whatmodel.sh / decision / jqv (DecisionBench 9216db57)

jqv (DecisionBench 9216db57)

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jqv (DecisionBench 9216db57) is an open-weights decision model from Octalab-Inc. Its published size is 32.762123264B parameters. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: Octalab-Inc/jqv, revision 9216db5781bf21249d130ec9da846c4624c16137; 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. 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.

57.0±1.7#137 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of jqv (DecisionBench 9216db57), out of 357 with a score.

#	model	score	where	released
137	Sieve 9B v2 Sthānika AI	57±1	model files	2026-10-13
138	Cerno 4B v1.1 apertixai	57±1	model files	2026-10-19
139	jqv (DecisionBench 9216db57) Octalab-Inc	57±2	model files
140	Trio-Spark DI (9B) MachineFi Labs	57±1	request access
141	openjev (DecisionBench ec4ff3df) razorback16	57±2	model files

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

$ what it's good at

workflows▁▁▁▂▁▂▂▆█▄▁▁ #40 of 114 · 99% of its score · 27 tests

Beats Clef-omni, Jebadiah 27B v2.1 (Ollaya Q4_K_M), vLLM-SR d3-mini (4B) here, though they rank higher overall. Trails StartLux-Decision v1.1 2B, Solar Decide 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	59.9% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 41/56	medium · avg 51%	▇▇▇▇▇▇▇▇ 20%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	78.0% ✎	▁▁▁▁▂▂▁▁▁▁▃█ beats 56/56	medium · avg 64%	▇▇▇▇▇ 13%	sharp · at its level
DecisionBench v1 · navigation (all-request accuracy) submitted	74.0% ✎	▁▁·▃▁▁▁▁▁▁▃█ beats 53/56	medium · avg 58%	▇▇▇▇ 11%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	49.0% ✎	▃▂▁▂▂▂▄▁▃▆██ beats 52/56	medium · avg 39%	▇▇▇▇ 9.5%	at its level
DecisionBench v1 · verification (all-request accuracy) submitted	59.0% ✎	▁▁▅▁▃▂▂▁▁▇▇█ beats 34/56	medium · avg 56%	▇▇▇ 6.4%	blunt · at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted	46.5% ✎	▂▂·▁▂▄▃▆▄█▁▁ beats 34/56	medium · avg 46%	▇▇ 5.3%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	47.1% ✎	▁▁▅▅▃▃▅█▃▃·▁ beats 35/56	medium · avg 45%	▇▇ 4.5%	at its level
DecisionBench v1 · triage (all-request accuracy) submitted	80.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 32/56	easy · avg 78%	▇▇ 4.1%	sharp

19 more tests

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · completion assessment (all-request accuracy) submitted	89.0% ✎	▁▁▂▃▄·▂▁▁▆█▂ beats 27/56	very easy · avg 86%	▇▇ 3.8%	sharp
DecisionBench v1 · content classification (all-request accuracy) submitted	79.0% ✎	▁····▁▁▁▁▁▁█ beats 39/56	easy · avg 73%	▇ 3.2%	typical
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	94.5% ✎	▃▃▂·▁▁▁▁▁▁█▅ beats 43/56	very easy · avg 86%	▇ 3.1%	sharp · maxed out · noisy
DecisionBench v1 · review scoring (all-request accuracy) submitted	60.0% ✎	▁······▂▃▅▇█ beats 43/56	medium · avg 52%	▇ 2.9%	at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	10.0% ✎	▁▁·▁▁▂▃▂▃▁▁█ beats 12/56	medium · avg 35%	▇ 2.4%	at its level · noisy
DecisionBench v1 · target selection (all-request accuracy) submitted	9.0% ✎	▁▁·▁▁▁▃▁▂▁▁█ beats 17/56	hard · avg 29%	▇ 2.2%	at its level · noisy
DecisionBench v1 · risk scoring (all-request accuracy) submitted	67.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 42/56	medium · avg 62%	▇ 1.9%	blunt · at its level
DecisionBench v1 · reasoning (all-request accuracy) submitted	69.0% ✎	▂·▁▁▆█▅▃▃▂▁▁ beats 48/56	medium · avg 52%	▇ 1.2%	at its level
DecisionBench v1 · document workflows (all-request accuracy) submitted	81.0% ✎	█▄▃▁▁▃▃▂▁·▁▁ beats 49/56	easy · avg 66%	▇ 0.8%	blunt · at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	87.6% ✎	█▁▂▁▁▁▁▁▂▅▁▁ beats 46/56	easy · avg 74%	▇ 0.7%	noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	63.3% ✎	▁▁▁▂▆▃▃█···▁ beats 50/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · safety gating (all-request accuracy) submitted	96.0% ✎	▂·····▆█▂▂▄▇ beats 44/56	easy · avg 77%	▇ 0.4%	noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	2.0% ✎	▂▁·▃▁▁▁▄▁▂▂█ beats 6/56	hard · avg 21%	▇ 0.4%	blunt · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	85.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 47/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	100.0% ✎	▁····▃▅▃▂▂·█ beats 53/56	easy · avg 82%	▇ 0.2%	noisy
DecisionBench v1 · context management (all-request accuracy) submitted	94.0% ✎	▁·····▁▇▆▁█▃ beats 54/56	easy · avg 73%	▇ 0.2%	blunt · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	12.9% ✎	▁·▁▄▇▄▅▄▂▁▄█ beats 6/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	12.0% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 4/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	0.0% ✎	█▁···▁·▂▆▅▂▂ beats 12/56	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/Qwen ↗

$ 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		= 57.0

$ improve this page

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