GLM-5-Turbo

Overview

GLM-5-Turbo is a foundation model deeply optimized for the OpenClaw scenario. It has been specifically optimized for the core requirements of OpenClaw tasks since the training phase, enhancing key capabilities such as tool invocation, command following, timed and persistent tasks, and long-chain execution.

Positioning

ClawBench Enhanced Model

Input Modalities

Text

Output Modalitie

Text

Context Length

200K

Maximum Output Tokens

128K

Capability

Thinking Mode

Offering multiple thinking modes for different scenarios

Streaming Output

Support real-time streaming responses to enhance user interaction experience

Function Call

Powerful tool invocation capabilities, enabling integration with various external toolsets

Context Caching

Intelligent caching mechanism to optimize performance in long conversations

Structured Output

Support for structured output formats like JSON, facilitating system integration

MCP

Flexibly integrate external MCP tools and data sources to expand use cases

Introducing GLM-5-Turbo

OpenClaw Native Model

From training data construction to the design of optimization objectives, we have systematically constructed a variety of OpenClaw tasks scenarios based on real-world agent workflows, ensuring that the model is truly capable of executing complex, dynamic, and long-chain tasks. We have significantly enhanced the following core capabilities:

Tool Calling—Precise Invocation, No Failures: GLM-5-Turbo has strengthened its ability to invoke external tools and various skills, ensuring greater stability and reliability in multi-step tasks, thereby enabling OpenClaw tasks to transition from dialogue to execution.
Instruction Following—Enhanced Decomposition of Complex Instructions: The model demonstrates stronger comprehension and decomposition capabilities for complex, multi-layered, and long-chain instructions. It can accurately identify objectives, plan steps, and support collaborative task division among multiple agents.
Scheduled and Persistent Tasks — Better Understanding of Time Dimensions, Uninterrupted Long Tasks: Significantly optimized for scenarios involving scheduled triggers, continuous execution, and long-running tasks. It better understands time-related requirements and maintains execution continuity during complex, long-running tasks.
High-Throughput Long Chains — Faster and More Stable Execution: For Lobster tasks involving high data throughput and long logical chains, GLM-5-Turbo further enhances execution efficiency and response stability, making it better suited for integration into real-world business workflows.

Resources

API Documentation: Learn how to call the API.
OpenClaw Guide: Learn how to integrate with OpenClaw.

Quick Start

The following is a full sample code to help you onboard GLM-5-Turbo 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-turbo",
        "messages": [
            {
                "role": "user",
                "content": "As a marketing expert, please create an attractive slogan for my product."
            },
            {
                "role": "assistant",
                "content": "Sure, to craft a compelling slogan, please tell me more about your product."
            },
            {
                "role": "user",
                "content": "Z.AI Open Platform"
            }
        ],
        "thinking": {
            "type": "enabled"
        },
        "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-turbo",
        "messages": [
            {
                "role": "user",
                "content": "As a marketing expert, please create an attractive slogan for my product."
            },
            {
                "role": "assistant",
                "content": "Sure, to craft a compelling slogan, please tell me more about your product."
            },
            {
                "role": "user",
                "content": "Z.AI Open Platform"
            }
        ],
        "thinking": {
            "type": "enabled"
        },
        "stream": true,
        "max_tokens": 4096,
        "temperature": 1.0
    }'

Install SDK

# Install latest version
pip install zai-sdk

# Or specify version
pip install zai-sdk==0.1.0

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-turbo",
    messages=[
        {
            "role": "user",
            "content": "As a marketing expert, please create an attractive slogan for my product.",
        },
        {
            "role": "assistant",
            "content": "Sure, to craft a compelling slogan, please tell me more about your product.",
        },
        {"role": "user", "content": "Z.AI Open Platform"},
    ],
    thinking={
        "type": "enabled",
    },
    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-turbo",
messages=[
    {
        "role": "user",
        "content": "As a marketing expert, please create an attractive slogan for my product.",
    },
    {
        "role": "assistant",
        "content": "Sure, to craft a compelling slogan, please tell me more about your product.",
    },
    {"role": "user", "content": "Z.AI Open Platform"},
    ],
    thinking={
        "type": "enabled",  # Optional: "disabled" or "enabled", default is "enabled"
    },
    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>0.3.0</version>
</dependency>

Gradle (Groovy)

implementation 'ai.z.openapi:zai-sdk:0.3.0'

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-turbo")
                .messages(
                    Arrays.asList(
                        ChatMessage.builder()
                            .role(ChatMessageRole.USER.value())
                                .content(
                                    "As a marketing expert, please create an attractive slogan for my product.")
                                .build(),
                        ChatMessage.builder()
                            .role(ChatMessageRole.ASSISTANT.value())
                                .content(
                                    "Sure, to craft a compelling slogan, please tell me more about your product.")
                                .build(),
                        ChatMessage.builder()
                            .role(ChatMessageRole.USER.value())
                                .content("Z.AI Open Platform")
                                .build()))
                .thinking(ChatThinking.builder().type("enabled").build())
                .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-turbo")
                .messages(
                    Arrays.asList(
                        ChatMessage.builder()
                            .role(ChatMessageRole.USER.value())
                            .content(
                                "As a marketing expert, please create an attractive slogan for my product.")
                            .build(),
                        ChatMessage.builder()
                            .role(ChatMessageRole.ASSISTANT.value())
                            .content(
                                "Sure, to craft a compelling slogan, please tell me more about your product.")
                            .build(),
                        ChatMessage.builder()
                            .role(ChatMessageRole.USER.value())
                            .content("Z.AI Open Platform")
                            .build()))
                .thinking(ChatThinking.builder().type("enabled").build())
                .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-turbo",
    messages=[
        {"role": "system", "content": "You are a smart and creative novelist"},
        {
            "role": "user",
            "content": "Please write a short fairy tale story as a fairy tale master",
        },
    ],
)

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

Get Started

Language Models

Vision Language Models

Image Generation Models

Video Generation Models

Image Generation Models

Audio Models

Capabilities

Tools

Agents

Overview

Positioning

Input Modalities

Output Modalitie

Context Length

Maximum Output Tokens

Capability

Thinking Mode

Streaming Output

Function Call

Context Caching

Structured Output

MCP

Introducing GLM-5-Turbo

OpenClaw Native Model

Resources

Quick Start

Get Started

Language Models

Vision Language Models

Image Generation Models

Video Generation Models

Image Generation Models

Audio Models

Capabilities

Tools

Agents

​ Overview

Positioning

Input Modalities

Output Modalitie

Context Length

Maximum Output Tokens

​ Capability

Thinking Mode

Streaming Output

Function Call

Context Caching

Structured Output

MCP

​ Introducing GLM-5-Turbo

OpenClaw Native Model

​ Resources

​ Quick Start

Overview

Capability

Introducing GLM-5-Turbo

Resources

Quick Start