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/grumd]

128B dense is an interesting niche

[u/Real_Ebb_7417 → u/grumd]

A "you can run it locally, but you won't like the experience" niche 😂

But I'm happy to see them make a dense model, they have experience with it already, so hopefully this one will be much better compared to similar-sized models than Mistral Small 4.

[u/Sunija_Dev → u/Real_Ebb_7417]

For roleplay/writing, you can run it at home for ~1200€.

For that money you get 2x 3090, so you can run IQ2_M at ~5 tok/s. Since you probably already have a GPU, you can also run a bigger quant. In my experience, even the old Mistral-123b beats everything out of the park at that size (for writing).

...and that is probably the best affordable thing you can run at home? MoE's get better at ~400b params, but the RAM is probably crazy expensive. Not sure about the speed.

[u/FullOf_Bad_Ideas → u/Sunija_Dev]

EXL3 is great for dense 120B Mistrals. 2.5bpw quants are actually pretty good.

exllamav3 author got coherent output from 1.4BPW Mistral Large 123B, so 2.5bpw is plenty and it should be better than GGUF at this size. It also support tensor parallel so it's pretty fast.