mistralai/Mistral-Medium-3.5-128B

Mistral Medium 3.5 128B

Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models.

Reasoning effort is configurable per request, so the same model can answer a quick chat reply or work through a complex agentic run. We trained the vision encoder from scratch to handle variable image sizes and aspect ratios.

Find more information on our blog.

> [!Note] > To speed up local inference using vLLM or SGLang, check out our released EAGLE model.

> [!Warning] > The Transformers config originally had an incorrect entry that caused long-context performance degradation. This has been fixed in this commit. GGUFs generated using the Transformers config prior to this commit are also affected. Please use the correct config for best performance.

Key Features

Mistral Medium 3.5 includes the following architectural choices:

• Dense 128B parameters.
• 256k context length.
• Multimodal input: Accepts both text and image input, with text output.
• Instruct and Reasoning functionalities with function calls (reasoning effort configurable per request).

Mistral Medium 3.5 offers the following capabilities:

• Reasoning Mode: Toggle between fast instant reply mode and reasoning mode, boosting performance with test-time compute when requested.
• Vision: Analyzes images and provides insights based on visual content, in addition to text.
• Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, and Arabic.
• System Prompt: Strong adherence and support for system prompts.
• Agentic: Best-in-class agentic capabilities with native function calling and JSON output.
• Large Context Window: Supports a 256k context window.

We release this model under a [Modified MIT License](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B/blob/main/LICENSE): Open-source license for both commercial and non-commercial use with exceptions for companies with large revenue.

Recommended Settings

• Reasoning Effort:
• 'none' → Do not use reasoning
• 'high' → Use reasoning (recommended for complex prompts and agentic usage)

Use reasoning_effort="high" for complex tasks and agentic coding.

• Temperature: 0.7 for reasoning_effort="high". Temp between 0.0 and 0.7 for reasoning_effort="none" depending on the task.

Generally, lower means answer that are more to the point and higher allows the model to be more creative. It is a good practice to try different values in order to improve the model performance to meet your demands.

• Top p: 0.95 for reasoning_effort="high". You can try different values but staying close should achieve best performance. Leave it to None (or 1.0) for reasoning_effort="none".

Benchmarks

Agentic Benchmarks

Mistral Medium 3.5 supersedes all our previous coding models, namely Devstral, across all benchmarks. It scores 91.4% on τ³-Telecom and 77.6% on SWE-Bench Verified. Due to its stronger agentic capabilities, Mistral Medium 3.5 replaces Devstral 2 in our coding agent, Vibe CLI.

!Mistral agentic benchmark !Mistral agentic benchmark SWE-bench !Mistral agentic vs competiting models benchmark

Instruction Following, Reasoning, and Coding Benchmarks

We compared Mistral Medium 3.5 with competing models on instruction following, reasoning (math), and coding benchmarks. Thanks to its unified capabilities, it achieves strong results across all these tasks and Mistral Medium 3.5 is now powering Le Chat.

!instruct reasoning and agentic benchmark

Usage

You can find Mistral Medium 3.5 support on multiple libraries for inference and fine-tuning.

We here thank every contributors and maintainers that helped us making it happen.

Mistral-Vibe

Use Mistral Medium 3.5 with Mistral Vibe.

Install

Install the latest version:

uv pip install mistral-vibe --upgrade

API Usage

Mistral Medium 3.5 can be selected by starting vibe. If it is the first time you launch vibe, it will:

• Create a default configuration file at ~/.vibe/config.toml.
• Prompt you to enter your API key if it's not already configured.
• Save your API key to ~/.vibe/.env for future use.

Now select mistral-medium-3.5 and start building !

Local server

If instead of pinging the Mistral API, you want to use a local vLLM server, you can do the following:

• 1. Spin up a vllm server as explained in [Usage - vllm](#vllm-recommended)
• 2. Add the model configuration in ~/.vibe/config.toml:

display_name = "Mistral Medium 3.5 (local vLLM)"
description = "Mistral Medium 3.5 mode using local vLLM"
safety = "neutral"

active_model = "mistral-medium-3.5" # Make sure this is the only active_model entry
[[providers]]
name = "vllm"
api_base = "http://:8000/v1"
api_key_env_var = ""
backend = "generic"
api_style = "reasoning"

[[models]]
name = "mistralai/Mistral-Medium-3.5-128B"
provider = "vllm"
alias = "mistral-medium-3.5"
thinking = "high"
temperature = 0.7
auto_compact_threshold = 168000

[tools.bash]
default_timeout = 1200

Notes:

• Make sure to overwrite `` with your server's url.
• Other inference backends are also supported. Please look at Mistral Vibe repo for more info.

Then restart vibe and "tab-shift" to "mistral-medium-3.5" mode.

Give it a try on some coding agentic tasks and start building some…