The Tool Desk
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Choose the way you want to use Kimi K3
The right route depends on whether you want a chat interface, a coding assistant, an application integration, or control over deployed model weights. Hosted access avoids operating inference hardware; self-hosting offers a different level of infrastructure control but brings substantial deployment requirements.
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| Route | Best fit | What you need to do |
|---|---|---|
| Kimi Web or mobile app | Chat and general knowledge work | Open the web experience or install the app, then use K3 where it is offered. |
| Kimi Code | Working with code in a terminal or supported IDE | Select Kimi K3 in the CLI or in the model dropdown of a supported VS Code or Cursor integration. |
| API | Building a programmatic workflow or application | Use Moonshot’s API documentation for credentials, endpoint details, and request format. |
| Downloaded weights | Operating a deployment on infrastructure you control | Obtain the weights under the Kimi K3 License and set up supported inference software on suitable accelerators. |
Moonshot lists Kimi Web, the mobile app, Kimi Work, Kimi Code, and the API as access routes. Its app page names iOS, Android, and HarmonyOS; availability can vary by platform and region. The official materials cited here do not establish current regional eligibility, account requirements, API pricing, or limits, so check the live service and documentation before relying on a particular option.
Use Kimi K3 in the web experience or mobile app
- Open Kimi Web or install the Kimi app for your supported device.
- Start a conversation and choose Kimi K3 if a model selector is available in your account.
- Give it the task and context it needs. Moonshot describes K3 as supporting native vision, so image-based tasks are part of its stated capabilities; what is available in a specific interface may depend on that product’s current features.
This is the simplest starting point if you want to use the model without configuring a development environment or running inference infrastructure. The hosted product handles the model serving; it does not mean the model weights are running on your device.
#1 Best Overall
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Select Kimi K3 in Kimi Code
In the CLI
- Open the Kimi Code CLI.
- Enter
/model. - Select
Kimi K3from the available models.
In a supported IDE integration
- Open Kimi Code in a supported VS Code or Cursor integration.
- Open the model dropdown.
- Choose
Kimi K3.
Moonshot’s access page describes these selection paths. The precise setup and integration availability may change, so use the current Kimi Code instructions for installation and supported editor versions.
Call Kimi K3 through the API
Moonshot’s repository identifies the model as kimi-k3 and says the API is available through platform.kimi.ai. It describes the API as compatible with OpenAI- and Anthropic-style integrations. Use the current official quickstart and repository examples for the exact endpoint, authentication, request fields, pricing, and rate limits; those details are not established here.
Rank #2
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Preserve the full assistant message in a conversation
The repository says thinking is always enabled and API responses include reasoning_content. For a multi-turn conversation or a tool call, pass the complete assistant message into the next messages array, including reasoning_content and tool_calls when present. Passing only the user-visible content omits fields the documented conversation flow expects.
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The repository documents a top-level reasoning_effort field with low, high, and max values; its stated default is max. Check the current sample code and API reference before implementing this field, because request support and interfaces can change.
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Deploy downloaded weights yourself
Moonshot makes full K3 weights available on Hugging Face under the Kimi K3 License. The repository recommends vLLM, SGLang, and TokenSpeed, while Moonshot’s technical blog recommends a supernode configuration with at least 64 accelerators. This is a specialized deployment route, not a recipe for a typical consumer PC. The cited materials do not establish a universal minimum GPU memory figure, a smaller supported hardware configuration, or a consumer desktop setup.
- Review the official Kimi K3 weights and license and determine whether the license and deployment terms suit your use.
- Choose an inference stack from the tools Moonshot names—vLLM, SGLang, or TokenSpeed—and follow its current K3-specific setup guidance.
- Plan for the accelerator infrastructure Moonshot recommends: a supernode with 64 or more accelerators. Confirm hardware compatibility and capacity with the current deployment documentation before acquiring or allocating infrastructure.
Open weights provide access to model files; they do not remove the practical cost and complexity of serving a 2.8-trillion-parameter model. The official recommendation is the meaningful baseline here, rather than an unsupported promise that a smaller setup will work.
Rank #4
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Understand what Kimi K3 is—and what the headline number means
Moonshot describes Kimi K3 as an open-weight mixture-of-experts model with 2.8 trillion total parameters, native vision, and a context window of up to one million tokens. Its technical report abstract gives 104 billion activated parameters, while Moonshot says 16 of 896 experts are activated. The 2.8T figure is the total parameter count, not the number activated for each token. Moonshot says the architecture uses Kimi Delta Attention and Attention Residuals and positions the model for long-horizon coding, knowledge work, and reasoning.
These are figures and capability descriptions from Moonshot and the report authors, not independent measurements or guarantees of performance for a particular task. A one-million-token context window describes the stated maximum context, not a promise that every interface, request, or deployment will accept that many tokens.
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Decide between hosted access, API use, and self-hosting
- Choose the web or mobile experience if you want the shortest path to trying K3 through a graphical interface.
- Choose Kimi Code if your work is centered on coding in a terminal or a supported IDE.
- Choose the API if you need to integrate K3 into software and can work with the current API’s authentication and message format.
- Consider self-hosting only if you have a reason to operate the weights yourself and can provision specialized accelerator infrastructure.
The available official information does not support a current price comparison or a definitive regional-availability comparison between these routes. Verify the live terms for your location and intended use, and compare hosted access costs with the full operational requirements of running infrastructure rather than treating downloaded weights as a no-cost deployment.
Quick Recap
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