Mistral Large 4
Mistral has launched a public preview of Mistral Large 4 (ML4), a 1 trillion-parameter natively multimodal model with 52 billion active parameters. The company says weights will be released by the end of the month. ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European datacenters. The source reports benchmark results across coding, agentic workflows, cybersecurity, and visual grounding, and describes a reduced-moderation access program for vetted cybersecurity partners.
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- Primary sourceHacker News · AI
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Mistral has launched a public preview of Mistral Large 4 (ML4), a 1 trillion-parameter natively multimodal model with 52 billion active parameters. The company says weights will be released by the end of the month. ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European datacenters. The source reports benchmark results across coding, agentic workflows, cybersecurity, and visual grounding, and describes a reduced-moderation access program for vetted cybersecurity partners.
Key findings from the stored brief
- ML4 is a 1 trillion-parameter natively multimodal model with 52 billion active parameters; a public preview API is available and weights are slated for end of month.
- On the Artificial Analysis Cyber Index, ML4 ranks among the top five models globally and leads open-weight models developed outside China.
- ML4 scores 61.7% on DeepSWE v1.1 and 59.9% on AutomationBench, per the company's reported figures.
- Mistral says it is red-teaming the model with cybersecurity leaders, partners, and state authorities who will access it with reduced moderation.
Source claims not independently checked; benchmark results are company-reported, and the full architecture and methodology details are promised for later.
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Why this may matter
The release targets open-weight frontier performance with self-deployment and sovereignty positioning, particularly for cybersecurity work where closed-model refusals can block legitimate defensive tasks. Its benchmark claims, if confirmed, would shift the open-model landscape.
Limits and open questions
Source claims not independently checked; benchmark results are company-reported, and the full architecture and methodology details are promised for later.
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