Multimodal AI

Multimodal AI describes models that accept and produce more than one kind of data. A multimodal model can read a screenshot and answer questions about it, transcribe a meeting recording, generate a diagram from a verbal description, or watch a 20-minute video and summarize the key moments. The dominant frontier models all ship multimodal capabilities by default. GPT-4o handles text, image, audio in and out. Claude 3.5 Sonnet reads images and PDFs alongside text. Gemini 1.5 Pro accepts hour-long video inputs. The shift from single-modal to multimodal happened fast and reshaped what AI agents can do in production.

In production the multimodal capability unlocks workflows that text-only models could not touch. A support agent reads a screenshot of an error and explains the fix without asking the user to describe what they see. A real-estate copilot looks at listing photos and generates compliant marketing descriptions. An internal compliance agent reads contract PDFs with embedded charts and extracts structured terms. A field-ops assistant watches a 90-second video of a broken machine and surfaces the relevant maintenance procedure from the knowledge base. None of these workflows survive in a text-only world because half the source material is not text.

The cost and latency profile of multimodal calls runs higher than text-only. An image input through GPT-4o consumes the equivalent of a few hundred to a few thousand tokens depending on resolution. A video input through Gemini 1.5 Pro can run into the tens of thousands of tokens for a single minute of footage. Production systems plan inference cost accordingly, often downscaling images before processing or sampling video frames at low rates. The AI Support Department ships multimodal screenshot triage as standard, and the AI Ops Department handles image-and-PDF document workflows where pure text models would miss half the content.