What Is Multimodal AI? — The Unified Text/Image/Audio/Video Architecture and How to Choose
In April 2026, the MMMU-Pro multimodal benchmark hit 81–83% across GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and Qwen 3.5 Omni — image understanding has effectively saturated. Architecture has migrated from stitched (separate encoders + adapter) to native omnimodal (all modalities as a shared token stream). This article covers what multimodal AI is (LMM/VLM/Omnimodal), the architectural divide and why it matters, what technically determines strength in each modality (video, audio, documents/UI, open models) plus a May 2026 comparison snapshot, four benchmarks to watch (MMMU-Pro, Video-MMMU, DocVQA, AudioBench), five use cases and what to judge each on, and the three hard limits (low-quality image guesses, mid-video accuracy, dialect/jargon audio) — grounded in current research and practical use.