Mistral launches Large 2 with 123 billion parameters

Mistral AI today launched Mistral Large 2, its new large language model. With 123 billion parameters — the values the system learns during training — the French company is targeting the tasks that matter most in professional use: programming, mathematical analysis, instruction following and work across multiple languages.

The launch matters for two reasons. Mistral is not just updating the flagship model it introduced in February; it is also releasing its weights under a research license and significantly lowering API access prices. That combination is unusual at the high end of the market, where the most capable models typically operate exclusively as closed services.

A large model for code and lengthy documents

Mistral Large 2 supports a context window of up to 128,000 tokens. In simple terms, it can receive and connect very long texts within a single conversation: technical documentation, code repositories, contracts or collections of reports. Supporting that volume does not guarantee that a model will reason well about every detail, but it removes a common practical limitation when working with large files.

The company is focusing on code generation and says the model supports more than 80 programming languages. It also strengthens function-calling capabilities, the mechanism that enables an assistant to decide whether to use an external tool — for example, query a database or run an operation in a program — rather than simply generate text.

In its evaluations, Mistral gives Large 2 a 92% score on HumanEval, a test of small Python programming problems; 84% on MATH, which focuses on mathematical exercises; and 84% on MMLU, a broad test of knowledge and comprehension. These are useful benchmarks for comparing models, although they do not replace testing with each company’s real-world data, code and workflows.

Multilingualism as a European priority

The language improvements are among the most significant elements of the announcement. Mistral Large 2 is designed to work in English, French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese and Korean, among other languages.

Most large models are trained on a far greater share of English-language content. That can lead to less reliable answers, poorer summaries or misunderstood instructions when they are used in other languages. For European companies that write, support customers or analyze documents in multiple languages, multilingual quality is not a secondary feature: it determines whether a model can be integrated into the workflow without first translating the information.

Mistral had already made French and other European languages one of its defining features. Large 2 expands that strategy at a time when the market is concentrating around US and Chinese labs, with models that are increasingly expensive to train and operate.

Weights available, but not open-source software

Mistral Large 2’s weights can be downloaded for research under the Mistral Research License. This allows researchers to study and test the model on their own infrastructure, which is not possible with fully closed services.

It is important to distinguish this from an open license without commercial restrictions. The research license does not automatically grant permission to use the model in commercial products or services. Companies seeking that use must contact Mistral to obtain the applicable terms or use the service hosted by the company and its partners.

The model is available through La Plateforme, Mistral’s API, at a price of $2 per million input tokens and $6 per million output tokens. It is also coming to platforms including Amazon Bedrock, Azure AI Studio, Google Cloud Vertex AI, IBM watsonx and NVIDIA NIM.

The price cut is significant: the previous Mistral Large launched at rates of $8 per million input tokens and $24 per million output tokens. For applications that process large volumes of text or continuously generate code, the per-query cost can determine which model is viable.

The decisive test will be whether those improvements hold up beyond benchmarks. Mistral Large 2 is entering a segment where quality matters, but so do privacy, ease of deployment and a predictable bill. The ability to research it locally and use it across multiple clouds gives customers and developers more options for comparing those factors.