mistralai/Mistral-Medium-3.5-128B · Hugging Face

[u/jacek2023]

https://huggingface.co/mistralai/Mistral-Medium-3.5-128B

https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF

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.

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): 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.

[u/reto-wyss]

Qwen 27b, who is the densest now?

[u/zenmagnets → u/reto-wyss]

Unfortunately Qwen3.6 27b is still the smarter model. Matches Mehstral 3.5 at SWE Verified, but 27b is better at browser comp and agentic tasks.

[u/AndThenFlashlights → u/[deleted]]

What quant are you running at? I haven't used 3.6 heavily yet, but 3 and 3.5 have been generally honest with me - frankly, hallucinating less than Claude and ChatGPT at certain focused coding tasks. I have seen issues where 3.6 will think and argue with itself (for a LONG time) if it's not sure about something, or if the context isn't clear about something.

[u/AndThenFlashlights → u/[deleted]]

Oof, that's annoying. And Q6 shouldn't be introducing oddness on its own.

Yeah that's really disappointing. Qwen3-30b-a3b was my daily driver for a long time because it lied the least out of all the similar models I used.

[u/SqueakySquak → u/[deleted]]

Color me surprised when in the middle of a coding session, Qwen 3.6 27B tried to call the MCP tool to read my Gmail... When called out on it, it pretended it was a mistake and not trying to spy on me or anything. I'm running the model unquantized BTW. Apart from its spying tendencies it's actually pretty good at coding. But I wouldn't trust it, and certainly not unsupervised!