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What is multimodal AI?
Last reviewed July 16, 2026
Multimodal AI refers to models that work in more than one medium: reading images, audio, or video as input, or producing them as output, alongside text. The two directions are separate capabilities: a vision model reads images but writes only text, while an image generator produces images from text. What a model accepts and what it can produce are the first things to check when picking one.
Input modality vs output modality
Input modality is what the model can perceive: vision models read screenshots, charts, and documents; audio-input models take speech directly. Output modality is what it can produce: image, video, and speech generation are usually separate specialist models rather than features of a chat model. Marketing blurs this distinction; capability tables should not.
How multimodal work actually runs
Practical pipelines chain specialists: transcribe a recording with a speech model, reason over the transcript with a text model, illustrate with an image model, narrate with a voice. The chat model coordinates; the media models execute. Metering differs per modality too: per image or per second of video and audio, rather than per token.
idapt runs text, image, video, speech, and transcription models in the same workspace: chat with vision models about your files, generate images and video, and turn any reply into audio.
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