OK, I'm 100% rooting for both Mistral and task focused small models.
But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.
Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.
Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.
If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited
I agree. I am a paying Le Chat Pro user, really rooting for a European alternative. But the quality difference between Mistral and the frontier labs is growing too big to ignore. It’s worrying to me that they didn’t talk much about new models at the conference, because that is really where their focus should be IMHO.
I am wondering what is keeping them back, though: Money? Compute? Skills? Training data? My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.
> I am wondering what is keeping them back, though: Money? Compute? Skills? Training data?
Not ruthless enough and no backing by a corrupt govt administration that has no morals but focuses on self-enrichment instead.
Might sound drastic but I think that's actually closer to the truth thn everbody likes to admit.
> My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.
My theory with no insider information: it’s a little of all of the above, but mostly money. To some extent, you can dig yourself out of a data hole with RL and a lot of compute. And you can buy a lot of compute and some data with a lot of money. Big labs have been operating in this regime for a while and it’s one of the drivers behind their costs beyond just scaling the weights and doing the actual training. Mistral just doesn’t have access to this level of compute or the money to try and muscle their way in.
I wouldn't be surprised if each of the frontier American labs and individually has compute access similar to the entire EU. Chinese firms are a more interesting comparison since there are a fair amount of great models there, and it's estimated about 15% of the ai relevant compute is in China versus maybe 5% in the EU under European companies (and 70% ish in the US is the most common ballpark I see)
It's a bit strange, but a huge handout from the EU/France and a huge AI lab investment round are different orders of magnitude. The necessary sums are just not politically possible. How do you sell spending the equivalent of ten USS Gerald Fords on a start-up? You don't.
This is tangential: and forgive my ignorance here, but is there an inherent reason why there aren't smaller, focused models from the frontier model providers?
I'm thinking something like a software-specific subset of Opus that is the default for use in Claude Code. Smaller, cheaper to deploy and consume, maybe faster.
OpenAI used to make Codex-specific models, but they stopped. What I've gathered from interviews and similar is that training two models isn't worth the (small) lift from having a coding-specific model. You're pre-training on everything anyway, and coding RL is reasonably useful for general-purpose models too.
agreed, the next price increase from frontier labs (and the inevitable limits decrease in subscription tiers) will have people thinking real hard about their model providers and that's when mistral should be ready. however, given their recent performance, I realistically don't have my hopes high up.
it doesn't matter the reason. This is a race and nobody will care or remember how the winners got there.
Mistral looks like it's fading away to irrelevance unless they can play alongside the similar sized models, or have some unique advantage other than being in Europe, for Europe. I was really excited for them back when they were startup that had the biggest European venture round ever. This space will have a few winners, and many losers. Google, plus either Anthropic or OpenAI most likely. Big models will see breakthroughs in inference performance/cost fall precipitously and small models will only exist on devices (Pixels and iPhones, cars, watches, bluetooth speakers, etc)
It’s not that I don’t agree with you, I am just pointing out why it’s hard to catch up to scaling laws given the European economic (capital) and political (US would be upset if they found out Europeans distill) constraints. China is only bound by economic constraints.
I tried it out on some dev tasks with their Mistral Vibe subscription, and the performance was pretty okay, both in regards to development and speed. Worse than Anthropic's models I'm used to but at 20 EUR per month it wasn't a bad deal - except that the 200k context size would more or less be a deal breaker in many cases.
> a decent proxy would be to build models that get the r/localLlama crowd excited
I don’t really disagree with your post, but this is not exactly right. That subreddit seems to go from hype train to hype train every week, I haven’t found anything really insightful in it for quite a while now.
Yeah. I run LLM models locally and for me 22B-32B is the largest I'm willing to invest in trying out.
Even though Mistral 4 has 6B active parameters per token (allowing 3-3.5 per token parameters to be loaded on a 4090), the ~240GB download + storage is pushing the limits of being able to try this out locally, especially if you are downloading and evaluating multiple models.
It also makes it harder for other people to make downstream finetunes like with what happened with the older Mistral/Magistral models.
I think machines like the DGX Spark are about to become a lot more common/popular. It’s big enough to run sparse 150-250B MoEs with enough throughout for a single user. Deepseek v4 Flash is #1 (in terms of usage) on OpenRouter because it’s good enough to be useful. You can run it on a Spark (though it runs better across 2, which is getting up there in cost)
We actually found the Mistral Small 4, quantized to 4bit was comparable to Qwen 3.6 27B and is roughly the same size. At least from our experience on our use cases, the quantization of the Mistral model worked far better than trying to quantize the Qwen family.
Fully agree to your point though, Mistral in general is far behind where I'd expect and Qwen in particular is crushing it at the smaller sizes.
Personally, I'd consider anything 20B params and above a "medium" model. Small being <20B and large >100B. I think obviously we can get to the huge 1-2T param models, but frankly the margin of accuracy improvement for the speed hit is kinda insane (1-2% for many metrics).
It's all relative. For local use I'd classify it by hardware (VRAM size) using FP8 or Q6 quantization:
1. tiny <2-3B -- easily runnable on lower-spec hardware
2. small 4-8B -- runnable on 8GB GPUs
3. medium 9-12B -- runnable on 12GB GPUs
4. large 13-24B -- runnable on 16GB (for the lower end models) and 24GB GPUs
5. very large 25-32GB -- runnable on 32GB GPUs
6. huge >32GB -- not easily runnable on consumer GPUs without compromising performance (offloading layers to the CPU/RAM), quality (heavy quantization, esp. at <= Q4), or price (investing in multi-GPU setups and/or server-grade hardware).
You could possibly split huge down further, as 70GB models (e.g. llama 3) are easier to get working than >120GB models and 1TB models are completely intractable.
> tiny <2-3B -- could run in a browser even, mac neo
Or a phone. I’m running Gemma 4 E2B in one of my apps on my 14 pro (which may or may not be killing my display through overheating. It might just be a coincidence).
Nobody trying to compete with Google, OpenAI, and Anthropic should be playing the small models / local models game.
Foundation model labs should be building very large reasoning models, then leaving it to the community to distill them down.
You can't scale a small model up, but you can scale a small model down.
I'm convinced the only way we'll have a seat at the table in the future and avoid total runaway takeoff is if there are very large models within 80% of the capabilities of the frontier models. Tiny RTX models do diddly squat to remain competitive.
Build open weights models for running on H200s. I'll spin them up on RunPod or Lambda.
I do think there's a chance open weight models have a bit of a moment with the costs of frontier models growing on business balance sheets. It's unfortunate from my "privacy loving" PoV that it's mostly Chinese models filling the gap. ( the top models on openrouter for instance ).
I have used Mistral models out of pure ideology for web agents and the like which aren't doing a lot of heavy lifting.
Antirez’s Deepseek 4 Flash implementation that can run on MacBooks also was a revelation. It runs decently on M5 Max 128GB and it’s pointing out other bottlenecks like prefill speed which will improve.
I thought distillation meant small models don't have to compete with the big models and can always eventually achieve close parity, but it's just a matter of time to do the distillation? (i.e. how much lag do you want to live with) Am I oversimplifying?
There is likely a theoretical limit to how much intelligence you can pack into a model of a given size (especially when stretching that over a large input context size).
Our evals are pretty complex so we only recently started testing ~30B class models, which are now becoming quite smart (on par with the frontier from 1 year ago). Mistral is far behind, but I'm rooting for them.
> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.
Mistral leaning into on-prem and European-hosted models is very smart.
Respectfully, I don't think it's "very" smart. It is a fair option given their limited options? Everyone is doing FDE or (customer engineering to be more transparent) because otherwise they will just be seen as markup on token cost. And the Neo-SaaS companies will take the money instead.
Isn’t that the usual EU startup playbook once they give up on the B2C or world-scale SaaS markets? Refocusing on large (European) enterprise B2B and government contracts?
It always felt to me this (enterprise B2B) was where European startups went to die.
It may be very smart for them, but it also shows that the EU has no desire, therefore no chance, to change and lead anything. The only thing it has is regulation.
We're talking about enterprise customers. The trivial answer is Mistral has sales teams and consultants from the same company that builds the models and from the EU.
i can invest in public markets in a lot of $10b sales and consultants businesses, who can also put mistral on premises (or do whatever the hell people ask for), it makes mistral sound like it is yet another one of those, not a growing $1T business.
>weird training biases that were required by the Chinese government
What is "weird training biases" to us might not be weird to them and vice versa. Just ask the Chinese what they think about LGBTQ+, Japanese, pride parades, Islam and colored minorities.
Every nation has its own biases injected in its domestic LLMs at this point. Otherwise they risk getting in trouble for hate speech/disinformation in the jurisdiction where they operate.
Same how Google Maps cleverly biases the lines of disputed borders based on where you are viewing it from. Or how Google maps switched 'Gulf of Mexico' to 'Gulf of America' in an instant when the orange man signed the paper. Google won't want to anger the US administration the same way how Mistral won't want to anger France and the EU, so Mistral will have all the EU prime directives injected into its LLMs no matter if they're ludicrous or not. The law is the law whether you agree with it or not. Companies want to survive and will pander to whatever the whims the regime they live under are at the current moment regardless of what is right or wrong.
But if I'm using a LLM for personal projects or generating a photorealistic choreographed fight between Tom Cruise and Brad Pitt, I don't care what its political biases are, I care if it solves my problem better and cheaper than the competition, and here the Chinese models could end up winning the consumer market, which is why you see Mistral and other EU alternatives focusing exclusive on B-2-B corporate market.
Except there's no such thing as the "European model" similar how Europe is not a country.
Mistral is mostly French and tends to have mostly French speaking customers, like BNP PAribas in Belgium. Germany will want its own domestic AI champions, maybe in partnership with Switzerland and Austria, similar to how Denmark already has invested in LLMs focused on the Nordic languages with money from Norway.
The biggest mistake is treating Europe like a single homogenous country/market.
The original question was "Yeah but why use mistral on premises instead of Qwen?". I think you and I agree on the answer.
I for one would love to see more country-specific models. There was a story here the other day about Norway’s National Library developing a LLM specialized in Norwegian: https://news.ycombinator.com/item?id=48270770
When the humans have a track record of corruption, it might make sense for a company to seek parallel opinions from a LLM so they can at least flag suspicious human decisions.
Assuming BNP Paribas leadership wants to stop the corruption of course.
That's just one side of the story, not following it on details, but their own le chat explained to me that the company was a capitalist succubus starving to build data center in some north European country. Hilarious if you ask me.
I really want Europe to be part of the AI development and research. And I strongly cheered for Mistral. But they are accumulating too much technological delay. This needs to be fixed, otherwise it will turn into yet another proof we are not able to run large tech with good results. Basically any Chinese lab is doing much better. It's not Mistral that created I don't want to say DeepSeek, but MiMo 2.5, Minimax 2.7, and so forth. There are only weaker and/or larger and slower (no MoE) models. Not good.
Europe shot itself in the dick with this hastily implemented at the height of mass hysteria bullshit and now no sane company will build anything there. an AI startup in the US or China can be a boy and his computer. in Europe, the boy needs a dozen lawyers.
Mistral's sinking into irrelevancy despite the head start they had, the very promising early models they released, and the funding they receive, might very well be the consequence of trying to comply with all that crap.
Possibly yes but let me remember you that France, Italy Germany were against the AI act, so here something very odd is happening, that the EU funding nations are getting marginalized by the countries they welcomed on key topics for our future, and I believe corruption could be a big part of what is happening, both internal to those three countries and at an even more alarming rate in other countries.
EU big nations getting marginalized: haha. The only reason there’s no US-like tariff on Chinese cars is because Germany was too scared it would lose its access to Chinese market.
It's not a matter of importance, but that bad actors as they tried to do in Italy, corrupting EU parliament, may be doing the same with counties are have less visibility. A weak EU is not also in the best interest of countries that wanted the AI act, and surely not in the interest of their citizens, but there could be pressures.
Well , there isn’t also the opposite take from TechCrunch where they say: Why Paris may be the most important AI city outside Silicon Valley. [0]
While the EU loves its regulation, I still feel it’s too early to write it down in the AI race. It will not replace Anthropic or OpenAI any time soon, but even Google and Meta fail to do that.
If AI continue to grow and expand, there is enough space for many more unicorns.
The problem is that statement is a bit too open to interpretation. Ever had Claude piss you off by being stupid and talking in circles? Sounds like manipulation of human behavior!
Compared to the UK Government which recently announced 10 million GBP for AI research, which will likely be scooped up by consultants. I think Europe is doing fine considering.
I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.
Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too
Wasn't even aware Mistral was around and I think that just shows you how irrelevant it has become and not a very good sign for EU in general when the best talent are working for American AI companies.
I saw Tibo's tweet a while back and it was basically a legitimate complaint about the extreme taxation he faced back in EU (France I think) and its pretty obvious how much of a hinderance top down centralized regulation is to innovation.
While I welcome competition and independence, nobody can argue with American innovation and its ability to attract the best of the best. Once it takes seat of the AI reigns there is very little chance for other countries to compete, very much similar to semiconductor field and how only a few select countries have the talent and monopoly over its particular supply chain.
It's clear to anyone looking in that whatever EU is doing is not working (not just AI) and will not work as they do not seem flexible or humble enough to steer itself.
Big tech has remote offices in every major European economy, and they pay well above top 90th percentile of market rate. It basically has a talent sucking effect on the entire economy.
Sounds like they don’t have a moat at all. It’s like software consultancy with a data centre. And then the article mentions many customers using these models on prem (so data centre is not really a plus).
What’s stopping any country backed startup from fine-tuning small open source models?
> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).
Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?
Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.
There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.
European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.
OTOH such things can be quite defensible, they just rarely become anything like as profitable.
I've said it before that Mistral is underrated. They are looking at real world use of LLMs and tooling. Bespoke models are very appealing to lots of non-tech centered companies and state agencies. Also, Mistral's actual platform is useful. While others are watching performance leaderboards like this is some eSports stream, they are building real world uses.
I was also at the event and was pretty disappointed. Most of the talks were pretty low on information. I was at the “build” stage, which supposedly was the technical stage, but the talks there didn’t really go into technical specifics.
OK, I'm 100% rooting for both Mistral and task focused small models.
But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.
Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.
Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.
If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited
I agree. I am a paying Le Chat Pro user, really rooting for a European alternative. But the quality difference between Mistral and the frontier labs is growing too big to ignore. It’s worrying to me that they didn’t talk much about new models at the conference, because that is really where their focus should be IMHO.
I am wondering what is keeping them back, though: Money? Compute? Skills? Training data? My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.
> I am wondering what is keeping them back, though: Money? Compute? Skills? Training data?
Not ruthless enough and no backing by a corrupt govt administration that has no morals but focuses on self-enrichment instead.
Might sound drastic but I think that's actually closer to the truth thn everbody likes to admit.
> My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.
Exactly.
My theory with no insider information: it’s a little of all of the above, but mostly money. To some extent, you can dig yourself out of a data hole with RL and a lot of compute. And you can buy a lot of compute and some data with a lot of money. Big labs have been operating in this regime for a while and it’s one of the drivers behind their costs beyond just scaling the weights and doing the actual training. Mistral just doesn’t have access to this level of compute or the money to try and muscle their way in.
Don’t they supposedly have a huge amount of EU support?
Or at least there’s been a lot of noise about that.
They can get what, 1B euros? 10B when everyone loses their mind? This doesn’t buy nearly enough compute nowadays.
Meanwhile, Anthropic and OpenAI have investors practically begging them to let them buy this much equity at mind-bogging valuations.
Huh. It’s weird then that the EU’s words are so many times stronger than their actions.
Like they intentionally want people to think negatively of them, and further doubt their credibility.
You think they're intentionally being bad because they can't manage to pump $65B into a startup on a whim...?
I wouldn't be surprised if each of the frontier American labs and individually has compute access similar to the entire EU. Chinese firms are a more interesting comparison since there are a fair amount of great models there, and it's estimated about 15% of the ai relevant compute is in China versus maybe 5% in the EU under European companies (and 70% ish in the US is the most common ballpark I see)
It's a bit strange, but a huge handout from the EU/France and a huge AI lab investment round are different orders of magnitude. The necessary sums are just not politically possible. How do you sell spending the equivalent of ten USS Gerald Fords on a start-up? You don't.
And a lot of the "funding" is through mutual deals with MSFT, Nvidia, etc. The Europeans have none of that and would need to pay in actual cash.
> task focused small models
This is tangential: and forgive my ignorance here, but is there an inherent reason why there aren't smaller, focused models from the frontier model providers?
I'm thinking something like a software-specific subset of Opus that is the default for use in Claude Code. Smaller, cheaper to deploy and consume, maybe faster.
OpenAI used to make Codex-specific models, but they stopped. What I've gathered from interviews and similar is that training two models isn't worth the (small) lift from having a coding-specific model. You're pre-training on everything anyway, and coding RL is reasonably useful for general-purpose models too.
Interesting. I'd have guessed there would be meaningful opex benefits to serving smaller models.
agreed, the next price increase from frontier labs (and the inevitable limits decrease in subscription tiers) will have people thinking real hard about their model providers and that's when mistral should be ready. however, given their recent performance, I realistically don't have my hopes high up.
DeepSeek is both cheaper and better than Mistral.
Because they distill
I suppose losing with dignity is a consolation.
it doesn't matter the reason. This is a race and nobody will care or remember how the winners got there.
Mistral looks like it's fading away to irrelevance unless they can play alongside the similar sized models, or have some unique advantage other than being in Europe, for Europe. I was really excited for them back when they were startup that had the biggest European venture round ever. This space will have a few winners, and many losers. Google, plus either Anthropic or OpenAI most likely. Big models will see breakthroughs in inference performance/cost fall precipitously and small models will only exist on devices (Pixels and iPhones, cars, watches, bluetooth speakers, etc)
It’s not that I don’t agree with you, I am just pointing out why it’s hard to catch up to scaling laws given the European economic (capital) and political (US would be upset if they found out Europeans distill) constraints. China is only bound by economic constraints.
Why doesn't Mistral distill?
Good question, given that American companies basically threw copyright law into the trash, I think they should.
Also, new Medium 3.5 is far more expensive than previous Mistral models, and much more expensive than e.g. Deepseek
I tried it out on some dev tasks with their Mistral Vibe subscription, and the performance was pretty okay, both in regards to development and speed. Worse than Anthropic's models I'm used to but at 20 EUR per month it wasn't a bad deal - except that the 200k context size would more or less be a deal breaker in many cases.
> a decent proxy would be to build models that get the r/localLlama crowd excited
I don’t really disagree with your post, but this is not exactly right. That subreddit seems to go from hype train to hype train every week, I haven’t found anything really insightful in it for quite a while now.
Yeah. I run LLM models locally and for me 22B-32B is the largest I'm willing to invest in trying out.
Even though Mistral 4 has 6B active parameters per token (allowing 3-3.5 per token parameters to be loaded on a 4090), the ~240GB download + storage is pushing the limits of being able to try this out locally, especially if you are downloading and evaluating multiple models.
It also makes it harder for other people to make downstream finetunes like with what happened with the older Mistral/Magistral models.
I think machines like the DGX Spark are about to become a lot more common/popular. It’s big enough to run sparse 150-250B MoEs with enough throughout for a single user. Deepseek v4 Flash is #1 (in terms of usage) on OpenRouter because it’s good enough to be useful. You can run it on a Spark (though it runs better across 2, which is getting up there in cost)
I don't agree that they are falling behind. Using both chat and cli I get what I need and it's comparable to "sota" when I compare.
We actually found the Mistral Small 4, quantized to 4bit was comparable to Qwen 3.6 27B and is roughly the same size. At least from our experience on our use cases, the quantization of the Mistral model worked far better than trying to quantize the Qwen family.
Fully agree to your point though, Mistral in general is far behind where I'd expect and Qwen in particular is crushing it at the smaller sizes.
Personally, I'd consider anything 20B params and above a "medium" model. Small being <20B and large >100B. I think obviously we can get to the huge 1-2T param models, but frankly the margin of accuracy improvement for the speed hit is kinda insane (1-2% for many metrics).
It's all relative. For local use I'd classify it by hardware (VRAM size) using FP8 or Q6 quantization:
1. tiny <2-3B -- easily runnable on lower-spec hardware
2. small 4-8B -- runnable on 8GB GPUs
3. medium 9-12B -- runnable on 12GB GPUs
4. large 13-24B -- runnable on 16GB (for the lower end models) and 24GB GPUs
5. very large 25-32GB -- runnable on 32GB GPUs
6. huge >32GB -- not easily runnable on consumer GPUs without compromising performance (offloading layers to the CPU/RAM), quality (heavy quantization, esp. at <= Q4), or price (investing in multi-GPU setups and/or server-grade hardware).
You could possibly split huge down further, as 70GB models (e.g. llama 3) are easier to get working than >120GB models and 1TB models are completely intractable.
As a Mac user:
1. tiny <2-3B -- could run in a browser even, mac neo
2. small 4-8B -- last of browser options, MacBook Air base
3. medium 9-24B -- 32GB machine, air or pro notebook or mini
4. large 25-48B -- 64GB, pro notebook or mini
5. x-large 49-100B -- 128GB MacBook Pro or Studio
6. Huge > 100B -- 256/512GB Mac Studio
> tiny <2-3B -- could run in a browser even, mac neo
Or a phone. I’m running Gemma 4 E2B in one of my apps on my 14 pro (which may or may not be killing my display through overheating. It might just be a coincidence).
Mistral is bad bad. For its use cases I feel like India’s Sarvam is doing better.
channeling Rocky (extraterrestrial) there I see :)
Nobody trying to compete with Google, OpenAI, and Anthropic should be playing the small models / local models game.
Foundation model labs should be building very large reasoning models, then leaving it to the community to distill them down.
You can't scale a small model up, but you can scale a small model down.
I'm convinced the only way we'll have a seat at the table in the future and avoid total runaway takeoff is if there are very large models within 80% of the capabilities of the frontier models. Tiny RTX models do diddly squat to remain competitive.
Build open weights models for running on H200s. I'll spin them up on RunPod or Lambda.
I do think there's a chance open weight models have a bit of a moment with the costs of frontier models growing on business balance sheets. It's unfortunate from my "privacy loving" PoV that it's mostly Chinese models filling the gap. ( the top models on openrouter for instance ).
I have used Mistral models out of pure ideology for web agents and the like which aren't doing a lot of heavy lifting.
Antirez’s Deepseek 4 Flash implementation that can run on MacBooks also was a revelation. It runs decently on M5 Max 128GB and it’s pointing out other bottlenecks like prefill speed which will improve.
I thought distillation meant small models don't have to compete with the big models and can always eventually achieve close parity, but it's just a matter of time to do the distillation? (i.e. how much lag do you want to live with) Am I oversimplifying?
There is likely a theoretical limit to how much intelligence you can pack into a model of a given size (especially when stretching that over a large input context size).
Our evals are pretty complex so we only recently started testing ~30B class models, which are now becoming quite smart (on par with the frontier from 1 year ago). Mistral is far behind, but I'm rooting for them.
Data at https://gertlabs.com/rankings
> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.
Mistral leaning into on-prem and European-hosted models is very smart.
Respectfully, I don't think it's "very" smart. It is a fair option given their limited options? Everyone is doing FDE or (customer engineering to be more transparent) because otherwise they will just be seen as markup on token cost. And the Neo-SaaS companies will take the money instead.
Who else will buy their AI?
and what other options do they have?
Also Mistral did just the right thing by acquiring Koyeb, to beef up their deployment at scale expertise.
Isn’t that the usual EU startup playbook once they give up on the B2C or world-scale SaaS markets? Refocusing on large (European) enterprise B2B and government contracts?
It always felt to me this (enterprise B2B) was where European startups went to die.
It may be very smart for them, but it also shows that the EU has no desire, therefore no chance, to change and lead anything. The only thing it has is regulation.
Yeah but why use mistral on premises instead of Qwen?
We're talking about enterprise customers. The trivial answer is Mistral has sales teams and consultants from the same company that builds the models and from the EU.
i can invest in public markets in a lot of $10b sales and consultants businesses, who can also put mistral on premises (or do whatever the hell people ask for), it makes mistral sound like it is yet another one of those, not a growing $1T business.
One reason might be that Mistral doesn't have a risk of weird training biases that were required by the Chinese government.
>weird training biases that were required by the Chinese government
What is "weird training biases" to us might not be weird to them and vice versa. Just ask the Chinese what they think about LGBTQ+, Japanese, pride parades, Islam and colored minorities.
Every nation has its own biases injected in its domestic LLMs at this point. Otherwise they risk getting in trouble for hate speech/disinformation in the jurisdiction where they operate.
Same how Google Maps cleverly biases the lines of disputed borders based on where you are viewing it from. Or how Google maps switched 'Gulf of Mexico' to 'Gulf of America' in an instant when the orange man signed the paper. Google won't want to anger the US administration the same way how Mistral won't want to anger France and the EU, so Mistral will have all the EU prime directives injected into its LLMs no matter if they're ludicrous or not. The law is the law whether you agree with it or not. Companies want to survive and will pander to whatever the whims the regime they live under are at the current moment regardless of what is right or wrong.
But if I'm using a LLM for personal projects or generating a photorealistic choreographed fight between Tom Cruise and Brad Pitt, I don't care what its political biases are, I care if it solves my problem better and cheaper than the competition, and here the Chinese models could end up winning the consumer market, which is why you see Mistral and other EU alternatives focusing exclusive on B-2-B corporate market.
> What is "weird training biases" to us might not be weird to them and vice versa.
I agree. That's why I think European companies might prefer a European model.
Except there's no such thing as the "European model" similar how Europe is not a country.
Mistral is mostly French and tends to have mostly French speaking customers, like BNP PAribas in Belgium. Germany will want its own domestic AI champions, maybe in partnership with Switzerland and Austria, similar to how Denmark already has invested in LLMs focused on the Nordic languages with money from Norway.
The biggest mistake is treating Europe like a single homogenous country/market.
>Similar to how Denmark already has invested in LLMs focused on the Nordic languages with money from Norway.
Would love to know more. Do you have a source on this?
The original question was "Yeah but why use mistral on premises instead of Qwen?". I think you and I agree on the answer.
I for one would love to see more country-specific models. There was a story here the other day about Norway’s National Library developing a LLM specialized in Norwegian: https://news.ycombinator.com/item?id=48270770
Because the lab working on Mistral is in the European Union.
Please don't run Chinese models for KYC operations.
Based on what? Is there any evidence of risk at all?
Lets hope the models can do a better KYC than the humans have been doing..because they are well known.
Or is this a case of the humans, now preparing for the excuse it was the AI failure?
"BNP Paribas Sentenced for Conspiring to Violate the Trading with the Enemy Act" - https://www.justice.gov/archives/opa/pr/bnp-paribas-sentence...
"BNP Paribas caught up in French money laundering investigation" - https://www.reuters.com/business/finance/bnp-paribas-caught-...
"BNP Paribas faces $246m fine in currency scandal" - https://www.bbc.com/news/business-40635070
"BNP Paribas caught in a Cypriot money laundering investigation" - https://www.lemonde.fr/en/les-decodeurs/article/2023/12/26/b...
In Money Laundering their track record is unmatched: https://violationtracker.goodjobsfirst.org/parent/bnp-pariba...
When the humans have a track record of corruption, it might make sense for a company to seek parallel opinions from a LLM so they can at least flag suspicious human decisions.
Assuming BNP Paribas leadership wants to stop the corruption of course.
They had years to fix it: https://violationtracker.goodjobsfirst.org/parent/bnp-pariba...
That's just one side of the story, not following it on details, but their own le chat explained to me that the company was a capitalist succubus starving to build data center in some north European country. Hilarious if you ask me.
I really want Europe to be part of the AI development and research. And I strongly cheered for Mistral. But they are accumulating too much technological delay. This needs to be fixed, otherwise it will turn into yet another proof we are not able to run large tech with good results. Basically any Chinese lab is doing much better. It's not Mistral that created I don't want to say DeepSeek, but MiMo 2.5, Minimax 2.7, and so forth. There are only weaker and/or larger and slower (no MoE) models. Not good.
https://en.wikipedia.org/wiki/Artificial_Intelligence_Act#Pe...
Europe shot itself in the dick with this hastily implemented at the height of mass hysteria bullshit and now no sane company will build anything there. an AI startup in the US or China can be a boy and his computer. in Europe, the boy needs a dozen lawyers.
Mistral's sinking into irrelevancy despite the head start they had, the very promising early models they released, and the funding they receive, might very well be the consequence of trying to comply with all that crap.
Possibly yes but let me remember you that France, Italy Germany were against the AI act, so here something very odd is happening, that the EU funding nations are getting marginalized by the countries they welcomed on key topics for our future, and I believe corruption could be a big part of what is happening, both internal to those three countries and at an even more alarming rate in other countries.
EU big nations getting marginalized: haha. The only reason there’s no US-like tariff on Chinese cars is because Germany was too scared it would lose its access to Chinese market.
> the EU funding nations are getting marginalized by the countries they welcomed
Thank you for reminding us that all animals are equal, but some are more equal
It's not a matter of importance, but that bad actors as they tried to do in Italy, corrupting EU parliament, may be doing the same with counties are have less visibility. A weak EU is not also in the best interest of countries that wanted the AI act, and surely not in the interest of their citizens, but there could be pressures.
Well , there isn’t also the opposite take from TechCrunch where they say: Why Paris may be the most important AI city outside Silicon Valley. [0]
While the EU loves its regulation, I still feel it’s too early to write it down in the AI race. It will not replace Anthropic or OpenAI any time soon, but even Google and Meta fail to do that.
If AI continue to grow and expand, there is enough space for many more unicorns.
[0] https://techcrunch.com/2026/05/28/why-paris-may-be-the-most-...
It's yet another time when EU is killing our own possibilities to build real competition to US or Chinese tech.
And yet another time they will be thinking aloud in few year "what happened that we are fully dependent on USA?"
So you're saying AI models should be allowed to freely "manipulate human behavior"?
That is almost a meaningless sentence. Cats and traffic lights both manipulate human behavior.
The problem is that statement is a bit too open to interpretation. Ever had Claude piss you off by being stupid and talking in circles? Sounds like manipulation of human behavior!
Compared to the UK Government which recently announced 10 million GBP for AI research, which will likely be scooped up by consultants. I think Europe is doing fine considering.
The first step would be indeed to join forces with UK, in order to don't be two entities, which is very unnatural to me.
No, we don’t need US’s Trojan horse in the EU
I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.
Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too
Wasn't even aware Mistral was around and I think that just shows you how irrelevant it has become and not a very good sign for EU in general when the best talent are working for American AI companies.
I saw Tibo's tweet a while back and it was basically a legitimate complaint about the extreme taxation he faced back in EU (France I think) and its pretty obvious how much of a hinderance top down centralized regulation is to innovation.
While I welcome competition and independence, nobody can argue with American innovation and its ability to attract the best of the best. Once it takes seat of the AI reigns there is very little chance for other countries to compete, very much similar to semiconductor field and how only a few select countries have the talent and monopoly over its particular supply chain.
It's clear to anyone looking in that whatever EU is doing is not working (not just AI) and will not work as they do not seem flexible or humble enough to steer itself.
Big tech has remote offices in every major European economy, and they pay well above top 90th percentile of market rate. It basically has a talent sucking effect on the entire economy.
Regardless of the business. Their website design is :chefs-kiss https://mistral.ai/
I love everything about Mistral's branding.
Le Chat was great, the rebrand to Vibe is meh
Oh most prominent eu ai company . Without reading an article predict next, will update after :
1. They give up on building competitive models. It’s time to drink wine not to struggle with competition
2. Because of #1 they will talk a bit about something around llms maybe coding agents , and after start talking about sovereignty.
3. They are going to start focusing on B2B implementation and deployment.
See what happened to Aleph Alpha...
As an European: 100x YES!
I really like the direction and the transparency of Mistral, among those players.
Even as a non European, it's great to see some competition from Europe against the US/Chinese models.
Sounds like they don’t have a moat at all. It’s like software consultancy with a data centre. And then the article mentions many customers using these models on prem (so data centre is not really a plus).
What’s stopping any country backed startup from fine-tuning small open source models?
> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).
Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?
Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.
There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.
European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.
OTOH such things can be quite defensible, they just rarely become anything like as profitable.
I've said it before that Mistral is underrated. They are looking at real world use of LLMs and tooling. Bespoke models are very appealing to lots of non-tech centered companies and state agencies. Also, Mistral's actual platform is useful. While others are watching performance leaderboards like this is some eSports stream, they are building real world uses.
I was also at the event and was pretty disappointed. Most of the talks were pretty low on information. I was at the “build” stage, which supposedly was the technical stage, but the talks there didn’t really go into technical specifics.
The papyrus talk was awesome though.