Key takeaways State-of-the-art enterprise retrieval: Embed 5 Pro achieves the highest average score of any model we tested - particularly across financial datasets, parsed PDFs, and visually rich documents.

A new Fast tier: Embed 5 Fast brings strong retrieval quality to latency - and cost-sensitive workloads, at $0.08 per million tokens.

One index, two models: Pro and Fast share an embedding space, so teams can index with Pro and query with either model without re-indexing.

Built for complex enterprise data: Embed 5 supports multimodal inputs and retrieval, 100+ languages, and a 128K-token context window for longer documents.

More efficient at scale: Matryoshka representations and lower-precision outputs reduce vector storage and search costs, while quantized weights lower serving requirements for private deployments.

Today, we're releasing Embed 5, a new family of embeddings models at the frontier of high-quality enterprise retrieval.

Embed 5 delivers stronger retrieval across complex enterprise data while giving teams more control over latency, cost, and deployment.

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