Multimodal AI
AI that understands and generates more than one type of media — text, images, audio, or video.
Multimodal AI refers to models that process or generate multiple types of data (modalities) — most commonly text + images, but increasingly audio, video, documents, and code.
Input modalities (what the model can receive):
- Vision — understand photos, screenshots, diagrams, charts (GPT-4o, Claude 3.5+, Gemini)
- Audio — transcribe and understand speech (Whisper, Gemini Live)
- Video — analyze video frames and sequences (Gemini 1.5+)
- Documents — PDFs, spreadsheets, slides with formatting intact
Output modalities (what the model can generate):
- Text — all frontier models
- Images — DALL·E, Imagen, Stable Diffusion, Midjourney
- Audio/Speech — ElevenLabs, Gemini Live, GPT-4o voice
- Code — all frontier models, often better than pure-text outputs
Practical implications: You can now paste a screenshot of an error and ask for a fix, share a chart and ask for insights, or describe an image with a voice note. Products like Claude's Projects and ChatGPT can maintain mixed-media context across a conversation.
The frontier in mid-2026: natively multimodal models that take any combination of inputs in a single prompt, with no intermediate conversion step.
In plain terms
The difference between a specialist who only reads reports versus a generalist who can read, listen, watch, and sketch — and respond in whichever format you need.