BGE-M3 and BGE Reranker in 2026: what the benchmarks say about dense, lexical, and multi-vector retrieval
BGE-M3 is designed as a single model that unifies dense, lexical, and multi-vector/ColBERT-style retrieval across 100+ languages and long inputs up to 8192 tokens — but its benchmark story is only meaningful if you read it alongside the reranker, because the model card shows reranking and multi-retrieval are complementary rather than interchangeable.