Cohere vs OpenAI Embeddings: Vector Search Compared
A technical deep-dive into embedding model quality, dimensions, multilingual support, and cost for production vector search systems.
Embeddings: The Foundation of AI Search
Embedding models convert text into numerical vectors that capture semantic meaning. They're the foundation of modern search, recommendation systems, and RAG pipelines. The quality of your embeddings directly determines the quality of your search results.
Cohere's Embed v4 and OpenAI's text-embedding-3-large are the two leading embedding models in 2026. Both produce high-quality vectors, but their characteristics differ in ways that matter for production systems.
Retrieval Quality
On the MTEB benchmark suite, Cohere Embed v4 scores 71.2% average across all tasks, versus OpenAI's 70.8%. The difference is marginal overall, but domain-specific performance varies more.
Cohere leads on document retrieval (+3%) and classification (+2%). OpenAI leads on semantic textual similarity (+2%) and clustering (+1%). For RAG applications, Cohere's retrieval advantage makes it the better default choice.
Multilingual Support
Cohere Embed v4 supports 100+ languages with consistent quality, making it the clear winner for multilingual search. OpenAI's model supports 50+ languages but shows quality degradation for low-resource languages.
For applications serving global audiences, Cohere's multilingual embeddings enable cross-language search—a query in English can find relevant documents in French, Japanese, or Arabic.
Dimensions and Efficiency
Both models support flexible dimensionality. Cohere offers 256, 512, and 1024 dimensions. OpenAI offers 256, 1024, and 3072 dimensions.
Higher dimensions capture more nuance but increase storage and compute costs. For most applications, 1024 dimensions offers the best quality-cost balance. At 256 dimensions (suitable for simple search), both models maintain 95%+ of their full-dimension quality.
Pricing
Cohere: $0.10 per million tokens. OpenAI: $0.13 per million tokens. Cohere is 23% cheaper.
For large-scale indexing (millions of documents), this price difference is significant. A 10-million document corpus costs approximately $50 with Cohere versus $65 with OpenAI.
The Verdict
Cohere Embed v4 wins for multilingual RAG applications and cost-sensitive deployments. OpenAI text-embedding-3-large wins for English-first applications and ecosystems already built on OpenAI.
Test both embedding models on your actual data through Vincony.com, which supports side-by-side embedding quality comparison with your own documents.