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gliner2.5-small-v1 (DecisionBench 7e6f537f)

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gliner2.5-small-v1 (DecisionBench 7e6f537f) is an open-weights decision model from Fastino. Its published size is 0.073881879B parameters. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: fastino/gliner2.5-small-v1, revision 7e6f537f10337497069276892a5ef435028252ce; 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.

22.5±1.7#336 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of gliner2.5-small-v1 (DecisionBench 7e6f537f), out of 357 with a score, then more from Fastino.

#modelscorewhereRAMreleased
336GLiNER2.5-Decide Fastino23±1model files
337Prumo (rule-based engine) tardellirs23±1source code
338gliner2.5-small-v1 (DecisionBench 7e6f537f) Fastino23±2model files
339Qwen-2.5-1B-RLCD (decision readout) harshatheg21±1source code
340Laya ConvAI Innovations21±1model files
more from Fastino
4Fastino GLiDE 1.2 Fastino73±1model files2026-10-22
313GLiNER2.5-Decide (DecisionBench 0872ab14) Fastino30±2model files
326gliner2-large-v1 (DecisionBench f32ea6ef) Fastino26±2model files

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

$ what it's good at

workflows▁▁▃▂▁▂▂▆█▄▁▁ #104 of 114 · 99% of its score · 27 tests
testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · action selection (all-request accuracy) submitted24.4% ✎▂▁▂▃▄▃▁▁▂█▇▄ beats 8/56medium · avg 51%▇▇▇▇▇▇ 15%typical
DecisionBench v1 · routing (all-request accuracy) submitted26.3% ✎▁▁▁▁▃▂▁▁▁▁▃█ beats 10/56medium · avg 64%▇▇▇▇▇ 12%sharp
DecisionBench v1 · completion assessment (all-request accuracy) submitted43.0% ✎▁▁▂▃▄·▂▁▁▆█▂ beats 9/56very easy · avg 86%▇▇▇▇▇ 12%sharp · at its level
DecisionBench v1 · bounded extraction (all-request accuracy) submitted24.6% ✎▃▃▂·▁▁▁▁▁▁█▅ beats 6/56very easy · avg 86%▇▇▇▇ 9.1%sharp · at its level · noisy
DecisionBench v1 · navigation (all-request accuracy) submitted20.0% ✎▁▁·▃▃▁▁▁▁▁▃█ beats 13/56medium · avg 58%▇▇▇ 7.5%sharp
DecisionBench v1 · triage (all-request accuracy) submitted41.0% ✎▁·▁·▂▃▄▃▂█▄▂ beats 9/56easy · avg 78%▇▇▇ 7.4%sharp · at its level
DecisionBench v1 · verification (all-request accuracy) submitted34.5% ✎▁▁▅▁▃▂▂▁▁▇▇█ beats 5/56medium · avg 56%▇▇ 6.2%blunt · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted18.0% ✎▃▂▁▃▂▂▄▁▃▆██ beats 8/56medium · avg 39%▇▇ 5.9%typical
19 more tests
testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · content classification (all-request accuracy) submitted32.0% ✎▁····▁▁▃▁▁▁█ beats 7/56easy · avg 73%▇▇ 4.5%at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted30.0% ✎▂▂·▁▃▄▃▆▄█▁▁ beats 10/56medium · avg 46%▇▇ 3.9%typical
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted25.3% ✎▁▁▅▅▃▃▅█▃▃·▁ beats 5/56medium · avg 45%▇ 3.3%typical
DecisionBench v1 · review scoring (all-request accuracy) submitted30.0% ✎▁······▂▃▅▇█ beats 8/56medium · avg 52%▇ 2.5%at its level
DecisionBench v1 · risk scoring (all-request accuracy) submitted37.0% ✎▁▁·▁▃▂▁▃▃█▇▃ beats 5/56medium · avg 62%▇ 2.1%blunt · at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted53.6% ✎█▃▂▁▁▁▁▁▂▅▁▁ beats 23/56easy · avg 74%▇ 1.1%at its level · noisy
DecisionBench v1 · reasoning (all-request accuracy) submitted41.3% ✎▂·▁▁▆█▅▃▃▂▁▁ beats 24/56medium · avg 52%▇ 1.1%at its level
DecisionBench v1 · document workflows (all-request accuracy) submitted49.6% ✎█▄▃▁▁▃▂▂▁·▁▁ beats 12/56easy · avg 66%▇ 0.9%blunt · at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted12.0% ✎▁▁·▁▁▂▃▂▃▁▁█ beats 16/56medium · avg 35%▇ 0.9%noisy
DecisionBench v1 · target selection (all-request accuracy) submitted9.0% ✎▁▁·▁▁▁▃▁▂▁▁█ beats 14/56hard · avg 29%▇ 0.8%noisy
DecisionBench v1 · safety gating (all-request accuracy) submitted44.0% ✎▂·····▆█▂▂▄▇ beats 5/56easy · avg 77%▇ 0.7%at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted34.8% ✎▁▁▁▃▆▃▃█···▁ beats 8/56medium · avg 52%▇ 0.5%blunt · at its level · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted63.0% ✎▁·▁▁·▁▁█▃▃█▂ beats 23/56easy · avg 71%▇ 0.4%blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted67.0% ✎▁····▃▅▃▂▂·█ beats 20/56easy · avg 82%▇ 0.4%at its level · noisy
DecisionBench v1 · context management (all-request accuracy) submitted64.0% ✎▁·····▁▇▆▁█▃ beats 23/56easy · avg 73%▇ 0.2%blunt · at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted9.0% ✎▂▁·▂▁▁▁▄▁▂▂█ beats 16/56hard · avg 21%▇ 0.2%blunt · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted6.8% ✎▁·▁▄▇▄▅▄▂▁▄█ beats 3/56easy · avg 66%▇ 0.2%at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted36.2% ✎▃▃▄▅▆▂▅▄▃▄█▃ beats 15/56easy · avg 68%▇ 0.1%blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted40.5% ✎█▁···▁·▃▆▅▂▂ beats 22/56medium · 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/fastino ↗

$ how the score was worked out · Hanno Labs DecisionBench v1 r… 99%

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= 22.5

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