Glossary
Multimodal Search searching with more than words.
Multimodal search lets people search with images, voice and video as well as text. How it works in AI Mode and Lens, and how Prefer fits in.
Multimodal Search is searching with more than text: a photo, a voice question, or a video, often mixed with words. Prefer tracks the text prompts and cited sources behind AI answers on five engines.
Multimodal search is searching with more than one kind of input: a photo, a voice question, a video, or a mix of these with typed words. Prefer tracks the text prompts and cited sources behind AI answers on five engines, and those cited pages are the same kind a multimodal answer links out to. The engine reads the image or audio, works out what you are asking, and returns an answer, often an AI-written one with links.
How multimodal search works#
A multimodal engine first turns the non-text input into something it can search with. For an image, it identifies the objects in it. Then it searches, reads sources and writes the answer.
Google describes this directly for AI Mode. In its April 2025 announcement, Google said AI Mode combines visual search in Lens with a custom version of Gemini, and uses its query fan-out technique to issue “multiple queries about the image as a whole and the objects within the image.” The answer comes back with links to dive deeper.
Fig. 01
How a multimodal query becomes an answer
Why it matters for AI search visibility#
An image query still ends in a written answer with links, so the pages that win are the ones that explain what is in the picture. If someone photographs your product and asks “is this any good?”, the engine searches for that product by name and category. Your page, reviews and comparisons are what it can cite.
That makes the visual side matter more than it used to. Google’s image guidance says it uses alt text, computer vision and the contents of the page to understand an image, and recommends placing images near relevant text. Its AI features guidance also lists “supporting your textual content with high-quality images and videos” as a best practice, and says there are no special extra requirements to appear in AI Overviews or AI Mode.
Fig. 02
Text search vs multimodal search
Example#
A shopper sees a running shoe on a train, takes a photo and asks AI Mode “is this better than the pair I have?” The engine identifies the shoe, searches for the model and its rivals, and writes a comparison with links. The brands cited are the ones with clear product pages, labelled product photos and honest third-party reviews, not the ones with the best photo alone.
Common confusions#
- Multimodal search vs visual search. Visual search (Google Lens is the best known) is one kind of multimodal search. Multimodal also covers voice, video and mixed inputs.
- Multimodal search vs multimodal models. A multimodal model is an AI that can read images or audio. Multimodal search is the product experience built on top, where that model is paired with retrieval and links.
- Multimodal search vs conversational search. Conversational search is about follow-up questions in a dialogue. A multimodal search can be conversational too, but the defining trait is the input type.
How to prepare#
- Fix the text answers first. Find which of your pages AI engines already cite; the free AI Visibility Checker gives a quick read.
- Use original product photos with descriptive alt text and filenames, placed next to the copy that names the product.
- Add structured data so names, prices and specs are machine readable. The free schema generator can help.
- Earn reviews and comparisons on third-party sites, since an image query often turns into “is this good?” or “what is it like?”
How Prefer helps#
Prefer does not run image or voice queries. It tracks the text prompts your buyers ask on ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, shows the sources behind each answer, and measures your share of voice against named competitors. Those cited pages are the ones multimodal answers lean on too. Run a free AI visibility audit to see where you stand.
In context
The term in a sentence.
Related questions
People also ask.
- What is an example of multimodal search?
- Is Google Lens multimodal search?
- How do I optimize images for AI search?
Questions
Asked plainly.
What is multimodal search in simple terms?
It means you can search with a photo, your voice or a video, not only typed words, and get an answer back. Prefer tracks the text side of AI search: the prompts buyers type into ChatGPT, Gemini, Perplexity and Google's AI Overviews and AI Mode, and the pages each engine cites. A typical multimodal search is pointing your camera at a product and asking where to buy it or how it compares.
Does Prefer track image or voice searches?
No. Prefer tracks text prompts on five engines (ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode), and shows the sources behind each answer. Multimodal answers are still written answers with links, so the pages Prefer shows being cited for your text prompts are a good guide to the pages an image or voice question will lean on too.
How do I optimize for multimodal search?
Start with the pages that already win text answers; Prefer shows which ones AI engines cite for your prompts. Then make your images understandable: descriptive alt text and filenames, images placed next to the text that explains them, and original photos of your real products, as Google's image guidance recommends.
Is multimodal search the same as visual search?
Visual search is one kind of multimodal search, where the input is an image. Prefer covers the text answers that follow those searches rather than the camera input itself. Multimodal is the wider term: it covers voice, video and any mix of inputs, such as a photo plus a typed question.
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