Large Enough

This latest generation continues to push the boundaries of cost efficiency, speed, and performance. Mistral Large 2 is exposed on la Plateforme and enriched with new features to facilitate building innovative AI applications.

Mistral Large 2 has a 128k context window and supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash.

Mistral Large 2 is designed for single-node inference with long-context applications in mind – its size of 123 billion parameters allows it to run at large throughput on a single node. We are releasing Mistral Large 2 under the Mistral Research License , that allows usage and modification for research and non-commercial usages. For commercial usage of Mistral Large 2 requiring self-deployment, a Mistral Commercial License must be acquired by contacting us .

You can use Mistral Large 2 today via la Plateforme under the name mistral-large-2407 , and test it on le Chat. It is available under the version 24.07 (a YY.MM versioning system that we are applying to all our models), and the API name mistral-large-2407 . Weights for the instruct model are available and are also hosted on HuggingFace .

We are proud to partner with leading cloud service providers to bring the new Mistral Large 2 to a global audience. In particular, today we are expanding our partnership with Google Cloud Platform to bring Mistral AI's models on Vertex AI via a Managed API. Mistral AI's best models are now available on Vertex AI, in addition to Azure AI Studio, Amazon Bedrock and IBM watsonx.ai .

A large fraction of business use cases today involve working with multilingual documents. While the majority of models are English-centric, the new Mistral Large 2 was trained on a large proportion of multilingual data. In particular, it excels in English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi. Below are the performance results of Mistral Large 2 on the multilingual MMLU benchmark, compared to the previous Mistral Large, Llama 3.1 models, and to Cohere’s Command R+.

Mistral Large 2 sets a new frontier in terms of performance / cost of serving on evaluation metrics. In particular, on MMLU, the pretrained version achieves an accuracy of 84.0%, and sets a new point on the performance/cost Pareto front of open models.

Following our experience with Codestral 22B and Codestral Mamba , we trained Mistral Large 2 on a very large proportion of code. Mistral Large 2 vastly outperforms the previous Mistral Large, and performs on par with leading models such as GPT-4o, Claude 3 Opus, and Llama 3 405B.

A significant effort was also devoted to enhancing the model's reasoning capabilities. One of the key focus areas during training was to minimize the model's tendency to "hallucinate" or generate plausible-sounding but factually incorrect or irrelevant information. This was achieved by fine-tuning the model to be more cautious and discerning in its responses, ensuring that it provides reliable and accurate outputs.