Overview
Kimi K3 is Moonshot AI's flagship multimodal model, built around a 1M-token context window and tiered deep reasoning. It keeps hold of the goal through long-document reading, cross-chapter references and multi-turn tool calls; combined with function calling, MCP tools and structured output, it can drive enterprise knowledge-base Q&A and complex automation pipelines.
1M-token context
Loads an entire technical manual, a long research report or a whole code repository in a single pass, with no drop-off in cross-chapter retrieval.
Tiered deep reasoning
Two effort levels, high and xhigh, schedule compute on demand to balance cost against quality.
Structured output
Native JSON Schema and function call support plugs straight into business systems and databases.
Open weights, self-hostable
The K3 series has open weights, so the model can be customized and integrated deeply.
Features
Pricing
Rate Limits & Context
Built-in Tools
code_interpreter
Responses API
web_extractor
Responses API
web_search
Responses API
API Reference
https://api.tokenfab.com/v1/chat/completionsRequest Example
curl https://api.tokenfab.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOKENFAB_API_KEY" \
-d '{
"model": "kimi-k3",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize this 300,000-word technical document and distill 5 key conclusions."}
]
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("TOKENFAB_API_KEY"),
base_url="https://api.tokenfab.com/v1"
)
response = client.chat.completions.create(
model="kimi-k3",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize this 300,000-word technical document and distill 5 key conclusions."}
]
)
print(response.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.TOKENFAB_API_KEY,
baseURL: "https://api.tokenfab.com/v1"
});
const response = await client.chat.completions.create({
model: "kimi-k3",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Summarize this 300,000-word technical document and distill 5 key conclusions." }
]
});
console.log(response.choices[0].message.content);Response Example
{
"id": "chatcmpl-kimi-k3-9d2k7f1",
"object": "chat.completion",
"created": 1784950000,
"model": "kimi-k3",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Key conclusions: (1) long context windows are the key differentiator of new-generation LLMs; (2) reasoning combined with tool calling improves performance on complex tasks; (3) caching significantly lowers the cost of long conversations; (4) structured output makes integration with business systems far more efficient; (5) multimodal capabilities and the open-source ecosystem are turning models into general-purpose AI infrastructure."
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 287412,
"completion_tokens": 96,
"total_tokens": 287508
}
}Request Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | ✓ | Model ID, e.g. kimi-k3 |
messages | array | ✓ | List of chat messages, each with role and content |
temperature | float | — | Sampling temperature, 0–2, default 0.6 |
top_p | float | — | Nucleus sampling, 0–1, default 0.9 |
max_tokens | int | — | Maximum output tokens |
stream | bool | — | Whether to stream the response, default false |
tools | array | — | Tool definitions for function calling |
tool_choice | string | — | Tool choice policy: auto / none / a named function |