Multimodal Models
In short
AI models that can understand and generate not just text, but also images, audio, video, and other data types — all within a single model.
Think of the difference between communicating with someone through text messages only, versus having a face-to-face conversation where you can show them documents, play them audio clips, and draw diagrams on a whiteboard. Multimodal AI is upgrading from texting-only to that full in-person meeting — the AI can “see,” “hear,” and “read” at the same time.
Most AI tools you’ve heard of — like ChatGPT, Gemini, and Claude — started as text-only LLMs (large language models). Early LLMs could only work with text. Multimodal models can process multiple types of input at once. You can show them a photo and ask “what’s in this image?”, upload a PDF with charts and ask for analysis, or provide audio and get a transcript with commentary. The model converts all these inputs into the same internal numerical representation, allowing it to reason across data types simultaneously.
As of 2026, most leading models — GPT-4o, Gemini 2.5 Pro, Claude Opus 4 — are multimodal to some degree. Different models support different modalities though — some accept image input but can’t generate images, so it’s always worth checking what a specific model actually handles.
This is a fast-growing area. Gartner predicts that 40% of generative AI solutions will be multimodal by 2027, up from just 1% in 2023.
Related
- LLMs - multimodal models evolved from text-only LLMs
- Computer Vision - the image/video processing side
- Numerical Representation - all modalities get converted to numbers
- Embeddings - modern embeddings work across modalities too