We are introducing two new embedding models: a smaller and highly efficient text-embedding-3-small model, and a larger and more powerful text-embedding-3-large model.

An embedding⁠ is a sequence of numbers that represents the concepts within content such as natural language or code. Embeddings make it easy for machine learning models and other algorithms to understand the relationships between content and to perform tasks like clustering or retrieval. They power applications like knowledge retrieval in both ChatGPT and the Assistants API, and many retrieval augmented generation (RAG) developer tools.

text-embedding-3-small is our new highly efficient embedding model and provides a significant upgrade over its predecessor, the text-embedding-ada-002 model released in December 2022⁠.

Stronger performance. Comparing text-embedding-ada-002 to text-embedding-3-small, the average score on a commonly used benchmark for multi-language retrieval (MIRACL⁠(opens in a new window)) has increased from 31.4% to 44.0%, while the average score on a commonly used benchmark for English tasks (MTEB⁠(opens in a new window)) has increased from 61.0% to 62.3%.