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What is it?
A multimodal model is an AI model that can process multiple types of information at the same time. Where a language model only understands and generates text, a multimodal model can analyse a photo, understand a spoken question, read a document containing tables, and respond based on all that combined input.
Well-known multimodal models include GPT-4o from OpenAI and Gemini 1.5 from Google. They are used for tasks where information from different sources comes together: an invoice with text and a table, a construction drawing with annotations, or a recording of a client conversation combined with written notes.
Why it matters for SMEs
For SMEs, multimodal models open up applications that are not possible with text alone. A great deal of business information does not exist purely as text: invoices, specifications, photos of damage or progress, scanned contracts. A multimodal model can process all those formats and extract meaning from them.
- Document processing becomes broader: a model can not only read the text in a PDF, but also understand and process tables, handwritten annotations, or images within it.
- Visual quality control becomes feasible: in construction or industry a model can analyse photos of progress or damage and compare them against a reference, without manual review for every item.
- Combining channels: spoken client feedback, emails, and forms can be processed together, giving you a more complete picture without manually aggregating everything.
Usability is expanding quickly: what was available only in research settings two years ago is now directly accessible via APIs for integration into existing workflows.
How it works
A multimodal model is trained on combined datasets of text, images, audio, and sometimes video, learning the connections between those modalities. During inference it processes all received inputs together and generates output based on the combined context.
- Input is provided: text, image, audio, or a combination.
- Each modality is converted into an internal representation the model understands.
- The model combines those representations in its reasoning.
- Based on the combined context it generates an answer, summary, or analysis.
The power lies in step three: a multimodal model draws connections between what is written, what is visible, and what is heard, in a way that separate single-modality models cannot.
Example in practice
Picture a real estate agency that regularly receives damage reports from tenants with attached photos. A multimodal model reads the damage description, analyses the attached photos, and automatically determines the damage category, urgency, and what type of contractor or tradesperson is needed. The colleague receives a prepared summary and task assignment, rather than having to route each report manually.
Comparison and misconceptions
An LLM processes text only. A multimodal model processes text, images, audio, and sometimes video in a combined line of reasoning. For tasks where information is available purely as text, an LLM is sufficient. As soon as images or other modalities are involved, a multimodal model is the appropriate choice.
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