bigmodel

GLM-5V-Turbo

bigmodel/glm-5v-turbo
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GLM-5V-Turbo 是智谱首个多模态 Coding 基座模型,面向视觉编程任务打造。能够原生处理图片、视频、文本等多模态输入,同时擅长长程规划、复杂编程和动作执行;深度适配 Agent 工作流,能够与 Claude Code、OpenClaw 等 Agent 深度协同,完成”看懂环境→规划动作→执行任务”的完整闭环。

Input / output modalities
文本 · 图像 · 视频 to 文本
Reference input / output price
Input¥5Output¥22per 1M tokens
Context window
200K
Added to catalog
Apr 3, 2026

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
智谱≤ 32K¥5¥22¥1.2200K128K
> 32K¥7¥26¥1.8

智谱

Latency
1.42s
Throughput
33 tokens/s
Context
200K

Pricing

Input
¥5/M tokens
Output
¥22/M tokens
Cached
¥1.2/M tokens

Tiered pricing

Pricing varies by input token range.

0–32K Token

Input tier
¥5/M tokens
Output tier
¥22/M tokens
Cached tier
¥1.2/M tokens

32K–∞ Token

Input tier
¥7/M tokens
Output tier
¥26/M tokens
Cached tier
¥1.8/M tokens

Specifications

Context
200K
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

GLM-5V-Turbo 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-5v-turbo", 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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