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To use Kimi K3 in an application, create a Kimi API key, select the kimi-k3 model, and send requests through Kimi’s Chat Completions API. The API is a pay-as-you-go developer service, separate from Kimi’s consumer membership products. K3 supports a documented context window of up to 1 million tokens, but your application still needs to budget output, handle truncation, and implement any tools or web access it needs.
What you need before making a Kimi K3 request
Kimi API Open Platform is Moonshot AI’s developer service for text generation, multi-turn conversations, file parsing, web search, and other capabilities. Kimi describes its API as compatible with the OpenAI API format and identifies Chat Completions as its primary inference interface. You need a developer account, an API key, a model name, and request parameters suited to your workload. See Kimi’s API documentation for current setup and request details.
The API is billed separately on a pay-as-you-go basis; it is not the same product as Kimi Membership or Kimi Code. Keep the key on your server or in a managed secret store rather than embedding it in a browser or mobile app, where users could extract it.
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Choose K3 for the workload, not just the context window
Kimi lists kimi-k3 as its flagship model for long-horizon coding and end-to-end knowledge work, with native visual understanding. Kimi’s current model-selection documentation, inspected in 2026, specifies a context window of up to 1M tokens. These are provider specifications, not independent evidence that K3 will outperform alternatives on your task.
K3 always runs in thinking mode. Kimi documents reasoning_effort values of low, high, and max, with max as the default. Select among models by balancing context needs, response speed, generation quality, cost, and whether thinking mode can be controlled. Kimi’s guidance distinguishes K3’s deep-reasoning orientation from K2.6, which can switch thinking mode on or off. Check the current Kimi model-selection guidance before choosing a model or relying on a particular parameter.
Make a Chat Completions request safely
Kimi says its platform follows the OpenAI API format, so an OpenAI-compatible client may be usable with the appropriate Kimi endpoint and credentials. Compatibility does not remove the need to verify provider-specific settings: the exact direct endpoint, authentication header, supported fields, and streaming format should come from Kimi’s current Chat API reference.
- Register for Kimi API Open Platform and create an API key in the developer console.
- Choose
kimi-k3and confirm the current endpoint, required authentication, and supported parameters in the Chat API reference. - Send a Chat Completions request with the conversation messages and a deliberate output ceiling. Use non-streaming JSON when you want the completed response as one result, or streaming when your interface should render output as it arrives.
- Handle the response’s finish reason and any tool calls in application logic; do not assume that a successful HTTP response means the full desired answer was generated.
Kimi documents both non-streaming JSON responses and streaming responses delivered as a server-sent events (SSE) stream. Because the overview alone does not specify the full request and event schemas, avoid copying a generic OpenAI code sample without checking Kimi’s current API reference for exact field names and stream parsing requirements.
Set output limits and detect truncation
Kimi’s troubleshooting documentation gives max_completion_tokens a default of 131,072 for kimi-k3 and describes the maximum output length as 1024*1024 - prompt_tokens. The parameter sets an upper limit; it does not ask the model to generate exactly that many tokens. A large ceiling can also be impractical if the prompt already consumes much of the available context.
Estimate input size with Kimi’s token-count estimation API, then reserve only the output capacity your task needs. Kimi notes that longer generated outputs generally take longer to complete; token counts do not convert to a fixed number of characters because character usage varies by text. If a response ends with finish_reason=length, the generation hit its limit and excess content was discarded. Treat that as a partial result: raise the output budget if context permits, or split the task and continue from the available result. See Kimi’s troubleshooting guidance for current token-limit and estimation details.
Keep repeated prompt prefixes stable for caching
On the direct Kimi platform, Kimi says the API automatically attempts to cache repeated initial context. You do not provide a cache ID, TTL, or extra request parameter for this behavior. To make later requests more likely to match the same prefix, keep the opening portion stable—especially system instructions, tool definitions, and long documents—and place request-specific content after it. Kimi does not promise a particular cache-hit rate or savings, so measure behavior with your own traffic.
Amazon Bedrock has a separate caching implementation. AWS documents implicit and explicit prompt caching for K3; its model card lists a minimum explicit cache checkpoint of 1,024 tokens and retention of at least 30 minutes. AWS says explicit cache controls can improve hit rate and thereby latency and cost. Those Bedrock details do not describe the direct Kimi API. Consult the AWS Kimi K3 model card for current hosting-specific behavior.
Integrate tools and web search explicitly
Kimi models do not access the internet, databases, or other external resources by default. Your application can provide official tools or custom tools through the tool-calling flow, but it must execute the requested operation and return its result; a tool call is not itself proof that the model accessed a resource.
Kimi’s troubleshooting documentation describes this sequence: append the assistant message containing its tool calls, run the corresponding operations, then send a role=tool message for each result with its tool_call_id matching the call ID. Kimi warns that calls can repeat. Add client-side detection for recurring calls with the same tool and arguments when they are not producing useful progress, and impose sensible limits on tool rounds.
Built-in web search is not an always-on capability. Kimi’s documentation says the web-search feature is being updated and is not recommended in the near term, while also describing a $web_search tool that must be declared in tools and handled through the normal tool-call flow. Verify its current availability and instructions before depending on it; if it is unavailable or unsuitable, use a search service your application controls.
Choose between direct Kimi access and Amazon Bedrock
Direct access uses Kimi’s own OpenAI-format-compatible API platform and pay-as-you-go billing. Amazon Bedrock separately documents Kimi K3 as a hosted model and recommends its Chat Completions API for this model. Bedrock’s model ID is moonshotai.kimi-k3; documented cross-region identifiers include us.moonshotai.kimi-k3 and global.moonshotai.kimi-k3. Its endpoint follows the pattern https://bedrock-runtime.{region}.amazonaws.com. Bedrock identifiers, authentication, regional availability, quotas, prices, and data-handling terms are AWS-specific, not direct Kimi API terms.
AWS’s model card listed the following Standard-tier prices per 1 million tokens when inspected in 2026. These are AWS Bedrock rates, not direct Moonshot API prices, and may change.
| Bedrock Standard tier | Input | Output | Cache read | 30-minute cache write |
|---|---|---|---|---|
| Global CRIS | $3.00 | $15.00 | $0.30 | $3.75 |
| US CRIS | $3.30 | $16.50 | $0.33 | $4.125 |
AWS states that Priority costs 1.75 times the applicable Global or US Standard per-token rate, while Flex costs 0.5 times that rate. Check the live Bedrock model card before estimating costs or deployment.
Compare the two routes using the requirements that affect your application:
Quick Recap
- Endpoint and authentication: direct Kimi and Bedrock have different provider-specific setup and credentials.
- Regions and availability: check the current service regions, cross-region routing, quotas, and account eligibility for your deployment.
- Pricing and caching: compare current input, output, and cache charges, and distinguish direct Kimi’s automatic prefix caching from Bedrock’s documented controls.
- Features: AWS lists image input and text output, client-side tool calling, and structured outputs; it lists no audio or video input. Confirm the exact feature and schema requirements for either provider.
- Operations and governance: assess latency on your workload and decide whether each provider’s operational model and data-handling terms meet your organization’s requirements. The published specifications alone do not settle those decisions.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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