Multimodal

A multimodal AI model can understand and reason across different modalities simultaneously. Rather than having separate models for vision and language, multimodal models like GPT-4o, Gemini, and Claude 3.5 Sonnet can analyze an image while discussing it in text, understand audio alongside a transcript, or generate code based on a screenshot.

Most current multimodal LLMs are primarily text models with additional encoder components (like CLIP for images) that project other modalities into the model's text embedding space. True native multimodality, where the model processes all modalities with the same architecture from training, is an active research frontier, with Google's Gemini and Meta's Llama 4 being early examples.

Multimodal capabilities unlock a wide range of applications: analyzing medical imaging alongside patient records, processing handwritten notes, understanding complex charts and diagrams, building visual coding assistants that can see a UI mockup and generate the code, and creating accessibility tools that describe images for visually impaired users.