Why it matters
- Embed v3 consistently ranks among MTEB's top embedding models — production-quality semantic search from day one.
- 100+ language support in the multilingual model makes it one of the most practical for global enterprise applications.
- Available on AWS Bedrock, Azure AI, and Google Vertex AI — meets enterprise deployment and compliance requirements.
- Compression options (int8, binary) reduce vector storage costs by up to 128× with minimal accuracy loss.
Key capabilities
- embed-english-v3.0: Highest-quality English embeddings; top MTEB scores for retrieval and classification.
- embed-multilingual-v3.0: 100+ language support in a single model; cross-lingual semantic search.
- Input types: Optimized embeddings for
search_document,search_query,classification, andclusteringpurposes. - Vector compression: float32, int8, and binary output types for storage/performance tradeoffs.
- Rerank API: Companion re-ranking model to improve RAG retrieval accuracy post-retrieval.
- Batch processing: Embed thousands of documents in parallel via batch API.
- Cloud marketplace: Available on AWS Bedrock, Azure AI Foundry, and Google Vertex AI.
- REST API: Simple POST endpoint with Python, TypeScript, Java, and Go SDKs.
Technical notes
- Models: embed-english-v3.0 (1024-dim); embed-multilingual-v3.0 (1024-dim)
- Languages: 100+ in multilingual model
- Output types: float32, int8, uint8, binary, ubinary
- Max input tokens: 512 tokens per input
- API: REST; SDKs: Python, TypeScript, Java, Go
- Pricing: Free trial tokens; pay-per-token for production ($0.10 per 1M tokens approx.)
- Company: Cohere; Toronto; founded 2019 by ex-Google Brain researchers
Ideal for
- RAG pipelines requiring high-quality document retrieval for LLM applications.
- Enterprise search systems requiring multilingual support across 100+ languages.
- Organizations on AWS/Azure/GCP who need embedding models available in their existing cloud marketplace.
Not ideal for
- Projects deeply integrated with OpenAI's ecosystem where text-embedding-3 aligns better.
- Image or multimodal embeddings — Cohere Embed is text-only (use CLIP-based models for images).
- Very long documents exceeding the 512-token limit per chunk — requires chunking strategy.
See also
- Pinecone — Vector database to store and search Cohere embeddings at scale.
- Voyage AI — Competing embedding model provider with domain-specific models.
- Jina Embeddings — Open-source-first embedding models with 8K token context.