What is a multimodal model?

A multimodal model is an AI model that can process and combine different types of data at the same time: text, images, sound and sometimes video. Instead of only reading words, it also “sees” and “hears”. It is the technology behind AI tools you can show a photo while asking a question in the same breath. In this article you will learn what a multimodal model actually is, how it differs from a regular language model, and why multimodal AI matters to you as a B2B marketer.

What is a multimodal model exactly?

A modality is a type of input: text is one modality, image is another, sound yet another. A regular language model works with a single modality, namely text. A multimodal model processes several at once and draws connections between them.

According to IBM, multimodal AI refers to systems that integrate information from different types of data into a single understanding. Important: it is not a collection of separate models running side by side. It is one neural network, trained on paired data, for example images with their accompanying captions or videos with transcripts. That is how the model learns that a particular image and a particular description belong together.

Under the hood, a multimodal model converts every modality into the same internal language: a series of numbers that holds meaning. A photo of product packaging, the word “packaging” and a spoken question about that packaging all land in the same space, which lets the model connect them. This is the same machine learning logic as with text models, just applied more broadly.

The modalities at a glance

To make it concrete what a multimodal model processes, here are the common modalities and why they matter for B2B content.

Modality	What the model does with it	Why it matters for B2B
Text	Reads and generates language, the basis of most answers	Remains the most important layer for citations in AI search engines
Image	Recognises objects, diagrams and text within images	Diagrams and product photos are understood as well
Audio	Turns speech into text and interprets tone	Relevant for podcasts, webinars and voice search
Video	Combines image and audio over time	Demos and explainer videos become readable in substance

How does it differ from a regular language model?

A large language model only understands text. A multimodal model builds on that by placing several input types in the same space, so it can compare and combine them. That opens up new possibilities:

Describing an image. You show a photo and the model explains what is on it.
Asking a question about an image. You upload a chart and ask for the key takeaway.
Generating combinations. From text to image, or from image plus question to a written answer.

Well-known examples are OpenAI’s GPT-4o, which handles text, image and audio in real time, and Google Gemini, built from the ground up to process multiple data types within a single architecture. Under the hood it is still deep learning, applied to more than text alone. Such models are usually also a foundation model: broadly trained and versatile in use.

Why this matters to you as a B2B marketer

The shift to multimodal has a practical consequence: AI no longer judges your text alone. When an AI search engine or assistant looks at your page, it can in principle also factor your images, diagrams and video into its understanding. That means visual content deserves the same care as written content.

Concretely:

Describe your visuals properly. Clear captions and alt texts help a model understand what is being shown.
Make charts and diagrams readable. What a human grasps instantly should also make sense to a model.
Ensure coherence. Text and image that reinforce each other send a more consistent signal.

All of this ties into generative engine optimization, the new layer on top of classic SEO. If you want AI to cite your company as a source, what counts is how well your full content, text and image alike, can be understood. That is exactly what we focus on with our GEO agency approach.

A concrete B2B example

Say a software company puts a comparison table on its pricing page as an image, beautifully designed but without a textual counterpart. A classic language model sees nothing of it: for the text layer, that table simply does not exist. A multimodal model can interpret the image, but it still remains risky to lock your most important message into a visual only. Put that same information down as readable text too, with a clear alt text on the image, and both a generative AI and a classic search engine understand what you offer. In practice, we see that this double readability, text plus image, makes the difference between being cited and being ignored.

Common mistakes

The most common mistake is thinking “multimodal” means you now have to produce video and audio everywhere. That is not the case. The real miss is subtler: putting important information exclusively in an image (an infographic, a table, a screenshot) without a textual counterpart. You are then trusting that every system will read your visual flawlessly, and that is a gamble.

A second mistake is meaningless alt texts such as “image1”, or leaving the field empty altogether. That is a missed opportunity, because a short, factual description helps both visitors using a screen reader and AI to place your visual. The third mistake is inconsistency: a chart that claims something other than the text around it sends a confusing signal.

Honestly: how much do you need to do about this right now?

Let us keep it realistic. For most B2B companies, the text layer is still the most important: that is where most answers and citations are drawn from. So you do not need to spin up a video strategy right away just because models are going multimodal.

What does pay off is describing your existing visuals decently and not hiding an important message exclusively in an image that nobody, human or model, can read. That is low-hanging fruit. As always: steer on customers and revenue, not on every technical trend. As a small team that moves fast, we pick the highest-impact moves first, and that is usually clarity, not more production.

The same reasoning applies if you want your brand to show up in AI assistants: getting found in ChatGPT is mostly about clear, well-structured content, not about stacking as many media formats as possible.

Frequently asked questions

What does “multimodal” mean? It means the model processes several types of input at once: text, image, sound and sometimes video. “Modality” stands for a type of data.

Is a multimodal model just several models put together? No. It is one neural network trained on paired data, so that it draws connections between, for example, an image and its accompanying text. That is precisely what sets it apart from separate models running side by side.

Which tools are multimodal? Well-known examples are OpenAI’s GPT-4o and Google Gemini. They can process text and image (and partly audio) within a single system.

What does this mean for my content? AI can interpret your visuals too, so give them clear captions and alt texts, and never put a core message exclusively in an image. That keeps your full content understandable for human and model alike.

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