# Top Multimodal Embedding Models > Source: > Published: 2026-07-24 03:37:44+00:00 Member-only story # Top Multimodal Embedding Models ## A research-led guide to image-text retrieval, visual documents, video, audio, multimodal RAG and model selection. When handling complex visual data like financial charts, medical scans, or legal schematics, broad semantic matches are not enough. If your ingestion pipeline strips away the granular, fine-grained details during chunking or embedding, your users cannot verify the information they find. The important question, here, is no longer whether cross-modal search works. It is which model, retrieval unit and pipeline design can make your hidden evidence findable without losing the details users need to verify. ## Beyond CLIP: The Best Multimodal Embedding Models for Search, RAG, and Real-World AI A practical, evidence-led comparison of the models that turn text, images, document pages, video and audio into searchable meaning. **THE CENTRAL IDEA** A multimodal retrieval system succeeds when it preserves the evidence that text extraction would flatten, omit or misunderstand. - A customer photographs a dining chair and asks for “the same shape, but weatherproof and narrow enough for a balcony.”