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title: Mistral’s Large 4 open weights arrive with a 1-point...
description: Mistral released open weights for Mistral Large 4 (ML4), nicknamed Le Chonk, on Hugging Face. The company claims it's now the best open agentic coding model,...
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og:title: Mistral’s Large 4 open weights arrive with a 1-point coding lead over GLM-5.3: “There’s nothing you can’t do with this model that you can do with competitors.” | daily.dev
og:description: Mistral released open weights for Mistral Large 4 (ML4), nicknamed Le Chonk, on Hugging Face. The company claims it's now the best open agentic coding model,...
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# Mistral’s Large 4 open weights arrive with a 1-point coding lead over GLM-5.3: “There’s nothing you can’t do with this model that you can do with competitors.”

**[The New Stack](https://daily.dev/sources/newstack)** · 6 min read · 1 upvotes · 0 comments

## Summary

Mistral released open weights for Mistral Large 4 (ML4), nicknamed Le Chonk, on Hugging Face. The company claims it's now the best open agentic coding model, scoring 41% on Terminal-Bench 4.0 versus GLM-5.3's 40%, up from 28% at its October preview. Mistral says Le Chonk uses 47% fewer tokens per task than GLM-5.3 and scored 79% on a cybersecurity benchmark versus GLM-5.3's 29%. VP of Science Pierre Stock says the reinforcement learning training ran on just 10 megawatts of power, and smaller models, including a laptop-sized one, are coming.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://thenewstack.io/mistral-chonk-coding-model>

## Questions this post answers

### What is Mistral Large 4 (Le Chonk) and how does it compare to GLM-5.3 on coding benchmarks?

Mistral Large 4, nicknamed Le Chonk, is Mistral's open-weight frontier model released on Hugging Face. It scores 41% on Terminal-Bench 4.0, narrowly beating GLM-5.3's 40%, and uses 47% fewer tokens per task on average than GLM-5.3, according to Mistral. It also scored 79% versus GLM-5.3's 29% on the CyberGym E2E cybersecurity benchmark.

_Comparing open-weight coding models before committing to one? daily.dev surfaces benchmark reports like this as they land._

### How much did Mistral's reinforcement learning training for Large 4 cost in compute?

Mistral says it ran the reinforcement learning phase for Large 4 on only 10 megawatts of power, which it describes as a fraction of what competitors use. The initial training took about two months on roughly 3,800 Nvidia Grace Blackwell GPUs in Mistral's European data centers, after which the Terminal-Bench 4.0 score rose from 28% to 41%.

_Tracking compute efficiency claims across AI labs helps when weighing which models to adopt, something daily.dev keeps developers current on._

### Will Mistral release smaller versions of its Large 4 coding model?

Yes, Mistral's VP of Science Pierre Stock confirmed a family of smaller Le Chonk models is planned, including one small enough to run on a laptop. These are expected to ship in the coming months as part of Mistral's broader strategy to extend ML4 as what Stock called 'a stepping stone' toward leading the open-weight AI frontier.

_Developers planning for lighter local coding models can follow releases like this through daily.dev._

---

Tags: [#ai](https://daily.dev/tags/ai), [#open-source](https://daily.dev/tags/open-source), [#ai-coding](https://daily.dev/tags/ai-coding), [#mistral-ai](https://daily.dev/tags/mistral-ai), [#glm](https://daily.dev/tags/glm)

[View this post on daily.dev](https://daily.dev/posts/mistral-s-large-4-open-weights-arrive-with-a-1-point-coding-lead-over-glm-5-3-there-s-nothing-you--uyiutwee0)

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