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Kimi K3 is available through Moonshot AI’s API and several managed inference services, with a million-token context window and rates that vary by provider and region. A common hosted rate is $3 per million uncached input tokens, $15 per million output tokens, and $0.30 per million cached input tokens. Before choosing a route, account for K3’s always-on reasoning, preserve its full assistant messages between turns, and check the provider’s current regional rates, limits, and terms.
What is the Kimi K3 API?
Kimi K3 is Moonshot AI’s open-weight, native multimodal model, designed for agentic tasks. Moonshot’s 2026 model documentation lists 2.8 trillion total parameters, 104 billion activated parameters, 896 experts with 16 selected per token, and a context length of 1,048,576 tokens. In a mixture-of-experts model, the activated-parameter figure describes the parameters used for a token, not the model’s total size.
The API lets applications send requests to K3 without operating the model themselves. You can call Moonshot directly or route requests through a managed provider such as Amazon Bedrock or Cloudflare Workers AI. Those choices can change the price, regional availability, interface, and operational controls, even when the underlying model is K3.
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K3’s large context and native vision are relevant when a task genuinely needs a large body of text, code, or multimodal input in one workflow. A long context limit is capacity, not a recommendation to send the maximum on every request: input tokens are billable, and large prompts can raise costs without improving a task that needs only a small excerpt.
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How much does Kimi K3 cost?
A common hosted rate card is $3.00 per million input tokens, $15.00 per million output tokens, and $0.30 per million cached input tokens. Amazon Web Services lists a different rate for some regions, so compare the actual provider, region, and cache-read rate you intend to use rather than assuming one universal price.
| Provider and service tier | Input per 1M tokens | Output per 1M tokens | Cached input or cache-read per 1M tokens |
|---|---|---|---|
| Cloudflare Workers AI rate published in 2026 | $3.00 | $15.00 | $0.30 |
| AWS Bedrock Global CRIS rate published in 2026 | $3.00 | $15.00 | $0.30 |
| AWS Bedrock US and India CRIS rates published in 2026 | $3.30 | $16.50 | $0.33 |
These figures are per million tokens, not per request. At the common $3/$15 rate, a request using 100,000 uncached input tokens and 10,000 output tokens would cost $0.30 for input plus $0.15 for output, or $0.45 before any other applicable charges. The same 100,000 input tokens billed at the $0.30 cache-read rate would cost $0.03. This arithmetic illustrates the listed rates; actual bills depend on the provider’s token accounting and applicable terms.
What changes the bill?
- Input volume: Large repositories, documents, and conversation histories can use many tokens. A million-token context window does not make those tokens free.
- Output volume: Output is priced higher than input on the listed rate cards. K3 always has thinking enabled and returns a
reasoning_contentfield, so account for the provider’s billed token usage rather than assuming only the visible final answer drives cost. The available documentation does not quantify a typical share of reasoning tokens. - Cache reads: The listed cache-read price is lower than uncached input, but the precise caching behavior and eligibility depend on the provider. Verify how a service applies caching before forecasting savings.
- Region and service tier: AWS’s published US and India CRIS rates are 10% above its Global CRIS figures. Other provider regions, quotas, and terms may differ.
Use the rate card for the specific route you will deploy, and recheck it before committing to a budget: rates and provider offerings can change. The prices above are the rates identified in 2026 provider materials, not a guarantee of future pricing.
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Moonshot documents an OpenAI-compatible and Anthropic-compatible API interface, which can make K3 easier to connect to clients built for those formats. Compatibility does not mean that every client feature behaves identically across Moonshot, Bedrock, Cloudflare, or another gateway; test the features your application depends on.
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Configure a direct Moonshot connection
Moonshot’s provider documentation lists https://api.moonshot.ai/v1 as the base URL and uses the KIMI_API_KEY and KIMI_BASE_URL environment variables. Set those values in your application’s secret and configuration system rather than embedding credentials in source code. Select the model name kimi-k3 in the client request.
- Obtain an API key through Moonshot’s platform and store it as
KIMI_API_KEY. - Set
KIMI_BASE_URLtohttps://api.moonshot.ai/v1for a direct Moonshot connection. - Configure an OpenAI-compatible or Anthropic-compatible client for the chosen interface, then select
kimi-k3. - Make a small test request and inspect the returned assistant message, including any reasoning and tool-call fields.
- Before relying on a feature in production, test it with the exact provider and client combination you plan to use.
Moonshot documents reasoning_effort settings of low, high, and max, with max as the default. Use a setting supported by your client and provider; a gateway may not expose every Moonshot-specific option in the same way.
How do you preserve K3 reasoning in a multi-turn tool call?
When continuing a conversation, append the complete assistant message returned by K3 to the message history. In particular, retain both reasoning_content and tool_calls rather than reconstructing a shortened assistant message from only the visible text.
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Multi-turn implementation checklist
- Keep the complete assistant response from the API, including fields your UI does not display.
- When K3 returns a tool call, execute it in your application and append the corresponding tool result using the client’s expected tool-message structure.
- Send the updated history on the next request; do not discard reasoning or tool-call fields as a cleanup step.
- Test at least one multi-step tool exchange with the exact SDK, gateway, and streaming mode used in production.
Because K3 always returns reasoning content, applications should also decide deliberately what to show, store, or pass through. The API requirement to preserve a field in conversation history does not by itself mean a product should display it to end users. Set interface behavior and data handling according to the application’s needs and applicable provider terms.
What can you use a 1,048,576-token context window for?
The million-token context makes K3 a candidate for workflows that need a large collection of material in one request or conversation, such as navigating a substantial code repository, analyzing long documents, or coordinating multimodal agent work. Its native multimodal design also makes vision-related use cases relevant, although the precise image or video payload formats and limits depend on the provider interface.
Context capacity should be treated as a ceiling, not a target. Sending a large input can cost more, and the context limit alone does not establish how quickly a request will run, how much output can be generated, or what the provider’s request limits are. For a repository task, for example, include the files and history needed to answer the question rather than automatically packaging every available file.
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Before building around a very large prompt, verify how your chosen provider handles context limits, cached input, image or video payloads, streaming, and maximum output. These are provider and client behaviors; the model’s stated context length does not prove that every gateway exposes the same capabilities.
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Which Kimi K3 deployment option should you choose?
Choose direct Moonshot if you want to call Moonshot’s documented API, or use a managed provider when its routing, service interface, or deployment environment better fits your application. Managed access may simplify integration into an existing stack, but it introduces another provider’s pricing, limits, and feature behavior to verify.
| Route | Documented identifier or configuration | What to evaluate |
|---|---|---|
| Moonshot API | Base URL https://api.moonshot.ai/v1; model kimi-k3 |
Client compatibility, API behavior, account terms, quotas, and data handling |
| Amazon Bedrock | moonshotai.kimi-k3, us.moonshotai.kimi-k3, in.moonshotai.kimi-k3, and global.moonshotai.kimi-k3 |
Region, model ID, CRIS rate, runtime interface, and service limits |
| Cloudflare Workers AI | Model slug moonshotai/kimi-k3; documented access includes JavaScript env.AI.run and cURL examples |
Current rate, Workers integration, request limits, and feature support |
| Vercel AI Gateway | Kimi K3 is listed with displayed rates | Confirm current provider availability, displayed price, routing, and feature support |
AWS recommends bedrock-runtime for new applications. Its documentation describes Chat Completions, Responses, Converse, and Invoke interfaces. The model IDs above differ by route, so use the identifier corresponding to your chosen Bedrock option rather than copying the direct Moonshot model name.
Can you self-host Kimi K3?
Moonshot describes K3 as open-weight and names vLLM, SGLang, and TokenSpeed as inference-engine options. That establishes that self-hosted inference is a possible route, but the cited documentation does not prescribe one hardware configuration or establish that a particular machine will meet your throughput, latency, or capacity needs.
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What should you check before using Kimi K3 commercially?
Review the Kimi K3 License for the exact way you plan to deploy, redistribute, or offer access to the model. The license describes a threshold for Model as a Service businesses and separate display requirements for certain large products; those conditions are distinct and should not be collapsed into a general claim that all commercial use is unrestricted or prohibited.
- Model as a Service threshold: The license says a licensee and its affiliates whose aggregate revenue exceeds $20 million in any consecutive 12-month period must sign a separate agreement with Moonshot before commercial use of the model in a Model as a Service business.
- Prominent display condition: The license requires prominent “Kimi K3” display for products with more than 100 million monthly active users or more than $20 million in monthly revenue.
- Stated exemptions: The license identifies exemptions for internal use and access through Moonshot official products or certified inference partners.
These are license-specific conditions, not a substitute for reading the full terms. If you intend to redistribute the model or build a commercial service on it, have counsel assess the license against your product, business structure, and revenue circumstances.
What are the main trade-offs when selecting a provider?
Compare more than the headline input rate. A practical provider choice balances cost, operational control, workflow compatibility, and governance; the lowest listed token price may not be the best fit if a required interface or region is unavailable.
- Cost: Compare uncached input, cache-read, and output prices, along with any regional or service-tier premium.
- Control: Decide whether you prefer a direct Moonshot API or managed routing through a service already used by your application.
- Latency and limits: Check provider-specific quotas, maximum output, geographic availability, and failover behavior for your workload.
- Workflow compatibility: Validate preservation of reasoning history, tool-call formats, structured output, and streaming.
- Data and governance: Check current provider retention terms, regional processing, and contractual commitments before sending sensitive material.
- License: If you self-host, redistribute, or offer model access commercially, assess the Kimi K3 License conditions for that use.
Frequently Asked Questions
Does Kimi K3’s context limit tell me the maximum output length?
No. The documented 1,048,576-token figure is the context length; it does not, by itself, establish a provider’s maximum output setting. Check the selected service’s current model limits for that separate value.
Which API styles can I use with Kimi K3 on Bedrock?
AWS documents Chat Completions, Responses, Converse, and Invoke. AWS recommends the bedrock-runtime interface for new applications.
Can I turn off Kimi K3 thinking?
Moonshot describes thinking as always enabled, so applications should plan for returned reasoning_content. Its documented effort settings are low, high, and max, with max as the default; these adjust effort rather than establish a way to disable thinking.
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