bigmodel

GLM-5.3-Flash

bigmodel/glm-5.3-flash
/v1/chat/completions/v1/messages/v1/completions/v1/responses/v1beta/models/*/v1/models/*

GLM‑5.3‑Flash为GLM‑5系列首款原生多模态模型,以低成本架构实现优于GLM‑5.2的能力,采用总参320B、激活参18B的稀疏与线性注意力混合架构,在保障长上下文精度的同时大幅降低算力与KV缓存开销;它将视觉原生融入代码流程,可识别界面渲染反馈完成前端、游戏、Blender及真实环境操控类任务,同时拓展至办公、金融研究、专业文档场景,能够拆解任务、调用工具、自检优化,一站式输出PPTX、PDF、DOCX、XLSX等成品文档。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥0.8Output¥2.8per 1M tokens
Context window
1M
Added to catalog
Aug 27, 2026

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
智谱¥0.8¥2.8¥0.231M128K
Tencent¥0.8¥2.8¥0.231M128K

智谱

Latency
12.09s
Throughput
35 tokens/s
Context
1M

Pricing

Input
¥0.8/M tokens
Output
¥2.8/M tokens
Cached
¥0.23/M tokens

Specifications

Context
1M
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/completions/v1/responses/v1beta/models/*/v1/models/*

Tencent

Latency
4.81s
Throughput
35 tokens/s
Context
1M

Pricing

Input
¥0.8/M tokens
Output
¥2.8/M tokens
Cached
¥0.23/M tokens

Specifications

Context
1M
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/completions/v1/responses/v1beta/models/*/v1/models/*

GLM-5.3-Flash 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-5.3-flash", 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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