Overview
Cohere Embed is a family of embedding models designed for semantic search, retrieval, and clustering. Embed v3 is the latest version, offering state-of-the-art performance on retrieval benchmarks.
Versions
Embed v3, v2
Dimensions
1024
Languages
100+ languages
Use Case
Semantic Search
✅Strengths
- ✓State-of-the-art retrieval performance
- ✓100+ language support
- ✓Optimized for RAG workflows
- ✓Low latency and cost
- ✓Easy integration with Cohere models
⚠️Weaknesses
- ✗API-only (no self-hosting)
- ✗Requires vector database for storage
- ✗Less known than OpenAI embeddings
- ✗Task-specific optimization needed
Best Use Cases
🔍 Semantic Search
Document retrieval
📚 RAG Systems
Knowledge bases
📊 Clustering
Document grouping
🏷️ Classification
Text categorization
🌐 Multilingual
100+ languages
📈 Recommendations
Similarity matching
Benchmarks
MTEB64.5%
BEIR71.2%
RetrievalTop-tier