Google Releases First Gemini Multimodal Embedding Model, Simplifying Complex AI Workflows

Embedding models convert data into vectors and are core components of retrieval-augmented generation, semantic search and recommendation systems. Text, images, audio and video previously were typically processed by separate models, with their outputs requiring additional alignment. By bringing Gemini’s multimodal capabilities to embedding technology, Google could reduce the complexity and cost of cross-media retrieval systems.

As of July 19, 2026, Google had released Gemini Embedding 2, its first multimodal embedding model based on the Gemini architecture. It natively supports text, images, audio and video, mapping all data types into a unified embedding space. Developers can use it directly for cross-media search and RAG. Google did not disclose pricing or specific cost savings.