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Model details

Mistral Large 3

Mistral Large 3 is a general-purpose, multimodal model designed to serve as a highly capable foundation for diverse applications. Built as a sparse mixture-of-experts, the architecture utilizes 41 billion active parameters out of a 675 billion total parameter count. This design allows the model to balance depth and efficiency, positioning it as a versatile tool for users who require a robust, state-of-the-art system capable of handling complex, multi-faceted inputs and generating high-quality text outputs.

The model was trained from scratch using a massive compute infrastructure consisting of 3,000 NVIDIA H200 GPUs, reflecting a significant investment in its foundational development. As a frontier instruction-fine-tuned model, it is engineered to excel in demanding production environments. By combining this intensive training lineage with a flexible, multimodal design, the model provides a strong, forward-looking solution for developers and enterprises seeking to integrate advanced, high-performance intelligence into their workflows.

Vercel AI Gatewaymistral/mistral-large-3mistral-large

Quick Info

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Provider
Vercel AI Gateway
Model key
mistral/mistral-large-3
Release date
Dec 2, 2025
Last updated
Dec 2, 2025
Knowledge cutoff
2024-10
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.50
Output token cost
$1.50

Limits

Output tokens
256,000 tokens
Context window
262,144 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Mistral Large 3

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CoverageBenchmark

DataCamp's third-party overview of the Mistral 3 release confirms Mistral Large 3 as the flagship of a unified model family where every tier supports multilingual prompts and multimodal inputs, providing developers a consistent baseline across sizes. The article specifies that Mistral Large 3 uses a sparse MoE architec DataCamp reports that Mistral Large 3 outperforms open-source competitors Kimi-K2 and DeepSeek-3.1 on important benchmark tests, framing Mistral's strategy around compact, open-weight models fine-tunable for domain tasks such as customer support routing, document classification, and code completion. The piece emphasize

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