Streamlake

KAT-Coder-Pro-V1

streamlake/kat-coder-pro-v1
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

专为 Agentic Coding 设计,全面覆盖编程任务与场景,通过大规模智能体强化学习,实现智能行为涌现,在代码编写性能上显著超越同类模型。

Input / output modalities
Not provided to Not provided
Reference input / output price
Input¥4Output¥16per 1M tokens
Context window
128K
Added to catalog
Nov 13, 2025

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Streamlake≤ 32K¥4¥16¥0.8128K32K
32K – 128K¥6¥24¥1.2
≥ 128K¥10¥40¥2

Streamlake

Latency
0.72s
Throughput
92 tokens/s
Context
128K

Pricing

Input
¥4/M tokens
Output
¥16/M tokens
Cached
¥0.8/M tokens

Tiered pricing

Pricing varies by input token range.

0–32K Token

Input tier
¥4/M tokens
Output tier
¥16/M tokens
Cached tier
¥0.8/M tokens

32K–128K Token

Input tier
¥6/M tokens
Output tier
¥24/M tokens
Cached tier
¥1.2/M tokens

128K–∞ Token

Input tier
¥10/M tokens
Output tier
¥40/M tokens
Cached tier
¥2/M tokens

Specifications

Context
128K
Max output
32K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

KAT-Coder-Pro-V1 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="streamlake/kat-coder-pro-v1", 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}")
                
              

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