Multimodal model

A multimodal model is an AI model that understands and combines different types of data at once, for example text, image, audio and video. Where a classic large language model works only with text, a multimodal model can look at a photo, answer a question about it and return the result as text.

How does a multimodal model work?

The model translates each type of input into a common internal representation, so that words, pixels and sound are expressed in the same “language”. This lets it connect modalities: it recognizes that the word “invoice” and an image of an invoice are about the same thing. Many modern generative models are now multimodal, such as the latest versions of ChatGPT, Gemini and Claude, which accept both text and image as input. Those same models now also power the AI Overviews in Google, which means visibility increasingly revolves around Answer Engine Optimization (AEO) rather than classic positions alone.

Why it matters to marketers and B2B companies

Multimodality opens practical applications that were impossible with text alone. Think of automatically reading and summarizing a scanned document, analyzing a product photo, or an automation flow that interprets an incoming image and chooses the right follow-up action. Developers usually connect such a model to their own systems via a software development kit (SDK). For B2B processes where information sits in PDFs, screenshots and forms, that saves a lot of manual work.

What to watch out for

A multimodal model adds capabilities, but also sources of error. A model can misinterpret an image with the same conviction with which it makes up text. So test every application on real examples before you trust it in a process, and build in a check at the points where a mistake can cost a customer or a deal. The technology is a lever, not a replacement for a final human check.