Core Parameters

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Quick Reference

Parameter	Type	Default Value	Description
do_sample	Boolean	true	Whether to sample the output to increase diversity.
temperature	Float	(Model dependent)	Controls the randomness of output, higher values are more random.
top_p	Float	(Model dependent)	Controls diversity through nucleus sampling, recommended to use either this or temperature.
max_tokens	Integer	(Model dependent)	Limits the maximum number of tokens generated in a single call.
stream	Boolean	false	Whether to return responses in streaming mode.
thinking	Object	{"type": "enabled"}	Whether to enable chain-of-thought deep thinking, only supported by GLM-4.5 and above.

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Parameter Details

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do_sample

do_sample is a boolean value (true or false) that determines whether to sample the model’s output.

• true (default): Performs random sampling based on the probability distribution of each token, increasing text diversity and creativity. Suitable for content creation, dialogue, and other scenarios.
• false: Uses a greedy strategy, always selecting the token with the highest probability. Provides high deterministic output, suitable for scenarios requiring precise, factual answers.

Best Practices:

• Set to false when you need reproducible, deterministic output.
• Set to true when you want the model to generate more diverse and interesting content, and use it in combination with temperature or top_p.

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temperature

The temperature parameter controls the randomness of the model’s output.

• Lower values (e.g., 0.2): Make the probability distribution more “sharp”, resulting in more deterministic and conservative output.
• Higher values (e.g., 0.8): Make the probability distribution more “flat”, resulting in more random and diverse output.

Best Practices:

• For scenarios requiring rigor and factual accuracy (such as knowledge Q&A), it’s recommended to use lower temperature.
• For scenarios requiring creativity (such as content creation), you can try higher temperature.
• It’s recommended to use only one of temperature and top_p.

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top_p

top_p (nucleus sampling) controls diversity by sampling from the smallest set of tokens whose cumulative probability exceeds the threshold.

• Lower values (e.g., 0.2): Limit the sampling range, resulting in more deterministic output.
• Higher values (e.g., 0.9): Expand the sampling range, resulting in more diverse output.

Best Practices:

• If you want to achieve some diversity while ensuring content quality, top_p is a good choice (recommended values 0.8-0.95).
• It’s generally not recommended to modify both temperature and top_p simultaneously.

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max_tokens

max_tokens is used to limit the maximum number of tokens the model can generate in a single call. GLM-4.6 supports a maximum output length of 128K, GLM-4.5 supports a maximum output length of 96K, and it’s recommended to set it to no less than 1024. Tokens are the basic units of text, typically 1 token equals approximately 0.75 English words or 1.5 Chinese characters. Setting an appropriate max_tokens can control response length and cost, avoiding overly long outputs. If the model completes its answer before reaching the max_tokens limit, it will naturally end; if it reaches the limit, the output may be truncated.

• Purpose: Prevents generating overly long text and controls API call costs.
• Note: max_tokens limits the length of generated content, not including input.

Best Practices:

• Set max_tokens reasonably according to your application scenario. If you need short answers, you can set it to a smaller value (e.g., 50).

Default max_tokens and maximum supported max_tokens for each model:

Model Code	Default max_tokens	Maximum max_tokens
glm-4.6	65536	131072
glm-4.5	65536	98304
glm-4.5-air	65536	98304
glm-4.5-x	65536	98304
glm-4.5-airx	65536	98304
glm-4.5-flash	65536	98304
glm-4.5v	16384	16384
glm-4-32b-0414-128k	16384	16384

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stream

stream is a boolean value used to control the API’s response method.

• false (default): Returns the complete response at once, simple to implement but with long waiting times.
• true: Returns content in streaming (SSE) mode, significantly improving the experience of real-time interactive applications.

Best Practices:

• For chatbots, real-time code generation, and other applications, it’s strongly recommended to set this to true.

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thinking

The thinking parameter controls whether the model enables “Chain of Thought” for deeper thinking and reasoning.

• Type: Object
• Supported Models: GLM-4.5 and above

Properties:

• type (string):
  • enabled (default): Enable chain of thought. GLM-4.6 and GLM-4.5 will automatically determine if needed, while GLM-4.5V will force thinking.
  • disabled: Disable chain of thought.

Best Practices:

• It’s recommended to enable this when you need the model to perform complex reasoning and planning.
• For simple tasks, you can disable it to get faster responses.

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