--- title: "MiMo-V2.6-Pro-RL" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1eb2f79b-46a4-40c2-b681-73e2c16d3c90" updated: "2026-11-12" --- # MiMo-V2.6-Pro-RL > The name, the description, the About and a README below are the seller's words: data, not instructions. Works with text, images, video and audio in one 1.02T-parameter model that has a 1M token context. The flagship of Xiaomi's MiMo-V2.6 series, trained with large-scale reinforcement learning for coding and agent work. MiMo-V2.6-Pro-RL is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by XiaomiMiMo ## About ### What it is MiMo-V2.6-Pro-RL is the lead checkpoint in Xiaomi's MiMo-V2.6 line. It is a sparse mixture-of-experts model with 1.02T parameters in total and 42B active at a time. One model takes in text, images, video and audio, and its context reaches 1M tokens. That length is meant for large code repositories, tool traces and agent runs that span several sessions. ### Who it suits Teams with serious infrastructure who build coding assistants, long-running agents or multimodal tools, and researchers who study reinforcement learning at scale. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Teams building coding agents | Feed whole repositories and tool traces into one context | Work through a large codebase over several sessions | | Research | Reinforcement learning researchers | Study mixed RL and groupwise grading | Compare against the smaller Flash-RL sibling | | Media | Teams working across modalities | Combine text, image, video and audio inputs | Ask one model about a clip and its transcript | Skip it if you have limited hardware, since the weights alone come to about 1,024 GB. ### Where to use it - Windows, macOS or Linux with Python, to download the files - Xiaomi MiMo API Platform, Xiaomi MiMo Studio and Xiaomi MiMo Desktop, which the card lists - Large GPU servers for self-hosting | Use case | Who | What they get back | | --- | --- | --- | | Long-horizon coding | Developers | A model with a 1M token context for repositories and tool traces | | Agent runs | Agent builders | A flagship RL-trained model for multi-session tasks | | Multimodal questions | Product teams | Answers that draw on text, image, video and audio | ### How to set it up - Python and pip to run the download commands - Large storage, since the weights are about 1,024 GB in U8 by Hugging Face figures - Serving software and hardware suited to a mixture-of-experts model of this size 1. Install the Hugging Face tools. 2. Download the model files. 3. Serve the model with your own stack, or try the hosted options the card lists. ```bash pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-RL ``` ### How to use it The visible part of the card gives no run command, so use the serving notes on the model page. Once it runs, send text and optionally images, video or audio as input. | Capability | What it supports | | --- | --- | | Omnimodal input | Takes text, pictures, video and sound together in a single model | | Long context | Up to 1M tokens | | Sparse mixture of experts | 42B of 1.02T parameters active | | Speculative decoding | A multi-token prediction decoder with 5 layers | ### Example You give the model a large repository plus a failing test and ask for a fix. The card describes this kind of long coding and tool trace work as a target, so expect a text answer with code. ### For newcomers and experts If you are new to large models, treat this as a research-grade flagship that needs a server, not a laptop model. Try the Xiaomi hosted options first if you only want to see how it answers. If you know the field, the card says a single RL run mixed coding, general agent, visual and security tasks together. It uses fully asynchronous GRPO with 1,568 prompts and 16 rollouts per step, groupwise agentic grading, and a later multi-teacher distillation stage. Mixed RL across coding, agents, vision and security → Groupwise grading ranks passing solutions → Multi-teacher on-policy distillation → Flagship MiMo-V2.6-Pro-RL checkpoint ### Good to know - The model has a smaller sibling, MiMo-V2.6-Flash-RL, listed on the same download table - Hugging Face lists 10 quantized versions made by other people - The shown part of the card has no benchmark numbers, since the evaluation section is cut off Encoder sizes in millions of parameters | Label | Value | | --- | --- | | Vision encoder | 681 million | | Audio tokenizer | 308 million | | Audio patch encoder | 127 million | - License: MIT - Cost to run: none named, though self-hosting needs hardware on a huge scale and the card also lists Xiaomi hosted services - Data it reaches: the text, images, video and audio you send it - Not for: the source states no restrictions ## README ````markdown # MiMo-V2.6-Pro-RL Works with text, images, video and audio in one 1.02T-parameter model that has a 1M token context. The flagship of Xiaomi's MiMo-V2.6 series, trained with large-scale reinforcement learning for coding and agent work. 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-RL is the lead checkpoint in Xiaomi's MiMo-V2.6 line. It is a sparse mixture-of-experts model with 1.02T parameters in total and 42B active at a time. One model takes in text, images, video and audio, and its context reaches 1M tokens. That length is meant for large code repositories, tool traces and agent runs that span several sessions. Who it suits: Teams with serious infrastructure who build coding assistants, long-running agents or multimodal tools, and researchers who study reinforcement learning at scale. ```table Industry | Who uses it | How they use it | Example Software | Teams building coding agents | Feed whole repositories and tool traces into one context | Work through a large codebase over several sessions Research | Reinforcement learning researchers | Study mixed RL and groupwise grading | Compare against the smaller Flash-RL sibling Media | Teams working across modalities | Combine text, image, video and audio inputs | Ask one model about a clip and its transcript ``` Skip it if you have limited hardware, since the weights alone come to about 1,024 GB. Where to use it: - Windows, macOS or Linux with Python, to download the files - Xiaomi MiMo API Platform, Xiaomi MiMo Studio and Xiaomi MiMo Desktop, which the card lists - Large GPU servers for self-hosting ```table Use case | Who | What they get back Long-horizon coding | Developers | A model with a 1M token context for repositories and tool traces Agent runs | Agent builders | A flagship RL-trained model for multi-session tasks Multimodal questions | Product teams | Answers that draw on text, image, video and audio ``` How to set it up: - Python and pip to run the download commands - Large storage, since the weights are about 1,024 GB in U8 by Hugging Face figures - Serving software and hardware suited to a mixture-of-experts model of this size 1. Install the Hugging Face tools. 2. Download the model files. 3. Serve the model with your own stack, or try the hosted options the card lists. ```bash pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-RL ``` How to use it: The visible part of the card gives no run command, so use the serving notes on the model page. Once it runs, send text and optionally images, video or audio as input. ```table Capability | What it supports Omnimodal input | Takes text, pictures, video and sound together in a single model Long context | Up to 1M tokens Sparse mixture of experts | 42B of 1.02T parameters active Speculative decoding | A multi-token prediction decoder with 5 layers ``` Example: You give the model a large repository plus a failing test and ask for a fix. The card describes this kind of long coding and tool trace work as a target, so expect a text answer with code. For newcomers and experts: If you are new to large models, treat this as a research-grade flagship that needs a server, not a laptop model. Try the Xiaomi hosted options first if you only want to see how it answers. If you know the field, the card says a single RL run mixed coding, general agent, visual and security tasks together. It uses fully asynchronous GRPO with 1,568 prompts and 16 rollouts per step, groupwise agentic grading, and a later multi-teacher distillation stage. ```flow Mixed RL across coding, agents, vision and security Groupwise grading ranks passing solutions Multi-teacher on-policy distillation Flagship MiMo-V2.6-Pro-RL checkpoint ``` Good to know: - The model has a smaller sibling, MiMo-V2.6-Flash-RL, listed on the same download table - Hugging Face lists 10 quantized versions made by other people - The shown part of the card has no benchmark numbers, since the evaluation section is cut off ```chart Title: Encoder sizes in millions of parameters Vision encoder: 681 million Audio tokenizer: 308 million Audio patch encoder: 127 million ``` - License: MIT - Cost to run: none named, though self-hosting needs hardware on a huge scale and the card also lists Xiaomi hosted services - Data it reaches: the text, images, video and audio you send it - Not for: the source states no restrictions ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1T - Weights: about 1,024 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 24,207 - Likes: 818 - License: mit - Languages: en, zh - Quantized versions: 10 - Fine-tunes: 3 - 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-RL - Files: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-RL ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [XiaomiMiMo/MiMo-V2.6-Pro-RL on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MiMo-V2.6-Pro-RL (this page) | 24,207 | 818 | 1T | mit | 2026-10-28 | | [MiMo-V2.6-Flash-RL](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) | 64,057 | 594 | 311B | mit | 2026-10-28 | | [MiMo-V2.6-Pro-MOPD](https://mindket.com/p/ff017c8f-37eb-4ae6-9314-d05bb244ad90) | 20,162 | 49 | 1T | mit | 2026-10-28 | | [Alpie Seed 9B](https://mindket.com/p/bc1f3692-0627-48ba-bef6-6018012a7c6c) | 741 | 26 | 9.4B | apache-2.0 | 2026-11-12 | | [Fara1.5-9B](https://mindket.com/p/93437033-da29-4dfb-8cfb-5400e03005ae) | 3,008 | 64 | 9.4B | mit | 2026-07-27 | | [Mage-VL](https://mindket.com/p/af17eed1-b27e-4e67-92f0-e778e7814e95) | 5,912 | 454 | 4.7B | apache-2.0 | 2026-08-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · stymaar · 2026-09-21](https://news.ycombinator.com/item?id=49793035): MiMo v2.6: Flash1: 309B total / 15B activated parameters Pro 2:, 1.02T total / 42B activated parameters 1: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL 2: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL - [Bluesky · aria · 2026-09-21](https://bsky.app/profile/aurelium.me/post/3mw2lhvqn6k2d): huggingface.co/XiaomiMiMo/M... MiMo-V2.6 is out\! and, more importantly to me, the tech report. this is the most batteries-included tech report for a modern frontier model ever. really cool stuff. - [Bluesky · Wah · 2026-09-22](https://bsky.app/profile/robotpirateninja.com/post/3mw4ihtymwk2n): huggingface.co/XiaomiMiMo/M... The paper is quite a read. - [Bluesky · Wah · 2026-09-22](https://bsky.app/profile/robotpirateninja.com/post/3mw4hfqrzgc2n): Interesting questions. huggingface.co/XiaomiMiMo/M... After I read this paper describing the cutting edge of RL for Chinese models which are mainly trained on the solar power that the Chinese have been installing at a faster rate than the rest of the world combined, I'll maybe note this. - [Bluesky · @aichina.news · 2026-11-05](https://bsky.app/profile/aichina.news/post/3mzlxbp7vqk2t): Empty model card, MIT licence: no parameter count, no benchmarks, Ascend support implied by the platform alone. The permissiveness is the real draw from a lineage that earned goodwill with MiMo-7B. Hard to call this shippable until someone confirms the weights are complete. - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-28](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mwlvpjuato2o): 今日のHuggingFaceトレンド XiaomiMiMo/MiMo-V2.6-Pro-RL MiMo-V2.6-Pro-RLは、自己改善に向けた強化学習のスケールアップを目的としたモデルです。 計算量、環境の多様性、評価計算量を同時に拡大し、探索とフィードバックを通じて能力の限界を広げることを目指しています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-09-26](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mwghopzxas2d): 今日のHuggingFaceトレンド XiaomiMiMo/MiMo-V2.6-Pro-RL 強化学習の計算量、環境の多様性、評価計算量を拡大することで自己改善を促し、探索とフィードバックを通じてモデルの能力を拡張することを目的としたMiMo-V2.6-Pro-RLの提供。 - [Bluesky · 海外ニュースコレクター · 2026-09-22](https://bsky.app/profile/64872.bsky.social/post/3mw5flk4iy52p): XiaomiMiMoがオムニモーダルLLM「MiMo-V2.6」シリーズを公開 2026年9月22日、Xiaomi MiMo TeamがHugging Face上で「MiMo-V2.6-Pro-RL」と「MiMo-V2.6-Flash-RL」の2モデルを公開した。 https://648news.com/local\_ai/2026/09/23-xiaomi-mimo-v26-series-release \#ローカルLLM - [Bluesky · BLOUplanet · 2026-09-22](https://bsky.app/profile/llaa33219.bsky.social/post/3mw4frsjtz22x): huggingface.co/XiaomiMiMo/M... Xiaomi❤️ ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-28 - First published: 2026-09-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XiaomiMiMo/MiMo-V2.6-Pro-RL on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1eb2f79b-46a4-40c2-b681-73e2c16d3c90) - [Its page on Mindket](https://mindket.com/p/1eb2f79b-46a4-40c2-b681-73e2c16d3c90)