What type of data inputs do multimodal models handle?

Multimodal models are designed to work with and analyze multiple forms of data simultaneously, which makes them highly versatile in understanding and generating relationships among different data types. The correct answer highlights that these models can process inputs such as text, images, and audio.

The ability to combine various modalities allows these models to build richer representations of information. For example, a multimodal model might analyze a video by combining the visual content (images) with auditory information (audio) while also incorporating any text associated with that video, such as subtitles or descriptions. This enhances the model's understanding and provides deeper insights than if it were limited to a single data type.

Considering the other options, they each impose restrictions that do not reflect the full capabilities of multimodal models. Limiting inputs to only textual or visual data would neglect the model's potential to integrate different forms of information, while focusing exclusively on structured datasets disregards the nuanced understanding that can be achieved through multimodal combinations. Thus, the correct choice rightly emphasizes the comprehensive nature of multimodal inputs.