Multimodal Search

Multimodal search is searching with more than one type of input, such as an image, a voice question, a video or a mix of these with text. Instead of typing keywords, a user might photograph a plant and ask how to care for it, and the search system interprets both the picture and the words together.

Written by Paul Hoda, SEO consultant since 2005. Last updated 8 November 2026.

How multimodal search works

Modern AI models can process several types of data at once. They convert images, audio and text into representations of meaning, often called embeddings, which can be compared with each other. This lets a system match a photo of a chair with product pages that describe similar chairs, or answer a spoken question about something on screen.

Google described its MUM model as multimodal when it announced it in 2021, and its Gemini models were designed to handle text, images, audio and video. These capabilities now sit behind several search features.

Examples of multimodal search

  • Google Lens: search using the camera or an image to identify objects, products, text, plants or landmarks.
  • Circle to Search: on supported Android phones, users circle something on their screen to search for it.
  • Image plus text questions: Google AI Mode, ChatGPT, Gemini and other assistants accept a photo with a written or spoken question.
  • Voice search: spoken queries, often longer and more conversational than typed ones.
  • Video understanding: some assistants can answer questions about a live camera view or a recorded video.

Why it matters for SEO

Multimodal search creates new ways for people to discover products, places and information without knowing the right words to type. If someone photographs a pair of trainers, the results will favour pages with clear, well-described images of that product. This makes visual assets a search entry point in their own right, closely related to visual search.

How to optimise for multimodal search

  1. Use original, high-quality photos that show products and subjects clearly from useful angles.
  2. Write descriptive alt text and meaningful file names.
  3. Place images near relevant text, with captions where helpful, so context supports the image.
  4. Add product schema and keep Merchant Center feeds accurate for shopping results.
  5. Follow image SEO basics: compressed files, responsive sizes and an image sitemap if needed.
  6. For video, provide transcripts, chapters and video structured data so the content can be understood without watching it.

Common mistakes

  • Relying only on stock photos that appear on many other sites.
  • Putting important text inside images without repeating it in HTML.
  • Leaving alt text empty or stuffing it with keywords.
  • Blocking image folders from crawling in robots.txt.
  • Assuming multimodal search only matters for retailers. Local businesses, publishers and service providers can all be found through images.

Key Takeaways

  • Multimodal search combines inputs such as images, voice, video and text.
  • Tools like Google Lens and AI assistants let users search with photos and questions together.
  • AI models compare meaning across different types of data to find matches.
  • Original images, descriptive alt text and nearby context help visual discovery.
  • Transcripts and structured data make video content easier to understand.

Related Terms

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