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The easiest way to use Kimi K3 is through Kimi Web or the Kimi mobile app. For software development, select it in Kimi Code; for an application, call Moonshot’s API. You can also download its open weights, but Moonshot recommends specialized supernode infrastructure with at least 64 accelerators—not an ordinary desktop.
Choose the way you want to use Kimi K3
| Route | Best fit | What to know |
|---|---|---|
| Kimi Web or mobile app | Using K3 through a graphical interface | Moonshot lists Kimi Web and apps for iOS, Android, and HarmonyOS. Check the official access page for current availability. |
| Kimi Code | Working in a terminal or supported IDE | Select Kimi K3 with /model in the CLI, or from the model dropdown in supported VS Code or Cursor integrations. |
| API | Building K3 into software or an automated workflow | The model identifier documented by Moonshot is kimi-k3. Check the current quickstart for credentials, endpoint, pricing, limits, and request fields. |
| Downloaded weights | Operating the model on infrastructure you control | Weights are available from Hugging Face under the Kimi K3 License. Moonshot recommends a supernode with at least 64 accelerators. |
Moonshot’s Kimi access page lists its products, while the official repository documents developer access and deployment options. Current pricing and regional eligibility are not established in the materials cited here, so confirm them with Moonshot before choosing a route.
What Kimi K3 is—and what “2.8T” means
Moonshot describes Kimi K3 as an open-weight, mixture-of-experts model with native vision and a context window of up to one million tokens. Its official materials position it for long-horizon coding, knowledge work, and reasoning. The 2.8 trillion figure is the model’s total parameter count, not the number activated for every response.
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Moonshot says 16 of 896 experts are activated. The technical report abstract gives 104 billion activated parameters. Those figures describe the model’s architecture; they do not, by themselves, establish performance on a particular task or hardware requirements for every deployment. Moonshot’s technical blog describes Kimi Delta Attention and Attention Residuals as components of the design. These are company-provided specifications and recommendations, not independent measurements.
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Use Kimi K3 in Kimi Code
In the command-line interface
- Start Kimi Code using Moonshot’s current setup instructions.
- At the CLI prompt, enter
/model. - Select
Kimi K3from the available models.
In a supported IDE integration
- Open Kimi Code in the supported VS Code or Cursor integration.
- Open the model dropdown.
- Choose
Kimi K3.
Integration availability and setup details can change; use the official Kimi access page for the current product instructions.
Call Kimi K3 through the API
Moonshot’s repository documents API access through platform.kimi.ai and describes compatibility with OpenAI- and Anthropic-style interfaces. The model identifier shown there is kimi-k3. Use Moonshot’s current quickstart to verify the endpoint, credentials, supported fields, limits, and pricing for your account.
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Preserve the full assistant response in multi-turn requests
The repository says thinking is always enabled and that responses include reasoning_content. For a multi-turn exchange or a tool call, include the complete assistant message in the next messages array—including reasoning_content and tool_calls when present. Passing only the user-visible content omits fields Moonshot says are needed to continue the interaction correctly.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The API documents a top-level reasoning_effort field with low, high, and max options; the documented default is max. Refer to the repository’s sample code and current documentation for the exact request and response shape.
Rank #3
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Self-hosting: open weights, substantial infrastructure
Moonshot makes full weights available through Hugging Face under the Kimi K3 License. Its repository recommends vLLM, SGLang, and TokenSpeed, and its technical materials recommend supernode configurations with 64 or more accelerators. That recommendation makes self-hosting a specialized infrastructure project; “open-weight” should not be read as “practical to run on a consumer PC.”
The cited official materials do not establish a complete minimum hardware configuration, such as a universal GPU-memory threshold or consumer-grade setup. Check Moonshot’s current deployment documentation and the license before planning a deployment. Do not infer that a particular accelerator count or memory capacity will meet your latency, concurrency, or context-length needs.
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Which route should you choose?
- Choose Kimi Web or the app if you want a graphical way to try K3 without integrating it into code.
- Choose Kimi Code if you want to use it from a terminal or a supported IDE workflow.
- Choose the API if your application needs programmatic access and you can handle credentials, request formatting, and current usage terms.
- Consider downloaded weights if operating the model on your own infrastructure is a requirement and you can provision and maintain the specialized hardware Moonshot recommends.
Moonshot’s statements about model capabilities and benchmark standing are company-reported. The cited materials do not provide independent hands-on testing, and benchmark results should be read in the context of their stated evaluation methods rather than treated as a guarantee for every use case.
Quick Recap
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