Technical Specifications of kimi-k2
| Item | Details |
|---|---|
| Model ID | kimi-k2 |
| Provider | Moonshot AI |
| Version referenced | kimi-k2-250905 |
| Context window | Up to 256k tokens |
| Tool calling | Supported |
| Performance | Standard API TPS up to 60–100 |
| Turbo performance | Turbo mode TPS up to 600–100 |
| Knowledge cutoff | Up to mid-2025 |
| Best for | Ultra-long context tasks, complex tool workflows, integration optimization |
What is kimi-k2?
kimi-k2 is CometAPI’s platform identifier for Moonshot AI’s Kimi K2 series 0905 version. It is designed for ultra-long-context workloads, with support for up to 256k tokens as well as frontend and Tool calls. The model is positioned for users who need strong performance on complex tasks, especially scenarios that benefit from reliable tool orchestration, long-document handling, and fast inference.
On CometAPI, kimi-k2 can be used as the model ID when sending compatible chat completion requests through the unified API, making it easier to integrate Moonshot AI capabilities into existing applications without changing provider-specific infrastructure.
Main features of kimi-k2
- Ultra-long context: Supports contexts up to 256k tokens, making it suitable for long documents, extended conversations, large codebases, and multi-step task execution.
- Enhanced Tool Calling: Built for strong tool-use performance with 100% accuracy claims, enabling seamless integration for complex workflows, external function execution, and automation-heavy applications.
- Efficient performance: Delivers TPS up to 60–100 through the standard API, helping reduce latency for production use cases that require responsive outputs.
- Turbo mode acceleration: Offers Turbo mode throughput up to 600–100, improving response speed for high-demand or large-scale inference scenarios.
- Integration-friendly design: Supports frontend and Tool calls, making it easier to embed into apps, agent systems, and operational pipelines.
- Stronger inference capability: Optimized to provide improved reasoning and task execution quality for sophisticated business and developer workflows.
- Recent knowledge coverage: Includes a knowledge cutoff up to mid-2025, which helps on tasks requiring relatively recent background knowledge compared with older-generation models.
How to access and integrate kimi-k2
Step 1: Sign Up for API Key
To use kimi-k2, first create an account on CometAPI and generate your API key from the dashboard. After you have your key, store it securely and use it in the Authorization header for all API requests.
Step 2: Send Requests to kimi-k2 API
Send requests to CometAPI’s compatible chat completions endpoint and specify kimi-k2 as the model name.
curl --request POST \
--url https://api.cometapi.com/v1/chat/completions \
--header "Authorization: Bearer YOUR_COMETAPI_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "kimi-k2",
"messages": [
{
"role": "user",
"content": "Summarize the following document and identify key action items."
}
]
}'
from openai import OpenAI
client = OpenAI(
api_key="YOUR_COMETAPI_KEY",
base_url="https://api.cometapi.com/v1"
)
response = client.chat.completions.create(
model="kimi-k2",
messages=[
{
"role": "user",
"content": "Summarize the following document and identify key action items."
}
]
)
print(response.choices[0].message.content)
Step 3: Retrieve and Verify Results
After receiving the response, parse the returned content from the API response object and validate that the output matches your task requirements. For production integrations, you should also verify structured fields, check tool outputs when applicable, and add retries, logging, and guardrails to improve reliability.