You should choose text-embedding-3-small if you want better efficiency and stronger semantic quality without increasing system complexity. The model is designed to generate embeddings that capture meaning more reliably while keeping vector size and compute costs low. For developers, this means fewer trade-offs between quality and performance when building text-based features.
In real systems, older embedding models often create larger vectors or require more compute per request, which increases latency and infrastructure cost. text-embedding-3-small improves on this by producing embeddings that are compact and consistent, making them easier to store and faster to compare. For example, if you are embedding millions of product descriptions or support tickets, smaller vectors directly reduce memory usage and indexing time. This matters not just during ingestion, but also during query-time similarity searches.
When used with a vector database such as Milvus or Zilliz Cloud, these efficiency gains become even more noticeable. Smaller embeddings mean faster indexing and lower storage costs in Milvus, while still maintaining strong retrieval quality. For teams operating at scale or under cost constraints, this makes text-embedding-3-small a sensible default choice for modern semantic systems.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small