Cohere Embed v4vsCommand R

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.

Command R, developed by Cohere, is an enterprise-grade model with 35 billion parameters and a 128K token context window optimized for retrieval-augmented generation. The model specializes in grounded generation, producing responses with accurate citations from provided source documents. It handles document search, summarization, information extraction, and conversational Q&A with built-in citation capabilities. Command R supports 10 languages and features production-ready RAG tooling that reduces hallucination on retrieval tasks. Priced at $0.15 per million input tokens and $0.60 per million output tokens, it offers cost-effective enterprise search capabilities. As an open-source model, it can also be self-hosted. Command R ranks #23 on the Chatbot Arena leaderboard, reflecting its specialized design for retrieval tasks rather than general-purpose benchmarks where broad models score higher.