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GLiNER2.5-Decide (DecisionBench 0872ab14)

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GLiNER2.5-Decide (DecisionBench 0872ab14) is an open-weights decision model from Fastino.

Catalogue identity notes

DecisionBench v1 comparison: fastino/GLiNER2.5-Decide, revision 0872ab149bd2f8a50ed5fc7ad8cfc3293e9a3bad; classifier; adapter gliner25_decide_classifier; probability source raw_classification_logits_softmax. Kept separate from newer or unpinned Decision Index recipes; standard prompt, not compact. No unverified runtime or OpenRouter price imported.

29.6±1.6#311 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of GLiNER2.5-Decide (DecisionBench 0872ab14), out of 357 with a score, then more from Fastino.

#	model	score	where	RAM	released
311	OpenDecision (DecisionBench a51e07b5) deepanwadhwa	30±2	model files
312	Crivo-68M tardellirs	30±1	model files
313	GLiNER2.5-Decide (DecisionBench 0872ab14) Fastino	30±2	model files
314	LiquidAI d1-omni-600M Liquid AI	30±1	local	~2.7GB
315	laya-typed-decisions (DecisionBench bc76315b) ConvAI Innovations	29±2	model files
	more from Fastino
4	Fastino GLiDE 1.2 Fastino	73±1	model files		2026-10-22
326	gliner2-large-v1 (DecisionBench f32ea6ef) Fastino	26±2	model files
336	GLiNER2.5-Decide Fastino	23±1	model files

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

$ what it's good at

workflows▁▁▁▃▁▂▂▆█▄▁▁ #95 of 114 · 99% of its score · 27 tests

Trails GLiNER2.5-Decide, vLLM-SR Decision 2.0 Kai 0.6B, LiquidAI d1-omni-600M here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · action selection (all-request accuracy) submitted	30.0% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 17/56	medium · avg 51%	▇▇▇▇▇▇ 15%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	27.0% ✎	▁▁▁▁▃▂▁▁▁▁▃█ beats 11/56	medium · avg 64%	▇▇▇▇▇ 13%	sharp · at its level
DecisionBench v1 · completion assessment (all-request accuracy) submitted	73.0% ✎	▁▁▂▃▄·▃▁▁▆█▂ beats 19/56	very easy · avg 86%	▇▇▇▇ 10%	sharp · at its level
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	31.3% ✎	▃▃▂·▁▁▁▁▁▁█▅ beats 13/56	very easy · avg 86%	▇▇▇▇ 10%	sharp · at its level · noisy
DecisionBench v1 · navigation (all-request accuracy) submitted	26.0% ✎	▁▁·▃▁▃▁▁▁▁▃█ beats 17/56	medium · avg 58%	▇▇▇ 8.7%	sharp · at its level
DecisionBench v1 · triage (all-request accuracy) submitted	62.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 20/56	easy · avg 78%	▇▇▇ 6.9%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	25.0% ✎	▃▂▁▂▂▃▄▁▃▆██ beats 15/56	medium · avg 39%	▇▇ 6.1%	typical
DecisionBench v1 · verification (all-request accuracy) submitted	47.5% ✎	▁▁▅▁▃▂▃▁▁▇▇█ beats 20/56	medium · avg 56%	▇▇ 5.8%	blunt · at its level

19 more tests

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · content classification (all-request accuracy) submitted	69.0% ✎	▁····▁▁▁▁▁▃█ beats 21/56	easy · avg 73%	▇▇ 4.3%	at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted	14.0% ✎	▃▂·▁▂▄▃▆▄█▁▁ beats 2/56	medium · avg 46%	▇▇ 3.9%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	18.5% ✎	▁▃▅▅▃▃▅█▃▃·▁ beats 2/56	medium · avg 45%	▇ 3.3%	at its level
DecisionBench v1 · review scoring (all-request accuracy) submitted	45.0% ✎	▁······▂▃▅▇█ beats 22/56	medium · avg 52%	▇ 2.4%	at its level
DecisionBench v1 · risk scoring (all-request accuracy) submitted	60.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 24/56	medium · avg 62%	▇ 1.9%	blunt · at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	0.0% ✎	▃▁·▁▁▂▃▂▃▁▁█ beats 1/56	medium · avg 35%	▇ 1.0%	noisy
DecisionBench v1 · reasoning (all-request accuracy) submitted	9.8% ✎	▃·▁▁▆█▅▃▃▂▁▁ beats 0/56	medium · avg 52%	▇ 1.0%	at its level
DecisionBench v1 · target selection (all-request accuracy) submitted	4.0% ✎	▁▁·▁▃▁▃▁▂▁▁█ beats 5/56	hard · avg 29%	▇ 0.9%	noisy
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	53.1% ✎	█▁▂▁▁▁▁▁▂▅▁▁ beats 22/56	easy · avg 74%	▇ 0.9%	at its level · noisy
DecisionBench v1 · document workflows (all-request accuracy) submitted	47.1% ✎	█▄▃▁▁▃▂▂▁·▁▁ beats 3/56	easy · avg 66%	▇ 0.8%	blunt · at its level
DecisionBench v1 · safety gating (all-request accuracy) submitted	68.0% ✎	▂·····▆█▂▂▄▇ beats 29/56	easy · avg 77%	▇ 0.6%	at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	29.6% ✎	▁▁▁▃▆▃▃█···▁ beats 5/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	82.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 38/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	94.0% ✎	▁····▃▅▃▂▃·█ beats 34/56	easy · avg 82%	▇ 0.3%	at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	7.0% ✎	▂▁·▂▁▁▃▄▁▂▂█ beats 13/56	hard · avg 21%	▇ 0.2%	blunt · noisy
DecisionBench v1 · context management (all-request accuracy) submitted	91.5% ✎	▁·····▁▇▆▁█▃ beats 48/56	easy · avg 73%	▇ 0.2%	blunt · at its level · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	0.0% ✎	▃·▁▄▇▄▅▄▂▁▄█ beats 0/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	21.3% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 6/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	0.0% ✎	█▁···▁·▂▆▅▂▂ beats 8/56	medium · 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

source	results	share of the score	licence
Hanno Labs DecisionBench v1 reviewed results · submitted; independent execution unverified	27	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%	CC0 1.0
prior · pull toward the middle		▇ 1%
weighted average		= 29.6

$ improve this page

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data updated 2026-11-03 02:37 UTC · sources and licences · json