Multimodal AI processes multiple types of input — text, images, audio, video — within a single model or system. This mirrors how humans understand the world: we don't process sight and sound separately.

Why Multimodal Matters

Single-modality models have fundamental limitations:

• A text-only model can't understand a chart, screenshot, or medical scan
• An image model can't follow written instructions
• Real-world tasks almost always involve multiple modalities

The Evolution

• 2021: CLIP (OpenAI) — aligned text and image embeddings in a shared space
• 2022: Flamingo (DeepMind) — few-shot visual question answering
• 2023: GPT-4V, Gemini — production multimodal LLMs with image understanding
• 2024: GPT-4o — native multimodal (text, vision, audio in one model)
• 2025: Gemini 2.5, GPT-5 — advanced reasoning across all modalities

Types of Multimodal Models

Architecture Patterns

1. Encoder fusion: Separate encoders for each modality, fused in a shared transformer. Most VLMs use this.
2. Early fusion: All modalities tokenized and mixed from the start. GPT-4o's approach.
3. Late fusion: Process modalities independently, combine at decision time. Simpler but less powerful.
4. Cross-attention: One modality attends to representations of another. Flamingo's approach.

Early fusion produces the most coherent cross-modal understanding but requires training from scratch on multimodal data.