GLM-5.2 - Overview - Z.AI DEVELOPER DOCUMENT

GLM-5.2 is a flagship model built for the era of long-horizon tasks. With truly usable 1M-token context, it has been tested to handle project-scale engineering context, delivering more stable long-task execution, more reliable adherence to engineering standards, and higher success rates in development scenarios. A single task can complete the full development workflow—from requirements to deployable products across multiple platforms.

Capability

Usage

Introducing GLM-5.2

Resources

API Documentation: Learn how to call the API.

Quick Start

The following is a full sample code to help you onboard GLM-5.2 with ease.

cURL
Official Python SDK
Official Java SDK
OpenAI Python SDK

Basic Call

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-api-key" \
-d '{
  "model": "glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks."
    },
    {
      "role": "user",
      "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack."
    }
  ],
  "thinking": {
    "type": "enabled"
  },
  "reasoning_effort": "max",
  "max_tokens": 4096,
  "temperature": 1.0
}'

Streaming Call

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-api-key" \
-d '{
  "model": "glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks."
    },
    {
      "role": "user",
      "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack."
    }
  ],
  "thinking": {
    "type": "enabled"
  },
  "reasoning_effort": "max",
  "stream": true,
  "max_tokens": 4096,
  "temperature": 1.0
}'

Install SDK

# Install latest version
pip install zai-sdk

# Or specify version
pip install zai-sdk==0.2.3

Verify Installation

import zai

print(zai.__version__)

Basic Call

from zai import ZaiClient

client = ZaiClient(api_key="your-api-key")  # Your API Key

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {
            "role": "system",
            "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks.",
        },
        {
            "role": "user",
            "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack.",
        },
    ],
    thinking={
        "type": "enabled",
    },
    reasoning_effort="max",
    max_tokens=4096,
    temperature=1.0,
)

# Get complete response
print(response.choices[0].message)

Streaming Call

from zai import ZaiClient

client = ZaiClient(api_key="your-api-key")  # Your API Key

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {
            "role": "system",
            "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks.",
        },
        {
            "role": "user",
            "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack.",
        },
    ],
    thinking={
        "type": "enabled",  # Optional: "disabled" or "enabled", default is "enabled"
    },
    reasoning_effort="max",
    stream=True,
    max_tokens=4096,
    temperature=0.6,
)

# Stream response
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)

Install SDKMaven

<dependency>
    <groupId>ai.z.openapi</groupId>
    <artifactId>zai-sdk</artifactId>
    <version>0.3.5</version>
</dependency>

Gradle (Groovy)

implementation 'ai.z.openapi:zai-sdk:0.3.5'

Basic Call

import ai.z.openapi.ZaiClient;
import ai.z.openapi.service.model.ChatCompletionCreateParams;
import ai.z.openapi.service.model.ChatCompletionResponse;
import ai.z.openapi.service.model.ChatMessage;
import ai.z.openapi.service.model.ChatMessageRole;
import ai.z.openapi.service.model.ChatThinking;
import java.util.Arrays;

public class BasicChat {
    public static void main(String[] args) {
        // Initialize client
        ZaiClient client = ZaiClient.builder().ofZAI().apiKey("your-api-key").build();

        // Create chat completion request
        ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
            .model("glm-5.2")
            .messages(
                Arrays.asList(
                    ChatMessage.builder()
                        .role(ChatMessageRole.SYSTEM.value())
                        .content(
                            "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks.")
                        .build(),
                    ChatMessage.builder()
                        .role(ChatMessageRole.USER.value())
                        .content(
                            "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack.")
                        .build()))
            .thinking(ChatThinking.builder().type("enabled").build())
            .reasoningEffort("max")
            .maxTokens(4096)
            .temperature(1.0f)
            .build();

        // Send request
        ChatCompletionResponse response = client.chat().createChatCompletion(request);

        // Get response
        if (response.isSuccess()) {
            Object reply = response.getData().getChoices().get(0).getMessage();
            System.out.println("AI Response: " + reply);
        } else {
            System.err.println("Error: " + response.getMsg());
        }
    }
}

Streaming Call

import ai.z.openapi.ZaiClient;
import ai.z.openapi.service.model.ChatCompletionCreateParams;
import ai.z.openapi.service.model.ChatCompletionResponse;
import ai.z.openapi.service.model.ChatMessage;
import ai.z.openapi.service.model.ChatMessageRole;
import ai.z.openapi.service.model.ChatThinking;
import ai.z.openapi.service.model.Delta;
import java.util.Arrays;

public class StreamingChat {
    public static void main(String[] args) {
        // Initialize client
        ZaiClient client = ZaiClient.builder().ofZAI().apiKey("your-api-key").build();

        // Create streaming chat completion request
        ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
            .model("glm-5.2")
            .messages(
                Arrays.asList(
                    ChatMessage.builder()
                        .role(ChatMessageRole.SYSTEM.value())
                        .content(
                            "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks.")
                        .build(),
                    ChatMessage.builder()
                        .role(ChatMessageRole.USER.value())
                        .content(
                            "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack.")
                        .build()))
            .thinking(ChatThinking.builder().type("enabled").build())
            .reasoningEffort("max")
            .stream(true) // Enable streaming output
            .maxTokens(4096)
            .temperature(1.0f)
            .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());
        }
    }
}

Install SDK

# Install or upgrade to latest version
pip install --upgrade 'openai>=1.0'

Verify Installation

python -c "import openai; print(openai.__version__)"

Usage Example

from openai import OpenAI

client = OpenAI(
    api_key="your-Z.AI-api-key",
    base_url="https://api.z.ai/api/paas/v4/",
)

completion = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "system", "content": "You are a senior full-stack software engineer, proficient in frontend development, backend architecture design, and modern web technology stacks."},
        {
            "role": "user",
            "content": "Design and build a personal blog website for me, including a homepage, article list page, and article detail page, using React + Node.js technology stack.",
        },
    ],
)

print(completion.choices[0].message.content)