Key Findings
Mistral Large 4 is a multimodal, multilingual Mixture-of-Experts (MoE) model capable of processing text and image inputs to generate text. It features approximately 1T total parameters, with 49B active parameters, and supports a context window of 1M tokens. As of October 29, 2026, it ranks as the fifth best open weights model on the Artificial Analysis Intelligence index.
Technical Details
The model’s text decoder consists of 72 layers with a hidden dimension of 6,144 and 64 attention heads. The architecture integrates Multi-head Latent Attention (MLA) with a DeepSeek Sparse Attention (DSA) indexer, where the DSA selects up to 2,048 key positions per query. The model employs a 3:1 RoPE to NoPE pattern across decoder layers and utilizes LeRope, a YaRN-scaled RoPE with one learned multiplier per frequency.
The MoE structure utilizes a softmax router to select four out of 128 experts per token, plus one shared expert, totaling five active experts per token. For vision, the model uses a 1.6B Pixtral encoder with 30 layers, a hidden dimension of 2,048, 16 attention heads, 14 x 14 patches, and a patch-merge projector with a spatial merge size of 2.
Cybersecurity and Coding Performance
Mistral Large 4 is specifically optimized for defensive security. It achieved an 82% score on the Artificial Analysis Cyber Index, outperforming several closed frontier models that refused the task. It also shows strength in malware analysis and vulnerability prioritization. In coding, it scored 61.7% on DeepSWE v1.1 and 59.4% on SWE-Atlas-QnA, with a combined Coding Agent Index score of 49.8%, placing it ahead of Qwen3.8 Max and DeepSeek V4 Pro 0813.
Significance and Outlook
Distributed under the Mistral Ecosystem Open-Weight License, the model allows users to maintain control over deployment and usage policies, avoiding the filters or API dependencies of closed-source models. This open-weight nature enables researchers to build custom systems around proprietary code bases and crash logs, or use TRL to fine-tune smaller models based on Mistral Large 4 traces.
Source: https://huggingface.co/blog/mistral-large4
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