# Introducing Kimi K3 > Source: > Published: 2026-07-16 15:04:12+00:00 ## Introducing Kimi K3 Kimi K3 is Kimi’s most capable model to date, with 2.8 trillion parameters. Built on Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, it offers native visual understanding and a 1M-token context window for frontier intelligence scenarios such as software engineering, knowledge work, and deep reasoning. In our evaluations, Kimi K3 delivers frontier-level performance. Among the models tested, its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol. For the complete benchmark results, see our[tech blog](https://www.kimi.com/blog/). The full model weights of Kimi K3 will be released in the coming days. More details on the architecture, training, and evaluation will be published together with the Kimi K3 technical report. ### The 3-trillion-scale open-source model Kimi K3 is the first open-source model to reach the 2.8-trillion-parameter scale. It is the latest step in Kimi’s continued push of model-scale boundaries: in 9 of the past 12 months, Kimi models have set new records for open-source model scale. Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). Both architectural updates are designed to help information flow more smoothly through longer sequences and deeper models. We also further increased the sparsity of the Mixture of Experts (MoE): with the Stable LatentMoE framework, the model efficiently activates 16 out of 896 experts. Together with improvements in training methodology and data recipes, these structural advances give K3 roughly 2.5x the overall scaling efficiency of K2, converting compute into capability more effectively.### Coding Kimi K3 is our most capable coding model to date. It sustains progress on long-horizon software engineering tasks: understanding large codebases, operating the terminal, coordinating tool calls, inspecting runtime behavior, and recovering autonomously from failed attempts with minimal human intervention. K3 improves most notably on tasks that combine software engineering, visual understanding, and spatial reasoning. It can move back and forth between source code and rendered output, using screenshots, logs, test results, and runtime state to decide the next change. This makes K3 especially well suited for game development, frontend engineering, CAD workflows, and infrastructure optimization.### Knowledge work K3 pushes the boundary of end-to-end knowledge work. On the GDPval-AA v2 leaderboard, Kimi K3 scores 1687. The benchmark evaluates AI models on real-world tasks across 44 occupations and 9 major industries; Kimi K3 ranks behind only Claude Fable 5 Max and GPT-5.6 Sol Max, and ahead of Claude Opus 4.8 Max at 1600. On AA-Briefcase, Kimi K3 scores 1527, ranking second among all models — behind only Claude Fable 5 Max and ahead of GPT-5.6 Sol Max (1495). AA-Briefcase is a private agentic knowledge-work benchmark developed by Artificial Analysis to evaluate frontier agentic capability in long-horizon knowledge work. Thanks to the 1M context window, in a single-agent setup Kimi K3 achieves a SOTA score of 91.2 on BrowseComp without context compression or additional context-management techniques, demonstrating outstanding capability on long-horizon, high-difficulty information-seeking tasks.## Get started The examples require Python 3.9+ and the OpenAI SDK. Install the SDK and initialize the client once; later Python examples reuse`client` . ## Basic call - Python - cURL ## Thinking effort K3 always has thinking mode enabled and supports configuring its thinking effort with the top-level`reasoning_effort` field. Do not use the K2.x `thinking` parameter. Thinking effort currently supports only the `max` level (default); more levels are coming soon. See [Thinking Effort](/docs/guide/use-thinking-effort)for usage.For multi-turn conversations and tool calls, add the complete assistant message returned by the API to the next request. Do not keep only `content` .## Streaming Streaming responses provide separate`reasoning_content` and final-answer `content` deltas. See [Streaming Output](/docs/guide/utilize-the-streaming-output-feature-of-kimi-api)for details. ## Vision input For vision messages,`content` must be an array of objects, not a serialized string. See [Vision Input](/docs/guide/use-kimi-vision-model)for formats and limits. - Local image - Video file ## Structured output Use`json_schema` with `strict: true` to constrain the final `message.content` . Parse only that field, not `reasoning_content` . Name and age schema Name and age schema [Structured Output](/docs/guide/response_format). ## Partial Mode Add an assistant message with`partial=True` at the end of `messages` to continue from a text prefix. Prepend that prefix when displaying the final result. [Partial Mode](/docs/guide/use-partial-mode-feature-of-kimi-api). ## Custom tools and `tool_choice` Use `tool_choice="required"` on the first turn to require at least one tool call. After executing every call, return the complete assistant message and append one tool result with the matching `tool_call_id` for each call. Minimal weather agent loop Minimal weather agent loop [Tool Choice](/docs/guide/use-tool-choice). ## Dynamic tool loading Place a complete tool definition in a`system` message without `content` . The tool becomes available from that message onward. Load a calculator dynamically Load a calculator dynamically - Include the complete `name` ,`description` , and`parameters` definition. - The declaration takes effect at its position in `messages` . - Keep this message in later request history; the server does not retain it. [Dynamic Tool Loading](/docs/guide/use-dynamic-tool-loading). ## 1M context and automatic caching Context caching is automatic for regular model requests; no cache ID, TTL, or extra parameter is required. Keep the long prefix unchanged so later requests can automatically attempt a cache hit.[Context Caching](/docs/guide/use-context-caching-feature-of-kimi-api). ## Official tools Official tools are integrated through Formula:- Fetch tool definitions from the Formula `/tools` endpoint. - Add those definitions to the Chat Completions `tools` field. - When the model returns `tool_calls` , submit each function name and arguments to the Formula`/fibers` endpoint. - Add the complete assistant message and Fiber output as the corresponding tool message. - Call Chat Completions again until the model returns a final answer. [Official Tools](/docs/guide/use-official-tools)for the complete client and API contract. Web search is being updated and is not recommended for use in the near term. ## Important limits `reasoning_effort` currently supports only`max` ; K3 always has thinking mode enabled.`max_completion_tokens` defaults to 131072 and can be set up to 1048576.`temperature=1.0` ,`top_p=0.95` ,`n=1` ,`presence_penalty=0` , and`frequency_penalty=0` are fixed; omit them from requests.- Return the complete assistant message unchanged in multi-turn conversations and tool calls. - Vision input does not support public image URLs. Use base64 or `ms://` , and make`content` an array of objects. - Web search is being updated and is not recommended for production workflows in the near term. ## FAQ How is Kimi K3 billed? How is Kimi K3 billed? Kimi K3 offers a 1M-token context and uses flat pay-as-you-go pricing — there is no tiering by context length. Input (with separate rates for cache hits and misses) and output are billed at uniform per-token prices. See [Kimi K3 pricing](/docs/pricing/chat-k3).## Model Pricing For token pricing details, refer to[Model Pricing](/docs/pricing/chat-k3). ## Related docs ## Thinking Effort Configure reasoning_effort. ## Vision Input Send images and videos. ## Structured Output Use strict JSON Schema. ## Partial Mode Continue from a prefix. ## Tool Choice Control whether the model calls tools. ## Dynamic Tool Loading Inject tool definitions on demand. ## Tool Calling Best Practices Combine tool-calling features. ## Official Tools Integrate Formula tools. ## Kimi K3 Pricing Review input and output prices.