Interface ModelProfile

Whether the model supports audio inputs.

When true, the model can process audio data as input, enabling capabilities like speech recognition, audio analysis, or multimodal interactions involving sound.

Whether the model can generate audio outputs.

When true, the model can produce audio data as part of its response, enabling capabilities like text-to-speech, audio generation, or sound synthesis.

Whether the model supports image inputs.

When true, the model can process images as part of its input, enabling multimodal interactions where visual content can be analyzed alongside text.

Whether the model can generate image outputs.

When true, the model can produce images as part of its response, enabling capabilities like image generation, editing, or visual content creation.

Whether the model supports image content in tool messages.

When true, tool responses can include images that the model can process and reason about. This enables workflows where tools return visual data that the model needs to interpret.

Whether the model supports image URL inputs.

When true, the model can accept URLs pointing to images rather than requiring the image data to be embedded directly in the request. This can be more efficient for large images or when images are already hosted.

Maximum number of tokens that can be included in the input context window.

This represents the total token budget for the model's input, including the prompt, system messages, conversation history, and any other context.

Maximum number of tokens the model can generate in its output.

This represents the upper limit on the length of the model's response. The actual output may be shorter depending on the completion criteria (e.g., natural stopping point, stop sequences).

Whether the model supports PDF document inputs.

When true, the model can process PDF files as input, allowing it to analyze document content, extract information, or answer questions about PDF documents.

Whether the model supports PDF content in tool messages.

When true, tool responses can include PDF documents that the model can process and reason about. This enables workflows where tools return document data that the model needs to interpret.

Whether the model supports reasoning or chain-of-thought output.

When true, the model can produce explicit reasoning steps or chain-of-thought explanations as part of its output. This is useful for understanding the model's decision-making process and improving transparency in complex reasoning tasks.

Whether the model supports structured output generation.

When true, the model can generate responses that conform to a specified schema or structure (e.g., JSON with a particular format). This is useful for ensuring the model's output can be reliably parsed and processed programmatically.

Whether the model supports tool calling (function calling).

When true, the model can invoke external tools or functions during its reasoning process. The model can decide which tools to call, with what arguments, and can incorporate the tool results into its final response.

Whether the model supports tool choice control.

When true, the caller can specify how the model should select tools, such as forcing the use of a specific tool, allowing any tool, or preventing tool use entirely. This provides fine-grained control over the model's tool-calling behavior.

Whether the model supports video inputs.

When true, the model can process video data as input, enabling capabilities like video analysis, scene understanding, or multimodal interactions involving moving images.

Whether the model can generate video outputs.

When true, the model can produce video data as part of its response, enabling capabilities like video generation, editing, or visual content creation with motion.