What is multimodal AI?
Multimodal AI works with more than one kind of information, such as text and images. The supported combinations depend on the model; the term does not mean every model can handle every file type.
By Exemplary AI · Updated · Illustrated explainer
The key idea
Different kinds of input can add context, but support and accuracy must be checked for each task.
The idea, illustrated.
Several kinds of evidence, one question
Figure 09A torn corner on the box.
Shipment H-18
Arrived Tuesday
Inspect packaging
The shipment identifier and task.
“The box arrived in this condition.”
Visible: packaging damage.
Reported: damage present on arrival.
Unknown: condition of the contents.
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Bring the inputs together
A photograph may show a damaged box, while a written note identifies the shipment and an audio note explains when it arrived.
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Prepare each input
The system may read an image, transcribe audio or extract text before combining the useful information.
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Keep the evidence distinct
The result should make clear which observations came from the image and which were stated by a person.
What does “modality” mean?
A modality is a kind of information: text, image, audio or video, for example. A vision-language model can work with images and text. An audio model may transcribe speech or generate it. These abilities are not interchangeable, and a supported input type says little on its own about reliability on a specific task.
The Visual Instruction Tuning paper is an example of research connecting a vision component and a language model to follow instructions about images. It helps explain the idea, but a result from one research model should not be read as a promise about another product or deployment.
Primary source: Liu et al.: Visual Instruction Tuning
More evidence can also mean more disagreement
A caption might describe a box as undamaged while the photograph shows a torn corner. The system should preserve that disagreement. Combining information does not mean turning everything into a single confident story.
Document work has similar challenges. A table can be visible in an image but lose structure when turned into plain text. A system can use visual layout to help interpret it, but small text, crowded pages and unusual formats still need checking. The route the file takes also matters: sending an image to an external model is a data transfer, just as sending text is.
An everyday example
Preparing an incident brief
A team supplies a shipment photograph and an inspector’s note. The system drafts a brief that separates visible damage from statements in the note and lists details that cannot be established from either.
A person checks the brief before it becomes a formal record. The model should not infer hidden damage, responsibility or intent from an image alone.
A common misconception
“Multimodal means it can understand any media.”
Each model has supported inputs, limits and failure modes. Audio, video and images may require different processing and different deployment arrangements.
How Exemplary helps
We help choose the right processing route.
Exemplary’s Model Hub brings supported model options into the platform. Our local runtime support covers language and embedding models; image, audio and video needs must be checked separately for the proposed deployment.
See model and deployment optionsSources & further reading
Primary publications and documentation. Accessed 22 September 2026.
- 01Visual Instruction Tuning
Liu et al. · NeurIPS · 2023