Kimi K3 Chat Completions API: How to Build an Integration

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 interface. The service accepts the OpenAI API format, supports both regular JSON responses and server-sent event (SSE) streaming, and is billed separately from Kimi’s consumer membership products. The key implementation decisions are how much output to allow, how to preserve repeatable prompt prefixes, and whether to handle tools yourself or deploy through Amazon Bedrock.

What the Kimi K3 API is—and what you need

Kimi API Open Platform is Moonshot AI’s developer service for model inference. Kimi describes it as supporting text generation, multi-turn conversations, file parsing, web search, and other capabilities. Chat Completions is its primary inference interface, and the platform is compatible with the OpenAI API format. That compatibility can make existing SDK tooling convenient, but confirm the exact endpoint, authentication header, request fields, and supported parameters in the Kimi Chat API reference before adapting or deploying code.

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You need a developer account, an API key, a model name, and a request configured for your workload. Register with the developer platform, create the key in its console, then use the API reference to confirm the current request schema and choose model parameters. Kimi’s API is pay-as-you-go and billed separately from Kimi Membership and Kimi Code; consumer access does not substitute for API credentials or API billing. See Kimi API platform documentation.

Kimi documents both non-streaming JSON responses and streaming responses delivered as SSE. Use a regular response when the application can wait for a complete result; streaming can display generated content as it arrives. For streaming, implement the event handling described in the current API reference rather than assuming the event format from another provider.

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Choose the model and reasoning settings

The model name documented for Kimi K3 is kimi-k3. Kimi describes it as its flagship model for long-horizon coding and end-to-end knowledge work, with native visual understanding. These are provider descriptions, not independent benchmark results. The model-selection guidance recommends considering context length, response speed, generation quality, price, and thinking mode rather than assuming one model fits every workload. See Kimi model selection guidance.

• Context: Kimi documents a context window of up to 1 million tokens for kimi-k3. This is a provider specification, not a guarantee that every request can use the full amount for input or output.
• Thinking: K3 always runs in thinking mode. The documented reasoning_effort settings are low, high, and max; max is the default.
• Workload fit: Consider a different model or setting if your priority is lower latency, different pricing, or control over whether thinking is enabled. Kimi’s guidance says K2.6 can switch thinking mode on and off.

Set output limits and handle truncation

max_completion_tokens sets the maximum generation budget; it does not make the model produce that many tokens. Kimi’s troubleshooting documentation lists a default of 131,072 for kimi-k3 and gives the maximum output length as 1024*1024 - prompt_tokens. Since the prompt consumes context, set the output ceiling with both the requested response size and the remaining context in mind. Kimi recommends using its token-count estimation API to estimate input size. Token counts do not convert to a fixed number of characters, and longer generations generally take longer to complete. See Kimi troubleshooting and model guidance.

Inspect the response’s finish_reason. A value of length means generation hit its limit and excess content was discarded. Treat that as a potentially incomplete answer: increase the output budget if context allows, reduce unnecessary prompt content, or change the task so the model can return a useful result within the available budget. Do not silently present a truncated response as complete.

Keep reusable prompt prefixes stable

On the direct Kimi platform, the API automatically attempts to cache repeated initial context; Kimi says you do not need to provide a cache ID, TTL, or extra request parameters. Its troubleshooting guidance recommends keeping the beginning of requests stable—especially system prompts, tool definitions, and long documents—so later requests can match the cached prefix. This is an optimization opportunity, not a promise of a particular cache hit rate, latency improvement, or saving; measure your own workload. See Kimi caching guidance.

Amazon Bedrock has a separate caching implementation. Its Kimi K3 model card lists implicit and explicit prompt caching, a minimum explicit cache checkpoint of 1,024 tokens, and retention of at least 30 minutes. AWS says explicit cache controls can improve cache hit rate and thereby latency and cost. Those checkpoint and retention details apply to Bedrock, not direct Kimi API requests. See the AWS Kimi K3 model card.

Integrate tools without creating repeat loops

Kimi says models do not access external resources such as the internet or databases by default. To connect a model to an application function, declare the available tools and handle the resulting tool calls in your client. In the documented Chat Completions flow, append the assistant message containing the tool calls, execute each call in your application, then provide a corresponding role=tool message for each result. Match each tool response’s tool_call_id to the associated call ID. Follow the current API reference for exact fields and message ordering.

Tool calls may recur. Kimi recommends client-side repeated-call detection when the same tool and arguments appear again without useful progress. Set limits or stop conditions for repeated calls so a stuck interaction does not loop indefinitely.

Web search needs explicit configuration

Do not assume the model is browsing the web automatically. Kimi’s troubleshooting documentation describes a $web_search tool that must be declared in tools and handled through the usual tool-call flow. The same page says the web-search functionality is being updated and is not recommended in the near term. Verify its current status and instructions before building a production dependency on it. See Kimi troubleshooting documentation.

Use the direct API or Amazon Bedrock?

Direct Kimi access uses Moonshot AI’s API platform and its pay-as-you-go billing. Amazon Bedrock also documents Kimi K3 and recommends the Chat Completions API for the model. These are different hosting routes: model identifiers, endpoints, authentication, regional availability, prices, and operating terms depend on the provider you choose. The AWS model ID is moonshotai.kimi-k3; cross-region identifiers include us.moonshotai.kimi-k3 and global.moonshotai.kimi-k3. AWS lists the runtime endpoint pattern as https://bedrock-runtime.{region}.amazonaws.com. Check the current provider documentation for credentials, supported regions, quotas, and data handling before deployment.

These are AWS-published Standard-tier rates on the model-card page inspected in 2026, not direct Moonshot API prices; confirm current rates before budgeting. AWS says Priority costs 1.75 times the applicable Global or US Standard per-token rate, while Flex costs 0.5 times that rate. The AWS model card also lists image input and text output, but not audio or video input, and documents client-side tool calling and structured outputs.

When choosing between providers, compare the actual endpoint and authentication process, regions and availability, current input/output and cache pricing, tool and structured-output support, caching behavior, latency and operational requirements, and your organization’s data-governance needs. The published documentation does not establish which route will be faster or more cost-effective for a particular workload.

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