---
title: "Open-source 149M-parameter model tops public sparse embedding benchmark"
url: https://www.parallelquant.com/posts/open-source-149m-parameter-model-tops-public-sparse-embedding-benchmark-242099
source_name: "MarkTechPost"
source_url: https://www.marktechpost.com/2026/09/19/linkup-research-releases-sparseup/
published: 2026-09-19T07:48:38.000Z
topics: ["open source", "research"]
publisher: "Parallel Quant"
---

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

*2026-09-19 · Source: [MarkTechPost](https://www.marktechpost.com/2026/09/19/linkup-research-releases-sparseup/)*

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.

**Topics:** open source, research

---
Read the original: https://www.marktechpost.com/2026/09/19/linkup-research-releases-sparseup/
Canonical: https://www.parallelquant.com/posts/open-source-149m-parameter-model-tops-public-sparse-embedding-benchmark-242099
Published by Parallel Quant — https://www.parallelquant.com