That's really great! Congratulations to everyone working at Mistral!

Now, perhaps Kimi 3.1 is missing from the benchmarks, and Deepseek 4.1 Flash is still has impressive!

I am curious about the comparisons. One thing I would like to see is score vs speed and cost. I don't know how big glm and kimi models are and what are their speed and cost. So I don't know if comparisons are apple or apple.

Very nice. I always expected it to beat z.ai's model by at least a little bit, since it's so similar and that it would be slightly behind Kimi. Maybe this can change with better post-training, but it's great to go from not having a model in this size class to making something competitive with z.ai, who of course have made several good large models.

A first step on a trip which will hopefully go quite far.

It looks specialized models of 27B to 52B active size are currently state of the art.

I think we will not see any significant increase of that numbers in next 4 - 5 years.

Does MoE depth justify itself to be stored and served as "one" model for particular tasks like agentic coding vs same active params non-MoE model?

In fact only weights available is still better than the closed llms. We can check neurons and confirm that the model is not malicious. However we still need to develop methods to confirm the models that are being served are matching those whose weights we can observe.

It's great that Europe finally has a good open-weights model.

I see the logo of my company in this picture and I don't have it my harness dropdown. I guess they hallucilied (vibe truthed?) the list just a little bit.

I would love to see a really competetive european model.

Sorry for the dumb question, but is there any useful non-trivial LLM that is open-source where you could bit reproducibly train it from sources?