Documentation Index
Fetch the complete documentation index at: https://docs.bigmodel.cn/llms.txt
Use this file to discover all available pages before exploring further.
概览
GLM-4.5V 是智谱基于 MOE 架构的视觉推理模型，以 106B 的总参数量和 12B 激活参数量，在各类基准测试中达到全球同级别开源多模态模型 SOTA，涵盖图像、视频、文档理解及 GUI 任务等常见任务。
定位
视觉推理
输入模态
视频、图像、文本、文件
输出模态
文本
上下文窗口
64K
GLM-4.5V 价格详情请前往价格界面
能力支持
深度思考
启用深度思考模式，提供更深层次的推理分析
视觉理解
强大的视觉理解能力，支持图片，视频，文件
流式输出
支持实时流式响应，提升用户交互体验
上下文缓存
智能缓存机制，优化长对话性能
推荐场景
前端复刻
前端复刻
支持将网页截图或完整浏览录屏输入模型，自动解析布局与交互逻辑，高精度还原页面元素与二级页面结构，生成可交互的 HTML 代码，便于直接使用或二次优化。
Grounding
Grounding
可根据文本描述精准定位指定人物或物体，支持按外貌、衣着等多条件组合筛选。适用于安检、质检、内容审核、遥感监测等实业场景，定位精度高。
GUI Agent
GUI Agent
识别并理解屏幕画面，执行点击、滑动等操作指令，精准完成如 PPT 修改、Word 编辑等任务，全程自动化，适用于各类办公场景，为智能体操作任务提供可靠支持。
复杂长文档解读
复杂长文档解读
支持对长文档进行深度解析，处理文本、表格、图形等多模态内容，可总结、翻译、提取关键信息，并在原有观点基础上提出新见解，适用于研报分析、科研、教育等专业场景。
图像识别与推理
图像识别与推理
结合强推理能力与丰富世界知识，在无需搜索的情况下推断图像背景信息。支持将图表、曲线等内容转为结构化数据，精确还原内容与布局，适用于无电子版表格的快速数字化处理，避免手动录入的繁琐与错误。
视频理解
视频理解
支持解析长时视频内容，精准识别并推理视频中的时间线、人物关系、事件发展及因果逻辑，适用于安防监控、影视内容分析、舆情事件追踪等领域，实现高效的视频信息抽取与洞察。
学科解题
学科解题
具备图文感知、知识储备与推理能力，能够解决复杂的图文结合题目，适用于 K12 教育场景中的解题和讲解需求。
使用资源
体验中心：快速测试模型在业务场景上的效果接口文档：API 调用方式
详细介绍
开源多模态 SOTA
GLM-4.5V 基于 GLM-4.5-Air，延续 GLM-4.1V-Thinking 技术路线进行迭代升级，在 41 个公开视觉多模态榜单中综合效果达到同级别开源模型 SOTA 性能，涵盖图像、视频、文档理解及 GUI 任务等常见任务。
应用示例
- 视频前端复刻
- 图片翻译
- GUI Agent
- 图表转换
- 学科解题
- 文档解读
- Grounding
输入
prompt：帮我生成这个video中所展示的html code ，需要包含视频中的点击、跳转、交互等
输出
代码略.渲染后的网页截图:
输入
prompt：将图中的文字翻译成中文输出
翻译结果：
输入
prompt：Modify the data in the first row on slide 4 to “89”, “21”, “900” and “None”输出
修改结果：
输入
prompt：将图中的表格转为markdown格式输出
代码略.渲染后的结果：
输入
prompt：解题输出
### 解题过程：\n\n
#### 题目分析：\n
圆形金属线圈静止，匀强磁场绕直径轴匀速
转动，等效于线圈在磁场中切割磁感线，产
生正弦式交变电动势。线圈内阻忽略不计，
故电动势全部作用于变压器原线圈。变压器
为理想变压器，电压表测量副线圈电压，电
流表测量副线圈电流。\n\n\n
#### 选项逐一分析：\n
##### **A. 图示位置磁场
方向与线圈平面垂直，电流表示数为0**
\n图示位置为中性面，磁通量最大，但磁
通量变化率为零，感应电动势瞬时值为0。
然而，电流表测量的是有效值（非瞬时值），
交流电的有效值不为零，故A错误。\n\n
#####
（其余内容省略...）
### 最终答案：B
输入
prompt：总结这篇研报的主要观点，并从专业的角度对其进行补充，得出对AI未来发展前景的预测。输出
一、研报核心观点总结
该研报围绕**AI发展的“新 Scaling、新范
式、新 TAM”**展开，核心观点如下：
1. 模型层：预训练 Scaling Law 有望开启
新起点
预训练阶段仍是模型性能提升的关键，但传
统 Transformer 架构面临参数扩张放缓的瓶
颈。近期新架构（如腾讯 Turbo-S 的
Transformer-Mamba 混合架构、Google
Gemini Diffusion 的扩散模型）的出现，
以及数据量（Meta Llama 4、
Google Gemini 达 2 万亿 token 级）和算力
集群（OpenAI 星际之门、xAI Colossus）
的支持，为预训练 Scaling Law 的延续提供
了新路径。
理论上，Ilya Sutskever 提出“模型架构改
进可能是 Scaling Law 的新方向”，若能突破
现有架构限制，模型性能有望进一步提升。
（其余内容省略...）
输入
prompt：Tell me the position of the couple in the picture. The short-haired guy is wearing a pink top and blue shorts, and the girl is in a cyan dress. Answer in [x1,y1,x2,y2] format.输出
The position of the couple in the
picture, where the short-haired
guy is wearing a pink top and blue
shorts, and the girl is in a cyan
dress, is [835,626,931,883].
调用示例
基础与流式
- cURL
- Python
- Java
- Python(旧)
基础调用流式调用
curl -X POST \
https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.5v",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG"
}
},
{
"type": "text",
"text": "Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format"
}
]
}
],
"thinking": {
"type":"enabled"
}
}'
curl -X POST \
https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.5v",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG"
}
},
{
"type": "text",
"text": "Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format"
}
]
}
],
"thinking": {
"type":"enabled"
},
"stream": true
}'
安装 SDK验证安装基础调用流式调用
# 安装最新版本
pip install zai-sdk
# 或指定版本
pip install zai-sdk==0.2.2
import zai
print(zai.__version__)
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的 APIKey
response = client.chat.completions.create(
model="glm-4.5v", # 填写需要调用的模型名称
messages=[
{
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG"
}
},
{
"type": "text",
"text": "Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format"
}
],
"role": "user"
}
],
thinking={
"type":"enabled"
}
)
print(response.choices[0].message)
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的 APIKey
response = client.chat.completions.create(
model="glm-4.5v", # 填写需要调用的模型名称
messages=[
{
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG"
}
},
{
"type": "text",
"text": "Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format"
}
],
"role": "user"
}
],
thinking={
"type":"enabled"
},
stream=True
)
for chunk in response:
if chunk.choices[0].delta.reasoning_content:
print(chunk.choices[0].delta.reasoning_content, end='', flush=True)
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end='', flush=True)
安装 SDKMavenGradle (Groovy)基础调用流式调用
<dependency>
<groupId>ai.z.openapi</groupId>
<artifactId>zai-sdk</artifactId>
<version>0.3.3</version>
</dependency>
implementation 'ai.z.openapi:zai-sdk:0.3.3'
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import ai.z.openapi.core.Constants;
import java.util.Arrays;
public class GLM45VExample {
public static void main(String[] args) {
String apiKey = ""; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
.apiKey(apiKey)
.build();
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("text")
.text("描述下这张图片")
.build(),
MessageContent.builder()
.type("image_url")
.imageUrl(ImageUrl.builder()
.url("https://aigc-files.bigmodel.cn/api/cogview/20250723213827da171a419b9b4906_0.png")
.build())
.build()))
.build()))
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
Object reply = response.getData().getChoices().get(0).getMessage();
System.out.println(reply);
} else {
System.err.println("错误: " + response.getMsg());
}
}
}
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import ai.z.openapi.core.Constants;
import java.util.Arrays;
public class GLM45VStreamExample {
public static void main(String[] args) {
String apiKey = ""; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
.apiKey(apiKey)
.build();
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("text")
.text("Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format")
.build(),
MessageContent.builder()
.type("image_url")
.imageUrl(ImageUrl.builder()
.url("https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG")
.build())
.build()))
.build()))
.stream(true)
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
response.getFlowable().subscribe(
// Process streaming message data
data -> {
if (data.getChoices() != null && !data.getChoices().isEmpty()) {
Delta delta = data.getChoices().get(0).getDelta();
System.out.print(delta + "\n");
}},
// Process streaming response error
error -> System.err.println("\nStream error: " + error.getMessage()),
// Process streaming response completion event
() -> System.out.println("\nStreaming response completed")
);
} else {
System.err.println("Error: " + response.getMsg());
}
}
}
更新 SDK 至 2.1.5.20250726基础调用流式调用
# 安装最新版本
pip install zhipuai
# 或指定版本
pip install zhipuai==2.1.5.20250726
from zhipuai import ZhipuAI
client = ZhipuAI(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4.5v", # 填写需要调用的模型名称
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "请帮我解决这个题目，给出详细过程和答案"
},
{
"type": "image_url",
"image_url": {
"url": "传入图片的 url 地址"
}
}
]
}
]
)
print(response.choices[0].message)
from zhipuai import ZhipuAI
client = ZhipuAI(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4.5v", # 填写需要调用的模型名称
messages=[
{
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cloudcovert-1305175928.cos.ap-guangzhou.myqcloud.com/%E5%9B%BE%E7%89%87grounding.PNG"
}
},
{
"type": "text",
"text": "Where is the second bottle of beer from the right on the table? Provide coordinates in [[xmin,ymin,xmax,ymax]] format"
}
],
"role": "user"
}
],
thinking={
"type":"enabled"
},
stream=True
)
for chunk in response:
if chunk.choices[0].delta.reasoning_content:
print(chunk.choices[0].delta.reasoning_content, end='', flush=True)
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end='', flush=True)
多模态理解
不支持同时理解文件、视频和图像。
- cURL
- Python
- Java
图片理解视频理解文件理解
curl -X POST \
https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.5v",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cdn.bigmodel.cn/static/logo/register.png"
}
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.bigmodel.cn/static/logo/api-key.png"
}
},
{
"type": "text",
"text": "What are the pics talk about?"
}
]
}
],
"thinking": {
"type": "enabled"
}
}'
curl -X POST \
https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.5v",
"messages": [
{
"role": "user",
"content": [
{
"type": "video_url",
"video_url": {
"url": "https://cdn.bigmodel.cn/agent-demos/lark/113123.mov"
}
},
{
"type": "text",
"text": "What are the video show about?"
}
]
}
],
"thinking": {
"type": "enabled"
}
}'
curl -X POST \
https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.5v",
"messages": [
{
"role": "user",
"content": [
{
"type": "file_url",
"file_url": {
"url": "https://cdn.bigmodel.cn/static/demo/demo2.txt"
}
},
{
"type": "file_url",
"file_url": {
"url": "https://cdn.bigmodel.cn/static/demo/demo1.pdf"
}
},
{
"type": "text",
"text": "What are the files show about?"
}
]
}
],
"thinking": {
"type": "enabled"
}
}'
安装 SDK验证安装图片理解传入 Base64 图片视频理解文件理解
# 安装最新版本
pip install zai-sdk
# 或指定版本
pip install zai-sdk==0.2.2
import zai
print(zai.__version__)
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4.5v",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://cdn.bigmodel.cn/static/logo/register.png"
}
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.bigmodel.cn/static/logo/api-key.png"
}
},
{
"type": "text",
"text": "What are the pics talk about?"
}
]
}
],
thinking={
"type": "enabled"
}
)
print(response.choices[0].message)
from zai import ZhipuAiClient
import base64
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的APIKey
img_path = "your/path/xxx.png"
with open(img_path, "rb") as img_file:
img_base = base64.b64encode(img_file.read()).decode("utf-8")
response = client.chat.completions.create(
model="glm-4.5v",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": img_base
}
},
{
"type": "text",
"text": "请描述这个图片"
}
]
}
],
thinking={
"type": "enabled"
}
)
print(response.choices[0].message)
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4.5v",
messages=[
{
"role": "user",
"content": [
{
"type": "video_url",
"video_url": {
"url": "https://cdn.bigmodel.cn/agent-demos/lark/113123.mov"
}
},
{
"type": "text",
"text": "What are the video show about?"
}
]
}
],
thinking={
"type": "enabled"
}
)
print(response.choices[0].message)
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key="your-api-key") # 填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4.5v",
messages=[
{
"role": "user",
"content": [
{
"type": "file_url",
"file_url": {
"url": "https://cdn.bigmodel.cn/static/demo/demo2.txt"
}
},
{
"type": "file_url",
"file_url": {
"url": "https://cdn.bigmodel.cn/static/demo/demo1.pdf"
}
},
{
"type": "text",
"text": "What are the files show about?"
}
]
}
],
thinking={
"type": "enabled"
}
)
print(response.choices[0].message)
安装 SDKMavenGradle (Groovy)图片理解传入 Base64 图片视频理解文件理解
<dependency>
<groupId>ai.z.openapi</groupId>
<artifactId>zai-sdk</artifactId>
<version>0.3.3</version>
</dependency>
implementation 'ai.z.openapi:zai-sdk:0.3.3'
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import java.util.Arrays;
public class MultiModalImageExample {
public static void main(String[] args) {
String apiKey = "your-api-key"; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
.apiKey(apiKey)
.build();
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("image_url")
.imageUrl(ImageUrl.builder()
.url("https://cdn.bigmodel.cn/static/logo/register.png")
.build())
.build(),
MessageContent.builder()
.type("image_url")
.imageUrl(ImageUrl.builder()
.url("https://cdn.bigmodel.cn/static/logo/api-key.png")
.build())
.build(),
MessageContent.builder()
.type("text")
.text("What are the pics talk about?")
.build()
))
.build()
))
.thinking(ChatThinking.builder()
.type("enabled")
.build())
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
Object reply = response.getData().getChoices().get(0).getMessage();
System.out.println(reply);
} else {
System.err.println("错误: " + response.getMsg());
}
}
}
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.util.Arrays;
import java.util.Base64;
public class Base64ImageExample {
public static void main(String[] args) throws IOException {
String apiKey = "your-api-key"; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU().apiKey(apiKey).build();
String file = ClassLoader.getSystemResource("your/path/xxx.png").getFile();
byte[] bytes = Files.readAllBytes(new File(file).toPath());
Base64.Encoder encoder = Base64.getEncoder();
String base64 = encoder.encodeToString(bytes);
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("image_url")
.imageUrl(ImageUrl.builder()
.url(base64)
.build())
.build(),
MessageContent.builder()
.type("text")
.text("What are the pics talk about?")
.build()))
.build()))
.thinking(ChatThinking.builder().type("enabled").build())
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
Object reply = response.getData().getChoices().get(0).getMessage();
System.out.println(reply);
} else {
System.err.println("错误: " + response.getMsg());
}
}
}
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import java.util.Arrays;
public class MultiModalVideoExample {
public static void main(String[] args) {
String apiKey = "your-api-key"; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
.apiKey(apiKey)
.build();
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("video_url")
.videoUrl(VideoUrl.builder()
.url("https://cdn.bigmodel.cn/agent-demos/lark/113123.mov")
.build())
.build(),
MessageContent.builder()
.type("text")
.text("What are the video show about?")
.build()
))
.build()
))
.thinking(ChatThinking.builder()
.type("enabled")
.build())
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
Object reply = response.getData().getChoices().get(0).getMessage();
System.out.println(reply);
} else {
System.err.println("错误: " + response.getMsg());
}
}
}
import ai.z.openapi.ZhipuAiClient;
import ai.z.openapi.service.model.*;
import java.util.Arrays;
public class MultiModalFileExample {
public static void main(String[] args) {
String apiKey = "your-api-key"; // 请填写您自己的APIKey
ZhipuAiClient client = ZhipuAiClient.builder().ofZHIPU()
.apiKey(apiKey)
.build();
ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
.model("glm-4.5v")
.messages(Arrays.asList(
ChatMessage.builder()
.role(ChatMessageRole.USER.value())
.content(Arrays.asList(
MessageContent.builder()
.type("file_url")
.fileUrl(FileUrl.builder()
.url("https://cdn.bigmodel.cn/static/demo/demo2.txt")
.build())
.build(),
MessageContent.builder()
.type("file_url")
.fileUrl(FileUrl.builder()
.url("https://cdn.bigmodel.cn/static/demo/demo1.pdf")
.build())
.build(),
MessageContent.builder()
.type("text")
.text("What are the files show about?")
.build()
))
.build()
))
.thinking(ChatThinking.builder()
.type("enabled")
.build())
.build();
ChatCompletionResponse response = client.chat().createChatCompletion(request);
if (response.isSuccess()) {
Object reply = response.getData().getChoices().get(0).getMessage();
System.out.println(reply);
} else {
System.err.println("错误: " + response.getMsg());
}
}
}