> Man, a lot of this discussion sounds like people cheering for the last kid crossing the finish line.

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:

https://isaiprofitable.com/

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.

> This is a pretty grim prognosis for European AI.

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

People were extremely dismissive of chinese models until recently. They went from 1 year behind frontier to 6 months behind frontier to 3 months behind frontier extremely fast.

Yes, it arrived months late. The waiting was too long I started losing faith. But when I saw Mistral’s series funding news, I knew they are on the right track. 3 billion EUR is just too significant

I don't think it will matter in a year or so. We are clearly topping out on useful intelligence for an increasing amount of tasks, as demonstrated by more and more models reaching the "useful" barrier.

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.

People are cheering for a kid that is gaining ground in an ongoing race.

Curious, why do you say the model is mediocre? I haven't tried it, so I can't pass any judgement... I've learned to distrust benchmark rankings. Are benchmarks and Artificial Analysis the yardstick you're using?

Doesn't matter, it's excellent, and it's European with European inference, which solves the pains of all my clients trying to build data lakes and processes on top of it.

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 thought they wouldn't release anything at all, it's not dead yet :)

Neat. Wait 3 months for the landscape to change entirely.

LLM development is jumpy. It’s hard to extrapolate very far ahead.

First few models will always be slow improving and worse. The way to improvement is working your way through a gajillion evals [1], finding bugs, gaps, and curating training data (this part involves human design as well as raw inference compute) to fix it. This is very time intensive and can't easily be "done once and then everyone has lesser work to do" since every model is different. Well, one way to accelerate it is to simply have more compute, which mostly openai and anthropic have[2].

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.

Reminds me of Gemini 3.5 Pro

hurr durr

Those are comments from Europe. The US is waking up now and I expect them to be much harsher.

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.