SkillRank verdict
Choose embeddings by retrieval quality on your own corpus, not by brand. OpenAI and Gemini are natural choices for their platform ecosystems, Cohere is strong for search-oriented products, and BGE is useful when open-weight control matters.
Decision Matrix
Choose by workflow, risk, and fit.
The matrix turns the written comparison into a scan-friendly decision surface. It uses the same editorial comparison rows and linked model profiles.
OpenAI
Strong default for OpenAI-first stacks / Validate current model naming and pricing before rollout
Gemini
Good fit for Google Cloud and Gemini API users / Check regional and platform constraints
Cohere
Search and enterprise retrieval focus / Evaluate rerank and multilingual behavior
What matters in RAG
The embedding model should retrieve the right passage before the answer model writes. Measure recall at k, grounded answer quality, multilingual coverage, latency, cost, and how well the model handles tables, boilerplate, duplicates, and short queries.
Hybrid retrieval often wins
Dense embeddings are powerful, but keyword and metadata filters still matter. Many production RAG systems combine dense vectors, sparse search, reranking, permissions, and freshness filters.
Migration cost
Changing embedding models can require reindexing the corpus. Before committing, estimate storage, indexing time, versioning, and how you will compare old and new indexes during migration.
Sources and next steps