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The MAD Podcast · 2 min · Oct 22, 2026

Mistral says its models speed senior researchers up two to four times

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 says their models speed senior researchers up two to four times when they run experiments end to end and handle infrastructure failures. He expects meaningful model work to grow from hours to days with ML5.

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

“it accelerates our most senior researcher by a factor two, three or four with our models.”
Pierre Stockat 1:14:53 · The MAD Podcast1:14:53

Takeaways

  1. Models can run experiments end to end and handle infrastructure failures.
  2. Pierre estimates a two-to-fourfold gain for senior researchers.
  3. He expects ML5 to do meaningful work over several days.
Watch 2:17 from 1:14:14

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 simulations4:43
  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

Up next · Training DataRobot training is converging on a mixed data recipe3:56

Transcript

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

  1. 1:14:17

    yeah do you guys use your own product to start doing some um you know ai research uh so perhaps the general theme of rsi and ai building ai is that what's the reality of this at mistral today yep uh we are pretty much investing heavily on it rsi is like uh i mean everyone is uh is saying they're doing it the reality is a bit more nuanced obviously uh what works super well is to augment the researcher by running experiments end-to-end with all the infra, by handling all the infra failures automatically. I think that's great and it accelerates our most senior researcher by a factor two, three or four with our models. And then I think the meta question is like can you go one layer up and basically task a model to improve itself over long periods of time?

  2. 1:15:13

    And that's what we call the flight time. Currently the flight time. So the time it takes for a model to do meaningful work is like a few hours. Tomorrow with ML5 is going to be a few days, for sure, of meaningful work. Without the moving and also with agent swabs. And again, this new paradigm is something we deeply believe in because it will allow us to close the gap with the best labs even faster.

  3. 1:15:43

    Don't forget that humans are very bad at uploading exponentials. We don't know how it works, but we can't comprehend it. So we're only a few months behind the best frontier models. It's distorted here, but for instance, an artificial index, ml4, lechonc. is on the level of GPT-6 Luna and it's like way better than all the previous Opus models. So again, we have a few months delay only and we count on the next paradigms that we discussed in this podcast to actually close the gap and really crock them because their mission is really to power the frontier and to define the frontier of tomorrow.

  4. 1:16:22

    Yeah. And on that note, as a last question, so reports say that Mistral Large 5 already be in pre-training.