Cohere releases Embed 5: Pro and Fast models share one embedding space and accept mixed text-and-image inputs with 128k context
Cohere released the Embed 5 embedding family, with embed-v5.0-pro for retrieval quality and embed-v5.0-fast for low latency; the two share an embedding space, can encode text, images and mixed text-and-image PDF pages into a single vector, and are available through Cohere’s Embed API, Microsoft Foundry and Amazon SageMaker.
Cohere announced Embed 5 in its official changelog, calling it its most powerful embeddings family yet, with major gains over Embed 4 on visually rich documents, financial filings, parsed PDFs, code and multilingual retrieval.
There are two variants: embed-v5.0-pro targets the highest retrieval quality and suits offline indexing; embed-v5.0-fast targets low latency and high throughput for interactive search, agent loops and high-volume queries. They share one embedding space, so a corpus indexed with one can be queried with the other; Cohere recommends indexing with Pro and querying with Fast.
Embed 5 encodes text, images and mixed text-and-image inputs such as PDF pages into a single vector, supports more than 100 languages and a 128k-token context, and offers Matryoshka dimensions from 256 to 2048 with float, int8 and binary outputs. Microsoft Foundry’s model page says images can be up to about 2 million pixels and that key visual features can be captured directly from screenshots of PDFs, slides, tables and figures, removing the need for complex document parsing.
Embed 5 is available through Cohere’s Embed API and on Microsoft Foundry and Amazon SageMaker; enterprises needing single-tenant deployment can use it through Cohere’s Model Vault. The changelog does not list pricing.
Sources
- Announcing Cohere's Embed 5 Models — docs.cohere.com
- Cohere-Embed-V5-Pro | Model Catalog | Microsoft Foundry — ai.azure.com