# Hugging Face — Welcome Le Chonk: Mistral Large 4 on Hugging Face

- Company: Hugging Face (huggingface.co)
- Announced: 2026-10-29
- Category: open-weight-release
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://huggingface.co/blog/mistral-large4
- Record: https://forck.live/items/29352-welcome-le-chonk-mistral-large-4-on-hugging-face
- Subject: Platform
- Open weights: yes
- Models affected: Mistral Large 4, Le Chonk
- License: Mistral Ecosystem Open-Weight License
- Context window: 1M tokens

Mistral released Mistral Large 4, also called Le Chonk, an open-weight multimodal Mixture-of-Experts model that takes text and images as input and generates text. The model has about 1T total and 49B active parameters with a 1M-token context window, and is distributed under the Mistral Ecosystem Open-Weight License, an MIT derivative that limits model-as-a-service offerings. Mistral positions it for defensive security work, where vulnerability research and incident response can look like attacks and closed-source filters may block them, and says open weights put both the usage policy and the deployment under the user's control. Mistral Large 4 scores 82% on the Artificial Analysis Cyber Index test that asks a model to reproduce a real vulnerability in open-source software and then patch it, a score Mistral says is the highest reported on that test. On cyber prompts from JailbreakBench, StrongREJECT and AgentHarm, its average refusal rate is higher than any other open-weight model, according to Mistral. The text decoder has 72 layers, a hidden dimension of 6,144 and 64 attention heads, combining Multi-head Latent Attention with a DeepSeek Sparse Attention indexer that selects up to 2,048 key positions per query, plus a 1.6B Pixtral vision encoder. As of October 29, 2026, it is the fifth best open-weights model on the Artificial Analysis Intelligence index, with 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, 28.3% on Terminal-Bench 4 and a combined Coding Agent Index score of 49.8%.

## Evidence

Verbatim from https://huggingface.co/blog/mistral-large4:

> Mistral Large 4 (aka Le Chonk) is a multimodal, multilingual, Mixture-of-Experts (MoE) model from Mistral. It takes text and images as input and generates text. It has about 1T total, 49B active parameter , and a context window of 1M tokens .
> 
> The model is released in two official variants:
> 
> There are also GGUFs created by Unsloth and the llama.cpp team. It’s distributed under the Mistral Ecosystem Open-Weight License (an MIT derivative that establishes limitations for model-as-a-service offerings).
> 
> Mistral Large 4 is particularly strong for cybersecurity, coding agents, and multimodality. In this post, we look at how the model works, its capabilities, architecture and more!
> 
> Table of Contents
> 
> What is new with Mistral Large 4
> 
> Mistral Large 4 combines a large pool of experts with sparse attention and a Pixtral vision encoder. The language model selects a small group of experts for each token, while its attention indexer selects which earlier token positions to attend to. These are two different forms of sparsity, where one selects feed-forward networks, the other selects context positions.
> 
> You can find all the checkpoints in the Mistral Large 4 collection .

## Follow-ups

- Unite.AI (reported, 29 Oct): Mistral’s 1-Trillion-Parameter Le Chonk Checkpoint Lands on Hugging Face — https://forck.live/items/29369-mistral-s-1-trillion-parameter-le-chonk-checkpoint-lands-on-hugging-face

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