Technical Specifications

Mistral Large 3

Mistral Large 3 represents a significant evolution in the Mistral AI model lineage, specifically engineered as a high-capacity, general-purpose multimodal foundation model. Built to handle complex enterprise workflows and production-grade assistant tasks, the model integrates native vision capabilities within a unified architecture. It is designed to operate as a central engine for retrieval-augmented generation (RAG) and sophisticated agentic systems, offering native support for function calling and structured JSON output. This instruct-tuned variant has been refined through post-training to ensure high adherence to system prompts and reliable instruction-following across diverse conversational contexts.

The technical foundation of Mistral Large 3 is a granular sparse Mixture-of-Experts (MoE) architecture that decouples total parameter capacity from inference-time computational cost. By utilizing a gating network to route tokens to a specific subset of experts, the model maintains a total of 675 billion parameters for expansive knowledge storage while activating only approximately 41 billion parameters per token. This architectural approach, combined with a 2.5 billion parameter integrated vision encoder, allows the model to process visual and textual data simultaneously. The training process utilized a massive cluster of 3,000 NVIDIA H200 GPUs, resulting in a model that supports a 256,000-token context window and advanced optimizations for modern hardware targets like NVIDIA Blackwell and Hopper architectures.

From an operational perspective, Mistral Large 3 provides versatility for large-scale deployments through support for high-efficiency quantization formats such as FP8 and NVFP4. These optimizations enable the serving of a model of this magnitude on single-node GPU configurations, such as an 8xH200 or 8xH100 setup, which traditionally would require multi-node infrastructure. The model demonstrates extensive multilingual capabilities, supporting over 40 languages and excelling in non-English conversational performance. This makes it an effective solution for global enterprises requiring a single, high-intelligence model capable of managing document understanding, code generation, and complex logical reasoning within a unified, open-weight framework.