TL;DRMultimodal models handle more than text — images, audio, and video alongside language.
A multimodal model takes and/or produces multiple data types: describe an image, answer questions about a chart, transcribe audio, generate pictures from text. The trick is mapping each modality into a shared representation the model can reason over jointly. Modern frontier models are natively multimodal — vision + language in one model — which unlocks document understanding, accessibility, and richer assistants.
Key points
Handle text + images + audio + video
Map modalities into a shared representation
Frontier models are natively multimodal
Unlocks document/chart/image understanding
Common mistakes
Assuming text-only limits modern models
Treating modalities as fully separate pipelines
Ignoring modality-specific failure modes
Try it: Name three tasks that require a multimodal model.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>multimodal: describe an image · read a chart
transcribe audio · text → picture
all modalities → shared representation</pre></body></html>