Embedding models map text to vectors so you can run semantic search, retrieval-augmented generation and clustering.
This hub covers general-purpose and scientific or biomedical embeddings for domain-specific retrieval.
2 models
OpenAI's highest-quality embedding model. Returns 3072-dim vectors by default and supports reducing dimensions via the dimensions parameter. Outperforms text-embedding-3-small and the older ada-002 on MTEB and multilingual MIRACL retrieval benchmarks, for cases where accuracy matters more than cost.
OpenAI's small, low-cost embedding model. Returns 1536-dim vectors by default and supports shortening output dimensions via the dimensions parameter without retraining. Replaced text-embedding-ada-002 with better retrieval quality at a fraction of the price, and is the default choice for general-purpose semantic search and RAG.
Listed for reference. These models have no verified price or are no longer served, so the API does not run them.
Try any model with a free generation, no signup. Then transparent per-use pricing, no subscription.
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