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open-alternative-jev (DecisionBench 4a85df18)

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open-alternative-jev (DecisionBench 4a85df18) is a source-available decision implementation from IkerMoel.

Catalogue identity notes

DecisionBench v1 comparison: IkerMoel/open-alternative-jev, revision 4a85df1831b537343c9e133b5150fa3a3b1ce98e; language-model; adapter jev-compatible-server-v1; probability source option-letter-logit-softmax. Kept separate from newer or unpinned Decision Index recipes; standard prompt, not compact. No unverified runtime or OpenRouter price imported.

55.7±1.7#150 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of open-alternative-jev (DecisionBench 4a85df18), out of 357 with a score.

#	model	score	where	released
150	Jegue 0.1 (9B) fdemelo	56±1	model files
151	djev-dev (DecisionBench f7f5b7f5) Davipar	56±2	model files
152	open-alternative-jev (DecisionBench 4a85df18) IkerMoel	56±2	source code
153	Open-Jev-9B (DecisionBench 47e96688) ZefanCai	56±2	model files	2026-09-20
154	Tev1-4B-experimental (DecisionBench 0b7becf0) Together	56±2	model files

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

$ what it's good at

workflows▁▁▁▂▁▂▂▆█▄▁▁ #50 of 114 · 99% of its score · 27 tests

Beats Clef-omni, Clef-flash, Bespoke Nimble 9B v3 here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Together Tev1 Qwen3.5-4B, Jebadiah 9B v2.1 (Ollaya Q8_0) 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	57.6% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 36/56	medium · avg 51%	▇▇▇▇▇▇▇▇ 20%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	70.7% ✎	▁▁▁▁▂▂▁▁▁▁▃█ beats 32/56	medium · avg 64%	▇▇▇▇▇ 13%	sharp · at its level
DecisionBench v1 · navigation (all-request accuracy) submitted	67.0% ✎	▁▁·▃▁▁▁▁▁▁▃█ beats 29/56	medium · avg 58%	▇▇▇▇ 11%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	39.0% ✎	▃▂▁▂▂▂▄▁▃▆██ beats 27/56	medium · avg 39%	▇▇▇▇ 9.2%	at its level
DecisionBench v1 · verification (all-request accuracy) submitted	63.0% ✎	▁▁▅▁▃▂▂▁▁▇▇█ beats 50/56	medium · avg 56%	▇▇▇ 6.3%	blunt · at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted	54.3% ✎	▂▂·▁▂▄▃▆▄█▁▁ beats 43/56	medium · avg 46%	▇▇ 5.2%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	44.5% ✎	▁▁▅▅▃▃▅█▃▃·▁ beats 29/56	medium · avg 45%	▇▇ 4.4%	at its level
DecisionBench v1 · triage (all-request accuracy) submitted	88.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 50/56	easy · avg 78%	▇▇ 4.3%	sharp

19 more tests

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · completion assessment (all-request accuracy) submitted	91.0% ✎	▁▁▂▃▄·▂▁▁▆█▂ beats 36/56	very easy · avg 86%	▇▇ 4.0%	sharp
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	92.9% ✎	▃▃▂·▁▁▁▁▁▁█▅ beats 30/56	very easy · avg 86%	▇ 3.4%	sharp · noisy
DecisionBench v1 · content classification (all-request accuracy) submitted	80.0% ✎	▁····▁▁▁▁▁▁█ beats 49/56	easy · avg 73%	▇ 3.3%	typical
DecisionBench v1 · review scoring (all-request accuracy) submitted	62.0% ✎	▁······▂▃▅▇█ beats 51/56	medium · avg 52%	▇ 2.9%	at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	25.0% ✎	▁▁·▁▁▂▃▂▃▁▁█ beats 30/56	medium · avg 35%	▇ 2.3%	at its level · noisy
DecisionBench v1 · target selection (all-request accuracy) submitted	19.0% ✎	▁▁·▁▁▁▃▁▃▁▁█ beats 25/56	hard · avg 29%	▇ 2.2%	at its level · noisy
DecisionBench v1 · risk scoring (all-request accuracy) submitted	70.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 51/56	medium · avg 62%	▇ 1.9%	blunt · at its level
DecisionBench v1 · reasoning (all-request accuracy) submitted	52.8% ✎	▂·▁▁▆█▅▃▃▂▁▁ beats 37/56	medium · avg 52%	▇ 1.2%	at its level
DecisionBench v1 · document workflows (all-request accuracy) submitted	69.6% ✎	█▄▃▁▃▃▂▂▁·▁▁ beats 39/56	easy · avg 66%	▇ 0.7%	blunt · at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	82.3% ✎	█▁▂▁▁▁▁▃▂▅▁▁ beats 34/56	easy · avg 74%	▇ 0.7%	noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	60.6% ✎	▁▁▁▂▆▃▃█···▁ beats 35/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · safety gating (all-request accuracy) submitted	97.0% ✎	▂·····▆█▂▂▄▇ beats 50/56	easy · avg 77%	▇ 0.4%	noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	10.0% ✎	▂▁·▂▁▁▁▄▁▂▂█ beats 18/56	hard · avg 21%	▇ 0.3%	blunt · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	81.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 35/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	99.0% ✎	▁····▃▅▃▂▂·█ beats 42/56	easy · avg 82%	▇ 0.2%	noisy
DecisionBench v1 · context management (all-request accuracy) submitted	93.5% ✎	▁·····▁▇▆▁█▃ beats 53/56	easy · avg 73%	▇ 0.2%	blunt · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	50.6% ✎	▁·▁▄▇▄▅▄▂▁▄█ beats 24/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	49.2% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 20/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	0.0% ✎	█▁···▁·▂▆▅▂▂ beats 15/56	medium · avg 42%	▇ 0.0%	blunt · at its level · noisy

$ where to run it

Implementation and setup instructions ↗
Published implementation and setup documentation verified. This entry describes a decision readout or evaluated configuration, not necessarily separate trained weights. Follow the referenced version and model settings; hardware compatibility has not been verified. Source · checked 2026-10-31.

Model repository and documentation: github.com/ikermoel ↗

$ 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		= 55.7

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

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