Multimodal AI

Multimodal AI refers to models and systems that can take in, reason over and sometimes produce more than one type of data, such as text, images, audio, video and documents. A multimodal model can read a photographed receipt, answer questions about a chart, summarize a video or describe what is on a screen, because it maps these inputs into a shared representation it can reason about alongside language.

By 2026 every frontier large language model family is multimodal on the input side, but the details differ. OpenAI's GPT-6 models (Astra, Sol and Luna, which replaced the GPT-5.6 family in ChatGPT in October 2026) accept text and images and return text; audio runs through separate speech and transcription models (OpenAI's model docs).5. Google's Gemini models accept text, images, audio and video, and Gemini 4 Argon, released on October 13, 2026, made agentic video understanding generally available, so the model can move through a long video and inspect transcripts and frames as needed (Google). The Gemini API also accepts a public YouTube URL directly as input. Anthropic's Claude models, now on the 5.5 generation, accept images and PDFs, and models from Claude 4.7 onward read images at up to 2576 pixels on the long edge, which helps with dense screenshots and documents (Anthropic's vision docs).

It helps to separate input from output. Reading an image is not the same as generating one. Most chat models output text and call specialized models for images, speech or video; Google's Gemini Omni family, introduced at I/O in May 2026, is aimed at generating video from a mix of text, image and audio inputs. Multimodal embeddings are a third piece: models such as Google's Gemini Embedding 2 place text, images, video, audio and PDFs in one vector space, which is what makes searching a product catalog with a photo practical.

Multimodality shows up across the products that shape AI visibility:

• Visual search in Google Lens, Circle to Search and Search Live, where the query is a photo or camera feed.
• Video understanding, where an assistant can summarize or quote a YouTube video rather than relying only on its title and description.
• Computer use agents, which look at rendered screenshots of a site to click, compare and buy, so a page's visual layout matters to them as well as its HTML.
• Document understanding, where uploaded PDFs, slides and spreadsheets are read with their charts and tables intact.

For GEO, the practical consequence is that images, video and layout are no longer invisible to AI systems, but they are also not yet weighed equally with text. Answer engines such as ChatGPT search, Perplexity and AI Overviews still retrieve and cite mostly text passages, so the words around an image, the captions on a chart and the transcript of a video remain the most reliable way to get that information into an answer. Visual quality matters more in shopping and visual search, where the image itself is matched. Inconsistency is a risk: if a product photo shows a different color or model than the text claims, a multimodal system can pick up either version and describe the product wrongly. The working practice for all of this is multimodal search optimization.