Cohere Embed v3vsCohere Embed v4

Cohere Embed v3, developed by Cohere, is an embedding model with a 512-token input limit designed for semantic search, retrieval-augmented generation, and clustering applications. The model generates dense vector representations of text that capture semantic meaning, enabling similarity-based search across document collections. Embed v3 supports 100+ languages and produces compact embeddings optimized for vector database storage and retrieval. It outperforms previous generations on the MTEB benchmark across multiple retrieval and classification tasks. Priced at $0.10 per million tokens, it offers cost-effective embedding generation for production search pipelines. The model serves as the foundation for enterprise search systems, recommendation engines, and RAG architectures. Embed v3 remains widely deployed despite the release of v4, due to its mature ecosystem of integrations and proven production reliability.

Cohere Embed v4, developed by Cohere, is the first multimodal embedding model in Cohere's lineup, processing both text and images into unified 128K-context vector representations. The model generates embeddings for semantic search, RAG pipelines, document retrieval, and visual search applications. Supporting 100+ languages, Embed v4 produces compact, efficient vectors optimized for modern vector databases. Its multimodal capability enables searching across mixed document types containing both text and visual elements. Priced at $0.12 per million tokens, it offers affordable embedding generation for production applications. The model represents a significant upgrade over text-only Embed v3, enabling unified search across document types. It is particularly valuable for enterprises with heterogeneous content including PDFs, presentations, and image-heavy documents that require combined text and visual understanding.