MiMo V2.6 Pro
9028°
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Xiaomi | 1,050,000 token context | $0.43/M input, $0.87/M output. MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding workloads. The model features a 1M-token context window and native multimodal capabilities. Optimized for agentic workflows, it delivers top-tier performance across coding, visual, general, and research scenarios, excelling at complex, long-horizon tasks with robust generalization across a diverse range of agent harnesses.
Guides for this model
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
Best local LLM by use case (October 2026): the right open model for every job
How much VRAM do I actually need for local AI?
Open weights vs open source vs API-only: what you are actually allowed to do
How to Install Ollama and Run a Local AI (Windows, Mac, Linux)
Captain's notes
Notes from 2026-10-13
MiMo-V2.6-Pro is Xiaomi's flagship model: a sparse Mixture-of-Experts system with 1.02 trillion total parameters (42 billion activated) that handles text, image, video and audio in a single model. Its defining feature is a reinforcement-learning pipeline designed to keep improving itself through exploration rather than separate per-domain fine-tuning. It suits teams running complex agentic workflows, long-context code or research tasks, and anyone who wants a capable open-weights frontier model at sub-dollar-per-million pricing.
Known for
- True omnimodal: text, image, video and audio handled natively in one model
- 1M-token context window — long enough for full repositories or multi-session agent runs
- Single mixed RL run across coding, visual, general and cybersecurity tasks
- Open weights available on Hugging Face for local or self-hosted deployment
- Self-improving reward loop that steers toward shorter, higher-quality solutions
Watch out for
- Over a trillion total parameters — running this locally demands serious hardware most people do not have.
- Licence terms are not detailed in the model card; check the Hugging Face repo before any commercial use.
- At this scale, inference latency on self-hosted setups will be high unless you have a large multi-GPU cluster.
Prompts to try
Multi-file refactor task
You have a Python service split across three files: a FastAPI router, a SQLAlchemy models file, and a Celery tasks file. The router imports directly from the models file and calls Celery tasks inline. Redesign the dependency structure so the router depends only on a service layer, write the service layer code, and explain each change you make.
Long-horizon research plan
I need to evaluate five open-source vector databases for a production RAG system handling 50 million documents, sub-100ms p95 latency, and hybrid keyword-plus-semantic search. Build a structured evaluation plan: define the criteria, propose a benchmark dataset strategy, list the specific tests to run for each criterion, and describe how you would score and compare results.
Image plus code debugging
Attach a screenshot of a Python traceback or a chart with unexpected output. Describe what the code is supposed to do, then ask: what is causing this error or anomaly, and what is the minimal code change that fixes it? Walk through your reasoning step by step before giving the fix.
Compare with
- MiMo-V2.6-Pro-UltraSpeedAPI-only model1,048,576 token context · $4.35/M input, $8.70/M output
- MiMo-V2.6-FlashOpen-weights model1,050,000 token context · $0.14/M input, $0.28/M output
- MiMo-V2.5Open-weights model1,050,000 token context · $0.14/M input, $0.28/M output
- MiMo-V2.5-ProOpen-weights model1,050,000 token context · $0.43/M input, $0.87/M output
More text models from xiaomi
Questions people ask
- Are the weights open and can I run MiMo-V2.6-Pro locally?
- Yes, the weights are published on Hugging Face under the repository XiaomiMiMo/MiMo-V2.6-Pro-RL. Running it locally is technically possible but demands substantial multi-GPU hardware given the 1.02 trillion total parameter count.
- What does MiMo-V2.6-Pro cost and what is the context window?
- Via Xiaomi's API it is priced at $0.43 per million input tokens and $0.87 per million output tokens, with a 1,050,000-token context window.
- How does MiMo-V2.6-Pro differ from MiMo-V2.6-Flash?
- Both come from the same RL training run; Flash is the smaller, faster sibling. Pro is the flagship checkpoint optimised for maximum capability on demanding tasks rather than speed or cost.
Captain's verdictA serious open-weights frontier model for agentic and long-context work — the price is right, but the hardware ask is not casual.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #22 Hottest Text / LLM model · #22 Hottest Text / LLM text model
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MiMo V2.6 Pro Popularity Trend
Heat score since 21 Sep 2026
Heat score
9,028°
This week
▲
50.4%
8,974° then, 9,028° now
This month
n/a
Wasn't released/tracked
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,699°
OpenRouter tokens
4,616,159,113,192 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 461,615 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+329°
Adoption rate
About 24,039 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 9,028°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.461,615 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,699°8,699°running total
- 3Add the adoption rate.About 24,039 downloads a day right now, measured between two readings, at a quarter of the weight.+329°9,028°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,028°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
MiMo V2.6 Pro is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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