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The MAD Podcast · 3 min · Oct 22, 2026

Mistral trained Le Chonk with less compute than Chinese rivals

Pierre Stock

VP of Science, Mistral AI

Mistral's first employee, now VP of Science, who leads model releases including the 1-trillion-parameter Mistral Large 4.

  • Matt TurckHost · General Partner, FirstMark Capital

    Publishes the annual MAD landscape of data and AI; sold TripleHop to Oracle.

About this segment

Pierre estimates Le Chonk used three to four times less compute than Chinese competitors. He says Mistral’s RL investment and research strategy help offset the gap.

From “Mistral Is SO Back: Inside Le Chonk, the West’s Biggest Open Model”, Oct 22, 2026. Watch the full episode

“That's, we think, three to four times less than the Chinese competitors.”
Pierre Stockat 19:17 · The MAD Podcast19:17

Takeaways

  1. He estimates Chinese competitors use three to four times more compute.
  2. Mistral’s RL investment is part of its approach to building capabilities.
  3. Pierre says strategy is harder to commoditize than tactics.
Watch 2:33 from 18:40

In this episode

Full episode
  1. 46:42RL training depends on calibrated tasks and resilient infrastructure5:40
  2. 27:28Mixture-of-experts models trade training complexity for inference capacity4:58
  3. 1:03:20A model pinged its trainer from inside a sandbox4:01
  4. 10:28Mistral’s in-house cluster teams fixed issues in two nights2:57

  5. 18:40Mistral trained Le Chonk with less compute than Chinese rivals2:33

  6. 53:35Mistral trains domain skills with tests, rubrics and simulations4:43Up next
  7. 1:07:40Open-model guardrails raise the cost of malicious use4:34
  8. 1:14:14Mistral says its models speed senior researchers up two to four times2:17

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 18:43

    Why do you think Chinese models are still at the very frontier? Is that a question of financing, to your point? They have more compute? Is that a question of technical capabilities? Why is that? I think that there's many aspects to this question. I think the first one is that, so what we've achieved, training ML4, Lechonk, Frontier, open-rate performance on agency can the best overall capabilities. It was trained on, as you said, a bit more than 3K GPU, so 10 megawatt of power.

  2. 19:21

    That's, we think, three to four times less than the Chinese competitors. And the other magnitude is like the percent are even fewer than what the big Frontier Labs in the US have. So again, I'm pretty far out of the team and we can dive into how it was made possible but so there's definitely the compute aspect. Then there's other things like the the method and the science you put behind the model. I think one of the key focus for Mistrol has been to make reinforcement learning work and this particular step in which we teach the model to interact with an environment and to learn with the rewards of the carrot and the stick, basically.

  3. 20:07

    This particular phase, it takes time to actually get exponential yield and to actually make it work. But once you've made it work, basically, you're independent on all the other models. You can't distill when you do reinforcement learning, basically. And I think that's a bet we're doing at Mistrol. we want also to to be able to have to train models with this technique and that's a big investment it takes a bit more time to materialize obviously i think that's also an explanation and then i think the the chinese in particular they're very very nice and very smart people so i think uh we we have the same talent density here the question is against like the means and And maybe the most important thing is the strategy, right?

  4. 20:55

    That's one thing that is hard to commoditize or to delegate yet to an agent. You can speak tactic with an agent. It's harder to speak strategy. And here, our strategy is different. Since we talked about talent density and allude to the fair,