Embed 5 Pro

Embed 5 Pro is the higher-accuracy tier of Cohere's Embed 5 family of multimodal embedding models (about 2.4B parameters). It embeds text, images, and interleaved text and image content into a single vector space and is suited for retrieval and RAG over long or domain-specific documents, visual and document search, and multilingual semantic search across 100+ languages.

It shares its embedding space with Embed 5 Fast, so a common pattern is to index a corpus with Pro and embed queries with Fast. Output dimensions are selectable from 256 to 2048, and `input_type` defaults to `search_document` when not set.