Multimodal AI

Multimodal AI refers to models that can process and generate more than one type of data — such as text, images, audio, and video — within a single system. A multimodal model can, for example, look at an image and answer questions about it, or turn a text prompt into a video.

How it works

Multimodal models are trained on paired data across modalities so they learn shared representations — linking the word “dog,” a photo of a dog, and the sound of barking. Leading 2026 foundation models are natively multimodal, handling text, images, and audio in one model.

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Why it matters

Multimodality expands AI from text into the visual and audio world. The MMMU benchmark, which tests multimodal reasoning, saw scores jump 18.8 points in a single year. See AI Models & Benchmarks Statistics 2026.