Sometimes it's ok to cheer for the last kid crossing the finish line because they're actually running a totally different race, and winning might look completely different.
When I look at what Mistral does vs other organizations I'm impressed:
They aren't profitable yet, but they're a lot closer than most and they're doing a hell of a lot with very little.
Pointless racing story:
I was in high school track with a really tough guy who was just not a runner. We went to a pretty messed up high school and if you screwed around in track practice sometimes the coach would make you run a crap race at the next meet, like steeplechase or hurdles. Well this guy and a few others screwed up and coach made them all run hurdles at a meet.
He hooked every single one and fell on his face. Every time he got back up and kept on running. By the time he hit the finish line his knees were bleeding halfway down to his ankles. We cheered like hell and he was smiling ear to ear.
Coach quit punishing us with races after that.
It looks like Mistral is middle of the pack, behind Anthropic and ahead of OpenAI on that front. All of those labs are way "ahead" of the cloud providers, but those providers are building infrastructure, not just training models, so it's not apples to apples.
Speaking from an absolute perspective I do think they are doing more with their money than either Anthropic or OpenAI.
I think it's an incomplete read. What's the point in competing for a sizeable percentage of your funding when the finish line is incrementally being moved each month? Better spend it on leapfrogs which they seem to have done.
Meanwhile Mistral have a natural ace in their pocket with respect to regulation in the form of CADA and the Cloud Sovereignty Framework. I can't think of another company that would qualify as SOV-3 under that regime
Political polarization is turning the world insane.
This barrier is not going to start moving dramatically. It will simply be mostly satisfied for most work we do. Mistral is going to get there, soonish, long before the economy takes an entirely different shape (in so far that even happens).
There will be super human intelligence tasks, tasks truly constrained by intelligence for quite a while. Those will be few and far between, relatively speaking. Mistral will have plenty of opportunity to capture the other stuff, with a fraction of the resources required that it took the frontier labs to get there first.
But Chinese models have very much earned their place. The same cannot be said of Europe, so far.
It's nowhere mediocre.
It's toes-to-toes with GLM-5.3 which is one of the best Open Weight model available (With Kimi K3) for general reasoning.
I just runned it on code reviews right now and it was able to catch some thread safety issue than DeepSeek-4.1 didn't. And DeepSeek-4.1 is by no means a bad model.
Nobody in the real world cares about minor benchmark differences in money losing coding agents.
And nobody in the real world is giving Altman or Musk their data.
I think we will see some horror stories come out with data leak in the next years.
LLM development is jumpy. It’s hard to extrapolate very far ahead.
When Europe does surprise us, I will be the first to commend their progress. But until then, this is where we're at.
This is mistrals first 1T-scale model and I expect the 4th or 5th generation to be close to the best for many purposes.
[1] These evals differ from the public ones like terminal-bench, are sometimes model-specific, need real, diverse usage to actually create, and are held secretly since quality of eval is the first driver behind the next step improvement of a model.
[2] It is not close. This model was trained on less than 4k GPUs, whereas astra used north of 100k GPUs.
And to the point of scale and training cluster, so what? Not only do Chinese labs have smaller clusters with less empowered GPUs, compute is Mistral's responsibility. You can't take away from other labs just because they fulfill that responsibility better.
The lack of compute is not really attributable in that sense to mistral. First of all it needs general investor and government willingness, which is easier in a larger economy like the US or China.
Second, you need widespread usage of your paid inference service for two reasons: one it pays off your compute cost, and two it speeds up the improvement process.
The vast majority of deepseeks paid customers are within china itself (since openai and anthropic services are not reachable from china) which gives it a market. But for someone in france, there is no reason to use a structurally slower developing model from mistral compared to using one from openai...except when data guarantees are needed, hence the landing page focus on sovereignty. As far as the dual use aspect goes, a model like this is more than enough, so the government will be happy.
I really want them to win as that's our last horse in the AI race, but ~200 research-oriented devs out of 1800 employees? I believe they agree it's pretty doomed and have pivoted.