Cohere ships Embed 5 with shared Pro and Fast vectors
Cohere released Embed 5 Pro and Fast for text, image and fused retrieval across more than 100 languages and contexts up to 128K tokens. Both models share an embedding space, allowing teams to index with Pro and query with Fast without rebuilding the index, and support Matryoshka dimensions plus float, int8 and binary outputs.
Why it matters: A compatible quality and latency tier can reduce query-path cost without forcing a second corpus index. The multimodal and compressed-output options also make storage, retrieval quality and private-serving requirements explicit engineering choices rather…