Text and multimodal embedding models ranked by the MTEB leaderboard: retrieval, classification, clustering, and STS.
Benchmark data last synced Apr 25, 2026
| Compare | ||||||
|---|---|---|---|---|---|---|
AA74.7 | Qwen/Alibaba | 65 | 1024 | 33K | 0.596B | |
AA72.7 | intfloat (Microsoft Research) | 62 | 1024 | 514 | 0.56B | |
AA72.5 | Qwen/Alibaba | 71 | 4096 | 33K | 7.6B | |
AA71.9 | Microsoft | 69 | 1024 | 33K | 0.596B | |
AA71.7 | Qwen/Alibaba | 70 | 2560 | 33K | 4B | |
AA70.8 | 68 | 1024 | 33K | 0.596B | ||
AA70.6 | Microsoft | 66 | 640 | 33K | 0.268B | |
BB69.8 | Jina AI | 67 | 768 | 8K | 0.212B | |
BB63.5 | Alibaba-NLP (Tongyi Lab) | 64 | 3584 | 33K | 7.1B | |
BB62.5 | NVIDIA | 69 | 4096 | 33K | 7.5B | |
BB60.2 | intfloat (Microsoft Research) | 61 | 4096 | 33K | 7.1B | |
BB59.9 | Octen AI | 69 | 4096 | 33K | 7.6B | |
BB58.7 | Microsoft | 74 | 5376 | 131K | 27B | |
BB56.6 | 72 | 3840 | 33K | 11.8B | ||
CC54.9 | CodeFuse-AI (Ant Group) | 66 | 2048 | 41K | 1.7B |