Cohere vs OpenAI Embeddings: Comparison for RAG and Search
Compare Cohere and OpenAI embedding models on quality, cost, and multilingual support. Discover which best fits your RAG and semantic search.
Cohere Embed
Cohere's embedding models (embed-v3, embed-multilingual-v3) are optimized for retrieval. Multilingual-v3 supports 100+ languages in one model. Cohere offers good retrieval quality and competitive pricing.
OpenAI Embeddings
OpenAI's text-embedding-3-small and text-embedding-3-large models for semantic search and RAG. Widely adopted, good documentation, and seamless integration with the rest of the OpenAI ecosystem.
Comparison table
| Feature | Cohere Embed | OpenAI Embeddings |
|---|---|---|
| Models | embed-v3, embed-multilingual-v3 (1024 dims) | text-embedding-3-small (1536), -large (3072) |
| Multilingual | 100+ languages in one model | Good, no dedicated multilingual model |
| Input type | search_document / search_query modes | Single model, optional dimensions |
| Cost | Competitive, free tier | Per token, often slightly higher |
| Integration | LangChain, LlamaIndex, Weaviate | Broadest ecosystem, all tools |
| RAG quality | Strong on retrieval benchmarks | Very good, many use cases documented |
Verdict
Cohere wins on multilingual and often on price. OpenAI wins on ecosystem and breadth. For Dutch or multilingual RAG Cohere embed-multilingual-v3 is a strong choice. For pure English and existing OpenAI stack OpenAI embeddings is logical.
Our recommendation
AVARC Solutions uses Cohere embed-multilingual-v3 for Dutch and EU projects requiring multiple languages. For English-only and clients already on OpenAI we choose text-embedding-3. Both are production-ready.
Frequently asked questions
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