Embedding model family that ranks well on retrieval benchmarks.
| Feature | BGE | OpenAI Text Embedding 3 |
|---|---|---|
| Dimensions | 1024 | 1536 by default, 256 to 3072 selectable |
| License | MIT | Proprietary |
| Max tokens | 512 | 8191 |
| Open weights | Yes | No |
| Provider | BAAI | OpenAI |
Shaded rows are identical for both; the differing rows are the ones that drive the recommendation below.
Generated from the differences above by structured rules, not by a model.
Choose BGE if…
Nothing in the recorded attributes distinguishes it in your favour.
Choose OpenAI Text Embedding 3 if…
Nothing in the recorded attributes distinguishes it in your favour.
model · Embedding Models
BGE is a family of embedding models trained specifically for retrieval, available in sizes small enough to run locally. It solves the problem that a general-purpose embedding model is not optimised for the asymmetric comparison a search query and a document require. It exists because retrieval quality is set at the embedding step, so a model trained for it improves everything downstream without changing anything else.
model · Embedding Models
Text Embedding 3 is OpenAI's hosted embedding family, notable for letting the caller choose a shorter output vector than the model natively produces. It solves the storage problem directly: embedding indexes are sized by dimensions times vectors, so trimming 3072 dimensions to 512 or 256 shrinks the index substantially at a small quality cost. It exists because that trade is one the caller is better placed to make than the model author, and it removes the need to retrain to change it.
