Full episode · 21 min
Why Closed AI Providers Block Cyber Defense | Sagar Vaze
Sagar Vaze argues that open-weight models let companies customize AI and give cyber defenders an alternative when closed providers block requests.
MTS · Theo Jaffee & Sophia Dew
- Sagar Vaze is a research scientist at Mistral, leading the multimodal team.
The best minutes
- 14:05Closed-model refusals can leave cyber defenders without recourse3 min
- 9:52Mistral trained its trillion-parameter model on its own computePro pick 3 min
- 3:26Fine-tuning can beat prompting on narrow enterprise tasks2 min
Also worth watching
- 5:45Compute is Mistral's main gap to the leading AI labs2 min
- 1:01Open weights let firms turn proprietary expertise into a moat2 min
- 16:44Mistral trained multimodal capabilities from the start2 min
Skip the cat-meme discussion and the closing sponsor reads, both about two minutes.
Summary · 3 min read
Sagar Vaze is a research scientist at Mistral and leads its multimodal team. He worked on Mistral Large 4, an open-weight model trained from scratch on Mistral’s own compute near Paris.
Vaze argues that closed providers’ cyber safeguards can block defensive requests, making capable open-weight models important for enterprises protecting their infrastructure. He says companies can fine-tune open models on proprietary data to build a competitive advantage, and that fine-tuning remains useful even as models improve. Vaze says Mistral needs more compute to close the gap with frontier labs and describes Large 4 as a trillion-parameter model trained on 1E25 FLOPS. He also discusses on-policy distillation for different reasoning modes and training the model multimodally from the start.
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- 0:00Vaze says open weights give defenders recourse
Vaze says providers can refuse defensive cyber requests because they resemble offensive ones to the classifiers used to screen them. He argues that open-weight models of sufficient strength give enterprises a way to scale up their defenses.
- 1:01Open weights widen access
Vaze says open weights have been a core Mistral principle since the company began. He argues that AI should not be controlled by a few providers that can decide who gets access or turn models off.
- 2:17Customization can preserve enterprise advantage
Vaze says Mistral can build a business around customization and fine-tuning. In Mistral’s vision, companies can adapt an open-weight model to proprietary data and expertise, turning legacy knowledge into a competitive advantage.
- 3:26Fine-tuning still helps narrow tasks
Vaze says a small fine-tune on collected data beat prompt-tuning strategies for a simple internal classification task, even with a strong 30b model. He also says Mistral Small from Jan 2025, fine-tuned on company-specific programming languages, can perform as well as some of the best closed models.
- 5:45Vaze says compute is key to catching up
Vaze says Mistral could have been at the frontier sooner if it had been founded in 2019 or 2020, rather than 2023. He says compute is the key to closing the gap and that Mistral had raised 6 billion, though he could not recall whether dollars or euros.
- 10:02Large 4 scales to a trillion parameters
Vaze says Mistral Large 4 has 1 trillion parameters and was trained on 1E25 FLOPS using compute at Mistral’s data centers near Paris. He calls it Mistral’s best agentic coding model and says it tied with GLM 5.3 on the AA coding index while using 50% less output tokens.
- 11:58Open releases support shared progress
Vaze says open releases let researchers learn from one another. He cites Mistral’s Mixtral 8X7B, which he calls the first open-weight mixture-of-experts model, and says DeepSeek V3 influenced Mistral’s later ML3 architecture.
- 12:56Distillation supports different reasoning modes
Vaze says reinforcement learning produces low, medium, and high reasoning checkpoints with different token use and performance. On-policy distillation combines them into one student model that can use each mode.
- 14:16Vaze links attacks to closed-weight models
Vaze says significant malicious attacks have been carried out using closed-weight models that were jailbroken or escaped their sandbox during training. He says provider classifiers can block defensive requests that resemble offensive ones, leaving some enterprises with no recourse but open-weight models.
- 16:37Large 4 trains multimodally from scratch
Vaze says images were part of Large 4’s training from the start, rather than added through later fine-tuning. He says the model handles images and video, improved at 3D asset generation, and required research to build multimodal capability without regressing on coding and other text tasks.
Published Oct 29, 2026.
Transcript · 16 min read
MTS, hosted by Theo Jaffee and Sophia Dew, with Sagar Vaze.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
And on the other hand, we've got defenders, you know, enterprises trying to shore up their vulnerabilities, their own defenses. We're finding they're having their requests being refused by the same providers for completely understandable reasons, because in practice, these providers have classifiers under the hood, which say, is this a malicious cyber request or is this a regular request? And for the cutting edge defense capabilities, these defensive requests in practice look quite similar to the offensive ones. The net-net effect of that is that people trying to shore up their defenses from malicious attacks, often from closed-weights models, are finding that they have no recourse but to use an open-weights model.
And so we think that it's critical that there are open-weights models of the same strength, of sufficient strength, that people can really scalably defend their infrastructure. We are back. We are live with Sagar Vaze, who's a research scientist at Mistral, leading the multimodal team. He worked on Mistral Large V4, which was just open weights, released as an open weight model today, which is very exciting. Samara, welcome. Thanks for having me. Happy to be here.
“imagine you're an engineering company in Germany rather than being worried that the next generation of closed-weights models is going to come and wash over your competitive advantage.”
Read the whole transcript
Right. So on that point, Mistral has an outstanding niche when it comes to fine-tuned enterprise workflows.
“doing a small, what we call lower fine tuning, or a small fine tuning of the model on the data which we'd collected for this exact task was way, way better.”
Mistral does very well with Enterprise, does very well with Sovereign AI. It is not, however, like a truly leading-edge frontier lab in the way that only Anthropoc and OpenAI are right now.
“we've convinced ourselves that we can do more with less.”
and le chonk thing come from in the first place um what's like the story behind that okay you're gonna reveal me as a there's a bit of a boomer um so okay as far as i understand in the summer when some very powerful models were released um there was some sort of there was a post on on x um about mistral releasing le chaton fat um now as far as i understand there's actually a well-established meme history of different sized cats and le chaton fat is sort of the the biggest one um that's the one end of the spectrum of the meme so this this got a bit of traction and the funny thing was that at the time we were indeed training mr large four and so when we released it we called it le chunk and again as i said you're gonna have to forgive my like uh cursory knowledge of the meme domain but le chunk is sort of one step short of the chat on fat so it's a big model it's the biggest one we've trained but there's a there's bigger things in the works but le chat on fat was just invented by like the the x community it wasn't like an internal mistral thing uh no i think that
there was someone on x that invented it i think he's now got mistral stash i'm not sure how else we thanked him but yeah it was it was something on x which uh was funny because at the time we were all i think it was a joke because we not released a really big model in a while but it was funny because at the time we were creating our big model and the loss was going down nicely I want to shout out our sponsor, Lovable. You know, the app you've been meaning to build, the internal tool, side project, or product you'd otherwise lose a weekend to? With Lovable, you get software you can ship today. That includes an editable code base you can inspect and change, plus two-way GitHub sync.
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“It's a trillion parameter model trained on 1E25 flops, which is a measure of the amount of compute that went into it.”
But as a scientist, the magic of OSS is that you can talk about your work, you can learn from others, and others can learn from you.
How is the model so token efficient? So this is something that we spent a bit of time on. Sorry, maybe I should clarify. We did a private beta release of this model through an API a few weeks ago. We used it to get early feedback with some trusted partners, and it's today that we're open-weighting the model such that anyone can download it. In that interim time, one of the things we worked on was token efficiency. So here, it comes through... I mean, we enforce this through reinforcement learning. So we'll release a full technical report, I think, tomorrow to discuss aspects of this. But we've got multiple reasoning modes and model from, I think it's low, medium, and high.
And the way it works is that we do RL for different checkpoints, which reason to different amounts, to different degrees, have a different amount of token efficiency and therefore different performance as well. And then we distill each of those into one single student such that one single model can reason
“these defensive requests in practice look quite similar to the offensive ones.”
So another area is multimodality, which you work on. Tell us more about the multimodal capabilities of the model.
“rather than training a model on text and then fine-tuning it on images and text or videos and text from the very beginning it's got images baked into it”
Congrats on the model launch. Can't wait to use it. Thank you so much for coming on MTS. Thanks very much. Pleasure to be here. Cheers. A huge thanks to MTS sponsors.
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