Kimi K2.6
2103°
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MoonshotAI | 262,144 token context | $0.65/M input, $3.41/M output. Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and can convert prompts and visual inputs into production-ready interfaces. Its agent swarm architecture scales to hundreds of parallel sub-agents for autonomous task decomposition - delivering documents, websites, and spreadsheets in a single run without human oversight.
Guides for this model
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
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
Kimi K2.6 is Moonshot AI's open-weight multimodal model built around agentic coding work: long-horizon tasks, UI generation from visual inputs, and swarm-based orchestration that can spin up to 300 parallel sub-agents. At 1T total parameters with 32B active via a MoE architecture, it punches above its compute cost. It suits developers and teams who need autonomous end-to-end pipelines rather than a chat assistant.
Known for
- End-to-end coding across Python, Rust and Go including front-end and DevOps work.
- Converts prompts and images into production-ready interfaces with animations and layouts.
- Agent swarm scales to 300 sub-agents and 4,000 coordinated steps in a single run.
- 256K token context window for large codebases and long documents.
- Open weights with a 400M-parameter vision encoder (MoonViT) built in.
Watch out for
- 1T total parameters means local deployment demands serious hardware; the 32B active figure does not tell the whole VRAM story.
- Output pricing at $3.41/M adds up fast on long agentic runs that generate thousands of tokens autonomously.
- Licence terms are not detailed on the maker's page; check the Hugging Face repository before any commercial use.
Prompts to try
Build a full-stack UI
You have a screenshot of a dashboard I am attaching. Recreate it as a self-contained React component with Tailwind CSS. Add a dark-mode toggle, make the chart interactive using Recharts, and output a single file I can drop into a Vite project.
Autonomous repo task
Clone the public repo https://github.com/expressjs/express, identify the three functions with the highest cyclomatic complexity, write a refactored version of each with inline comments explaining the changes, and produce a markdown report summarising what you changed and why.
Multi-language port
Here is a Python script that parses a CSV, aggregates rows by a key column and writes a summary JSON. Port it faithfully to idiomatic Go and idiomatic Rust. For each version, note any behaviour differences caused by type system or error-handling constraints.
Compare with
- Kimi LatestAPI-only model1,048,576 token context · $0.29/M input, $24.00/M output
- Kimi K3Open-weights model1,048,576 token context · $0.30/M input, $11.94/M output
- Kimi K2 ThinkingOpen-weights model262,144 token context · $0.60/M input, $2.50/M output
- Kimi K2.7 CodeOpen-weights model262,144 token context · $0.67/M input, $3.35/M output
More text models from moonshotai
Questions people ask
- Are the weights open and can I run Kimi K2.6 locally?
- Yes, the weights are open and available on Hugging Face at moonshotai/Kimi-K2.6. The model is 1T total parameters with 32B active via MoE, so local deployment requires substantial hardware; check the VRAM guides before committing.
- What does Kimi K2.6 cost via API and what is the context limit?
- Moonshot AI prices it at $0.65 per million input tokens and $3.41 per million output tokens, with a 256K token context window.
- How is K2.6 different from K2.5?
- K2.6 adds native multimodal input via a built-in MoonViT vision encoder, expands the agent swarm to 300 sub-agents, and shows meaningful benchmark gains over K2.5 across agentic search, coding and vision tasks.
Captain's verdictIf your work is autonomous coding pipelines or vision-to-UI generation, K2.6 is the most capable open-weight option in that niche right now.
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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How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
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.
Kimi K2.6 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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