Migrate to GLM-5.3

This guide explains how to migrate your calls from GLM-5.2 or other earlier models to our latest flagship model GLM-5.3, covering samminpling parameter differences, streaming tool calls, reasoning_effort parameter, and other key points.

GLM-5.3 Features

• Context and output: Maximum context 1M, maximum output 128K.
• Support for streaming output during tool calling process (tool_stream=true), real-time retrieval of tool call parameters.
• Forced deep thinking by default (thinking={ type: "enabled" }), with the reasoning depth controlled by reasoning_effort.
• Support for the reasoning_effort parameter to control the reasoning effort level of the model when chain-of-thought thinking is enabled.
• Superior code performance and advanced reasoning capabilities.

Migration Checklist

• Update model identifier to glm-5.3
• Sampling parameters: temperature default value 1.0, top_p default value 0.95, recommend choosing only one for tuning
• Deep thinking: Forced thinking={ type: "enabled" } for complex reasoning/coding
• Control reasoning effort: Configure reasoning_effort to decide between low (light thinking), high (enhanced thinking), or max (deep thinking, default)
• Streaming response: Enable stream=true and properly handle delta.reasoning_content and delta.content
• Streaming tool calls: Enable stream=true and tool_stream=true and stream-concatenate delta.tool_calls[*].function.arguments
• Maximum output and context: Set max_tokens appropriately (GLM-5.3 maximum output 128K, context 1M)
• Development environment verification: Conduct use case testing and regression, focus on randomness, latency, parameter completeness in tool streams

Start Migration

1. Update Model Identifier

• Update model to glm-5.3.

resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Briefly describe the advantages of GLM-5.3"}]
)

2. Update Sampling Parameters

• temperature: Controls randomness; higher values are more divergent, lower values are more stable.
• top_p: Controls nucleus sampling; higher values expand candidate set, lower values converge candidate set.
• temperature defaults to 1.0, top_p defaults to 0.95, not recommended to adjust both simultaneously.

# Plan A: Use temperature (recommended)
resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Write a more creative brand introduction"}],
    temperature=1.0
)

# Plan B: Use top_p
resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Generate more stable technical documentation"}],
    top_p=0.8
)

3. Deep Thinking (Forced)

• GLM-5.3 continues to support deep thinking capability, which is forced on by default and returns an error if disabled.
• Recommended to enable for complex reasoning and coding tasks:

resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Design a three-tier microservice architecture for me"}],
    thinking={"type": "enabled"}
)

• Control reasoning effort, default max for deep reasoning:

resp = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Design a three-tier microservice architecture for me"}],
    thinking={"type": "enabled"},
    reasoning_effort="max"
)

4. Streaming Output and Tool Calls (Optional)

• GLM-5.3 supports real-time streaming construction and output during tool calling process, disabled by default (False), requires enabling both:
  • stream=True: Enable streaming output for responses
  • tool_stream=True: Enable streaming output for tool call parameters

response = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "How's the weather in Beijing"}],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather conditions for a specified location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {"type": "string", "description": "City, eg: Beijing, Shanghai"},
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                    },
                    "required": ["location"]
                }
            }
        }
    ],
    stream=True,
    tool_stream=True,
)

# Initialize streaming collection variables
reasoning_content = ""
content = ""
final_tool_calls = {}
reasoning_started = False
content_started = False

# Process streaming response
for chunk in response:
    if not chunk.choices:
        continue

    delta = chunk.choices[0].delta

    # Streaming reasoning process output
    if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
        if not reasoning_started and delta.reasoning_content.strip():
            print("\n🧠 Thinking Process:")
            reasoning_started = True
        reasoning_content += delta.reasoning_content
        print(delta.reasoning_content, end="", flush=True)

    # Streaming answer content output
    if hasattr(delta, 'content') and delta.content:
        if not content_started and delta.content.strip():
            print("\n\n💬 Answer Content:")
            content_started = True
        content += delta.content
        print(delta.content, end="", flush=True)

    # Streaming tool call information (parameter concatenation)
    if delta.tool_calls:
        for tool_call in delta.tool_calls:
            idx = tool_call.index
            if idx not in final_tool_calls:
                final_tool_calls[idx] = tool_call
                final_tool_calls[idx].function.arguments = tool_call.function.arguments
            else:
                final_tool_calls[idx].function.arguments += tool_call.function.arguments

# Output final tool call information
if final_tool_calls:
    print("\n📋 Function Calls Triggered:")
    for idx, tool_call in final_tool_calls.items():
        print(f"  {idx}: Function Name: {tool_call.function.name}, Parameters: {tool_call.function.arguments}")

See: Tool Streaming Output Documentation

5. Testing and Regression

First verify in development environment that post-migration calls are stable, focus on:

• Whether responses meet expectations, whether there’s excessive randomness or excessive conservatism in output
• Whether tool streaming construction and output work normally
• Latency and cost in long context and deep thinking scenarios

More Resources

Concept Parameters

Common model parameter concepts and sampling recommendations

Tool Streaming Output

View tool streaming output usage details

API Reference

View complete API documentation

Technical Support

Get technical support and help