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kev-0.6b (DecisionBench dece6dba)

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kev-0.6b (DecisionBench dece6dba) is an open-weights decision model from Jared Palmer. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: jaredpalmer/kev-0.6b, revision dece6dba8d43f0f7ded45e9f5b9df12474d90843; decision-model; 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 ↗

37.2±1.6#273 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of kev-0.6b (DecisionBench dece6dba), out of 357 with a score, then more from Jared Palmer.

#	model	score	where	released
273	Sieve 0.8B Sthānika AI	38±1	model files	2026-10-13
274	Amber 0.4B MLukauskas	37±1	model files
275	kev-0.6b (DecisionBench dece6dba) Jared Palmer	37±2	model files
276	jiwo 0.8B v2 eljiwo	37±1	model files	2026-10-10
277	Gevva E2B davidburhans	37±1	model files
	more from Jared Palmer
88	Kev 27B Jared Palmer	62±1	model files	2026-09
159	kev-4b (DecisionBench 485ace87) Jared Palmer	55±2	model files
162	kev-8b (DecisionBench c80773da) Jared Palmer	55±2	model files

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

$ what it's good at

workflows▁▁▁▂▁▃▂▆█▄▁▁ #91 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	35.7% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 21/56	medium · avg 51%	▇▇▇▇▇▇▇ 17%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	55.7% ✎	▁▁▁▁▂▂▁▁▃▁▃█ beats 23/56	medium · avg 64%	▇▇▇▇▇▇ 14%	sharp · at its level
DecisionBench v1 · navigation (all-request accuracy) submitted	52.0% ✎	▁▁·▃▁▁▁▁▃▁▃█ beats 23/56	medium · avg 58%	▇▇▇▇ 10%	sharp · at its level
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	75.9% ✎	▃▃▂·▁▁▃▁▁▁█▅ beats 22/56	very easy · avg 86%	▇▇▇ 8.6%	sharp · at its level · noisy
DecisionBench v1 · completion assessment (all-request accuracy) submitted	50.0% ✎	▁▁▂▃▄·▂▁▁▆█▂ beats 11/56	very easy · avg 86%	▇▇▇ 8.5%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	28.0% ✎	▃▂▁▂▂▂▄▁▃▆██ beats 18/56	medium · avg 39%	▇▇▇ 6.6%	at its level
DecisionBench v1 · triage (all-request accuracy) submitted	55.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 15/56	easy · avg 78%	▇▇ 6.2%	sharp · at its level
DecisionBench v1 · verification (all-request accuracy) submitted	39.0% ✎	▁▁▅▃▃▂▂▁▁▇▇█ beats 11/56	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	35.1% ✎	▂▂·▁▂▄▃▆▄█▁▁ beats 18/56	medium · avg 46%	▇▇ 4.1%	at its level
DecisionBench v1 · content classification (all-request accuracy) submitted	55.0% ✎	▁····▁▁▁▁▃▁█ beats 16/56	easy · avg 73%	▇▇ 4.1%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	30.8% ✎	▁▁▅▅▃▃▅█▃▃·▁ beats 17/56	medium · avg 45%	▇ 3.4%	at its level
DecisionBench v1 · review scoring (all-request accuracy) submitted	38.0% ✎	▁······▂▃▅▇█ beats 14/56	medium · avg 52%	▇ 2.4%	at its level
DecisionBench v1 · risk scoring (all-request accuracy) submitted	56.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 20/56	medium · avg 62%	▇ 1.8%	blunt · at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	45.0% ✎	▁▁·▁▁▂▃▂▃▁▃█ beats 33/56	medium · avg 35%	▇ 1.3%	noisy
DecisionBench v1 · target selection (all-request accuracy) submitted	36.0% ✎	▁▁·▁▁▁▃▁▂▁▃█ beats 32/56	hard · avg 29%	▇ 1.2%	noisy
DecisionBench v1 · reasoning (all-request accuracy) submitted	40.3% ✎	▂·▁▁▆█▅▃▃▂▁▁ beats 19/56	medium · avg 52%	▇ 1.0%	at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	49.6% ✎	█▁▂▁▁▁▁▁▂▅▁▁ beats 9/56	easy · avg 74%	▇ 0.8%	at its level · noisy
DecisionBench v1 · document workflows (all-request accuracy) submitted	50.4% ✎	█▄▃▁▁▃▂▂▁·▁▁ beats 19/56	easy · avg 66%	▇ 0.7%	blunt · at its level
DecisionBench v1 · safety gating (all-request accuracy) submitted	55.0% ✎	▂·····▆█▂▂▄▇ beats 16/56	easy · avg 77%	▇ 0.5%	at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	38.7% ✎	▁▁▁▂▆▃▃█···▁ beats 17/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	53.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 14/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	45.0% ✎	▁····▃▅▃▂▂·█ beats 6/56	easy · avg 82%	▇ 0.3%	at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	31.0% ✎	▂▁·▂▁▁▁▄▁▂▂█ beats 38/56	hard · avg 21%	▇ 0.2%	blunt · noisy
DecisionBench v1 · context management (all-request accuracy) submitted	48.0% ✎	▁·····▁▇▆▁█▃ beats 4/56	easy · avg 73%	▇ 0.2%	blunt · at its level · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	29.6% ✎	▁·▁▄▇▄▅▄▂▁▄█ beats 17/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	86.5% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 35/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	57.7% ✎	█▁···▁·▂▆▅▂▂ beats 35/56	medium · avg 42%	▇ 0.0%	blunt · at its level · noisy

$ where to run it

Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. 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/jaredpalmer ↗

$ 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		= 37.2

$ improve this page

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