Cohere Embed

Enterprise-Grade Text Embedding Models

Cohere Embed is Cohere's state-of-the-art text embedding model offering, purpose-built for enterprise semantic search, retrieval, and classification applications. Text embeddings convert words, sentences, and documents into numerical vectors that capture semantic meaning, enabling AI systems to find semantically similar content rather than just keyword matches — a fundamental capability for modern RAG applications and semantic search.

Best-in-Class Embedding Quality

Cohere Embed models consistently rank at the top of MTEB (Massive Text Embedding Benchmark), the standard evaluation for embedding model quality. They support over 100 languages, making them suitable for global enterprise deployments. The models come in multiple sizes optimizing the quality-cost-latency tradeoff for different application requirements — from high-throughput classification to maximum accuracy retrieval.

• State-of-the-art performance on standard embedding benchmarks
• 100+ language support for multilingual applications
• Multiple model sizes for different quality/cost tradeoffs
• Compression-friendly embeddings for efficient storage
• Enterprise security and data privacy guarantees

For Enterprise AI Applications

Enterprises building internal knowledge search systems, customer support RAG applications, document classification pipelines, and recommendation systems use Cohere Embed for its combination of quality, multilingual coverage, and enterprise-grade data handling guarantees that general-purpose embedding endpoints may not provide.