Type
Spec
Rate
Input
per 1M prompt tokens
$0.95
Output
per 1M generated tokens
$4.00
Web search
per call when invoked
$0.015
Kimi K2.7 Code and Kimi K2.7 Code Highspeed side by side: specs, capabilities, parameters, and live pricing from the EmpirioLabs catalog.
Moonshot AI Text GenerationKimi K2.7 Code and Kimi K2.7 Code Highspeed are both Moonshot AI models in the Text Generation category, available through the EmpirioLabs API.
| Spec | Kimi K2.7 Code | Kimi K2.7 Code Highspeed |
|---|---|---|
| Model ID | kimi-k2-7-code | kimi-k2-7-code-highspeed |
| Author | Moonshot AI | Moonshot AI |
| Released | Jun 16, 2026 | Jun 16, 2026 |
| Context window | 256K tokens | 256K tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Input | Text Image Video | Text Image Video |
| Output | Text | Text |
| Structured output | JSON Schema | JSON Schema |
| Region | International | International |
| Endpoints | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/kimi-k2-7-code:generateContent | POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/kimi-k2-7-code-highspeed:generateContent |
| Capabilities |
Live rates from the EmpirioLabs catalog for both models. Each rate card always matches what the API charges.
Request parameters each model lists on EmpirioLabs, with the allowed values or range where one applies. A dash marks a parameter the model does not list.
| Parameter | Kimi K2.7 Code | Kimi K2.7 Code Highspeed |
|---|---|---|
| max_tokens | 1 to 262144 Default: 32768 | 1 to 262144 Default: 32768 |
| stop | string | string |
| tool_web_search | boolean Default: false | boolean Default: false |
| max_tool_calls | 1 to 20 Default: 5 | 1 to 20 Default: 5 |
| response_format | enum | enum |
Supports text, image, and video inputs with 256K context, function calling, JSON mode structured output, and built-in web search at $0.015 per invoked call. Thinking is always on and cannot be disabled; reasoning tokens are billed as output tokens. Temperature and other sampling overrides are ignored because the model service uses fixed sampling settings. Multi-step function calling through the API must replay the assistant message with its reasoning_content field intact.
The faster-serving tier of Kimi K2.7 Code. Supports text, image, and video inputs with 256K context, function calling, JSON mode structured output, and built-in web search at $0.015 per invoked call. Thinking is always on and cannot be disabled; reasoning tokens are billed as output tokens. Temperature and other sampling overrides are ignored because the model service uses fixed sampling settings. Multi-step function calling through the API must replay the assistant message with its reasoning_content field intact.
Kimi K2.7 Code serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id kimi-k2-7-code. Both models are called through the same endpoint. Set model to kimi-k2-7-code or kimi-k2-7-code-highspeed. Get an API key from the EmpirioLabs dashboard.
curl https://api.empiriolabs.ai/v1/chat/completions \
-H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k2-7-code",
"messages": [
{"role": "user", "content": "Write a haiku about the ocean."}
]
}'Both models were released on Jun 16, 2026. Both models have a context window of 256K tokens. Kimi K2.7 Code lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens). The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.
Kimi K2.7 Code lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens). Both live rate cards are on this page and always match what the API charges.
Yes. Both models use POST /v1/chat/completions with the same EmpirioLabs API key, so most integrations switch by changing the model value. Check the parameters table on this page first if you rely on a model-specific parameter.
Yes. Open each model in the EmpirioLabs playground and run the same prompt on both, with the parameters the API exposes.
Check out our pricing or reach out if you want your own model deployed on our stack.