Multimodal AI

What it is

IBM defines multimodal AI as machine-learning models that can process and integrate information from multiple types of data, such as text, images, audio and video. Some models accept images and text but only output text. Others also generate images or audio.

How it works

1. Inputs of different types are converted into a shared internal representation.
2. The model reasons across them, for example reading a chart and its caption together.
3. Output can be text, or another type of data if the model supports it.

Examples from vendor docs (checked 2026-10-17): OpenAI says all of its latest models accept text and image input and produce text output. Google lists a multimodal embedding model, Gemini Embedding 2, that maps text, images, video, audio and PDFs into one space for search. Which inputs and outputs a model supports lives on each model page, so check there rather than here.

Benefits

• Ask questions about screenshots, receipts, slides or product photos.
• Turn recordings into summaries and action items.
• Fewer separate tools in a pipeline.

Limits and risks

• Reading small text or dense charts can still fail, so verify numbers.
• Images and audio may contain personal data.
• Supported input types differ by model.