Embedding
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An embedding is a numeric vector representing text, used for semantic search and retrieval.
Embedding models are much cheaper per token than generation models, which makes the per-call cost feel negligible — right up to the point you embed a large corpus. The bill lands in the initial indexing run and again on every re-index after a model change.
Estimate before you run: corpus tokens × rate. And consider the Batch API, since indexing is rarely latency-sensitive.