Medium is the new large.

At Mistral AI, we are continuously pushing the frontier for both open models (Mistral Small, Mistral Large, Pixtral, many others) as well as enterprise models (Mistral OCR, Mistral Saba, Ministral 3B / 8B, and more). All the way from Mistral 7B, our models have consistently demonstrated performance of significantly higher-weight and more expensive models. And today, we are excited to announce Mistral Medium 3, pushing efficiency and usability of language models even further.

Highlights.

1. Mistral Medium 3 introduces a new class of models that balances

2. • SOTA performance

   • 8X lower cost

   • simpler deployability to accelerate enterprise usage

3. The model leads in professional use cases such as coding and multimodal understanding

4. The model delivers a range of enterprise capabilities including:

5. • Hybrid or on-premises / in-VPC deployment

   • Custom post-training 

   • Integration into enterprise tools and systems

The perfect balance.

Mistral Medium 3 delivers frontier performance while being an order of magnitude less expensive. For instance, the model performs at or above 90% of Claude Sonnet 3.7 on benchmarks across the board at a significantly lower cost ($0.4 input / $2 output per M token). 

On performance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems. 

Additionally, Mistral Medium 3 can also be deployed on any cloud, including self-hosted environments of four GPUs and above. 

Top-tier performance.

*Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline.

Human Evals

In addition to academic benchmarks we report third-party human evaluations that are more representative of real-world use cases. Mistral Medium 3 continues to shine in the coding domain and delivers much better performance, across the board, than some of its much larger competitors.