Text embedding model family trained on weakly supervised pairs.
| Feature | E5 | 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 | Microsoft | 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 E5 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
E5 is an embedding model family trained on large collections of weakly supervised text pairs, with the task described in the input so one model serves queries, passages and classification. It solves the problem that an embedding model trained on one kind of pair transfers poorly to another. It exists because describing the task in the text is a cheap way to make one model cover several retrieval settings.
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.
