Multimodal Model (Multimodal AI)

A multimodal model is an AI model that can take in, and sometimes produce, more than one type of data, such as text, images, audio and video, within a single model. Gemini, OpenAI GPT models and Claude all accept images alongside text, which is why you can upload a screenshot or a product photo and ask questions about it. “Multimodal AI” is the broader name for systems built this way.

It is worth separating this from multimodal search. Multimodal search is the search experience, such as Google Lens, Circle to Search or an image query in AI Mode. A multimodal model is one of the engines behind experiences like these, and one you can call yourself through an API.

How it works

As MLforSEO’s image SEO guide explains, most image-understanding models combine a few ideas. The image is cut into small patches that a transformer reads much like words. Contrastive training, as in OpenAI’s CLIP, puts images and their captions close together in a shared embedding space. A vision encoder is then connected to a language model so it can reason over image “tokens” next to your text. The exact architectures of the commercial models are not public.

As of October 2026, the official documentation describes:

• Gemini API: image understanding (PNG, JPEG, WebP, HEIC, HEIF), object detection with bounding boxes, plus separate audio and video understanding.
• Claude: image input in JPEG, PNG, GIF and WebP.
• OpenAI API: image input in PNG, JPEG, WebP and non-animated GIF.

How it’s used in SEO and marketing

• Drafting alt text at scale with a vision model, then having a person check it.
• Critiquing YouTube thumbnails and titles together, as in our YouTube SEO audit guide.
• A first pass on the tone of video or audio content, treated as a hint, as our sentiment analysis guide recommends.
• Turning one format into another (video to text, image to text) as part of content transformation.
• Reading charts, screenshots and PDFs from reports so you can query them.

Example

You export your last 20 YouTube thumbnail URLs with their titles and send them to a multimodal model with specific questions: is the text readable at small sizes, does the image match the title, do the thumbnails look alike? The model returns notes per video. You then check the flagged ones yourself before redesigning anything.

Limitations and things to watch

• Text in images can be misread. OpenAI’s documentation lists non-Latin text, small text, rotated images and precise spatial reasoning among its vision limitations.
• Recognition without context. A model can tell a photo shows a black Labrador; it cannot know which specific dog it is unless the page says so. Keep key facts in HTML text.
• Images cost tokens, so large batches get expensive.
• Outputs can be confidently wrong, so review anything you publish, especially alt text and product details.

Learn it on MLforSEO

• Image SEO in 2026: The Complete Guide to Image Optimisation, Advanced Tactics and Multimodal Search
• How to do a YouTube SEO Audit for improved organic search visibility
• Sentiment Analysis: A Deep Dive Into the Theory, Methods, and Applications
• Introduction to Machine Learning for SEO, including the content transformation module