Mistral AI unveils Mistral Large 4 with 1 million token context
Mistral AI has unveiled Mistral Large 4, featuring a 1 million token context window, with model weights scheduled for release at the end of October 2026.
What happened?
Mistral AI has launched a public preview of Mistral Large 4, a Mixture-of-Experts model containing approximately 1 trillion total parameters and 52 billion active parameters. The model features a 1 million token context window and supports multimodal inputs including both text and images. Model weights are scheduled to be made available for download and local deployment on 27 October 2026.
Key facts
| Totalt antal parametrar | Cirka 1 000 miljarder (1 biljon) |
|---|---|
| Aktiva parametrar | Cirka 52 miljarder |
| Kontextfönster | 1 miljon token |
| Datum för förhandsvisning | 6 oktober 2026 |
| Planerat viktsläpp | 27 oktober 2026 |
Why it matters
With Mistral Large 4, the company is expanding capacity for long-context tasks and complex document analysis in open-weight enterprise models. Providing downloadable model weights allows organisations to manage local operations and customisation without relying solely on external cloud services.
Who is affected?
The model is targeted at enterprises and developers building applications in software development, financial analysis, cybersecurity, and automated workflows. Organisations requiring local infrastructure will be impacted when the model weights become available for internal deployment under the applicable license terms.
Impact on the EU
As a French AI developer, Mistral AI operates within the EU, and the preview is currently available via their Mistral Studio platform. The model is undergoing specific safety testing with vetted partners prior to the public release of the weights, though license terms and specific regulatory compliance will be fully established only upon the wider launch.
What else you should know
Pre-release testing of the model weights is focusing heavily on cybersecurity risks. Performance metrics are based on Mistral's internal data and have not yet been verified through independent benchmarking.
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