Multimodal AI:
one model, many senses

A unimodal model processes one type of input. A pure text LLM is unimodal: text in, text out.

A multimodal model can take several input types and processes them through encoders that convert each modality into vectors compatible with the model's internal representation. From the model's perspective, an image and a paragraph become similar tensors. The same attention mechanism reasons over both. Most current multimodal models still output only text, though models that also generate images and audio are arriving fast.

Multimodal lets you ask questions like read this chart and tell me the trend, identify the bug in this UI screenshot, transcribe this voice memo and extract the action items, or summarise this podcast.

It does not magically solve grounding: the model can still hallucinate about what it sees. But it removes the friction of converting modalities by hand. Many real workflows (design review, document scanning, accessibility) are now feasible in a single prompt where they used to require a pipeline.