Multimodal AI
Models and systems that take in or produce more than one type of data, such as text, images, audio and video, within a single interface.
Definition
Early language models worked on text alone. Multimodal models accept several kinds of input, most commonly text and images, and increasingly audio, video and documents, and reason across them in one pass. Some also generate images or speech. Under the hood, each input type is converted into tokens or embeddings that share a representation with text, so a question about a photograph is answered the same way as a question about a paragraph.
Most current frontier models from the major providers are multimodal to some degree. Practical uses include reading screenshots and scanned forms, describing and classifying images, interpreting charts, extracting tables from PDFs, and, with audio models, listening and speaking. A separate but related idea is multimodal search, where one embedding space holds both text and images.
Example: an insurer receives a photo of a damaged bumper and a short description. One multimodal request identifies the vehicle area, estimates the damage category, checks the description for consistency with the image and drafts the claim note.
What it means in practice
- Much business data is not clean text: scans, photos, screenshots, recordings. Multimodal models can read it directly.
- One model instead of separate OCR, vision and language pipelines simplifies architecture and maintenance.
- Image and audio inputs cost more tokens and need their own evals; accuracy on charts and handwriting varies.
- Visual inputs are an additional channel for prompt injection (text hidden in images).
How Teksmobile uses it
Multimodal models power our document processing work, where scanned invoices, forms and contracts become structured records, and sit alongside specialised models in our computer vision work. Audio modalities feed our voice AI and speech-to-text services.
Related terms
Part of the Teksmobile AI Glossary. For longer reads, see our guides.
Frequently asked questions
Can a multimodal model replace OCR?
For many documents, yes: it reads layout, tables and handwriting together and returns structured fields. For high-volume, simple forms, traditional OCR may still be cheaper. We usually benchmark both.
Are images more expensive than text?
Usually. An image is converted into a number of tokens that depends on its size, often equivalent to several hundred words. Resize images to what the task needs.
Is multimodal the same as computer vision?
Computer vision is the older, specialised field of image analysis. Multimodal LLMs bring vision into the same model that handles language, which makes building mixed tasks much simpler, though specialised vision models still win on some narrow jobs.