Got curious, sign up, add money to account, try to use. Can't, it's a labs model. Fine, let's enable labs. Can't, unspecified error. Fine, lets contact customer support as instructed, can't no customer support, just a half-assed FAQ, that seems vibe-coded and searched poorly, totally irrelevant answers coming up for all queries tried. Then it hit me:
If AI makes good customer support, then why does no AI company use theirs to provide customer support?
No one ever thought it made good customer support. It makes cheap customer support, and quite a lot of companies already have shitty customer support because they don't care about it being good, so they're thrilled to get to cut costs further.
It's "good" from the perspective of a company that's annoyed to have to spend money on actually fixing things.
I laughed and cried at this comment. It's so uncannily EU. Just spent 18 months landing an EU enterprise contract. Signed today and sent it back and got an automated message 'sorry will be on vacation til end of July...' This is the fourth vacation emails I got since corresponding with this contact window for the past 1 year.
You would think Europeans would be masters of continuity than. But no, usually it is a form of narcissism and selfishness that is utterly indifferent to the needs of others that just says “oh well, I guess you’ll have to just wait for me to get back from my vacation and on my terms to continue on with your life”.
It’s a common disgusting mentality wide spread across Europe.
How is people having reasonable work place environments related to shit customer support and companies trying to optimize for reducing costs? Seems highly unrelated.
Tangential, but I'm pretty sad about EU having absolutely nothing in the actual SotA LLM market. Especially given the recent events of US completely restricting the actual SotA models.
Has this been just pure lack of funding and infra?
Mistral has raised $4B+ which is a decent chunk of change, albeit not in the league of OpenAI/Anthropic/xAI.
The hard part is justifying pure LLM development financially. Models are all very similar. OpenAI justified it originally by being a 'charity' dedicated to pure research (not financial). Anthropic justified it by saying OpenAI didn't care enough about safety and splitting from them (not financial). Elon justified it by saying that AI would be woke and untruthful unless he built Grok (not financial). Google did Gemini because, well, they're where it all started and because AI research was one of the core missions Larry & Sergey gave it when they started it (but then sat on it for financial reasons).
Then there's the Chinese models. It's unclear what their motives are tbh. I've never seen a really great explanation, only hypotheses. But as they're giving them away for free or very underpriced, their motivation doesn't seem to be financial either.
But Mistral is a normal company. It doesn't have rich backers giving it money based on narratives about cosmic destiny, so it needs to justify what it's doing with ROI. So that more or less rules out large scale LLM training.
There's also EU regulation to consider. When I looked at this in the past I found lots of odd rules that kill off any chance of having a European tech industry. The UK had one that said you could only crawl the internet for research purposes! And without the First Amendment you're at much greater risk of being prosecuted for things your models say. See how Germany has taken Google to court over things its models put in its search result pages.
So the benefit isn't clear and the legal risks are very high.
The EU simply doesn't have a proper common market, especially when it comes to capital. Having more people than the US and a big economy in aggregate doesn't matter much if you can't efficiently pool resources. Could we in Europe have 100 billion fundraises for a new lab? If not, then it's over and you can give up.
I agree with your analysis, at least as long as we shall remain strictly inside the free market methodologies. And having a common capital market would be good no matter what.
But there are other ways to pool resources than the free market. Airbus was not made dynamically in a market, neither was the LHC. 100 billion € is a lot, it's half of the total allocated aid from Europe to Ukraina. Which can be read in two ways, either 'helping Ukraine is already weighing us down, another similar cost is too much for some IT toy ', or 'Europe has the ability to collect massive amount of capital when it needs to, and AI is a existential threat which justifies it'.
Software in general, AI as well, is a rich get richer market. The big American companies can afford to (and very much do) scoop up European talents and upcoming European companies. And if they don't want to buy them, they can undercut them to bankruptcy. We live in a colony economy, with human capital as the raw produce, and it all gets funneled to the USA.
The only way to avoid this is to stop playing the game as it is today, and start using proper industrial policy to build up a competitive industry (like China did). There has been no appetite for that the last decades, but Trump is making it completely clear that the state is back, and Europe is slowly acknowledging it as well.
Which is a mistake the EU makes again and again. If you put onerous requirements on everyone, this means that the most well-capitalized firms will be able to shoulder the regulatory overhead the easiest. But who can't? New European startups. This already killed part of the tech sector with the GDPR while Google and Meta just hire 100 lawyers and are done with it.
[citation needed] here. The tech sector is still well and alive in EU, and outside adtech (which was hit hard by GDPR - that was the point) doesn't seem to have been visibly impacted.
Given that American and Chinese models exist in an environment where the executive can and will pull models because the vibes are off, or they don't think a company is sufficiently deferential or politically aligned, this feels like false attribution.
Leanstral 1.5 - June 30, 2026
An updated Lean 4 formal proof engineering model optimised for automated theorem proving and autoformalization. 119B total parameters, 6.5B active.
What a coincidence! I just released OpenATP earlier today. OpenATP is an open-source Python package and CLI for agentic automated theorem provers. It includes support for Leanstral with Mistral’s Vibe harness. The previous production Leanstral model was deprecated on May 22nd. I will update the package to point to Leanstral 1.5 ASAP!
I'm not sure I understand the Weights policy. This site says the weights are Apache-licensed, suggesting it's open weights. But I can't find a download link. Their Huggingface profile seems to only provide an earlier snapshot [0]. Any pointers on whether/where we can or will be able to download the weights?
I would have preferred actual proof objects, as in Metamath's: separate the actual proof from the heuristics used to find it (also valuable, but a different thing).
Real talk, does anyone use anything from Mistral because it performs the best, by whatever secular metric of your choosing? Or is it only used "because EU"? Just focus on answering the question. I wonder if anyone has observed it perform better on any objective metric in any rigorous setting.
I use their Voxtral Mini STT audio model to automatically transcribe my podcasts into markdown.
Out of all the STT models I've tried, it's both the best performing and one of the cheapest!
It's really accurate, feeding the episode notes and the podcast description ensures all names are properly spelled, and speaker diarization works really great.
(I just do a Gemini flash pass at the end to identify the speakers, so it shows the host name instead of "Speaker 1")
For writing and languange learning it's very decent, especially Mistral Large. The pricing is very good too. I really like the consistently low time to first token and good token per second. Claude, especially in the past, would be very inconsistent, often with outages. Mistral mostly just always works and is very fast.
Technical questions are unfortunately hit or miss. I'm lately pretty much always using a system prompt that emphasizes short answers [1], and Opus regularly one-shots it while Mistral needs a follow up. I use big-AGI as a model router [2] (dumb name, great software), which makes switching midway very easy though. For coding I'm still using Claude Code mostly out of inertia (although I really want to move to an OSS harness) and the one time I tried their `vibe` tool months ago it was a bit rough.
Mistral TTS with diarization is also great and cheap. That's the only thing for which I use their web UI.
[1] Give a short but helpful answer to the question the user asks. When helping with a computer-related task, unless the user asks, don't give any installation or setup instructions, but just get straight to the point. When the user asks a follow up question, give a more complete and longer answer while still not overexplaining. When the user prefaces the question with "short mode off" in any question, give a full and well considered reply.
We are not Mistral's target audience. For instance I don't know if Leanstral performs the best as a "formal proof engineering model optimised for automated theorem proving and autoformalization" because I don't even know wth that is or who else does it.
Mistral themselves focus more on b2b; financial services, manufacturing, stuff like that, and they get some big clients that way.
Despite not being their target, I started using them because they have many open models. I continue using them because, yeah EU, but also because the community is great and the tool makes me think more than Claude does. Last, I stick with them because they are one of the few AI companies that are up-front about their environmental impact and are actually trying to minimize it while still providing a decent product.
If you can express a solution in Lean you can formally prove or disprove it. Formal verification is making a debut in traditional engineering toolkits.
For a defense project we're working on, we basically have a hard requirement to use european cloud provider + european llm
We cannot use open source LLMs on-prem, I asked. So that's basically a hard requirement to use mistral, even though Chinese models are strictly better on every dimension.
I use it as my workhorse for coding and general chat questions, because it's good enough 80% of the time, and indeed it's french/european (with heavy US capital tho...).
We complain too much about not having enough major competitors in the IT space, to not support a burgeoning one even if it's less powerful than SOTA labs
just used mistral for a database/scraping creation tool and ended at <10k€ in token costs (via openrouter), beating gpt5.4-mini in output quality and speed and costs after actual testing A/B fairly. so its a super scoped task to be performed hundreds of thousands of time for some automation and mistral just did it better across all dimensions that gpt-5.4-mini. of course thats not a headline in terms of frontier model competitiveness, but for "the boring parts" it just was flat out better than anything else consistently. bonus points it handles mixed-language-content with nuances surprisingly well to turn web content in the wild into structured data really good and fast.
I made a game (https://prose-or-con.com) where you pick whether writing is AI or human. Mistral is a bonkers weird writer. So weird I fell for it a couple of times because I thought, "No way a model writes this weird." Not, like, incorrect grammar or spelling or anything, just...off-kilter. Kinda sassy.
Yes, it's on the todo list, but I need more data. Only a half dozen people have played it and submitted a score. I'm storing the hashes of passages people got right and wrong so I can make exactly that chart at some point. I think both "the most human-like AI" and "the most AI-like human" are both interesting pieces of data, but I don't know either yet.
> Mistral because it performs the best, by whatever secular metric of your choosing?
I am. I use them primarily through their vibe CLI.
Reason is simple: They are cheaper (by almost one order of magnitude compared to Claude) and still do the job pretty well.
For small programming tasks, quick prototyping, refactoring or anything verbose and not requiring a context too large: I first go to Mistral and then eventually to Claude if I'm unsatisfied.
I also found out some of their models to be more responsive than OpenAI ones (which is not so surprising considering the size).
My tasks are mainly C++ and Python programming. People in other languages might not share my enthusiasm.
I use it because EU and API pricing is decent to me. And support is awesome also. They reply the same day or at most the next day, and they follow the ticket great. It isn't that bad, but neither the best.
A few months ago, I had some data cleaning to do; their small model was surprisingly efficient and got the job done for 0.2x what I expected to run (Anthropic Sonnet / Haiku). Their TTS / STT is also roughly at the frontier, at least for French.
But I admit I only consider them because they're from France. Haven't seen a dimension where they're competitive for general users
I still prefer Mistral Nemo 12B for text summarisation tasks. It has a nice style. The Mistral Small 24B is also decent. I have a YouTube transcript summariser which I like these for.
However these days I usually have Qwen 3.6 27B already loaded so I mostly just use that instead.
OCR is off the charts good on every metric you can think of.
LLMs are a near-afterthought at this point if you don’t have data residency requirements. I love them and they’re slightly underrated, their models are consistently well-trained, open, but as you note, behind. There is no metric that will say they’re ahead in anything.
Hmm, not sure I'd agree. I really like google's offering there (they suck at coding agents but their OCR is good value for money - well up till the latest flash model which has got wicked expensive). See also https://www.ocrarena.ai/leaderboard
I know these leaderboards are iffy, but at least my experience has been somewhat similar.
This went to market horribly (if you can even call it that), just look at the comments. Mistral played themselves big time over the past ~18 months. Non-competitive products and models combined with bad marketing and GTM...Oh Europe
If AI makes good customer support, then why does no AI company use theirs to provide customer support?
They do! E.g. Cursor. See earlier discussions like "Cursor IDE support hallucinates lockout policy, causes user cancellations"[1].
[1]: https://news.ycombinator.com/item?id=43683012
It's "good" from the perspective of a company that's annoyed to have to spend money on actually fixing things.
It’s a common disgusting mentality wide spread across Europe.
Sample of two, but I'm assuming french companies don't like to being contacted n English.
https://web.archive.org/web/20260630223430/https://docs.mist...
Has this been just pure lack of funding and infra?
The hard part is justifying pure LLM development financially. Models are all very similar. OpenAI justified it originally by being a 'charity' dedicated to pure research (not financial). Anthropic justified it by saying OpenAI didn't care enough about safety and splitting from them (not financial). Elon justified it by saying that AI would be woke and untruthful unless he built Grok (not financial). Google did Gemini because, well, they're where it all started and because AI research was one of the core missions Larry & Sergey gave it when they started it (but then sat on it for financial reasons).
Then there's the Chinese models. It's unclear what their motives are tbh. I've never seen a really great explanation, only hypotheses. But as they're giving them away for free or very underpriced, their motivation doesn't seem to be financial either.
But Mistral is a normal company. It doesn't have rich backers giving it money based on narratives about cosmic destiny, so it needs to justify what it's doing with ROI. So that more or less rules out large scale LLM training.
There's also EU regulation to consider. When I looked at this in the past I found lots of odd rules that kill off any chance of having a European tech industry. The UK had one that said you could only crawl the internet for research purposes! And without the First Amendment you're at much greater risk of being prosecuted for things your models say. See how Germany has taken Google to court over things its models put in its search result pages.
So the benefit isn't clear and the legal risks are very high.
But there are other ways to pool resources than the free market. Airbus was not made dynamically in a market, neither was the LHC. 100 billion € is a lot, it's half of the total allocated aid from Europe to Ukraina. Which can be read in two ways, either 'helping Ukraine is already weighing us down, another similar cost is too much for some IT toy ', or 'Europe has the ability to collect massive amount of capital when it needs to, and AI is a existential threat which justifies it'.
The only way to avoid this is to stop playing the game as it is today, and start using proper industrial policy to build up a competitive industry (like China did). There has been no appetite for that the last decades, but Trump is making it completely clear that the state is back, and Europe is slowly acknowledging it as well.
and now they get to sit in the chair in the corner and watch as its citizens use American and Chinese models.
Leanstral 1.5 - June 30, 2026 An updated Lean 4 formal proof engineering model optimised for automated theorem proving and autoformalization. 119B total parameters, 6.5B active.
https://web.archive.org/web/20260630223430/https://docs.mist...
GitHub: https://github.com/henryrobbins/open-atp
Docs: https://open-atp.henryrobbins.com
[0] https://huggingface.co/mistralai/Leanstral-2603
Technical questions are unfortunately hit or miss. I'm lately pretty much always using a system prompt that emphasizes short answers [1], and Opus regularly one-shots it while Mistral needs a follow up. I use big-AGI as a model router [2] (dumb name, great software), which makes switching midway very easy though. For coding I'm still using Claude Code mostly out of inertia (although I really want to move to an OSS harness) and the one time I tried their `vibe` tool months ago it was a bit rough.
Mistral TTS with diarization is also great and cheap. That's the only thing for which I use their web UI.
[1] Give a short but helpful answer to the question the user asks. When helping with a computer-related task, unless the user asks, don't give any installation or setup instructions, but just get straight to the point. When the user asks a follow up question, give a more complete and longer answer while still not overexplaining. When the user prefaces the question with "short mode off" in any question, give a full and well considered reply.
[2] https://github.com/enricoros/big-AGI
The new Mistral Medium 3.5 is also a big improvement over devstral-2
I think its dumb.
Their support is hidden away in a chat bubble at the bottom. But they do respond promptly.
Its decent, but after switching to Google i wouldn't go back
Mistral themselves focus more on b2b; financial services, manufacturing, stuff like that, and they get some big clients that way.
Despite not being their target, I started using them because they have many open models. I continue using them because, yeah EU, but also because the community is great and the tool makes me think more than Claude does. Last, I stick with them because they are one of the few AI companies that are up-front about their environmental impact and are actually trying to minimize it while still providing a decent product.
If you can express a solution in Lean you can formally prove or disprove it. Formal verification is making a debut in traditional engineering toolkits.
We cannot use open source LLMs on-prem, I asked. So that's basically a hard requirement to use mistral, even though Chinese models are strictly better on every dimension.
We complain too much about not having enough major competitors in the IT space, to not support a burgeoning one even if it's less powerful than SOTA labs
I’ve also found it very good at pulling info from pdfs. Even a complicated festival with multiple venues and timetables.
I am. I use them primarily through their vibe CLI.
Reason is simple: They are cheaper (by almost one order of magnitude compared to Claude) and still do the job pretty well.
For small programming tasks, quick prototyping, refactoring or anything verbose and not requiring a context too large: I first go to Mistral and then eventually to Claude if I'm unsatisfied.
I also found out some of their models to be more responsive than OpenAI ones (which is not so surprising considering the size).
My tasks are mainly C++ and Python programming. People in other languages might not share my enthusiasm.
Nope. This is not my experience.
Public pricing in token/$ is only part of the equation.
Mistral tooling to consume significantly less tokens-per-given-task than the Anthropic ones.
My bills currently reflects that.
But I admit I only consider them because they're from France. Haven't seen a dimension where they're competitive for general users
Are you trying to instruct me like an LLM?
[1]: https://github.com/maxim/ringbinder
However these days I usually have Qwen 3.6 27B already loaded so I mostly just use that instead.
LLMs are a near-afterthought at this point if you don’t have data residency requirements. I love them and they’re slightly underrated, their models are consistently well-trained, open, but as you note, behind. There is no metric that will say they’re ahead in anything.