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A state-of-the-art language model designed to excel in complex reasoning tasks, including mathematical problem-solving, programming challenges, and scientific inquiries. The model integrates advanced reasoning capabilities through its innovative architecture, making it suitable for a wide range of applications that require deep understanding and logical deduction.
Only model
and messages
are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding , which lists all available parameters along with notes on how to use them.
If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our .
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
512
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Available options: JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so
Parameters for audio output. Required when audio output is requested with modalities: ["audio"]
Output types that you would like the model to generate
This tool searches the web for relevant results to use in a response
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