bge-large-en-v1.5 and multilingual-e5-large are both feature-extraction models. See each entry for specifics.
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.
Multilingual-E5-Large is a 560-million-parameter multilingual embedding model from Microsoft Research, supporting 100+ languages via an XLM-RoBERTa backbone. Trained with E5's instruction-following approach (prepending 'query:' or 'passage:' prefixes), it achieves strong MTEB multilingual retrieval scores. MIT licensed with ONNX and OpenVINO export.
Pick based on your compute budget and specific task requirements.