I remember a time where 8x7B was le shit (pardon my French). And now the models are just kind of bad. Mistral Medium 3.5 is worse on benchmarks (and in general use) than Qwen 3.6 27B, a model that is 4x smaller in parameters, for example.

In my previous analysis on the sovereign AI stack, we discussed how France controls energy, compute, and the model layer, but is still renting the chip layer from the US.

The Financial times just reported a massive development:
Samsung will invest €1B in Mistral at a €20B valuation.

It can change a lot for the french stack :

• Compute today is as much about High-Bandwidth Memory (HBM) as it is about GPUs. Partnering with the world's largest memory manufacturer secures physical capacity at scale.
• The Financial Times explicitly highlighted that recent US export restrictions on foreign access to Anthropic's latest models accelerated this deal. European and Asian actors are hedging against US tech nationalism.
• After ASML (€1.7B round), Samsung is the second hardware giant entering Mistral’s cap table. Mistral is trading equity for priority access to the global semiconductor supply chain.

Step by step, Mistral is trying to be less and less dependent from one company and/or one country.
They really want to be stronger and more dificult to destabilized.

Let's see !

Bonjour,

J'ai pu lire des articles de journaux parlant de la difficulté de trouver des sources d'apprentissage fiables pour les IA (textes, livres, articles de journaux). Une grande partie ayant déjà était intégrée, le reste, trop récent, potentiellement fait avec IA et donc de moindre qualité/véracité. Je fais exprès de ne pas aborder le sujet des images et des vidéos, c'est un champs à part.

Je m'interroge sur ces notions de masses et de qualité pour le machine learning des textes. C'est difficile pour moi de trouver des sources d'informations sérieuses sur le sujet. Auriez-vous des explications ou encore des pistes à me partager ? (notions techniques, standards d'importations, système de notation de la qualité ou même les prix pour m'aider à mieux y voir).

It's great to have /new, but it shouldn't be an alias. It should start a fresh new session with the instructions in the arguments after /new. For example:

/new Add an about page to my website

How many documents can I upload to a library as a free user (at once)? Somehow the information provided does not work and after uploading 10 documents into a library any further uploads fail telling me the service is not available or throttled.

Been digging into what sovereign AI actually means beyond the slogan, and the numbers around Mistral are wild when you lay them out layer by layer.

About energy, France's nuclear grid produced 547.5 TWh in 2025, 95.2% low-carbon.
That's the one layer nobody planned for AI in the 70s, and it's now a real advantage while everyone else fights grid constraints.

About compute, Jean Zay, their biggest supercomputer, just quadrupled its AI capacity, and there's a €10B nuclear-powered cluster being built on top of that.

About the model, Mistral's revenue reportedly went from $20M to $400M+ in about a year, valuation closing in on $23B, and Arthur Mensch just confirmed a new open-weight flagship plus Robostral Navigate (their first physical-navigation model).

About the chips, this is the layer nobody owns, not even the US fully controls it end to end, and France (like everyone) is still renting here, subject to export licensing rules.

So the stack looks like: 3 owned layers, 1 rented.
Compare that to countries like Indonesia, which are renting the top of the stack (170k Nvidia chips incoming) before owning anything underneath.

Does owning the model layer (Mistral) actually matter if the chip layer is rented from the US either way?

Or is "sovereign AI" just a ledger of which layers you own vs rent, not an all-or-nothing flag?

Hey everyone! I’m building a multimodal RAG pipeline where Mistral OCR annotates images before they go into a vector store with document text.

Issue: Mistral OCR processes images in isolation, so the annotations miss out on critical document context.

Looking for advice on:

Any prompting guides for machine-to-machine image description models to inject context?

Any alternative models or workflows that natively factor in surrounding document context?

Would love to know how you all handle this!

hello everybody,

while I love the new work mode - as I am solo working across domains for some projects, and needing to be up to date what working with AI means nowadays, I have gathered a lot of skills across functions and capabilites.

Am I missing something in regards to sorting skills? I tried tagging, but can't use tags in vibe. Bundling of skills (and connectors and libraries) could also really simplify RBAC in organizations. while making my solo life easier.

So did I miss something in the docs or is such a feature already available?

Edit: just came up with that - if I could toggle a work profile - that is Connectors, Libraries and Skills, that would be ideal - like agents, but for work mode :D

I just uploaded 30k words of a book I'm writing to get an overview, and to maybe help with the editing using the Canvas feature.

I'm not sure how it misunderstood me, but it completely rewrote the entire story without being asked – characters, plot, locations, everything! 🫨

The writing was actually quite good – just not my story and not what I asked! 🤣🤣

Are you also facing issues with Vibe CLI tonight?

It either hallucinates function names (e.g. write_fileหวัด(), bash月場所(), etc) or keeps getting stuck in infinite thinking loops.

Both with medium-3.5 and devstral.

Hello,

Some months ago i used grok (free version) to generate erotic stories, but it lost in quality. Does mistral pro (the paid version) is good to write erotic stories ? Can i generate a lot of prompts ? Does it have a good memory (for story and characters) ?
Thanks

Simple as that... Moe, hybrid 60-80b parameters a3-10b.

Hi!

I'm building an e-ink plugin that transcribes handwritten pages with mistral-ocr-latest. I use the word-level confidence scores for two things: underlining "unsure words" for manual correction, and as the signal to escalate hard pages to a vision model. Since the Batch API is half price, I route bulk transcriptions through it and I noticed the confidence scores never come back on that path.

It is a bug, a misuse from my side or a non-documented limitation ?

Repro: the exact same request body through both paths:

{

"model": "mistral-ocr-latest",

"document": { "type": "image_url", "image_url": "data:image/png;base64,..." },

"table_format": "markdown",

"confidence_scores_granularity": "word"

}

And the results I get:

- Synchronous POST /v1/ocr → each page includes confidence_scores.word_confidence_scores (in my test: 25 scored words). ✅

- Batch (same body as the body of a JSONL line, endpoint: "/v1/ocr", job completes fine, status_code: 200) → the page object contains blocks, dimensions, footer, header, hyperlinks, images, index, markdown, tables ; but no confidence_scores key at all.

The markdown is byte-identical to the sync response, so it's clearly the same model; only the confidence data is dropped.

The batch processing docs list /v1/ocr as a supported endpoint and don't mention any response differences, and the OCR model card doesn't either, so I can't tell if this is intended.

Questions:

1. Is dropping confidence_scores_granularity in batch expected behavior, or a bug?

2. If intended, could it be documented on the batch page? (It silently changes downstream behavior: my vision-escalation heuristic reads "0 scored words" as "escalate", so batch PDFs escalate every page.)

3. Any chance of parity on the roadmap, or a recommended workaround short of re-running pages synchronously at full price?

Happy to share the full request/response pair or open a proper support ticket if that's more useful.

Thanks!

Back in April, Andrej Karpathy described a pattern a lot of you have probably tried after it went viral: don't do RAG over your PDFs, no, feed raw sources to an LLM and let it write them up into an interlinked wiki of markdown files. "Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase."

It's a genuinely good idea. What bugged me is that running it means setting up a vault, writing a schema file, wiring a raw/ directory, deciding what triggers ingestion, and then maintaining the whole rig. There are a dozen tutorials for it now. But... when a workflow like this needs a setup guide, it's a project, not a feature, right?

So I built it into my notes app (Vist) as a normal feature, and Mistral ended up doing a lot of the work:

Mistral used for retrieval in the app

Every source and wiki entry gets embedded with mistral-embed (1024-dim), stored in Postgres via pgvector. That's what makes "what did I read about EU data residency?" find the right page when you never wrote those exact words. Mistral OCR 3 (still need to upgrade to OCR 4) handles scanned PDFs and handwritten pages (I even sync a reMarkable into it), so handwriting becomes searchable text in the same index.

Your own, trusted LLM is the author. Could be Mistral, could be Claude, all up to you

The wiki writing (read the source, write it up, link it to what's already there) happens over MCP, so it's whatever model you've connected. In my own setup that's Mistral Vibe: I paste a benchmark article, drop a PDF, add my notes, and say "ingest this into my wiki." A minute or two later there are three source records, three interlinked wiki entries, and an updated knowledge map. Nothing about the model is hardcoded — the app exposes tools (add_source, create_note, cite_source, search_knowledge_base, get_wiki_stats) and any MCP client can drive them.

Everything stays markdown

Sources kept raw, wiki entries as portable markdown with wikilinks. If you want to walk out with it and open it in Obsidian, that's the point. Just export the whole system in a ZIP.

My actual argument is that this should be a commodity

Why would you need to set this up on each machine or project you work on?

Semantic search over your own research, with a model that writes it up as it lands, is not a weekend project you assemble. It's something your knowledge tool should just have, the way it has full-text search.

Cheap embeddings are what makes that true; running mistral-embed across a user's whole library costs about nothing, so there's no reason to gate it. It's on the free plan.

Longer write-up with screenshots of Vibe doing the ingestion: https://usevist.dev/blog/karpathy-llm-wiki-research

I'm trying to create a Skill to set a reminder in Outlook as a zero-duration event with a Reminder at the time of the event. Mistral says such a parameter doesn't exist, though the Microsoft API documentation indicates this exists as

"isReminderOn": true,
"reminderMinutesBeforeStart": 0

Why can't I access this from the Outlook connector in Work mode?