text-embedding-3-small — reviews, specs & pricing
Cost-efficient text embeddings for high-volume RAG.
Summary
Text-embedding-3-small is OpenAI's highly efficient and cost-effective embedding model designed to convert textual data into numerical vectors. It offers a significant performance upgrade and drastically lower pricing compared to its predecessor, text-embedding-ada-002. Additionally, it supports native dimension reduction, allowing developers to balance storage costs and retrieval accuracy for high-volume applications.
Sample use case
A global e-commerce enterprise can leverage text-embedding-3-small to build a highly scalable semantic search and recommendation engine. By embedding millions of product listings and customer search queries, the system can instantly match user intent with relevant products in real-time. This architecture supports cost-efficient Retrieval-Augmented Generation (RAG) for virtual shopping assistants while keeping vector database storage costs to a minimum.
Specifications
- Provider: openai
- License: closed
Pros
- Highly cost-effective pricing
- Supports flexible dimension truncation
- Outperforms text-embedding-ada-002
- Low latency for high-volume applications
Cons
- Lower retrieval accuracy than text-embedding-3-large
- Dependent on OpenAI's proprietary API
- Strictly limited to text embeddings
Average rating 0.0 from 0 community reviews on Reviuws.