Kimi K3 Is Live on APIMaster.ai: 1M Context for Coding, Agents, and Knowledge Work

What is Kimi K3?

Kimi K3 is Moonshot AI's most capable model to date. According to the official Kimi K3 documentation, it has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear-attention mechanism, together with Attention Residuals.

The model is designed for workloads that must keep working across large codebases, long documents, many tool calls, or extended research context. Its native multimodal architecture accepts text and visual input, while its one-million-token window can hold substantially more project or document context than a conventional chat model.

What can Kimi K3 do?

Kimi K3 combines long-context reasoning with production-oriented API controls.

Capability	What it enables
Long-horizon coding	Analyze large repositories, plan multi-file changes, generate code, and work through extended software-engineering tasks
1M-token context	Process up to 1,048,576 input-and-output tokens in one context window for large codebases, document sets, and long conversations
Native visual understanding	Analyze images and video supplied as base64 data or Kimi file references alongside text
Always-on reasoning	Perform deep reasoning and return separate reasoning_content; reasoning_effort currently supports max
Agent tool use	Call custom tools, require tool use with tool_choice, and load tool definitions dynamically during a conversation
Structured output	Produce strict JSON Schema responses for extraction, automation, and reliable downstream parsing
Partial Mode	Continue generation from an assistant-provided prefix when an output must start in a controlled format
Automatic context caching	Reuse unchanged long prefixes without manually creating cache IDs or setting a TTL

These features make Kimi K3 a strong fit for coding agents, repository analysis, document intelligence, research assistants, workflow automation, and other tasks where a model must retain context and act across multiple steps.

How does Kimi K3 reasoning work?

Kimi K3 always reasons. The API uses the top-level reasoning_effort field rather than the older K2.x thinking parameter. At launch, the only supported level is max, which is also the default.

Streaming responses separate the reasoning trace into reasoning_content and the final answer into content. For multi-turn conversations and tool loops, applications must return the complete assistant message to the next request, including reasoning and tool-call fields, instead of retaining only the visible answer.

How much does the Kimi K3 API cost?

The official Kimi K3 pricing page lists flat per-token pricing with no long-context tiers.

Price basis	Input, cache hit	Input, cache miss	Output	Context window
Kimi official API	$0.30/M	$3.00/M	$15.00/M	1,048,576 tokens
APIMaster current marketplace route	Check live route	$3.00/M	$15.00/M	Model limit applies

APIMaster route availability and prices are shown live in the model marketplace. Check the current card before moving production traffic because channel supply, quotas, and pricing can change.

Is Kimi K3 OpenAI API compatible?

Yes. Kimi K3 uses the Chat Completions interface and APIMaster provides an OpenAI-compatible base URL, so most applications only need a new API key, base URL, and model ID.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_APIMASTER_KEY",
    base_url="https://apimaster.ai/v1",
)

response = client.chat.completions.create(
    model="kimi-k3",
    reasoning_effort="max",
    messages=[
        {
            "role": "user",
            "content": "Review this service architecture and propose a safe migration plan.",
        }
    ],
)

print(response.choices[0].message.content)

K3 fixes several sampling fields: temperature=1.0, top_p=0.95, n=1, and both penalty values are fixed. The official documentation recommends omitting those fields rather than trying to override them.

How can developers use Kimi K3 tool calling?

Kimi K3 supports standard ToolCalls plus two controls useful for larger agents:

• Tool choice constraints: set tool_choice="required" when the model must call at least one supplied tool.
• Dynamic tool loading: introduce a complete tool definition later in the message history instead of placing every possible tool in the initial prompt.

Dynamic loading can reduce prompt size for agents with large tool catalogs. Every tool result must retain its matching tool_call_id, and the full assistant message must remain in the conversation history.

What should teams know before using Kimi K3 in production?

Kimi's launch documentation lists several operational constraints:

• K3 always reasons, and only reasoning_effort="max" is currently available.
• max_completion_tokens defaults to 131,072 and can be raised as high as 1,048,576.
• Vision input does not accept public image URLs; use base64 data or an ms:// file reference.
• Multi-turn and tool-call flows must preserve the complete assistant message.
• Kimi says its official Web Search tool is being updated and does not currently recommend it for near-term production use.

For production systems, test response latency and token consumption with representative long contexts before setting limits or budgets.

How to buy and use Kimi K3?

Kimi K3 is available now through APIMaster.ai.

1. Register for an APIMaster account.
2. Add wallet credit with a supported payment method.
3. Open the model marketplace and select Kimi K3.
4. Create an API key in the APIMaster console.
5. Set the model to kimi-k3 and the base URL to https://apimaster.ai/v1.

APIMaster gives developers one OpenAI-compatible API key for Kimi K3 and other leading models, live route pricing, pay-as-you-go billing, and a model fingerprint tester for checking route behavior before production deployment.

Why access Kimi K3 through APIMaster.ai?

One API for multiple model families

Use one integration for Kimi, Claude, GPT, Gemini, DeepSeek, and other models instead of maintaining separate provider SDKs and balances.

Transparent live route pricing

The marketplace displays the active Kimi K3 channel, input and output prices, availability, and operational data in one place.

Pay-as-you-go access

Start with a small balance, validate your workload, and scale usage without committing to a separate subscription for every provider.

Route verification

Use APIMaster's free AI model fingerprint tester to inspect whether an API route behaves like the model advertised by its provider.

FAQ

What is the Kimi K3 model ID on APIMaster?

Use kimi-k3 in Chat Completions requests.

How large is the Kimi K3 context window?

Kimi K3 supports 1,048,576 tokens, shared by input and output in the active context.

Does Kimi K3 support images and video?

Yes. K3 has native visual understanding. The official API accepts local image data encoded as base64 and uploaded video through Kimi file references; public image URLs are not supported at launch.

Does Kimi K3 support function calling?

Yes. It supports ToolCalls, parallel-compatible agent loops, tool_choice constraints, and dynamic tool loading.

Is Kimi K3 suitable for coding agents?

Yes. Moonshot positions K3 specifically for long-horizon coding, software engineering, end-to-end knowledge work, and deep reasoning. Teams should still benchmark it against their own repositories and tool workflows.

Where can I get a Kimi K3 API key?

Create an APIMaster account, add credit, and generate an OpenAI-compatible API key from the console. Use kimi-k3 as the model ID.

Sources

• Kimi K3 official quickstart and capability guide
• Kimi K3 official pricing
• Kimi API tool-calling documentation

Kimi K3 is currently part of APIMaster's 40%-off pricing on DeepSeek, Kimi K3, MiniMax M3, and GLM-5.2 — check the live route before you scale usage.

Kimi K3 is already live in the APIMaster marketplace. Register now to create an API key and test the one-million-token flagship model with your own coding, agent, or knowledge-work workload.