What is the context length of Mistral Medium 3.5?

It supports a 256k token context window.

Is the model available as open source?

Yes, it is released with open weights on Hugging Face under a modified MIT license.

How does the pricing work for the API?

The API costs $1.5 per million input tokens and $7.5 per million output tokens.

Can I run Mistral Medium 3.5 on my own hardware?

Yes, it can be self-hosted on as few as four GPUs, with deployment guides available.

Mistral Medium 3.5

A 128B model for coding, reasoning, and long tasks

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What is Mistral Medium 3.5

Mistral Medium 3.5 is a 128B dense language model that unifies instruction-following, reasoning, and coding in a single set of weights. It features a 256k context window and configurable reasoning effort, making it suitable for complex, long-running tasks. The model achieves 77.6% on SWE-Bench Verified and is available as open weights on Hugging Face, empowering engineers to run self-hosted inference.

Key Features

128B dense model with state-of-the-art coding and reasoning performance

256k token context window for handling long documents and conversations

Configurable reasoning effort per request for flexible responses

Open weight release under modified MIT license for self-hosting

Scores 77.6% on SWE-Bench Verified and 91.4 on τ³-Telecom

Use Cases

Software engineers using Vibe CLI for asynchronous, cloud-based coding agents
Developers needing a powerful LLM for complex multi-step reasoning tasks
Teams running self-hosted inference for data-sensitive applications
Users of Le Chat's Work mode for research, inbox triage, and cross-tool workflows

Why do startups need this tool?

Mistral Medium 3.5 offers startup-friendly open weights, enabling self-hosting to avoid per-token costs and maintain data privacy. Its strong performance on coding and reasoning tasks accelerates development cycles, while the 256k context supports long-form agentic workflows. Startups can leverage the same model for both quick queries and complex, multi-step tasks without switching between different models.