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Default Thinking Behaviour

Thinking is activated by default in GLM-4.7, different from the default hybrid thinking in GLM-4.6.
If you want to disable thinking, use: 
"thinking": {
    "type": "disabled"
}

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Interleaved thinking

We support interleaved thinking by default (supported since GLM-4.5), allowing GLM to think between tool calls and after receiving tool results. This enables more complex, step-by-step reasoning: interpreting each tool output before deciding what to do next, chaining multiple tool calls with reasoning steps, and making finer-grained decisions based on intermediate results.
When using interleaved thinking with tools, thinking blocks should be explicitly preserved and returned together with the tool results.
The detailed interleaved thinking process is as follows. Description

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Preserved thinking

GLM-4.7 introduces a new capability in coding scenarios (the default status is on for coding endpoint, and off for other scenarios, and currently not configurable publicly): the model can retain reasoning content from previous assistant turns in the context. This helps preserve reasoning continuity and conversation integrity, improves model performance, and increases cache hit rates—saving tokens in real tasks. The detailed Preserved thinking process is as follows. Description

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Turn-level Thinking

“Turn-level Thinking” is a capability that lets you control reasoning computation on a per-turn basis: within the same session, each request can independently choose to enable or disable thinking. This is a new capability introduced in GLM-4.7, with the following advantages:
  • More flexible cost/latency control: For lightweight turns like “asking a fact” or “tweaking wording,” you can disable thinking to get faster responses; for heavier tasks like “complex planning,” “multi-constraint reasoning,” or “code debugging,” you can enable thinking to improve accuracy and stability.
  • Smoother multi-turn experience: The thinking switch can be toggled at any point within a session. The model stays coherent across turns and keeps a consistent output style, making it feel “smarter when things are hard, faster when things are simple.”
  • Better for agent/tool-use scenarios: On turns that require quick tool execution, you can reduce reasoning overhead; on turns that require making decisions based on tool results, you can turn on deeper thinking—dynamically balancing efficiency and quality.

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Example Usage

This applies to both Interleaved Thinking and Preserved Thinking—no manual differentiation is required. Remember to return the historical reasoning_contentto keep the reasoning coherent. 
""""Interleaved Thinking + Tool Calling Example"""

import json
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.z.ai/api/paas/v4/",
)

tools = [{"type": "function", "function": {
    "name": "get_weather",
    "description": "Get weather information",
    "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
}}]

messages = [
    {"role": "system", "content": "You are an assistant"},
    {"role": "user", "content": "What's the weather like in Beijing?"},
]

# Round 1: the model reasons and then calls a tool
response = client.chat.completions.create(model="glm-4.6", messages=messages, tools=tools, stream=True)
reasoning, content, tool_calls = "", "", []
for chunk in response:
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
        reasoning += delta.reasoning_content
    if hasattr(delta, "content") and delta.content:
        content += delta.content
    if hasattr(delta, "tool_calls") and delta.tool_calls:
        for tc in delta.tool_calls:
            if tc.index >= len(tool_calls):
                tool_calls.append({"id": tc.id, "function": {"name": "", "arguments": ""}})
            if tc.function.name:
                tool_calls[tc.index]["function"]["name"] = tc.function.name
            if tc.function.arguments:
                tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments

print(f"Reasoning: {reasoning}\nTool calls: {tool_calls}")

# Key: return reasoning_content to keep the reasoning coherent
messages.append({"role": "assistant", "content": content, "reasoning_content": reasoning,
                 "tool_calls": [{"id": tc["id"], "type": "function", "function": tc["function"]} for tc in tool_calls]})
messages.append({"role": "tool", "tool_call_id": tool_calls[0]["id"], 
                 "content": json.dumps({"weather": "Sunny", "temp": "25°C"})})

# Round 2: the model continues reasoning based on the tool result and responds
response = client.chat.completions.create(model="glm-4.6", messages=messages, tools=tools, stream=True)
reasoning, content = "", ""
for chunk in response:
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
        reasoning += delta.reasoning_content
    if hasattr(delta, "content") and delta.content:
        content += delta.content

print(f"Reasoning: {reasoning}\nReply: {content}")