I think it really depends on what you’re doing. I use mistral for many tasks in https://phrasing.app and they blow models many times their size out of the water.

None of my tasks use reasoning though (reasoning actually kills the performance) so perhaps that’s why. Still, I just had to rewrite my pipeline, and mistral was both faster, cheaper, and substantially better than any alternative

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.

> 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.

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.

> they've fallen into irrelevancy right now

It's a very charitable take, as Mistral has never really left the realm of irrelevancy.

It's only a matter of time before EU falls back to hosting Chinese models in EU datacenters.

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 find Mistral Medium 3.5 with OpenCode is perfectly fine if you're willing to talk to it in a more fine-grained way about actual code. For me that's fine because even with huge frontier models I don't like trying to vibe prompt like a product manager.

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.

Mistral is entering the "let's extract has much money from EU taxpayers as we can" phase of European tech company which did not get bought by a US one.

They'll end like Dailymotion, just a zombie company.

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.

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).

Nawh, they trained on test since Llama 2, no wonder.

> 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.

Mistral is bad bad. For its use cases I feel like India’s Sarvam is doing better.