text-embedding-3-large vs text-embedding-3-small
Direct comparison between openai and openai for embedding models. Specs, prices, strengths, weaknesses — decide on facts.
At a glance
- · Cheaper: text-embedding-3-small
- · text-embedding-3-large: 3072 dimensions — top accuracy on MTEB benchmarks
- · text-embedding-3-small: One of the cheapest embedding models per million tokens
Spec comparison
| Spec | text-embedding-3-large | text-embedding-3-small |
|---|---|---|
| Provider | openai | openai |
| Input per 1M tokens | $0.13 / 1M | $0.02 / 1M |
| Output per 1M tokens | $0.00 / 1M | $0.00 / 1M |
| Context window | 8,191 tok | 8,191 tok |
text-embedding-3-large — strengths
- +3072 dimensions — top accuracy on MTEB benchmarks
- +Dimension can be shortened via API (Matryoshka) — save storage without re-embedding
- +Multilingual, strong on cross-language retrieval
text-embedding-3-small — strengths
- +One of the cheapest embedding models per million tokens
- +Clearly better than the old ada-002 at lower price
- +Default pick for RAG prototypes and mid-sized corpora
text-embedding-3-large — weaknesses
- −Pricier than 3-small, often with no noticeable quality edge on small corpora
- −Full 3072-dim is storage-intensive
text-embedding-3-small — weaknesses
- −Behind 3-large on the hardest MTEB tasks