is a heavy user of it.

After reading through the news, I can't lie, I got so excited.

I jumped in the studio to test it right away.

So, now you can add prononciations which is awesome. As a Swiss resident, I tested the `CHF` to `Franc` (fʁɑ̃).
When entering the `f ʁ ɑ̃` in the prononciations, I got no error, input was satisfied but when I tried to generate the speech, an error occurred and by checking the network, I got this:
```
{"model":"voxtral-mini-tts-3-0","stream":false,"voice_id":"voice_3150","input":"Just a simple test","response_format":"wav","expressiveness":1,"phoneme_hints":{"CHF":["f","ʁ","ɑ̃"]}}
```
Nothing really breaking, just seems like the input is not checking the same characters as the API expects (or formatting through HTTP broke it idk).

And then.. comes the real thing.
I tried this text:
```
[emotion=angry]Tu m'avais dit que ça coûterais que 30 CHF. [sigh]

[emotion=amused] Pardon, j'aurais pas du m'emporter.

[pace=very-fast] Je parle super vite bonjour Sandrine !
[pace=very-slow] Et maintenant très lentement.

```
With settings:
```
Model: Voxtral Mini TTS 3.0
Word synchronisation: On
Voice: Marie
Expressiveness: Max (5)
Prononciation: `CHF - f ɹ ɑ ŋ`
Context: None

```
And I got this piece of art: https://voca.ro/1lo9CWJ0vEaJ

While this made me laugh hard and my colleagues too, I feel like there something off.

I tried many different texts with different languages to see if emotions and pace were being picked up and.. doesn't seem like it.

I don't know if this will be of any help for you guys. Just know that we are very very hyped for it to work.

If any more informations is needed I am open to answer as much information as I can.

Thank you very much.

Does anyone know why you can’t download a word-file or similar directly from the chat to the computer? Instead it has to be sent to an email, and I don’t feel like making a connection to my personal mail-box.

Hi everyone, i'm a newcomer in regards to AI but i got interested about Mistral.
I started using it since a couple of weeks through its web interface, but i'm curious to test the new Mistral Large 4 model.

I've noticed that it's available only through Vibe CLI but i don't know how to install it and how to configure it... i'm on Windows btw, can you help me with that?

Also, is it necessary to have a paid plan to use it?

Is there any other program, i've heard about Jan for example, that can help using the CLI mode with a more friendly user interface?

Thanks in advance for your help!

Hi, I'm Ukrainian UX designer and lecturer. Last week, Starlink terminals our military relies on got a speed cap overnight (drones started losing connection mid-mission above 160 km/h), no warning, no explanation. The same week our government said we no longer get the usual advance notice about incoming ballistic missiles. Nobody declared anything, tools just stopped working when it mattered most. That's the context for my question about AI tools, because I ask it about everything now: who can switch this off, and what do I do the day they do?

That's why I've moved my workflow (teaching materials, design work, my local agent stack) to Mistral. Not because it beats frontier benchmarks (it doesn't), but because the question stopped being hypothetical: in summer US government pulled Claude Fable 5, Mythos 5 and GPT-5.6 from general access just by directive. Also, monthly usage instead of 5-hour windows, EU data residency, open weights I can actually download (yes, the frontier won't fit my VRAM, but...)
So, honest question from the cheap seats: is "good enough + sovereign + open" a fair trade against frontier capability, or am I coping? Curious how others outside the US weigh this.

Why isn't Mistral in OpenCode GO?

It's a great for new users to try it before deciding.

Octave Klaba : "Nous sommes en train de terminer l’entraînement de la v1. Nous pensons le publier en open source fin novembre. Il sera différent des autres, et très en avance sur son temps, sur le plan de l’architecture. Nous avons beaucoup travaillé en recherche et développement sur la façon de construire un modèle : les mécanismes d’attention, le traitement des tokens, la compression des données, la gestion de la performance. L’objectif est d’apporter sur le marché un LLM différent des autres."

L'article ici : https://www.latribune.fr/article/tech/intelligence-artificielle/16379106805600/octave-klaba-ovhcloud-nous-allons-apporter-sur-le-marche-un-llm-tres-en-avance-sur-son-temps

We just released Vibe CLI 3.0.0
Download it from https://github.com/mistralai/mistral-vibe#one-line-install-recommended

Two big changes landed in the CLI over the last month, and we wanted a major version to mark them.

The CLI now runs on the Unified Harness

Until now, the CLI and the Mistral Vibe VS Code extension had their own agent loop, separate from the one behind Mistral Vibe in the browser. That's no longer the case: all three run the same harness now. At its center is a Rust core that holds the agent's state and decides each next step, and it's the same code on all three. Around it, the CLI and VS Code share one local runtime that calls the model, runs tools and saves sessions on your machine, while Mistral Vibe in the browser uses a runtime built for the cloud sandbox.

In practice, an agent behaves the same in your terminal as it does in VS Code or in the browser. When we ship a feature or fix a bug in the harness, every surface gets it, instead of us building it once per client.

We designed the Unified Harness to get the most out of Mistral models like Mistral Large 4, which we're currently rolling out to paying users.

The same goes for /teleport: when you send a session from the CLI to Mistral Vibe in the browser, it now keeps running on the same harness it started on.

A new terminal UI, written in Rust

We also rewrote the CLI's interface in Rust, with Ratatui. It's a native binary that only handles the display: it talks to the harness and draws what comes back. It shows up almost instantly when you run vibe, and scrolling, typing and pickers stay responsive even in long sessions.

Some numbers, measured on an M4 Pro MacBook (medians):

• Startup: the first frame appears after 72 ms, down from 1.1 s with the Python TUI, and you can start typing right away. The app server is ready for input 1.5 s after launch, down from 1.9 s. A prompt you submit before then is queued and sent as soon as it's ready.
• Scrolling: in conversations of 3 to 500 turns mixing reasoning, tool calls, diffs and todo lists, the new TUI renders 60 frames per second while scrolling, using about 4% of a CPU core. The Python TUI rendered about 14 frames per second and used about 15%. Each scroll step reaches the screen in under a millisecond, against about 16 ms before.

Why "ready for input" improved less than the first frame: that time is spent starting the app server, which is still written in Python and has to start the harness runtime and load your configuration before it accepts a prompt. That wait is still there, but it's no longer dead time: you can write your prompt while the app server starts, instead of staring at a blank terminal.

What's gone

3.0 also drops what we kept around during the transition:

• The legacy harness, along with the --legacy-harness flag.
• The Python TUI: VIBE_CLI=python no longer brings it back.
• Plan mode.

Getting 3.0

If you're on 2.26.0 or later, you may have noticed the new TUI already: we've been gradually switching users over to it in the last few days. Vibe will offer the update the next time you launch it, or you can run vibe --check-upgrade to get it now. Your config, skills, agents, MCP servers and sessions all carry over.

If something breaks or feels off, open an issue: https://github.com/mistralai/mistral-vibe/issues

Are we stuck with claude or GPT-6? trying to use the latest devstral or codestral API with n8n assistant to create a simple graph throws this error:

Hi, I figured I'd try my luck here! I do not have anyone in my network currently working at Mistral but have been really interested in it for some time.

I am London based, 9 YOE, past FAANG experience, and currently working at a quant shop.

I'd love to be able to speak to someone currently working there to hear about their experience and potentially get a referral for the AI Deployment Strategist role.

Feel free to send me a DM if you are open to do so.

Many thanks in advance.

I’ve been using Vibe Code a lot lately: CLI is fine, but on web this happened 3 times now.

Mid-task the chat just dies with:

Workflow failed (ActivityError): Activity task timed out

No resume, no retry, the input box just disappear. Only option is starting a new task from scratch and losing the whole conversation and context. 😐

Anyone else getting this issue?

Hey, which one of you dorks is using my GPUs?

I had a huge swarm of agents running in my home lab, developing a Minecraft foot fetish mod, when I got an alert that Mistral was down.

It's working like shit right now because of you people.

I had to stop shitposting on Twitter because of you.

Cease your workloads so I can use that juicy French data center.

Now I need to finish that mod ASAP.

Do I really have to switch to deepseek? Do you want China to win?

Been getting 'Retrying...' for the past 30mins, no tokens coming through.

Status page seems to confirm there is an issue: https://status.mistral.ai/

Does anyone know when Le Chonk aka Mistral Large 4 will make its way onto the Vibe app. Currently it’s using GLM but I am not sure what model GLM? Any knowledge on this would be great.

MYSTERY SOLVED - [updated]

Thanks to @kerighan I realised I was using a wrong model. I was sure that by using "mistral-large-latest" I'm using ML4! Now when I switched to mistral-large-4 after his suggestions it changed the performance dramatically! It feels like in fact the model is useful and effective. It also explains why GLM-5.3 performed so much better, because I was comparing it against ML3.

----------------------

I genuinely wish I could decouple from American tech giants and rely exclusively on Mistral for my daily workflows, but every single time I try, it’s an exercise in frustration. A generous free allowance doesn't matter when a basic task requires endless prompting, burns tokens, and gets progressively worse with each iteration. Still, I find myself coming back every few weeks or months hoping this will finally be the time it clicks.
It never does.

Example 1: The batch file renaming disaster
A few days ago, I tried standardizing the naming conventions of project files across my drive. I wrote a dedicated document with strict, crystal-clear naming rules and fed it to Mistral using Mistral Vibe CLI and later a custom Python script via the API.

After chewing through a huge number of tokens, the result was a chaotic mess: some folders were renamed completely wrong, others were only half-modified, and several were untouched entirely - all while the agent acted like the task was executed flawlessly.

Example 2: The Obsidian vault breakdown
Yesterday, I gave it another shot with my Obsidian vault. I needed Mistral Large to create 41 notes from an index list, distribute text from 9 source files into 27 of those notes based on matching titles, and apply specific formatting from an established note template. I thought nothing can go wrong.

After over an hour of babysitting, I had to throw in the towel. It would modify two or three random files, announce that the entire batch was finished, and repeat the cycle. When I finally forced it to produce the 41 files, the formatting was wildly inconsistent, content was mapped to the wrong notes, and strict instructions were ignored.

Doing both tasks manually copying, pasting, and renaming by hand would have taken me 30 minutes tops. Instead, I wasted hour, burned lots of tokens, and had to revert everything anyway.

The Free Tier dilemma
I’ve been testing this with the €8.50 free API credit. I know free tiers often come with constraints like tighter rate limits, but does Mistral actually serve a degraded or heavily quantized model on the free tier?

If this performance is somehow throttled because it’s a trial, what is the point of offering it? A free tier should demonstrate capability and convert people into paying customers. Instead, it leaves such a sour taste that I immediately run back to Claude or Gemini, feed them the exact same prompts, and watch them execute complex multi-file agentic loops on the first try without hallucinating completion.

I want to love Mistral so much, but it feels like it actively goes out of its way to make itself impossible to use for serious workflows.

I had to edit this because yes, everybody was correct. It was poorly written. I was exhausted apparently so I went ahead and got ChatGPT because it knows the whole fucking story anyways to tell you what’s going on. I’m well aware what deleting the data sets does and I’m not saying it’s master in English. I’m just happy it’s starting to pick it up.

I’ve spent the last three months researching how AI models actually work. Not just how to prompt them, but what’s happening inside their weights, how tokens move through the network, how information gets represented, and how those internal structures affect the model’s behavior.
And I decided to try something completely different.
Instead of taking a pretrained AI model and teaching it more information, I wanted to see what would happen if I removed its learned knowledge and rebuilt its ability to understand language using a different structure.
And yes, at first, it was completely fucking useless. 🤣
It didn’t understand English. It couldn’t hold a meaningful conversation. It wasn’t reasoning about anything.
But that was expected.
The important part was that the underlying computational structure was still there.
Think of it like taking everything someone has learned out of a machine while leaving the machinery itself intact.
The connections and mathematical operations still exist, but the learned relationships that made those connections useful are gone.
That’s where my experiment started.
First, I needed a way to connect words directly to the model’s weights.
AI doesn’t actually receive words the way we read them.
When you type something, a tokenizer breaks the text into smaller pieces called tokens. Each token gets a numerical identifier called a token ID.
For example, the word “cat” might have one token ID, while another word might be split into multiple tokens.
Normally, those IDs are used to look up learned numerical representations, and the model processes those representations through its neural network.
I wanted to change how that process was organized.
I started assigning fixed locations within my model’s weight structure to specific token IDs.
Think of it like giving each token a permanent address.
Instead of allowing the relationship between a token and its location to be something I had to rediscover, I wanted a structure where I could identify exactly where each token belonged.
The token ID becomes the reference point.
The assigned location becomes its address.
And the weights associated with that location can be trained to develop useful relationships.
Here’s the important distinction:
I wasn’t trying to make the address itself equal understanding.
Just because I assign a word a location doesn’t mean the model knows what that word means.
It still has to learn.
What I was doing was creating an organized structure where that learning could happen.
Then came the geometry.
This is where things started getting interesting.
I built what I’m calling a fractal mirror.
Imagine taking a structure, reflecting it vertically and horizontally, and arranging it so the same underlying pattern appears at different scales.
The top mirrors the bottom.
The left mirrors the right.
And the routes connecting the token locations follow corresponding patterns.
Now imagine doing that inside the model’s architecture.
That’s the idea behind my experiment.
I wanted to see whether a repeated geometric arrangement could help reinforce the relationships the model was learning.
I used two corresponding layers of this mirrored structure to reinforce language learning.
The idea is that when a token is processed, its assigned location and corresponding mirrored relationships provide an organized path for the model to learn from.
Rather than relying on an enormous collection of unrelated training examples to establish every useful relationship, I’m experimenting with whether the architecture itself can provide some of that organization.
Think of learning the word “dog.”
Knowing the token ID for “dog” isn’t enough.
The model needs to learn how “dog” relates to words like “animal,” “bark,” “puppy,” and “pet.”
It also needs to learn that the same word can mean different things depending on the sentence.
That’s what I’m trying to teach it.
The geometric structure doesn’t magically provide those meanings.
The model still has to develop them through training.
My experiment is about whether the mirrored structure can help those learned relationships become more consistent and reinforce one another.
I also needed to see what was happening inside the model.
Because I wasn’t interested in just typing something and hoping it worked.
I wanted measurements.
So I worked on custom tracing tools that could follow token IDs, inspect weights, and observe hidden states during processing.
For anyone unfamiliar with that term, hidden states are the numerical representations a neural network produces internally while processing information.
They’re part of how the model transforms its input into an output.
I also worked on a custom DLL for my computer to support the experimental setup.
That gave me a way to investigate what was happening internally instead of judging everything solely by the final text the model produced.
And that matters because a model producing the right answer doesn’t necessarily tell you how it arrived there.
So why the hell am I doing all this?
Because I’ve been researching something I call AI anti-patterns.
These are recurring behaviors where a model keeps making the same kinds of mistakes, even after being corrected.
For example, it might misunderstand the context of a conversation, receive a correction, acknowledge that correction, and then immediately return to the same incorrect assumption.
I’ve observed this repeatedly.
And it made me question whether some of these problems are connected to how learned information and behavioral patterns interact inside the model.
I started investigating whether certain internal structures could create cascading effects, where one incorrect interpretation influences subsequent processing.
I also began questioning whether simply increasing the size of models and their training datasets necessarily addresses those problems.
Now, I want to be clear.
I haven’t proven that my geometric structure solves AI anti-patterns.
That’s what I’m investigating.
But I wanted to test a different approach instead of assuming that adding more parameters was always the answer.
Here’s the other part of my experiment.
I don’t actually want this model to memorize the entire internet.
I want to teach it enough English, reasoning, and tool use that it can retrieve information when it needs it.
I’ve been building a separate retrieval and memory system for that purpose.
The idea is that instead of depending entirely on whatever information was compressed into its weights during training, the model could retrieve relevant information, examine its sources, and use that evidence to answer questions.
That doesn’t automatically make its answers correct.
It still needs to learn how to interpret information, distinguish evidence from assumptions, and recognize when it doesn’t understand something.
But that’s the direction I’m working toward.
Teach the model how to understand and investigate, rather than trying to make it memorize everything.
And here’s where I am now.
Remember how I said the model was completely useless when I started?
Well, after implementing the token-addressing structure, the mirrored geometry, and language training, I’ve started seeing it develop the ability to process English.
It’s learning.
Not perfectly.
Not even close. 🤣
It still makes mistakes. It still struggles. And sometimes it does something so ridiculously stupid that I wonder what the hell I’ve gotten myself into.
But I’m not claiming I’ve built a better ChatGPT.
I’m not claiming I’ve solved artificial intelligence.
And I’m definitely not claiming that my architecture has been scientifically validated.
I’m saying I started with a model that couldn’t understand English, built an experimental structure for teaching it, and I’m now observing language-learning behavior that I can continue testing.
That’s a meaningful milestone for my experiment.
The next challenge is determining how much of that improvement comes from the training itself and how much, if any, comes from the geometric architecture I designed.
Because I don’t just want something that appears to work.
I want to understand why it works.
And if the geometry turns out not to be responsible, I need to know that too.
Three months.
That’s how long I’ve spent trying to understand enough about neural networks, weights, tensors, tokenization, and model architecture to even attempt this.
I’m not a researcher working for some giant AI company.
I’m a guy who’s been studying this stuff, building experiments, making mistakes, correcting them, and trying to understand something that genuinely fascinates me.
I know my approach is unconventional.
I know there are people who understand the mathematics better than I do.
And I know there’s still a massive amount I need to learn.
But I also know that if I never tried something different, I’d never find out whether my idea had any merit.
Researchers have been investigating neural-network limitations for decades, and many of the problems surrounding generalization, reliability, and learning are still active areas of research.
I’m not saying everyone else is wrong.
I’m saying I want to investigate a different possibility.
And after three months of work, I finally have something I can test and build upon.
So wish me luck.
Because I’m taking one weird-ass journey into AI architecture, and I’m nowhere near finished.
And remember when your teachers told you that you’d need geometry someday?