whatmodel.sh / decision / JevK5 9B v0.3.3

JevK5 9B v0.3.3

+ contribute

JevK5 9B v0.3.3 is an open-weights decision model from alibiserikbay. Its published size is 9.65B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jevk5-9b-v0.3.3; reported model JevK5 9B v0.3.3. Type: LoRA; technique: autoregressive. Comparison entry; no current run command, API price or speed inferred from the benchmark. 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 ↗

Publisher parameter count; includes the served backbone, not just adapter size.

54.4±1.2#165 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of JevK5 9B v0.3.3, out of 357 with a score, then more from alibiserikbay.

#	model	score	where	released
165	JoshuaSP diffgemma (decision readout) JoshuaSP	55±1	source code
166	decisio 0.4.0 (Gemma 4 12B) (decision readout) aminry	54±1	source code	2026-10-04
167	JevK5 9B v0.3.3 alibiserikbay	54±1	model files
168	Jevfire Qwen3.8-27B (decision readout) kikoncuo	54±1	source code
169	Open-Jev-9B (JevBench mini) Zefan Cai	54±2	source code
	more from alibiserikbay
206	JevK5 v0.3 alibiserikbay	50±1	model files
328	JevK5 Lite alibiserikbay	25±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #136 of 250 · 7.5% of its score · 6 tests

Beats Cerno 4B v1.1, Trio-Spark DI (9B), Decider 35B-A3B here, though they rank higher overall. Trails Amber 0.4B, OneJev1.6-0.8B, TokenRhythm NeoHorse-Jev-4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: API-Bank (accuracy) submitted	71.5% ✎	▁▁▁▁▁▁▁▂▃▃█▂ beats 95/236	easy · avg 70%	▇ 2.3%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	93.3% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 113/246	very easy · avg 92%	▇ 2.0%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	18.2% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 85/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	82.6% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 162/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	62.8% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 101/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.0% ✎	no other model yet		—	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

retrieval & classification▁▁▁·▁▁▁▁▃▃█▅ #196 of 258 · 6.5% of its score · 7 tests

Trails Bosun v3.1 0.6B LoRA, Mola-68M, Bosun v3.1 1.7B LoRA here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	43.3% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 131/246	medium · avg 38%	▇ 2.4%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	40.1% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 61/246	medium · avg 47%	▇ 2.2%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	74.4% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 120/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	62.9% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 100/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	81.5% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 69/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	75.1% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 61/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	66.6% ✎	no other model yet		—	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

language understanding▂▂▂▃▂▅▆▃█▆▄▃ #96 of 256 · 23% of its score · 9 tests

Beats Turion Jev 31B, FlexDM · Gemma-4-31B (decision readout), jev-brain · Gemma-4-31B (decision readout) here, though they rank higher overall. Trails Decider 4B, jiwo 4B v2, Sieve 4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	78.9% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 144/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	93.3% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 162/246	very easy · avg 85%	▇▇ 5.4%	sharp
Decision Index 0.4.1: ANLI (macro-F1) submitted	66.7% ✎	▁▂▃▅▅█▂····▁ beats 136/246	medium · avg 60%	▇▇ 5.3%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	59.1% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 179/246	medium · avg 44%	▇ 2.3%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	19.4% ✎	▁··▂▃▃▅▇█▇▃▃ beats 105/246	hard · avg 22%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	74.7% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 112/246	easy · avg 71%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	48.7% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 70/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	77.1% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 146/246	easy · avg 66%	▇ 0.8%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	88.0% ✎	▁···▁▁▁▁▃▆█▃ beats 122/246	easy · avg 84%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #122 of 247 · 9.3% of its score · 6 tests

Beats Bobcat 1.3 (31B), JEV-35B, OneJev1.6-4B here, though they rank higher overall. Trails vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B), Crivo-400M, Amber 0.4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	68.9% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 146/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	60.2% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 98/246	medium · avg 60%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	88.8% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 126/246	easy · avg 83%	▇ 1.2%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	65.6% ✎	▁········▁▇█ beats 198/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	7.9% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 91/237	hard · avg 21%	▇ 0.8%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	44.0% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 118/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #111 of 263 · 45% of its score · 14 tests

Beats OneJev1.6-4B, Seb-9B v2, Mica v0.5 4B here, though they rank higher overall. Trails PurpleMIST-Flash-1.0 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	58.0% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 146/246	medium · avg 46%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	79.8% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 156/246	easy · avg 70%	▇▇▇ 7.1%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	65.4% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 106/246	easy · avg 66%	▇▇▇ 6.4%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.7% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 156/246	very easy · avg 90%	▇▇ 4.1%	sharp · maxed out
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	47.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 135/246	medium · avg 45%	▇ 3.4%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	82.2% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 135/246	easy · avg 78%	▇ 3.2%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	44.4% ✎	▂▆▅▅▆█▆▃▁··▁ beats 150/246	medium · avg 39%	▇ 3.1%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.3% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 147/246	very easy · avg 96%	▇ 2.1%	sharp · maxed out · noisy

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	30.1% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 149/246	hard · avg 22%	▇ 1.3%	at its level
Decision Index 0.4.1: MuSR (accuracy) submitted	52.0% ✎	▁▁▃█▅▄▁▁▁▁▁▁ beats 43/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	51.4% ✎	██▄▃▃▄▁▁···▁ beats 118/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	64.2% ✎	▁███▆▅▃▃▁▁▁▁ beats 93/246	easy · avg 71%	▇ 1.0%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	14.0% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 103/246	hard · avg 16%	▇ 0.6%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	12.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 134/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.2% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	47.6900 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 134/247	avg 41.206 points	▇▇ 5.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	40.6700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 131/247	avg 38.861 points	▇ 2.9%	blunt

$ where to run it

Download model files ↗
Published model files verified. This is a verified repository snapshot; the benchmark does not establish an identical weight revision. 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/alibiserikbay ↗

$ how the score was worked out · Decision Index 0.4.1 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
Decision Index 0.4.1 · submitted; independent execution unverified	42	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%	CC BY 4.0
prior · pull toward the middle		▇ 1%
weighted average		= 54.4

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.0%	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json
Decision Index 0.4.1	Decision Index 0.4.1: SGD/SGD-X (macro-F1) [correct]	66.6%	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

$ 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

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

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