Introducing Embed 5—A new family of frontier embedding models
Cohere has released Embed 5, a new family of embedding models aimed at improving enterprise retrieval. The release includes two tiers: Embed 5 Pro, optimized for maximum quality across multimodal, multilingual, financial, code, and parsed‑document retrieval, and Embed 5 Fast, designed for latency‑ and cost‑sensitive workloads. Both tiers share a single embedding space, allowing teams to index with Pro and query with either model without rebuilding the index. Embed 5 is available on the Cohere API, Model Vault, Microsoft Foundry, and Amazon SageMaker, priced at $0.12 per million tokens for Pro and $0.08 for Fast.
The Core Development
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. Embed 5 Pro is optimized for maximum quality across multimodal, multilingual, financial, code, and parsed-document retrieval. Embed 5 Fast brings highly competitive performance to latency- and cost-sensitive workloads. Both tiers share a single embedding space, so teams can index with Pro and query with either model without rebuilding the index.
Embed 5 establishes the retrieval foundation for search, RAG, and agentic workflows, surfacing more relevant context while filtering out noise before it reaches expensive generative models. Use Embed to improve answer quality and user experience while helping keep downstream inference costs under control.
Deployment & Availability
Embed 5 is generally available today on the Cohere API and Model Vault , Microsoft Foundry , and Amazon SageMaker . Pricing is $0.12 per million tokens for Pro and $0.08 per million tokens for Fast.
Strategic Perspective & Ecosystem Impact
Embed 5 Pro delivers our strongest retrieval performance to date. It achieves the highest average score of any model we tested across ViDoRe V3, financial documents, parsed PDFs, image retrieval, and across key business languages.
Embed 5 is also the first model family evaluated with RCP-nDCG@10 , our latest retrieval methodology. Instead of scoring only against a limited set of fixed labels, it evaluates retrieved documents against query-specific relevance criteria, capturing relevant results and giving a fuller view of performance on your own corpus. 1 Read more about RCP-nDCG@10 .
Embed 5 excels with visually rich documents where meaning lives in tables, charts, diagrams, and layout — not just text. On ViDoRe V3 , which features documents sampled across key enterprise domains, including financial filings, technical manuals, regulatory material, government reports, textbooks, and lectures, Embed 5 Pro averages 85.8 — an impressive 8.8 gain from Embed 4. 2
Why it matters
The introduction of a high‑quality, multilingual, multimodal embedding family that balances performance and cost is significant for AI‑driven search, RAG, and agentic workflows, enabling more efficient and accurate retrieval in enterprise settings.