Multimodal AI
Multimodal AI can process and generate text, images, audio and video together, unlike systems mastering only one modality.
Early LLMs were text-only. GPT-4V (2023) added vision; Gemini 1.5 and Claude 3 followed. In 2026 all frontier models are multimodal: they understand and generate images, sometimes audio too (GPT-4o, Gemini Live). Multimodal opens use cases: screenshot debugging, UI automation, document analysis with charts, real-time voice conversations.
Example
A marketer uploads a competitor landing-page screenshot to Claude. Claude extracts structure, identifies calls-to-action, evaluates visual hierarchy and suggests improvements — cross-modal analysis.
Frequently asked questions
Are multimodal models more expensive?
Image tokens count extra (Claude: ~1,500 tokens per image). Audio processing: proportional to duration. For specific use cases, dedicated single-modality models are sometimes cheaper.
Related terms
Further reading
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