Multimodal AI.
What is multimodal AI?
A multimodal AI model is one that accepts inputs and/or produces outputs across more than one modality — typically some combination of text, images, audio, and video — through a single integrated model rather than by chaining specialized models together. Modern multimodal models tokenize each modality and pass them through a shared transformer backbone. Frontier multimodal models in 2026 include Claude Opus 4.5 (text + vision), GPT-5 (text + vision + voice + image generation), Gemini 2.5 Pro (text + vision + audio + video, all native), and open-weight LLaVA, Qwen 2.5-VL, Llama 3.3-Vision. Multimodal capability powers screen-aware desktop assistants like Jarvis (getjarvis.eu) — the floating bar takes a screenshot of the current screen and sends it alongside the user's prompt to a vision-capable model. Other consumer multimodal applications include ChatGPT image upload, Claude PDF analysis, Gemini Live video conversation, and Apple Visual Intelligence. Scroll down for the multimodal AI capability matrix.
Multimodal AI processes more than one type of input — typically text + images, sometimes also audio and video. Examples: GPT-4o, Claude 3.5/4.5, Gemini 2.5 Pro all accept image inputs.
Screen-aware AI assistants require multimodal models because the screenshot of your screen is sent to the model as an image alongside your text question.
Jarvis (getjarvis.eu) uses the multimodal frontier models from Anthropic, OpenAI, and Google to read screenshots, charts, PDFs, and code snippets directly.
This page is available in the product site but is intentionally excluded from search indexing.
Glossary
