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Mistral Large 3

Active Parameters

41B

Context Length

256K

Modality

Multimodal

Architecture

Mixture of Experts (MoE)

License

Apache 2.0

Release Date

2 Dec 2025

Knowledge Cutoff

Oct 2024

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

87.99 GB VRAM

Consumer

4x RTX 4090

24GB VRAM

Datacenter

2x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

256,000 tokens

184.49 GB VRAM

Consumer

9x RTX 4090

24GB VRAM

Datacenter

3x NVIDIA A100

80GB VRAM

Apple Silicon

2x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 12.3k · Context: 256K · Vocab: 32.8kx 88 layersRMSNormPre-AttentionMulti-Head Attention96Q / 8KV headsHead dim: 128+RMSNormPre-FFNSparse MoE FFN (2/16 experts)SwiGLU+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#110

BenchmarkScoreRank

0.516

23

Professional Knowledge

MMLU Pro

0.80

37

Web Development

WebDev Arena

1230

85

General Text

Text Arena

1414

86

Rankings

Overall Rank

#110

Coding Rank

#94

About 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.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

96

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

1,000,000

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

Linear Attention

-

Linear Attention Ratio

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

12,288

Number of Layers

88

FFN Intermediate Size (Dense)

28,672

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

32,768

Mixture of Experts

Total Expert Parameters

675.0B

Number of Experts

16

Active Experts

2

Shared Experts

-

FFN Intermediate Size (per Expert)

-

Dense Layers Before MoE

-

About Mistral Large 3

Mistral Large 3 is a state-of-the-art general-purpose multimodal model with a granular Mixture-of-Experts architecture. With 675B total parameters and 41B active parameters, it delivers frontier performance for production-grade assistants, retrieval-augmented systems, and complex enterprise workflows.


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