What is Kimi and how it can help you be more productive
Kimi isn’t just “China’s long-context chatbot” anymore. What started in October 2023 as Moonshot AI’s consumer assistant with a then-record context window has grown into a full open-weight platform: frontier-scale models, agentic products, native multimodal understanding, and the largest open-weight release ever. On July 16, 2026 Moonshot shipped Kimi K3 – a 2.8-trillion-parameter sparse MoE flagship with a 1-million-token context window and native vision – followed by full open weights on July 27.
In this guide we explain what Kimi is in 2026, how the K3 / K2.7 / K2.6 / K2.5 family works, what Agent Swarm, Kimi Code, and OK Computer can do, what the plans cost, and how to use it well.
What is Kimi?
Kimi is an AI assistant and family of large language models developed by Moonshot AI, a Beijing lab founded in March 2023 by Yang Zhilin (CEO), Zhou Xinyu, and Wu Yuxin – classmates at Tsinghua University. Yang earned his PhD at Carnegie Mellon and previously worked at Google Brain and Meta, and built Moonshot around one idea: long context is a first-class feature, not an afterthought. The company name nods to Pink Floyd’s The Dark Side of the Moon.
The Kimi chatbot entered closed beta in October 2023 and opened to the public that November with 128,000 tokens of lossless context – at the time, the first model that could actually accept that much in one go. Unlike closed US labs, Kimi’s defining trait since then has been open weights plus aggressive long-context engineering plus agentic coding. Most Kimi models ship with downloadable weights on Hugging Face so you can self-host, fine-tune, and keep data on your own infrastructure.
Today Kimi is not just a chat window. Depending on the plan and the task it can:
- Answer questions & explain concepts
- Write, rewrite, summarize & translate
- Search the live web with citations
- Reason step by step with thinking modes
- Analyze files, spreadsheets, PDFs, images & video
- Write, debug, test & ship code
- Coordinate parallel agents (Agent Swarm)
- Generate slides, sites & documents
- Hold real-time voice conversations
- Run locally on your own hardware with open weights
Kimi K3 and the K2 family
K3 is a 2.8-trillion-parameter Mixture-of-Experts model with 896 experts, of which 16 are active per token. It is built on two architectural innovations Moonshot developed in-house: Kimi Delta Attention (KDA), which cuts memory use and speeds up long-context generation, and Attention Residuals, which stabilize training across depth. K3 debuted near the top of independent leaderboards – behind only the strongest closed models on composite indexes, while leading some coding and front-end arenas.
Two notes on the wider lineup. First, K3’s weights are “open” with a caveat: the Kimi K3 License requires large inference providers to negotiate separately and large products to display attribution – the older K2 family ships under a more permissive modified MIT license. Second, behind K2.5 sit K2 (Jul 2025, the original trillion-parameter open release), K1.5 (Jan 2025, reasoning), and the Kimi-VL vision line.
How does Kimi work?
Kimi models are Mixture-of-Experts transformers trained natively on text, images, audio, video, and code together. Only a fraction of total parameters activate per token – that is how K3 can be 2.8T total but cost ~104B active per forward pass. A thinking-level setting controls how much the model reasons step by step before answering: trivial prompts get a fast response, hard ones trigger deliberate multi-step reasoning. K3 runs at maximum thinking effort by default.
Modern Kimi goes beyond next-token prediction by combining the model with tools and, in Swarm mode, parallel sub-agents. A typical request flows through several stages:
- 1
Understand the request
Reads the prompt, conversation history, uploaded files/images/video, and any connected tools, memory, or MCP servers.
- 2
Reason through the problem
For complex tasks, the thinking level lets it work through the problem step by step instead of answering in one pass. K3 always thinks; effort accepts low, high, and max.
- 3
Decide which tools to use
Web search, code execution, file analysis, image or video generation, or hand-off to sub-agents in Swarm mode.
- 4
Generate the response or take the action
Produces the answer, the document, the code, or the finished deliverable – sometimes after sub-agents have worked in parallel for minutes or hours.
- 5
Refine with follow-ups
Context compaction, checkpoints, and plan mode keep long sessions coherent across hundreds of thousands of lines of code or dozens of papers.
What can you do with Kimi?
The agentic shift: Swarm, Code, Claw, and OK Computer
The biggest change since 2023 is not that answers got better – it is that Kimi moved from conversation toward execution. Instead of “you ask, it answers,” the workflow is increasingly “you define a goal, Kimi researches, analyzes, uses tools, and comes back with something finished.”
Moonshot’s path here has been distinctive. While Western labs built agentic features into chat apps, Moonshot open-sourced the weights and let the ecosystem build around them – Cursor, Harvey, and others ship products on Kimi models. The strategy is fewer surfaces that each do more, backed by weights anyone can download and modify.
Kimi timeline: 2023–2026
Moonshot AI is founded in Beijing by Yang Zhilin, Zhou Xinyu, and Wu Yuxin.
Kimi chatbot enters closed beta in October and opens to the public in November with 128K lossless context – a record at the time.
Context caching enters public beta, making repeated long-context workloads cheap.
Kimi Explore Edition (now Kimi Researcher) launches globally with autonomous search; monthly active users later exceed 36 million.
Kimi K1.5 reasoning model ships, claiming parity with contemporary o1-class models on math, coding, and multimodal tasks.
Kimi K2 released – the first trillion-parameter open-weight MoE (1T total / 32B active) with same-day weights.
OK Computer agentic feature added; K2-Instruct scores strongly on SWE-bench Verified.
Kimi K2.5 ships – native multimodality plus Agent Swarm v1 for parallel multi-agent work.
Kimi K2.6 launches as a general-purpose multimodal model with improved long-context code generation and reasoning.
Kimi K2.7 Code ships as a specialized coding model with 256K context and multi-step tool invocation.
Kimi K3 unveiled: 2.8T parameters, 896 experts, KDA and Attention Residuals, 1M-token context, native multimodal – debuting near the top of independent leaderboards.
Full K3 model weights released under the Kimi K3 License – the largest open-weight model ever published at 1.56 TB.
How to use Kimi well
The prompt pattern that works
Give clear role + task + format, attach real files or codebase context, and iterate. For long-horizon work, use Kimi Code in plan mode and let Agent Swarm decompose: one agent for archaeology across a large repo, one for implementation, one for tests.
How large is the context window?
Kimi K3 supports a standard 1-million-token context window – roughly 750,000 words – with flat per-token pricing across the entire window. That is enough to hold an entire codebase, a book-length document, or hours of video transcripts in a single prompt. The K2 family supports 256K tokens (about 192,000 words), and the legacy K2 supports 128K.
Practical rule of thumb: a token is roughly 0.75 English words, and about 1.5 tokens per word once you factor in punctuation and spacing. Deep reasoning and Agent Swarm consume part of that budget, and attention is not perfectly uniform across the full window.
What does Kimi cost?
| Plan | Price | Model access | Limits |
|---|---|---|---|
| Adagio | $0 | K3 chat, file upload, web search | ~6 agent credits, no Swarm / Code / Claw |
| Moderato | $19/mo | K3 + K2.6 | 60 agent credits, 2 concurrent tasks, 25 Swarm runs |
| Allegretto | $39/mo | K3 + K2.6 | 150 agent credits, 50 Swarm runs, Kimi Claw |
| Allegro | $99/mo | K3 + K2.6, heavy agent work | 360 agent credits, 4 concurrent tasks, 120 Swarm runs |
| Vivace | $199/mo | Maximum everything | 720 agent credits, 240 Swarm runs, 8 sub-agents |
| API · Kimi K3 | $3.00 / $15.00 | per 1M tokens in/out, 1M context | $0.30 cached input · frontier pricing |
| API · K2.7 Code | $0.95 / $4.00 | per 1M tokens in/out, 256K context | Specialist coding endpoint |
| API · K2.6 | $0.95 / $4.00 | per 1M tokens in/out, 256K context | General-purpose workhorse |
| API · K2.5 | $0.60 / $3.00 | per 1M tokens in/out, 256K context | Multimodal value tier |
Annual billing saves roughly 20%, and the app and API are separate wallets. K3 undercuts comparable closed flagships on list price, and measured cost per completed task is often lower. The chart above reflects September 2026 – check Moonshot’s official pages for current rates.
Frequently asked questions
Heads-up:Kimi is a fast-moving product. Plan names, model names, and prices change frequently – the details above reflect September 2026 (K3 released July 16, weights July 27). Check Moonshot’s official Kimi site and API docs for current availability and limits.