Embedding Model Comparison Table

Side-by-side comparison of every major embedding model: dimensions, max input tokens, price per million tokens, MTEB benchmark score, and whether it's open-weight. Filter, sort, and click any model for the full spec card.

How to use the Embedding Model Comparison Table

Sort by MTEB score (overall retrieval quality), price (cheapest first), embedding dimensions (smaller = cheaper to store), or max input. Filter to open-weight models if you want to self-host. MTEB scores are the official aggregated leaderboard average — higher is better.

How to pick an embedding model

Three factors: quality (MTEB), price (for both initial corpus embedding and per-query), dimensions (storage cost in your vector DB scales linearly).

For most production RAG: start with text-embedding-3-small (cheap, 1536-dim, MTEB ~62) or voyage-3-lite. Move to text-embedding-3-large or voyage-3 if retrieval quality is your bottleneck. For multilingual: multilingual-e5-large open-weight, or cohere-embed-multilingual-v3.

The 256-dim variants (where supported via dimensions param) cut storage 6x at modest quality cost — worth measuring on your data.