Moonshot

Kimi K2.7 Code

moonshot/kimi-k2.7-code
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Kimi K2.7 Code 是 Kimi 迄今最智能的 Coding 模型,在长上下文中更可靠地遵循指令,能以更高的成功率完成编程任务。同时支持文本、图片与视频输入,仅支持思考模式,对话与 Agent 任务。模型上下文长度 256k,支持长思考擅长深度推理,支持自动上下文缓存功能,ToolCalls、JSON Mode、Partial Mode 等能力

Input / output modalities
Not provided to Not provided
Reference input / output price
Input¥6.5Output¥27per 1M tokens
Context window
256K
Added to catalog
Jun 15, 2026

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Moonshot AI¥6.5¥27¥1.3256K32K
Other-A¥6.5¥27¥1.3256K32K

Moonshot AI

Latency
3.28s
Throughput
75 tokens/s
Context
256K

Pricing

Input
¥6.5/M tokens
Output
¥27/M tokens
Cached
¥1.3/M tokens

Specifications

Context
256K
Max output
32K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Other-A

Latency
9.82s
Throughput
67 tokens/s
Context
256K

Pricing

Input
¥6.5/M tokens
Output
¥27/M tokens
Cached
¥1.3/M tokens

Specifications

Context
256K
Max output
32K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Kimi K2.7 Code code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="moonshot/kimi-k2.7-code", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

No public applications are currently listed for this model.

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