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
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
4x RTX 4090
24GB VRAM
Datacenter
2x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
256,000 tokens
Consumer
9x RTX 4090
24GB VRAM
Datacenter
3x NVIDIA A100
80GB VRAM
Apple Silicon
2x Apple M3 Max
128GB VRAM
Rank
#110
| Benchmark | Score | Rank |
|---|---|---|
StackUnseen | 0.516 | 23 |
Professional Knowledge | 0.80 | 37 |
Web Development | 1230 | 85 |
General Text | 1414 | 86 |
Overall Rank
#110
Coding Rank
#94
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.
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
-
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.
APX AI
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