Multimodal AI
Also known as: Multimodal, Multimodal Model, Vision Language Model
AI systems that can process and generate multiple types of content—text, images, audio, and video—within a single model.
Multimodal AI processes multiple types of input—text, images, audio, video—enabling richer interactions than text-only models.
Capabilities
- Image understanding: Describe, analyze, answer questions about images
- Document processing: Read PDFs, charts, screenshots
- Video analysis: Understand scenes, transcribe, summarize
- Audio processing: Transcription, voice understanding
- Cross-modal generation: Text→image, image→text
Leading Models
| Model | Modalities |
|---|---|
| GPT-4o | Text, images, audio |
| Gemini | Text, images, audio, video |
| Claude 3 | Text, images, PDFs |
| LLaVA | Open-source vision-language |
Applications
- Accessibility tools (image descriptions)
- Document understanding at scale
- Visual question answering
- Creative workflows
- Robotics and embodied AI
Implications
Multimodal models understand the world more like humans do—through multiple senses simultaneously—enabling more natural and capable AI assistants.