Model:
Kimi K3 always reasons and currently supports only max.
Maximum reasoning plus final-answer tokens. Kimi K3 defaults to 131072 when omitted.
USD estimate $1.31-$2.62: highest recharge package uses $1 = 2,000 credits; entry package uses $1 = 1,000 credits.
History
Saved locally in this browser
0 running · 0 completed
Input
≈ $0.002-$0.004
Output
≈ $0.01-$0.02
Cache
≈ $0.0002-$0.0004
Context
Max output
Authentication
Every request needs a Bearer token in the Authorization header. Create an API key in the console.
Authorization: Bearer YOUR_API_KEYhttps://apiany.ai/v1/chat/completionsRequest parameters
| Parameter | Type | Required | Notes |
|---|---|---|---|
model | string | Required | Model identifier to invoke. Use this model's ID. |
messages | array | Required | Conversation history in OpenAI chat format (role + content). |
stream | boolean | Optional | When true, the response streams back as server-sent events. |
Request example
curl "https://apiany.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k3",
"messages": [
{
"role": "user",
"content": "Analyze the architecture risks in this repository and propose a phased migration plan."
}
],
"reasoning_effort": "max",
"max_completion_tokens": 131072
}'Response example
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "kimi-k3",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 12,
"completion_tokens": 18,
"total_tokens": 30
}
}Moonshot AI flagship model
Kimi K3 API access brings Moonshot AI's flagship model to applications with native vision and a context window of up to one million tokens. Kimi K3 can work across text, images, code, and extended task history in one model workflow.
Kimi K3 is designed for difficult repository work, multimodal knowledge synthesis, complex reasoning, and long-horizon agent tasks. Fewer handoffs and retries can reduce total task cost when one capable route can keep the evidence, plan, and intermediate results together.
Kimi K3's capacity does not make irrelevant context useful. Production systems still need retrieval, careful context and state preservation, application-controlled permissions, executable tests, and output verification before results trigger downstream actions.
Try Kimi K3 in the playground
Sources
The quotations below are from official API documentation that APIAny implements against.
The Chat Completions API endpoint will generate a model response from a list of messages comprising a conversation.
The Gemini API provides access to Google's most capable generative AI models.
Use cases
The Kimi K3 API is used for product workflows that need this model's output. Test the same inputs in the playground, compare related models, and choose the route that meets your quality, latency, and budget requirements.
01
Use Kimi K3 across large repositories to follow dependencies between files and carry a plan through patches, tests, and tool results. The application keeps control of permissions and runs the final test validation before changes are accepted.
Try Kimi K3 in the playground
02
Give Kimi K3 long documents, images, tables, and technical evidence to produce structured, traceable analysis. Retrieve only relevant context and keep citations or source links available for review.
Try Kimi K3 in the playgroundAPIAny advantages
Kimi K3 on APIAny is a production route with one key, live prices, status, and usage records, so you do not maintain a separate provider integration.
Call Kimi K3 with an APIAny key and a documented request format. Keep your application integration stable while model versions and upstream routes evolve behind the gateway.
Review the live Kimi K3 pricing rules before a request, estimate credits in the playground, and inspect request-level usage records after the call completes.
Core capabilities
Kimi K3 API features are the input, output, and workflow traits that matter when you evaluate this model. Available request parameters remain visible in the live API reference on this page.
/01
Use a large context window for codebases, documents, conversation history, and multi-step instructions while monitoring token cost.
/02
Combine supported text and visual inputs so Kimi K3 can reason over more than one content format in the same workflow.
/03
Apply Kimi K3 to implementation, debugging, refactoring, repository navigation, and technical explanation with test-based verification.
/04
Use Kimi K3 to break down multi-step problems, compare alternatives, and produce structured conclusions that can be reviewed.
/05
Integration
Integrating the Kimi K3 API is a three-step path from playground test to authenticated production request. The page keeps the model identifier, request schema, pricing, and response examples together. Move from a representative prompt to monitored production traffic with explicit quality and fallback checks.
Sign in to APIAny, create a project API key, and assign only the model scope and budget controls your application needs.
Copy the request example from this page, set the model field to the selected Kimi K3 version, and send it to the documented APIAny endpoint.
Track status, latency, credits, and returned usage. Compare versions or related models with the same workload before shifting production traffic.
FAQ
Practical answers about Kimi K3 capabilities, APIAny access, long-running workflows, and production evaluation.
Kimi K3 is Moonshot AI's flagship model for native-vision, long-context, coding, reasoning, knowledge-work, and agent tasks. APIAny provides authenticated model access, live pricing, a Playground, and the request Reference on this page.
Moonshot AI positions Kimi K3 with a context window of up to one million tokens, which can accommodate large repositories, document collections, images, and long task histories. That official model capacity is distinct from the request fields currently exposed by APIAny. Check the live Reference for the model identifier, supported inputs, and present parameter limits before building a production request.
03
Use Kimi K3 for multi-step work that requires tool choice, preserved state, feedback, and revision. The host application validates tool arguments and permissions, records progress, and handles failures or retries.
Try Kimi K3 in the playground
Use model status, channel health, retry policy, and usage logs to operate Kimi K3 as part of a production workflow instead of treating it as an isolated demo.
Let Kimi K3 prepare or select tool calls while your application enforces permissions, validates arguments, and records actions.
/06
Render supported Kimi K3 text output incrementally for interactive assistants and long-running generation tasks.
/07
Use familiar request patterns where supported, while APIAny handles authentication, model selection, billing, and usage records.
Kimi K3 is positioned for long-horizon coding that can follow dependencies, maintain a plan, apply patches, read tool results, and revise after feedback. Give it a scoped repository view and explicit acceptance criteria instead of an unrestricted workspace. Your application should retain permission control and run the final build, tests, and code review.
Yes. Kimi K3 has native vision and can combine supported visual input with long documents, tables, and technical evidence for multimodal knowledge work. Retrieve the relevant sources, ask for structured and traceable output, and verify conclusions against the original evidence.
Create an APIAny key, use the endpoint shown in the live Reference, and select the listed Kimi K3 model identifier. The official model capability does not imply that every possible option is an APIAny request field. Start with the page's current example, test it in the Playground, and treat the Reference as authoritative for supported parameters.
Store the conversation, plan, tool calls, tool results, and approval decisions in application-managed state rather than assuming one request owns the workflow. On each step, send only the relevant state and validate tool names, arguments, permissions, and results. Use checkpoints so failures can resume safely without replaying completed side effects.
Choose a lighter model when the task is short, narrow, high-volume, or latency-sensitive and does not benefit from native vision, deep context, or long-horizon coordination. Compare representative tasks rather than model labels alone. Route straightforward work to the smallest model that meets quality targets, while reserving Kimi K3 for cases where fewer handoffs or retries improve total task economics.
Track first-success rate and total completion time, not only single-request latency. Also monitor retries, token usage, valid tool-call rate, human edits, and fallback rate by task type so quality and total task cost remain visible.
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