Embedding Dimension Chooser
256 vs 1024 vs 3072 — quality vs cost
📚 Learn more — how it works, FAQ & guide Click to expand
Learn more — how it works, FAQ & guide
Click to expand
Embedding dimension chooser
Pick the optimal embedding size. Quality × storage × latency tradeoff.
How to use this tool
- 1
Pick use case
Search, classification, clustering, RAG.
- 2
Enter scale + quality need
Documents, latency, storage budget.
- 3
See recommendation
256 vs 1024 vs 3072 with reasoning.
Frequently Asked Questions
What is embedding dimensionality?
The length of the vector representation. 256 = compact, fast, less accurate. 3072 = rich, slow, most accurate. text-embedding-3-small has 1536 default but can truncate. 3-large goes to 3072.
Why smaller is often better?
Storage: 4 bytes × dim × vectors. 10M vectors at 3072 = 117 GB. At 512 = 19 GB. Query latency scales too. Matryoshka learning (new models) lets you truncate without major quality loss.
When MUST I go high-dim?
Legal/medical/scientific where exact nuance matters. Cross-lingual. Long-tail entity disambiguation. Not for typical product search or recommendation.
You might also like
🔒
100% Privacy. This tool runs entirely in your browser. Your data is never uploaded to any server.