Mistral AI
Deploy Mistral's model family on Together AI. State-of-the-art performance, Apache 2.0 open weights, and native multilingual support.
Why Mistral AI on Together AI?
Designed for production workloads that need consistent performance and operational control.
State-of-the-art at 8× lower cost
Mistral models deliver enterprise-grade performance at a fraction of the price. Mistral Medium 3 benchmarks at state-of-the-art while cutting costs by 8× versus closed-source alternatives.
Multilingual and transparent by design
Native support across English, French, Spanish, German, Italian, and more. Magistral's reasoning chain is fully visible — transparent thinking you can follow and verify across languages.
From frontier to edge, open licensed
Apache 2.0 licensing, on-premises deployment, and fine-tuning on proprietary data. SOC 2 Type II certified and HIPAA compliant on Together AI's US-based infrastructure.
Meet the Mistral AI family
Explore top-performing models across text, image, video, code, and voice.
Breakthrough technical innovations
Explore all the game-changing architectural advances that make Mistral AI models shine.
- Mixture of Experts (MoE)
Sparse expert routing activates only 37B out of 671B parameters for each token in V3. Advanced load balancing without auxiliary losses maintains performance while reducing computational cost.
- Group Relative Policy Optimization
New RL approach that removes separate value networks in RLHF, using grouped relative advantage estimation to cut compute requirements while maintaining training stability.
- Native Reasoning Transparency
First reasoning model to expose complete thinking process in <think> tags. Native reasoning capabilities built into model foundation through large-scale reinforcement learning.
- MetaP Training
First successful implementation of FP8 mixed precision training on a 671B parameter model. Pioneering reinforcement learning approach without supervised fine-tuning as preliminary step.
- Multi-Head Latent Attention
Innovative attention mechanism that reduces KV-cache memory requirements while maintaining modeling performance. Optimized for efficient inference deployment.
- Multi-Token Prediction
Novel training objective that allows the model to predict multiple tokens simultaneously. Enhanced performance and efficiency through advanced training techniques.
Deployment options
Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.
Real-time
A fully managed inference API that automatically scales with request volume.
Best for
Batch
Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.
Best for
Dedicated Model Inference
An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
Best for
Dedicated Container Inference
Run inference with your own engine and model on fully-managed, scalable infrastructure.
Best for