> ## Documentation Index
> Fetch the complete documentation index at: https://docs.z.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Migrate to GLM-5.1
This guide explains how to migrate your calls from GLM-5.1 GLM-5 GLM-4.7 or other earlier models to Z.AI GLM-5.1, our most powerful coding model to date, covering sampling parameter differences, streaming tool calls, and other key points.
## GLM-5.1 Features
* Support for larger context and output: Maximum context 200K, maximum output 128K.
* New support for streaming output during tool calling process (`tool_stream=true`), real-time retrieval of tool call parameters.
* Same as GLM-5 series, supports deep thinking (`thinking={ type: "enabled" }`), when enabled will think compulsorily.
* Superior code performance and advanced reasoning capabilities.
## Migration Checklist
* [ ] Update model identifier to `glm-5.1`
* [ ] Sampling parameters: `temperature` default value `1.0`, `top_p` default value `0.95`, recommend choosing only one for tuning
* [ ] Deep thinking: Enabled or disable `thinking={ type: "enabled" }` as needed for complex reasoning/coding
* [ ] 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-5.1 maximum output 128K, context 200K)
* [ ] Prompt optimization: Work with deep thinking, use clearer instructions and constraints
* [ ] 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-5.1`.
```python theme={null}
resp = client.chat.completions.create(
model="glm-5.1",
messages=[{"role": "user", "content": "Briefly describe the advantages of GLM-5"}]
)
```
### 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.
```python theme={null}
# Plan A: Use temperature (recommended)
resp = client.chat.completions.create(
model="glm-5.1",
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-5.1",
messages=[{"role": "user", "content": "Generate more stable technical documentation"}],
top_p=0.8
)
```
### 3. Deep Thinking (Optional)
* GLM-5.1 continues to support deep thinking capability, enabled by default.
* Recommended to enable for complex reasoning and coding tasks:
```python theme={null}
resp = client.chat.completions.create(
model="glm-5.1",
messages=[{"role": "user", "content": "Design a three-tier microservice architecture for me"}],
thinking={"type": "enabled"}
)
```
### 4. Streaming Output and Tool Calls (Optional)
* GLM-5 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
```python theme={null}
response = client.chat.completions.create(
model="glm-5.1",
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](/guides/tools/stream-tool)
### 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
Common model parameter concepts and sampling recommendations
View tool streaming output usage details
View complete API documentation
Get technical support and help