bigmodel

GLM-4.5-AirX

bigmodel/glm-4.5-airx
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GLM-4.5-AirX为GLM-4.5-Air的极速版,响应速度更快,专为大规模高速度需求打造。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥4Output¥12per 1M tokens
Context window
128K
Added to catalog
Jul 29, 2025

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
智谱≤ 32K¥4——128K96K
≥ 32K¥8——
≤ 2K—¥12—
≥ 2K—¥16—
≥ 0—¥32¥0.8

智谱

Latency
1.43s
Throughput
38 tokens/s
Context
128K

Pricing

Input
¥4/M tokens
Output
¥12/M tokens
Cached
¥0.8/M tokens

Tiered pricing

Pricing varies by input token range.

0–32K Token

Input tier
¥4/M tokens
Input tier
¥4/M tokens

32K–∞ Token

Input tier
¥8/M tokens

0–2K Token

Output tier
¥12/M tokens

2K–∞ Token

Output tier
¥16/M tokens

0–∞ Token

Output tier
¥32/M tokens
Cached tier
¥0.8/M tokens
Cached tier
¥0.8/M tokens
Cached tier
¥1.6/M tokens

Specifications

Context
128K
Max output
96K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GLM-4.5-AirX 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="bigmodel/glm-4.5-airx", 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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