Open-source 149M-parameter model tops public sparse embedding benchmark

Linkup Research released SPARSEUP, an open-source sparse embedding model built on a 149M-parameter ModernBERT backbone under an Apache 2.0 license. It scores 56.4 nDCG@10 on the BEIR-13 retrieval benchmark, which the company says is the best public result for a sparse encoder under 150M parameters, and reaches over 97% recall in about 380 microseconds per query when paired with the Seismic index.

Why it matters: Sparse embedding models are cheaper to run and more interpretable than dense ones, making them attractive for large-scale search infrastructure. A small, permissively licensed model beating prior public results lowers the barrier for teams to build retrieval systems without depending on proprietary embedding APIs.