🎨 Multimodal AI Models
Models that understand and generate multiple types of content - text, images, audio, and video - opening new possibilities for AI applications.
Multimodal Models Overview
GPT-4 Turbo with Vision
Capabilities:
- ✓ Text input/output
- ✓ Image input
- ✓ Up to 10 images
- ✓ Diagram understanding
Best for:
Document analysis, screenshot interpretation, visual QA
Claude 3.5
Capabilities:
- ✓ Text input/output
- ✓ Image input
- ✓ High visual accuracy
- ✓ Chart/diagram comprehension
Best for:
Scientific paper analysis, technical documentation
Gemini
Capabilities:
- ✓ Text
- ✓ Image
- ✓ Video
- ✓ Audio
- ✓ Multiple modalities in one
Best for:
Video analysis, multimedia content understanding
DALL-E 3
Capabilities:
- ✓ Text to image
- ✓ High quality
- ✓ Style control
- ✓ Accurate text in images
Best for:
Professional image generation, concept visualization
LLaVA
Capabilities:
- ✓ Text input/output
- ✓ Image understanding
- ✓ Open source
- ✓ Fine-tunable
Best for:
On-device vision-language, research
🔮 The Future: Full Multimodality
We're moving toward truly unified multimodal systems that seamlessly handle text, images, audio, and video inputs/outputs in a single model.
- Unified Embeddings: All modalities share same embedding space
- Cross-Modal Reasoning: Connect understanding across modalities
- Generation: Generate any modality from any input
- Efficiency: Shared parameters reduce model size