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Each call that you send to a model includes parameter values that control how the model generates
a response. The model can generate different results for different parameter values. Experiment with
different parameter values to get the best values for the task. The parameters available for
different models may differ. The most common parameters are the following:
Max output tokens
Temperature
Top-P
Seed
Max output tokens
Maximum number of tokens that can be generated in the response. A token is
approximately four characters. 100 tokens correspond to roughly 60-80 words.
Specify a lower value for shorter responses and a higher value for potentially longer
responses.
Temperature
The temperature is used for sampling during response generation, which occurs whentopPandtopKare applied. Temperature controls the degree of randomness in token selection.
Lower temperatures are good for prompts that require a less open-ended or creative response, while
higher temperatures can lead to more diverse or creative results. A temperature of0means that the highest probability tokens are always selected. In this case, responses for a given
prompt are mostly deterministic, but a small amount of variation is still possible.
If the model returns a response that's too generic, too short, or the model gives a fallback
response, try increasing the temperature.
Gemini models support a temperature value between 0.0 and 2.0. Models have a default
temperature of 1.0.
Top-P
Top-P changes how the model selects tokens for output. Tokens are selected
from the most probable to least probable until the sum of their probabilities
equals the top-P value. For example, if tokens A, B, and C have a probability of
0.3, 0.2, and 0.1 and the top-P value is0.5, then the model will
select either A or B as the next token by using temperature and excludes C as a
candidate.
Specify a lower value for less random responses and a higher value for more
random responses.
Seed
When seed is fixed to a specific value, the model makes a best effort to provide
the same response for repeated requests. Deterministic output isn't guaranteed.
Also, changing the model or parameter settings, such as the temperature, can
cause variations in the response even when you use the same seed value. By
default, a random seed value is used.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# Experiment with parameter values\n\nEach call that you send to a model includes parameter values that control how the model generates\na response. The model can generate different results for different parameter values. Experiment with\ndifferent parameter values to get the best values for the task. The parameters available for\ndifferent models may differ. The most common parameters are the following:\n\n- Max output tokens\n- Temperature\n- Top-P\n- Seed\n\nMax output tokens\n-----------------\n\nMaximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.\n\nSpecify a lower value for shorter responses and a higher value for potentially longer\nresponses.\n\nTemperature\n-----------\n\nThe temperature is used for sampling during response generation, which occurs when `topP` and `topK` are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of `0` means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.\n\nIf the model returns a response that's too generic, too short, or the model gives a fallback\nresponse, try increasing the temperature.\n\nGemini models support a temperature value between 0.0 and 2.0. Models have a default\ntemperature of 1.0.\n\nTop-P\n-----\n\nTop-P changes how the model selects tokens for output. Tokens are selected from the most probable to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is `0.5`, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\n\nSpecify a lower value for less random responses and a higher value for more\nrandom responses.\n\nSeed\n----\n\nWhen seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.\n\nThis is a preview feature.\n\nWhat's next\n-----------\n\n- Explore examples of prompts in the [Prompt gallery](/vertex-ai/generative-ai/docs/prompt-gallery).\n- Learn how to optimize prompts for use with [Google models](/vertex-ai/generative-ai/docs/learn/models) by using the [Vertex AI prompt optimizer (Preview)](/vertex-ai/generative-ai/docs/learn/prompts/prompt-optimizer)."]]
