Create a chat completion
Generates a chat completion based on the provided input. The completion can be streamed to the client as it is generated.
Query parameters
Specific provider to use (default determined by model)
Request body
Model ID to use
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API. This value is now deprecated in favor of max_completion_tokens, and is not compatible with o-series models.
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
How many chat completion choices to generate for each input message.
If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.
Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.
Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. The bias is added to the logits generated by the model prior to sampling.
A unique identifier representing your end-user, which can help to monitor and detect abuse.
If set to true, the model response data will be streamed to the client as it is generated using server-sent events.
Whether to enable parallel function calling during tool use.
The format of the reasoning content. Can be raw or parsed. When specified as raw some reasoning models will output <think /> tags. When specified as parsed the model will output the reasoning under reasoning or reasoning_content attribute.
Constrains effort on reasoning for reasoning models. Currently supported values are minimal, low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.
Response
Successful response
A unique identifier for the chat completion.
The Unix timestamp (in seconds) of when the chat completion was created.
The model used for the chat completion.
The object type, which is always chat.completion.