Multimodal AI
Models that understand and generate across multiple input types — text, images, audio, and video.
Multimodal models process combinations of text, images, audio, and video in a single forward pass. GPT-4o, Claude 3.5+, Gemini 2, and open models like Llama 3.2 Vision have collapsed the boundary between what used to be separate NLP and computer-vision pipelines.
For enterprises, multimodal capability opens use cases that were previously multi-model engineering projects: 'here is a screenshot of an error, tell me what the user is trying to do' or 'watch this five-minute video and extract the action items'.
Cost and latency for multimodal inputs are still higher than text-only, but falling fast. Vision inputs typically add 1,000–3,000 tokens per image at current tokenizers.
Key points
- Single model replaces multi-step OCR + captioning + LLM pipelines
- Vision + text is production-ready; video and audio-in still maturing
- Native tool-calling on multimodal inputs unlocks new agent designs
- Prompt caching materially reduces cost for repeated image contexts
Common use cases
Frequently asked
Related terms
A neural network trained on massive text corpora to understand and generate human-like language.
The field of AI that enables machines to interpret and act on visual information from images and video.
An LLM-driven system that plans, calls tools, and executes multi-step tasks autonomously.
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