What is multimodal AI?

What is multimodality?

Multimodal AI is an AI system that can process and generate more than one type of data. Unlike a purely language model — which only processes text — a multimodal model can simultaneously work with text, images, audio, video, or code.

The goal is to bring AI closer to the way humans perceive: we too continuously combine what we see, hear, and read.

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Examples of multimodal models

• GPT-4o (OpenAI) — processes and generates text, images, and audio in one model
• Gemini (Google) — designed as a multimodal model from the ground up
• Claude 3.5 (Anthropic) — understands images and documents alongside text
• DALL-E, Midjourney, Stable Diffusion — text-to-image models
• Sora (OpenAI) — text-to-video

How does multimodal AI work?

Different modalities are converted to a shared representation space. Text is tokenized; images are divided into patches (small blocks of pixels); audio is converted to spectrograms. All representations are then processed by a shared transformer architecture.

The model learns the connections between modalities: what a dog looks like, how a dog sounds, and what the word 'dog' means are internally linked.

Applications

• Medical image analysis combined with patient records
• Customer service that understands screenshots and answers text questions
• Accessibility tools that describe images for the visually impaired
• Video analysis for security systems or sports analysis
• Code generation based on a sketch or mockup

The future

Multimodality is becoming the standard. Models that only process text are considered limited. The next frontier: real-time processing of video streams and robot control based on visual input.