Embed English V3.0
Embed English V3.0 is Cohere's English-optimized embedding model, delivering top-tier retrieval quality for English-language applications. It outperforms multilingual models on English benchmarks by focusing its capacity entirely on English linguistic nuance.
For teams building English-only applications, this model offers the best quality at the most efficient cost point, making it the default recommendation for English-language RAG and search systems.
Key Features
English-optimized for maximum retrieval quality
1024-dimensional embeddings
Top performance on English retrieval benchmarks
Support for search, classification, and clustering tasks
Cost-efficient for English-only applications
Ideal Use Cases
English-language semantic search engines
English document RAG pipelines
Customer support knowledge base retrieval
English content classification
Technical Specifications
| Dimensions | 1024 |
| Modality | Text → Embedding |
| Provider | Cohere |
| Category | Embedding |
| Language | English (optimized) |
| Input Types | search_document, search_query, classification, clustering |
API Usage
1 curl -X POST https://api.vincony.com/v1/chat/completions \ 2 -H "Authorization: Bearer YOUR_API_KEY" \ 3 -H "Content-Type: application/json" \ 4 -d '{ 5 "model": "cohere/embed-english-v3.0", 6 "messages": [ 7 { "role": "user", "content": "Hello, Embed English V3.0!" } 8 ] 9 }'
Replace YOUR_API_KEY with your Vincony API key. OpenAI-compatible endpoint — works with any OpenAI SDK.
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Frequently Asked Questions
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