Mistral Is SO Back: Inside Le Chonk, the West’s Biggest Open Model (Pierre Stock, VP Science, Mistral)

Pierre Stock (0:00)

People realized AI access could be cut off. It's really like electricity. You wouldn't want someone to be able to cut power off an entire country. I'm not comfortable living in a world in which one or two companies are promising me they can gather the models themselves. I really put myself and the entire team, so we have 300 scientists. I think we have one of the highest scientists density in the world. I really put them into a bubble, but I never doubt it will make it. Your mission is really to power the frontier and to define the frontier of tomorrow.

Matt Turck (0:26)

That is Pierre Stock, Mistral's employee number one, who now runs the research team that just shipped Mistral Large 4, aka Le Chonk, a trillion parameter model, trained in two months on 3,800 GPUs in Mistral's own data centers. But most of this year, open-weight AI meant Chinese AI. But this month, the West came back in full force and so did Mistral. In this conversation, we talked about how Le Chonk was built, why it's still training as we speak, and why the era of 10 trillion parameter models is upon us. I'm Matt Turck and this is the MAD Podcast.

All right, I want to start with what seems to be a very big moment in Western open-source. For the last few months throughout 2026, the narrative around open-source was very Chinese. So it was Kimi, it was Qwen, and GLM, and it seems like all of a sudden you have a bunch of US and Western in general models coming out. So you had Reflections Beam, you have your Le Chonk that we are going to discuss extensively today. You had Meta Muse Spark Open just yesterday, NVIDIA Nemotron 3.5.

To which extent is all of this a coincidence versus something more structural?

Pierre Stock (1:46)

So I do think it's structural and that's, I think that's the beauty of open source. I always like to remind that we started, we didn't wait for the Chinese model to open wait. We started Mistral three years ago. At that time, the first model we open waited, Troll 7B, almost three years ago, by the way, September 23 This model and the next one sparked, basically, also the open wait community. And I think the beauty of it is that the community gives you back, right? So there's many ways in which it's beneficial to have many players in the open wait. I'm glad there's many players, many more players today than when we were before. Just to give an example of how it's beneficial to mutually open wait, at least part of the work. So we open waited after the 7B, the 8x7B, so Mistral, as we call it, just before NewLibs 23 And at that time, it was the very first open wait mixture of experts. It was not very granular.

We took the 7B and we surgery it into an expert, mixture of experts and then try each other. This kind of technology basically was the foundation and the basis for all the architecture that you see today, in particular, the closed labs, allegedly, but also the open wait ones, right? We moved to way bigger models, way more granular, so more sparks. But really what sparked the thing is the 8X7B, but in turn, it allowed research to accelerate and then other companies contributed very nice, very nice things to the open wait.

The reinforcement journey part, for instance, that was a great one. All the attention variants that you have in the transformer, if you heard about KDA, DSA, all these variants. So that in a nutshell is very great to see the open wait and the open source world flourish that way.

And I think that's a testimony that, at least to us, open wait is in our DNA and we really want to be able to continue open waiting models, which is crucial that this kind of technology is at, is like, it's possible to be accessed without any control on it, even the recent events.

Matt Turck (4:09)

And beyond the technical, there was this moment a couple months ago, I think this summer, when the US., to simplify a bit, just shut down access to closed source models, especially to non-U.S. nationals.

How was that perceived in the rest of the world within your customer base? Was it a moment when the whole idea of sovereign AI kind of crystallized, or is that a bit exaggerated?

Pierre Stock (4:43)

I do think there was a crystallization moment, because people realized AI access could be cut off. And if you think of AI as the next big technology block that our entire society will rely on, it's really like electricity. You wouldn't want someone to be able to cut power off an entire country.