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Mistral Releases Large 4 Open Weights Under a Modified MIT License That Requires a Commercial Agreement for Model-as-a-Service Businesses

Mistral released Mistral Large 4 weights under a modified MIT license with a commercial-license condition for Model-as-a-Service operators; Artificial Analysis scores it 26.

AI & Machine Learning Models
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Editor's Note ·

Correction:
The article says the Herald "found no independent replication of Mistral's FrontierCode, Terminal-Bench or cyber figures in the sources reviewed." The VentureBeat article it cites reports Artificial Analysis's own measurements: 25% on Terminal-Bench 4.0 (against Mistral's 41%, from different evaluation contexts), 15% on FrontierCode 2.0 (against Mistral's 19.7%, with DeepSeek at 29% and GLM-5.3 at 22% in Artificial Analysis's results), a Cyber Index score of 51, and a Coding Agent Index score of 48. VentureBeat says the FrontierCode result does not support extending Mistral's first-place claim beyond the company's own comparison.
Clarification:
The "Conflicting Figures" section presents 49 billion and 52 billion active parameters as a disagreement between sources. The Hugging Face model card itself states 49 billion active parameters per token, or 52 billion including embeddings and output layers, and VentureBeat reports the same explanation. The two figures count different things rather than contradicting each other.

Overview

Mistral AI has released the weights of Mistral Large 4 (ML4), the model it nicknames Le Chonk, under what it calls a modified version of the MIT license. Mistral’s release post says the weights belong to an “improved” version of the model that first appeared in public preview earlier in the month. The Hugging Face blog post on the release is dated October 29, 2026, and Artificial Analysis lists the same release date. The Herald previously reported on the preview, whose headline referred to open weights as promised.

What We Know

License. The license text on Hugging Face permits use, copying, modification, further training, distribution, sublicensing and sale of the model. Section 2 requires a separate commercial license from Mistral before commercial use if the licensee or any of its affiliates operates a Model as a Service business, defined as giving a third party access to the model or its derivatives for inference or post-training in a way that lets that party exercise meaningful control over inputs, parameters or training data. Section 3 says the requirement does not apply to internal use, or to use of the model accessed through Mistral’s official products. VentureBeat describes it as a custom license rather than a standard open-source one.

Checkpoints. The model card lists FP8 and NVFP4 variants, a 1M-token context window, and a mixture-of-experts design with 128 routed experts, four of them active per token. Hugging Face’s blog says GGUF versions were created by Unsloth and the llama.cpp team. The model card says the checkpoint ships with a speculative decoding draft head, and that Mistral measured up to 1.9x higher throughput than serving the target model alone, adding that actual gains depend on hardware and workload.

API pricing. As read on November 4, 2026, Mistral’s release post lists $0.70 per million input tokens, $0.07 per million cached input tokens and $2.10 per million output tokens on Mistral’s own API, and Artificial Analysis lists $0.70 per million input tokens and $2.10 per million output tokens. The Herald’s earlier preview article reported API pricing of $1.36 per million input tokens and $4.18 per million output tokens from the preview announcement.

Where to get it. Mistral says the model is available in Mistral Studio, through the Mistral API, on OpenRouter, through NVIDIA-hosted endpoints and an NVIDIA NIM container, and through Microsoft Foundry, with downloadable weights on Hugging Face.

Performance Claims

The following figures are Mistral’s own unless noted.

  • On Cognition’s private FrontierCode 2.0 benchmark, Mistral reports a score of 19.7% for ML4.
  • On Terminal-Bench 4.0, Mistral reports 41%, an improvement of 13 percentage points over its preview.
  • On the Artificial Analysis Coding Agent Index, Mistral reports 48%, which it describes as effectively tied with GLM-5.3 while using 47% fewer tokens per task on average.
  • Mistral reports a 51% score on the Artificial Analysis Cyber Index and an average refusal rate of 96.6% on malicious cyber requests, which it calls the highest it measures among open models.

Independent measurements are more mixed. Artificial Analysis gives ML4 (High) a score of 26 on its Intelligence Index, above the 23 median for comparable open-weight models. VentureBeat reports that the score is up from 25 for the preview and six points behind the open-weight leaders, DeepSeek V4.1 Flash 1017 and GLM-5.3. The same article reports a cost of $2.06 per Intelligence Index task against $5.54 for the preview, a 63% reduction, and a hallucination rate of 28% against 41% for the preview. The model card likewise says that the leading open-weight models from China score higher on that aggregate index.

VentureBeat quotes Mistral’s Marjorie Janiewicz on why customers want downloadable weights: “I don’t want to get disconnected. I want to own my model.”

Conflicting Figures

Sources differ on the active parameter count. The model card and the repository name give about 52 billion active parameters out of about 1 trillion, and Artificial Analysis lists 52 billion active out of 1.1 trillion. The Hugging Face blog says about 1 trillion total and 49 billion active. The Herald’s preview article reported 49 billion active parameters from the preview coverage at the time.

What We Don’t Know

  • The Herald found no independent replication of Mistral’s FrontierCode, Terminal-Bench or cyber figures in the sources reviewed.
  • The sources reviewed do not state how many organizations operate Model as a Service businesses that would need the commercial license, or what the commercial terms are.
  • Mistral’s post says it is modifying guardrails after observing cybersecurity-related use in live traffic, but the sources reviewed do not describe the changes in detail.