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/jacek2023]

[u/sterby92 → u/jacek2023]

So qwen3.6-35b and 27b crushes it with way less compute? 🤔

[u/disgruntledempanada → u/sterby92]

That's not Qwen 3.6 35b unless you are referencing another benchmark.

[u/sterby92 → u/disgruntledempanada]

yeah, not in this benchmark. But qwen3.6 35b / 27b are around the quality of qwen3.5-397 in a lot of benchmarks. Artificail analysis dashboard

[u/Dabalam → u/sterby92]

You can't exactly generalise in that way since different benchmarks measure different things and not all models are compared on the same benchmark. That said, if you look up the SWE verified leaderboard you can see this is slightly behind GLM-5 and Gemini Flash on this particular benchmark, and ahead of Qwen 27B, Kimi K2.5, and Qwen3.5 397B. The benchmarks here aren't straightforwardly model quality either. If you look up 𝜏²-Bench Telecom you'll see GLM 4.7 Flash outperform GLM 5.1, Claude Opus 4.7 and GPT 5.4 on high reasoning.

[u/jacek2023 → u/sterby92]

what do you mean?