MiMo-V2.6-Pro-MOPD

MiMo-V2.6-Pro-MOPD

Handles text, image, video and audio with a 1T-parameter mixture of experts tuned to repeat tool calls less in agent work. For teams that can host models of this size.

Made by XiaomiMiMo on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: MiMo-V2.6-Pro-MOPD is an upgraded checkpoint of Xiaomi's MiMo-V2.6-Pro-RL, a sparse mixture of experts model with about 1 trillion parameters. In the upgrade, teachers that each specialize in one domain pass their skills to a single student model. It also reduces a failure where an agent keeps issuing the same tool call and makes no progress.

Who it suits: It suits teams that can host a huge model and run agents that call tools, and researchers who study how to combine several teachers into one student.

Industry | Who uses it | How they use it | Example
AI platforms | Infrastructure teams | Host a 1T-parameter mixture of experts model | A shared model behind an internal agent service
Software | Agent builders | Run tool-using agents that should not loop on the same call | A coding agent that keeps making progress
Research | Language model researchers | Study multi-teacher on-policy distillation | Read how mixRL and SFT teachers are combined

People with a single ordinary computer can skip it, since the weights come to about 1,024 GB.

Where to use it:

• Python with the transformers library, on a server large enough to hold the weights
• Agent setups where the model calls tools and needs to avoid repeating itself
• Windows, macOS or Linux with Python, for downloading the files

Use case | Who | What they get back
Tool-using agents | Agent builders | Fewer repeated, near-identical tool calls
Long inputs | Developers | A context window of 1M tokens
Mixed media input | Product teams | A model that takes text, image, video and audio

How to set it up: You need a few things first.

• Python and pip, for the Hugging Face hub tool
• Disk space for about 1,024 GB of weights
• A server or cluster that can run a model with 42B activated parameters

1. Install the hub tool with pip.
2. Download the repository files.
3. Follow the technical report and blog the card links for how the model is run.

pip install -U huggingface_hub
hf download XiaomiMiMo/MiMo-V2.6-Pro-MOPD

How to use it: The part of the card shown here gives no run command. You send the model text, images, video or audio inside whatever agent or serving setup you build. Xiaomi MiMo also runs its own API platform, studio and desktop app, which the card links.

Title: Model summary
Part | Detail
Architecture | Sparse mixture of experts, 1.02T total and 42B activated parameters
Context length | 1M tokens
Inputs | Text, image, video and audio
Vision encoder | A 681M MiMo ViT with 28 layers
Audio encoder | A 308M audio tokenizer plus a 127M audio patch encoder
Speculative decoder | A 5-layer multi-token prediction block

Example: You put the model inside an agent and ask it to fix a failing test in a repository. The card says this checkpoint lowers the chance of the agent calling the same tool again and again while appearing busy.

For newcomers and experts: If you are new, this is a huge chat and agent model, not something you run on a laptop. A first step is to read the technical report the card links before you plan hardware.

The MOPD2 stage distills several domain teachers into the student model on-policy. Some teachers are trained on verifiable tasks with reinforcement learning, and others on synthetic demonstrations for open-domain tasks. Three streams feed each update: full rollouts supervised by teachers, prefixes taken from teacher rollouts, and prefixes taken from demonstrations. A short specialized-teacher run handles the repeated tool calls.

Domain teachers supervise the student
Three streams feed each update
Student model is updated on-policy
Checkpoint repeats tool calls less

Good to know:

• License: MIT.
• Cost to run: no API key or paid service named for the open weights. The cost is hardware for about 1,024 GB of weights.
• Data it reaches: only what you feed it, including any tools you connect in an agent setup. The card names no built-in file, web or account access.
• Not for: small machines. The card's visible part also gives no run command or benchmark numbers, so check the technical report before relying on it.

On Hugging Face, 14 November 2026

• Task: text generation
• Library: transformers
• Parameters: 1T
• Weights: about 1,024 GB in U8
• Formats: Safetensors
• Downloads in the last 30 days: 20,162
• Likes: 49
• License: mit
• Languages: en, zh
• Quantized versions: 9
• Fine-tunes: 4
• Tags: transformers, safetensors, mimo_v2, text-generation, multimodal, vision-language, audio, agent, video-understanding, long-context, conversational, 8-bit

Links

• Model: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD
• Files: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download XiaomiMiMo/MiMo-V2.6-Pro-MOPD

Then follow the usage in its model card. Nothing in this kit runs by itself.