Cohere Embed v3

Cohere Embed v3 is Cohere's third-generation embedding model — designed specifically for enterprise retrieval applications with strong performance on production RAG workloads. The family includes variants in multiple sizes (English, Multilingual, Light English, Light Multilingual) supporting 100+ languages and producing 1024-dimensional vectors. Cohere's distinguishing feature is the input-type parameter that lets callers specify whether they're embedding a search query or a document — Cohere optimizes each case differently, improving retrieval quality. Performance on MTEB and similar benchmarks places Cohere Embed v3 competitive with the strongest commercial embedding offerings. The model supports compression to int8 and binary vectors for reduced storage and faster search. Available through Cohere's API, AWS Bedrock, Azure AI, and Google Vertex AI. Real-world deployments include semantic search at major enterprises (Oracle, McKinsey, Notion, Booking.com), multilingual customer support knowledge retrieval, and RAG systems where retrieval quality matters. AI governance, AI compliance, and AI risk management programs deploy Cohere Embed v3 in retrieval-heavy enterprise AI applications worldwide supporting responsible AI through high-quality vector search.

Centralpoint Brokers Cohere Embed v3 With Full Vendor Diversity: Oxcyon's Centralpoint AI Governance Platform routes embeddings to Cohere alongside OpenAI, Voyage, BGE, and other models — your choice per workload. Centralpoint meters every call, keeps prompts and skills on-prem, and embeds retrieval chatbots into your portals via one JavaScript line.


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