Welcome Le Chonk: Mistral Large 4 on Hugging Face

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:

- FP8

- NVFP4

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!

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 .

Cybersecurity Capabilities

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 the software. Mistral reports that several closed frontier models score near zero on the same test because they refuse to attempt the work. This is super important for defenders, because fixing a vulnerability usually requires reproducing the vulnerability. In Mistral’s internal testing, Mistral Large 4 was also useful for malware analysis, vulnerability prioritisation and writing detection rules, which the model was not explicitly trained for.

Mistral mainly positions the model for defensive security work. Vulnerability research and incident response often appear like attacks in model input, so closed source filters can block them. A team that depends on an API can also lose access in the middle of an incident. Crucially, open weight models put both the usage policy and the deployment under the user’s control. That said, the model still declines harmful requests: on cyber prompts from JailbreakBench, StrongREJECT and AgentHarm, its average refusal rate is higher than any other open weight model.

For community builders, an open weight model like Mistral Large 4 unlocks a load of downstream use cases. You could build your own cyber system around your code base, crash logs, and malware samples. For example, with TRL you could fine-tune smaller models from Mistral Large 4 traces patching your code base. In fact, reproduce-then-patch tasks use a verifiable reward, so it fits with RL environments that could be used to further RL smaller security models. Open weights also let researchers measure the model’s refusal behaviour directly instead of relying on the provider’s description.

In short, cyber capabilities like ML4’s in the open are a huge boost for the community.

We have interviewed Pierre Stock about the cyber capabilities of the model, you can watch it here .

Architecture

Mistral Large 4’s text decoder has 72 layers, a hidden dimension of 6,144, and 64 attention heads. Its attention blocks combine Multi-head Latent Attention (MLA) with a DeepSeek Sparse Attention (DSA) indexer (a direction in other models such as GLM 5.3). MLA keeps a compressed KV representation, and DSA selects up to 2,048 key positions for each query.

For each decoder block, there’s a 3:1 RoPE to NoPE pattern, i.e. 3 decoder layers employ RoPE and every fourth layer uses NoPE. The model also shares KV state across layers. The first decoder layer uses a dense feed-forward network. The other 71 layers use MoE blocks, in which a softmax router selects four of the 128 experts for each token. One shared expert always runs alongside the selected experts, for a total of 5 active experts per token.

Regarding RoPE, Mistral uses LeRope , a YaRN-scaled RoPE with one learned multiplier per frequency.

Vision encoder

Mistral Large 4 employs a 1.6B Pixtral encoder. Pixtral has 30 layers, a hidden dimension of 2,048, and 16 attention heads. It uses 14 × 14 patches and a patch-merge projector with spatial merge size 2. You can see the encoder pipeline below.

Benchmark Results

Mistral Large 4 delivers strong benchmark performance across coding, cybersecurity, multimodal understanding, and agentic workflows, positioning it as a competitive addition to the open-weight ecosystem. As of October 29, 2026, the model is the fifth best open weights model on Artificial Analysis Intelligence index.

On coding, ML4 achieves 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, and 28.3% on Terminal-Bench 4. Its combined Coding Agent Index score of 49.8% places it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max.