Kimi K3 API
Choose Kimi K3
A premium reasoning route for visual front-end prototyping, repository-scale coding, large evidence sets, long-running agents, and complex knowledge work that benefits from a 1.05M-token working context.
Kimi K3
Moonshot flagship reasoning model
kimi-k3Screenshot-to-UI and interactive prototypes, repository-wide engineering, multi-document synthesis, tool-heavy agents, and difficult tasks where fewer retries or less human correction can justify a premium route.
Kimi K3 pricing
Estimate a request with the same interactive pricing experience as GPT-5.6. All user groups use the official Kimi K3 rate.
Token calculator
Enter the token mix for one request.Estimated request cost
Kimi K3Minimum charge: 0.01 credits per request.
Budget guide
Approximate requests using the current token mix.For quick testing
For regular development
For production evaluation
Model pricing
| Model | Context | Input tokens | Cache read tokens | Output tokens |
|---|---|---|---|---|
Kimi K3kimi-k3 | All context sizes | $3.000 / 1M204 cr / 1M$3.000 official price | $0.300 / 1M20.4 cr / 1M$0.300 official price | $15.000 / 1M1020 cr / 1M$15.000 official price |
Kimi K3
All context sizesUSD and credits are shown per 1M tokens. Live backend pricing takes priority over these frozen fallback rates.
Kimi K3 API for long-context coding and agent workflows
Access Moonshot’s 2.8-trillion-parameter flagship through EvoLink’s unified API. Kimi K3 combines visual understanding, front-end prototyping, a 1,048,576-token context window, prompt caching, always-on reasoning, structured output, and tool use for repository-scale engineering and complex knowledge work.
Where Kimi K3 earns a place in a production model stack
Kimi K3 should not become the default route simply because it is the newest model. Its strongest fit is work where long context, sustained reasoning, or complex tool sequences can reduce retries, handoffs, and human correction.
Kimi K3 for repository-scale coding
Use K3 when an engineering task depends on architecture documents, related services, test history, large diffs, and constraints spread across many files. Measure accepted patches and review time rather than isolated code-generation quality.
Kimi K3 for long-document analysis
Keep connected specifications, research papers, contracts, logs, or knowledge-base evidence in one working context. Retrieval and document structure still matter: a 1M-token window does not make irrelevant context useful.
Kimi K3 for tool-calling agents
K3 fits workflows with multi-step tool selection, structured outputs, code execution, or repeated external actions. Preserve complete assistant messages, reasoning content, tool-call IDs, arguments, and tool results across turns.
When a lighter model is the better route
Short chat, classification, rewriting, simple extraction, and latency-sensitive UI actions rarely need maximum reasoning or a 1M-token context. Keep these requests on a cheaper route and escalate only when task difficulty justifies K3.
What early Kimi K3 tests suggest—and what still needs proof
Launch-week community tests are useful for choosing evaluation workloads, not for declaring a universal winner. Treat the strongest demos and the sharpest complaints as hypotheses to verify on the same tasks, tools, budgets, and acceptance criteria.
The clearest early signal is visual front-end work
Early users repeatedly highlight screenshot-to-UI reconstruction, polished web prototypes, interactive pages, small games, and animation. Test Kimi K3 with your design system, responsive breakpoints, accessibility checks, and framework conventions before turning a strong demo into a production assumption.
General coding quality is not settled by front-end demos
A visually convincing page does not prove reliable backend changes, repository-wide refactors, tool execution, or multi-hour agent stability. Evaluate real issues, tests, multi-file diffs, tool failures, and accepted patches instead of extrapolating from one generated interface.
Latency and token efficiency can change the price decision
Some early reports describe long reasoning before useful output. Measure time to first usable result, total wall-clock time, reasoning and output tokens, retries, and human correction so the Kimi K3 API is judged by completed-task economics rather than list price alone.
Open weights would not make self-hosting lightweight
Interest in Kimi K3 open weights is high, but a 2.8-trillion-parameter footprint still implies serious memory, parallelism, quantization, throughput, and operations planning. Verify the latest Moonshot release terms and deployment details before treating local inference as an easy fallback.
Why Kimi K3 can handle these workloads
K3 is most useful when long context, sustained reasoning, and reusable prompt prefixes work together. Context capacity alone does not improve an answer; the workload still needs relevant evidence, clear structure, and an output budget.
A 1M-token workspace, not a target to fill
The 1,048,576-token window can keep related code, specifications, and prior tool results available without excessive chunking. Retrieval and context compaction still matter because irrelevant input competes for attention and increases processing cost.
Sustained reasoning needs an output budget
K3 fits work that requires extended analysis, planning, and execution across multiple steps. Reasoning and final-answer tokens both contribute to usage, so treat the 131,072-token default output limit as capacity rather than a normal completion size.
Prompt caching pays off when prefixes stay stable
Repository instructions, system prompts, reference material, and tool schemas create the strongest cache opportunity when their ordering remains consistent. Frequent model or prompt-structure changes can force the long prefix to be processed again.
What to verify before routing production traffic to Kimi K3
A suitable workload can still fail because the integration uses the wrong identifier, assumes the wrong product limit, or drops agent state between turns. Verify the request surface and conversation contract before evaluating model quality.
Use the API model ID, not a Kimi Code alias
Use kimi-k3 on the EvoLink API route for both Chat Completions and Anthropic Messages.
Confirm which product surface owns the context limit
EvoLink records a 1,048,576-token model context. Some coding clients or Kimi membership plans may expose a smaller default, so confirm the actual route and client configuration instead of assuming every K3 surface behaves identically.
Replay complete assistant and tool state
Multi-turn agents should retain complete assistant messages, reasoning content, tool-call IDs, arguments, and tool results. Keeping only the final text breaks state continuity and can make later steps fail even when the context window is large enough.
Start a clean session after material route changes
Changing the model or a major reasoning configuration can invalidate reusable context and prior assumptions. Start a new session after a material route change, then remeasure cache hits, completion length, and tool behavior.
Compare cost per accepted task, not token price alone
Kimi K3 earns a premium route only when it reduces chunking, retries, failed tool sequences, or human rework on the same production workload. Evaluate identical task sets instead of comparing isolated prompt prices.
If K3 produces usable results with fewer retries and less review effort, a higher token rate can still reduce total task cost. If those gains do not appear, keep the workload on a lighter route.
Compare leading long-context models after workload testing
EvoLinkFirst verify whether Kimi K3 reduces retries and review effort on your tasks. Then compare price, context, caching, and workload fit to choose the production route.
| Model | Kimi K3 | Claude Opus 4.8 | Gemini 3.1 Pro |
|---|---|---|---|
| Input / output | $3 / $15 | $4.5 / $22.5 | $1.68 / $10.08 |
| Context | 1.05M | 1M | 1M |
| Caching | Automatic cache reads | Read + write | Context cache |
| Best for | Screenshot-to-UI and interactive prototypes, repository-wide engineering, multi-document synthesis, tool-heavy agents, and difficult tasks where fewer retries or less human correction can justify a premium route. | Premium baseline for long-running coding agents, complex review, and judgment-heavy professional workflows. | Long-context multimodal route for document-heavy work, visual evidence, and cross-provider fallback testing. |
Related models
Claude Opus 4.8
Premium baseline for long-running coding agents, complex review, and judgment-heavy professional workflows.
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Gemini 3.1 Pro
Long-context multimodal route for document-heavy work, visual evidence, and cross-provider fallback testing.
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DeepSeek V4
Cost-sensitive baseline for high-volume coding, reasoning, and agent workloads with a 1M context window.
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GPT-5.6
Sol, Terra, and Luna tiers provide a direct comparison for capability, latency, and cost-routing flexibility.
View modelRelated reading for production teams
Kimi K3 API Guide
Explore model IDs, official pricing, 1M context, reasoning behavior, caching, migration risks, and production workload fit.
Read guide
Kimi K3 on EvoLink
Follow EvoLink availability, route verification, pricing status, compatibility checks, and production launch updates.
Read guideKimi K3 API FAQ
Is the Kimi K3 API available through EvoLink?
Yes. Kimi K3 is available as a production model with live backend pricing and official fallback rates.
What model ID should I use for the Kimi K3 API?
Use kimi-k3 for both Chat Completions and Anthropic Messages.
Can I keep using the OpenAI SDK or Anthropic Messages?
Yes. Use the same EvoLink API key with compatible Chat Completions or Anthropic Messages requests and select kimi-k3.
Does the Kimi K3 API support 1M context or only 256K?
The EvoLink route records 1,048,576 tokens. The 256K figure generally comes from a particular Kimi Code plan or client configuration.
How should I use the Kimi K3 1M-token context window?
Keep related code, documents, and tool results together, but use retrieval, stable cached prefixes, and context compaction instead of filling the window by default.
Is Kimi K3 good for front-end development and visual coding?
Early community tests are promising for screenshot-to-UI reconstruction, web prototypes, animation, small games, and visual interaction. Validate the advantage with your own components, responsive rules, accessibility checks, and code-quality standards.
Is the Kimi K3 API fast and token-efficient?
There is not yet a stable independent answer. Some launch-week reports describe long reasoning before useful output, so measure time to usable result, total tokens, retries, and accepted-task rate on representative workloads.
Why can Kimi K3 usage increase after switching models?
A material model or configuration change can invalidate reusable context. Start a clean session and remeasure cache-hit tokens and completion length.
How should I set the Kimi K3 output budget?
Limit output according to task complexity and monitor both reasoning and final-answer tokens. The 131,072-token limit is capacity, not a routine target.
Is Kimi K3 a good default for real-time or high-volume requests?
Test latency and cost first. Short chat, classification, rewriting, and lightweight extraction usually belong on a smaller route.
How should I compare Kimi K3 with GPT, Claude, GLM, or DeepSeek?
Evaluate K3 for long-context and complex-agent gains, GPT and Claude as frontier capability baselines, and GLM or DeepSeek as cost-sensitive baselines.
What should a production Kimi K3 evaluation measure?
Track first-pass success, accepted deliverables, retries, output tokens, cache hits, valid tool calls, time to accepted result, human correction, and fallback rate.