whatmodel.sh / decision / mojev (DecisionBench 0c8695b6)

mojev (DecisionBench 0c8695b6)

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mojev (DecisionBench 0c8695b6) is an open-weights decision model from MoLeMo-Lab. Its published size is 0.854036544B parameters.

Catalogue identity notes

DecisionBench v1 comparison: MoLeMo-Lab/mojev, revision 0c8695b6252f4205907433d4e196a94f032e60c3; decision-model; adapter mojev-hf; probability source native_candidate_logits_softmax. Kept separate from newer or unpinned Decision Index recipes; standard prompt, not compact. No unverified runtime or OpenRouter price imported.

Model information sources

· Parameter count ↗

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

37.1±1.6#276 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of mojev (DecisionBench 0c8695b6), out of 357 with a score.

#	model	score	where	released
276	jiwo 0.8B v2 eljiwo	37±1	model files	2026-10-10
277	Gevva E2B davidburhans	37±1	model files
278	mojev (DecisionBench 0c8695b6) MoLeMo-Lab	37±2	request access
279	Crivo-400M tardellirs	37±1	model files
280	InternLM Intern-Decision Qwen3.5-2B Shanghai AI Laboratory (InternLM)	37±1	model files

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

$ what it's good at

workflows▁▁▁▂▃▂▂▆█▄▁▁ #92 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	35.0% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 19/56	medium · avg 51%	▇▇▇▇▇▇▇ 17%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	47.7% ✎	▁▁▁▁▂▂▁▃▁▁▃█ beats 19/56	medium · avg 64%	▇▇▇▇▇▇ 14%	sharp · at its level
DecisionBench v1 · navigation (all-request accuracy) submitted	46.0% ✎	▁▁·▃▁▁▁▁▃▁▃█ beats 22/56	medium · avg 58%	▇▇▇▇ 10%	sharp · at its level
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	38.4% ✎	▃▃▃·▁▁▁▁▁▁█▅ beats 16/56	very easy · avg 86%	▇▇▇ 8.6%	sharp · at its level · noisy
DecisionBench v1 · completion assessment (all-request accuracy) submitted	56.0% ✎	▁▁▂▃▄·▂▁▁▆█▂ beats 16/56	very easy · avg 86%	▇▇▇ 8.5%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	37.0% ✎	▃▂▁▂▂▂▄▁▃▆██ beats 24/56	medium · avg 39%	▇▇▇ 6.6%	at its level
DecisionBench v1 · triage (all-request accuracy) submitted	65.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 22/56	easy · avg 78%	▇▇▇ 6.3%	sharp · at its level
DecisionBench v1 · verification (all-request accuracy) submitted	45.0% ✎	▁▁▅▁▃▃▂▁▁▇▇█ beats 18/56	medium · avg 56%	▇▇ 5.6%	blunt · at its level

19 more tests

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · document record classification (all-request accuracy) submitted	32.7% ✎	▂▂·▁▂▄▃▆▄█▁▁ beats 15/56	medium · avg 46%	▇▇ 4.1%	at its level
DecisionBench v1 · content classification (all-request accuracy) submitted	65.0% ✎	▁····▁▁▁▁▁▃█ beats 19/56	easy · avg 73%	▇▇ 4.1%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	33.5% ✎	▁▁▅▅▃▃▅█▃▃·▁ beats 20/56	medium · avg 45%	▇ 3.4%	at its level
DecisionBench v1 · review scoring (all-request accuracy) submitted	29.0% ✎	▁······▂▃▅▇█ beats 7/56	medium · avg 52%	▇ 2.4%	at its level
DecisionBench v1 · risk scoring (all-request accuracy) submitted	62.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 27/56	medium · avg 62%	▇ 1.8%	blunt · at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	25.0% ✎	▁▁·▁▁▂▃▂▃▁▁█ beats 29/56	medium · avg 35%	▇ 1.3%	noisy
DecisionBench v1 · target selection (all-request accuracy) submitted	32.0% ✎	▁▁·▁▁▁▃▁▂▁▃█ beats 31/56	hard · avg 29%	▇ 1.2%	noisy
DecisionBench v1 · reasoning (all-request accuracy) submitted	31.4% ✎	▂·▁▁▆█▅▃▃▂▁▁ beats 7/56	medium · avg 52%	▇ 1.0%	at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	73.4% ✎	█▁▂▁▁▃▁▁▂▅▁▁ beats 31/56	easy · avg 74%	▇ 0.8%	at its level · noisy
DecisionBench v1 · document workflows (all-request accuracy) submitted	54.5% ✎	█▄▃▁▁▃▂▂▁·▁▁ beats 27/56	easy · avg 66%	▇ 0.7%	blunt · at its level
DecisionBench v1 · safety gating (all-request accuracy) submitted	49.0% ✎	▂·····▆█▂▂▄▇ beats 10/56	easy · avg 77%	▇ 0.5%	at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	55.7% ✎	▁▁▁▂▆▃▃█···▁ beats 31/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	77.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 30/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	92.0% ✎	▁····▃▅▃▃▂·█ beats 33/56	easy · avg 82%	▇ 0.3%	at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	17.0% ✎	▂▁·▂▁▁▁▄▁▃▂█ beats 26/56	hard · avg 21%	▇ 0.2%	blunt · noisy
DecisionBench v1 · context management (all-request accuracy) submitted	72.0% ✎	▁·····▁▇▆▁█▃ beats 29/56	easy · avg 73%	▇ 0.2%	blunt · at its level · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	95.4% ✎	▁·▁▄▇▄▅▄▂▃▄█ beats 39/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	95.3% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 41/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	50.9% ✎	█▁···▁·▂▆▅▂▂ beats 27/56	medium · avg 42%	▇ 0.0%	blunt · at its level · noisy

$ where to run it

Request access to model files ↗
Gated files: the publisher requires access approval. 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/MoLeMo-Lab ↗

$ 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		= 37.1

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