whatmodel.sh / decision / gte-reranker-modernbert-base (DecisionBench f7481e60)

gte-reranker-modernbert-base (DecisionBench f7481e60)

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gte-reranker-modernbert-base (DecisionBench f7481e60) is an open-weights decision model from Alibaba. Its published size is 0.149605633B parameters. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: Alibaba-NLP/gte-reranker-modernbert-base, revision f7481e6055501a30fb19d090657df9ec1f79ab2c; classifier; adapter jev-compatible-server-v1; probability source native_probability_distribution. Kept separate from newer or unpinned Decision Index recipes; standard prompt, not compact. No unverified runtime or OpenRouter price imported. Current repository metadata checked separately; an unpinned alias is not evidence that today’s weights reproduce this historical run.

Model information sources

· Weights licence ↗ · Parameter count ↗

Published served parameter count; not a RAM or download-size measurement.

7.4±2.1#354 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of gte-reranker-modernbert-base (DecisionBench f7481e60), out of 357 with a score.

#modelscorewhereRAMreleased
353Nirnaya 0.4B kshitijthakkar16±1model files2026-10-06
354LFM2.5-350M-RLCD notnotsamuel16±1model files
355Zev (decision readout) bhubbard15±1source code
356gte-reranker-modernbert-base (DecisionBench f7481e60) Alibaba7±2model files
357certo-decision-model (DecisionBench 7628c0da) altslate7±2model files

see all decision models · find the ones that fit your machine

$ what it's good at

workflows▃▁▁▂▁▂▂▆█▄▁▁ #113 of 114 · 98% of its score · 27 tests

Beats Supersonic Labs Julia 1 here, though they rank higher overall. Trails certo-decision-model (DecisionBench 7628c0da) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · action selection (all-request accuracy) submitted15.6% ✎▃▁▂▃▄▃▁▁▂█▇▄ beats 2/56medium · avg 51%▇▇▇▇▇▇ 15%typical
DecisionBench v1 · completion assessment (all-request accuracy) submitted16.0% ✎▃▁▂▃▄·▂▁▁▆█▂ beats 1/56very easy · avg 86%▇▇▇▇▇ 12%sharp
DecisionBench v1 · routing (all-request accuracy) submitted8.7% ✎▃▁▁▁▂▂▁▁▁▁▃█ beats 0/56medium · avg 64%▇▇▇▇ 10%sharp
DecisionBench v1 · verification (all-request accuracy) submitted29.0% ✎▃▁▅▁▃▂▂▁▁▇▇█ beats 0/56medium · avg 56%▇▇▇ 8.4%blunt · at its level
DecisionBench v1 · triage (all-request accuracy) submitted9.0% ✎▃·▁·▂▃▄▃▂█▄▂ beats 1/56easy · avg 78%▇▇▇ 7.8%sharp
DecisionBench v1 · record classification (all-request accuracy) submitted11.0% ✎▃▂▁▂▂▂▄▁▃▆██ beats 2/56medium · avg 39%▇▇ 6.2%typical
DecisionBench v1 · bounded extraction (all-request accuracy) submitted19.1% ✎▃▃▂·▁▁▁▁▁▁█▅ beats 2/56very easy · avg 86%▇▇ 5.5%sharp · noisy
DecisionBench v1 · navigation (all-request accuracy) submitted9.0% ✎▁▃·▃▁▁▁▁▁▁▃█ beats 3/56medium · avg 58%▇▇ 5.3%sharp · too hard for it
19 more tests
testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · content classification (all-request accuracy) submitted15.0% ✎▁····▁▃▁▁▁▁█ beats 3/56easy · avg 73%▇▇ 4.7%typical
DecisionBench v1 · document record classification (all-request accuracy) submitted12.1% ✎▃▂·▁▂▄▃▆▄█▁▁ beats 0/56medium · avg 46%▇▇ 4.4%typical
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted26.3% ✎▁▁▅▅▃▃▅█▃▃·▁ beats 12/56medium · avg 45%▇▇ 3.8%typical
DecisionBench v1 · review scoring (all-request accuracy) submitted19.0% ✎▁······▃▃▅▇█ beats 2/56medium · avg 52%▇ 2.9%typical
DecisionBench v1 · risk scoring (all-request accuracy) submitted17.0% ✎▃▁·▁▂▂▁▃▃█▇▃ beats 0/56medium · avg 62%▇ 2.8%blunt · at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted50.9% ✎█▁▂▁▁▁▁▁▂▅▁▁ beats 14/56easy · avg 74%▇ 1.6%at its level · noisy
DecisionBench v1 · document workflows (all-request accuracy) submitted48.9% ✎█▄▃▁▁▃▂▂▁·▁▁ beats 8/56easy · avg 66%▇ 1.3%blunt · at its level
DecisionBench v1 · reasoning (all-request accuracy) submitted32.2% ✎▂·▁▁▆█▅▃▃▂▁▁ beats 9/56medium · avg 52%▇ 1.3%typical
DecisionBench v1 · safety gating (all-request accuracy) submitted49.0% ✎▂·····▆█▂▂▄▇ beats 11/56easy · avg 77%▇ 0.9%at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted23.0% ✎▁▁▃▂▆▃▃█···▁ beats 3/56medium · avg 52%▇ 0.7%blunt · at its level · noisy
DecisionBench v1 · target selection (all-request accuracy) submitted7.0% ✎▁▁·▁▁▁▃▁▂▁▁█ beats 11/56hard · avg 29%▇ 0.6%too hard for it · noisy
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted8.0% ✎▁▁·▁▁▃▃▂▃▁▁█ beats 9/56medium · avg 35%▇ 0.6%too hard for it · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted49.0% ✎▁·▁▁·▁▁█▂▃█▂ beats 7/56easy · avg 71%▇ 0.6%blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted47.0% ✎▁····▃▅▃▂▂·█ beats 9/56easy · avg 82%▇ 0.5%at its level · noisy
DecisionBench v1 · context management (all-request accuracy) submitted60.0% ✎▁·····▁▇▆▁█▃ beats 20/56easy · avg 73%▇ 0.4%blunt · at its level · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted15.4% ✎▁·▁▄▇▄▅▄▂▁▄█ beats 8/56easy · avg 66%▇ 0.2%at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted2.0% ✎▂▁·▃▁▁▁▄▁▂▂█ beats 8/56hard · avg 21%▇ 0.2%blunt · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted11.7% ✎▃▃▄▅▆▂▅▄▃▄█▃ beats 3/56easy · avg 68%▇ 0.2%blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted0.7% ✎█▁···▁·▂▆▅▂▂ beats 16/56medium · avg 42%▇ 0.0%blunt · at its level · noisy

$ where to run it

Download model files ↗
Published model files verified. This is the revision recorded by the benchmark. A local run command and hardware requirements have not been verified for this entry. Source · checked 2026-10-31.

Model repository and documentation: huggingface.co/Alibaba-NLP ↗

$ how the score was worked out · Hanno Labs DecisionBench v1 r… 98%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Hanno Labs DecisionBench v1 reviewed results · submitted; independent execution unverified27CC0 1.0
prior · pull toward the middle
weighted average= 7.4

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