bge-small-en-v1.5 vs bge-large-en-v1.5

bge-small-en-v1.5 and bge-large-en-v1.5 are both feature-extraction models. See each entry for specifics.

bge-small-en-v1.5

Pipeline: feature extraction
Downloads: 34,386,222
Likes: 451

Small English dense embedding model from BAAI's BGE (BAAI General Embedding) series, producing 384-dimensional vectors via MIT license. Optimized for MTEB retrieval benchmarks through a retrieval-focused training strategy, it achieves competitive scores relative to its parameter count. Suited for embedding workflows where throughput and cost matter more than peak accuracy.

bge-large-en-v1.5

Pipeline: feature extraction
Downloads: 14,929,062
Likes: 657

BGE-Large-EN-v1.5 is BAAI's highest-capacity English embedding model in the v1.5 series, producing 1024-dimensional vectors. It achieves top MTEB retrieval scores among its generation of English-only embedding models, at the cost of higher compute and storage than BGE-small or BGE-base. MIT licensed with ONNX export support.

Key differences

See individual model pages for architecture and use cases.

Common ground

Both are open-source models on HuggingFace.

Which should you pick?

Pick based on your compute budget and specific task requirements.