What are multimodal AI models, and how do they process different types of data?

A multimodal model handles more than one type of data — text, images, audio, video — often together, rather than a single modality. The core trick is turning every modality into the same kind of representation: each input is passed through a modality-specific encoder (a vision encoder for images, an audio encoder for sound) that produces embeddings in a shared vector space, which a language model or fusion layer then reasons over jointly. That's how a model can take an image plus a question and answer in text, or match a caption to a photo. Because everything becomes embeddings, the model can relate concepts across modalities — connecting the word 'dog' to a picture of one. The hard parts are aligning the spaces well and handling the very different sizes and structures of each data type.