Jev-Omni

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Jev-Omni is an open-weights decision model from akhilaaa3, released 22 September 2026. Its published size is 11.96B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jev-omni; reported model Jev-Omni. Type: LoRA + head; 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

Release date ↗ · Weights licence ↗ · Parameter count ↗

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

50.9±1.1#195 of 355 decision modelsfrom 58 benchmark results across 3 sources

$ how it compares

The decision models ranked either side of Jev-Omni, out of 357 with a score.

#	model	score	where	released
195	Shisa DE-2 (repeat) Shisa.AI	51±4	model files
196	ZTC-Judge-9B (DecisionBench 05d154a5) FINAL-Bench	51±2	model files
197	Jev-Omni akhilaaa3	51±1	model files	2026-09-22
198	LiquidAI d1-3B Liquid AI	51±1	model files
199	djev-spark (DecisionBench 1444f3e9) mmastrac	51±2	source code

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$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #78 of 250 · 6.9% of its score · 6 tests

Beats Sieve 27B v2, allec-oavg (Gemma 4 31B), Jev 1.13 here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	96.8% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 174/246	very easy · avg 92%	▇ 2.1%	sharp · maxed out · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	52.4% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 54/236	easy · avg 70%	▇ 2.1%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	63.0% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 75/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	42.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 117/243	medium · avg 37%	▇ 0.9%	sharp · at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	64.4% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 124/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	77.6% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #131 of 258 · 5.3% of its score · 7 tests

Beats Quyet-1.0-Large, Tern-35B-A3B, garas (Gemma 4 31B) here, though they rank higher overall. Trails vLLM-SR vela3-fast (Vela 3.0, 0.4B), crystal-0.8b, Kas 4B 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	46.5% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 173/246	medium · avg 38%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	53.5% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 137/246	medium · avg 47%	▇ 1.8%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	65.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 85/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	80.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 172/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	16.9% ✎	▁▁▁▁▃▁▁▂▃▆█▄ beats 15/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	31.0% ✎	▁·▁▁▁▁▃▁▂█▇█ beats 10/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	50.4% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #166 of 256 · 20% of its score · 9 tests

Beats PurpleMIST-Flash-1.0, Hopper 12B (trained) here, though they rank higher overall. Trails DecisionTune 1.0, Decider 2B, vLLM-SR d3-lite (0.8B) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy) submitted	81.0% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 87/246	very easy · avg 85%	▇▇ 5.4%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	82.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 177/246	easy · avg 74%	▇▇ 4.7%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	64.7% ✎	▁▂▃▅▅█▂····▁ beats 128/246	medium · avg 60%	▇▇ 4.5%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	41.1% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 95/246	medium · avg 44%	▇ 1.8%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	69.8% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 71/246	easy · avg 71%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	54.6% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 91/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	16.0% ✎	▁··▂▃▃▅▇█▇▃▃ beats 85/246	hard · avg 22%	▇ 0.8%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	80.0% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 174/246	easy · avg 66%	▇ 0.7%	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	90.8% ✎	▁···▁▁▁▁▃▆█▃ beats 163/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #167 of 247 · 7.8% of its score · 6 tests

Beats PurpleMIST-Flash-1.0 here, though they rank higher overall. Trails Crivo-17M, Crivo-32M v2, Crivo-68M 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	59.9% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 100/246	medium · avg 61%	▇ 3.4%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.5% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 134/246	medium · avg 60%	▇ 1.5%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	76.7% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 80/246	easy · avg 83%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	60.5% ✎	▁········▁▇█ beats 107/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	4.2% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 69/237	hard · avg 21%	▇ 0.6%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	41.5% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 88/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #133 of 263 · 39% of its score · 14 tests

Beats Winnow-12B (DecisionBench b6ac22b0), Cerno 4B v1.1, Amber Hybrid 4B (decision readout) here, though they rank higher overall. Trails djev diffgemma (decision readout), Kev 9B v2, Bespoke Nimble 9B v2 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	48.9% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 108/246	medium · avg 46%	▇▇▇ 8.3%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	77.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 136/246	easy · avg 70%	▇▇▇ 6.3%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	66.6% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 112/246	easy · avg 66%	▇▇ 5.5%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.0% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 141/246	very easy · avg 90%	▇▇ 4.4%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	72.1% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 99/246	easy · avg 78%	▇ 2.8%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	27.8% ✎	▁··▄█▆▆▆▅▅▃▁ beats 70/246	medium · avg 45%	▇ 2.8%	sharp · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.0% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 127/246	very easy · avg 96%	▇ 2.5%	sharp · maxed out · noisy
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	36.7% ✎	▂▆▅▅▆█▆▃▁··▁ beats 94/246	medium · avg 39%	▇ 2.5%	blunt · at its level

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	26.7% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 114/246	hard · avg 22%	▇ 1.0%	typical
Decision Index 0.4.1: MuSR (accuracy) submitted	55.7% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 78/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	50.7% ✎	██▄▃▃▄▁▁···▁ beats 114/246	medium · avg 58%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	66.8% ✎	▁███▆▅▃▃▁▁▁▁ beats 108/246	easy · avg 71%	▇ 0.8%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	12.8% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 92/246	hard · avg 16%	▇ 0.5%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	10.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 31/246	very hard · avg 13%	▇ 0.0%	blunt

vision▁·▃▂▃▃▇▄▄█▄▃ #56 of 79 · 11% of its score · 14 tests

Beats QEV-2B, Shisa DE-2 (repeat) here, though they rank higher overall. Trails Valen-4B, JPT-4B Qwen3.5-4B LoRA, LiquidAI d1-3B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted	72.0500 points ✎	▁·▁▁▃▁▄▄▃▅█▂ beats 26/61	avg 63.423 points	▇ 1.2%	blunt
Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted	29.5900 points ✎	▁▁▅▁▄▁▄█▄▃▇▇ beats 29/61	avg 22.797 points	▇ 1.2%	blunt
Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted	79.7500 points ✎	▁·▁▁▁▁·▂▁▃▅█ beats 32/61	avg 66.987 points	▇ 1.1%	blunt
Decision Index Vision 0.4: private same skills (points)	52.0500 points ✎	▁▁▃▃▁▂▄▆▃▃▅█ beats 26/61	avg 48.067 points	▇ 1.0%	blunt
Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted	42.6200 points ✎	▆▃▃▆▄▄▇██▁▆▆ beats 42/61	avg 28.443 points	▇ 0.9%	blunt
Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted	55.8800 points ✎	▁·▂▂▁▃▃▅▅▃▂█ beats 21/61	avg 53.651 points	▇ 0.8%	blunt
Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted	72.1800 points ✎	▂▁▁▁▁▃▁▅▅▆██ beats 32/61	avg 62.137 points	▇ 0.8%	blunt
Benchmark Heaven imagejevbench v0.3.0: intelligence (points)	37.5429 points ✎	▃·▃▃··▅█▅▅·▃ beats 5/12	avg 32.235 points	▇ 0.7%	blunt

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted	85.2500 points ✎	▁·▁▂▁▁▃▃▂▂▅█ beats 20/61	avg 83.859 points	▇ 0.6%	blunt
Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted	11.0200 points ✎	▂▄▇▆█▄▄▇▂··▁ beats 5/61	avg 19.676 points	▇ 0.6%	blunt
Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted	-4.5500 points ✎	▃···▁▂▂▂█▄▂▃ beats 1/61	avg 44.994 points	▇ 0.6%	blunt
Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted	73.5300 points ✎	▁▁▁▁▂▁▁▁▄█▃▃ beats 25/61	avg 67.662 points	▇ 0.5%	blunt
Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted	91.8500 points ✎	▁··▁▁▁▁▁▂▄█▁ beats 36/61	avg 85.184 points	▇ 0.3%	blunt
Benchmark Heaven imagejevbench v0.3.0: calibration (points)	70.9573 points ✎	▃···▃·▅█▅▃▃▅ beats 5/12	avg 68.584 points	▇ 0.3%	blunt

other tests4 not in a task · 9.0% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	41.6200 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 106/247	avg 41.206 points	▇▇ 4.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	47.1600 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 148/247	avg 38.861 points	▇ 2.3%	blunt
Benchmark Heaven jevbench v1.6.1: intelligence (points)	55.5302 points ✎	▃···▃·▃▃█▅▅█ beats 4/13	avg 32.166 points	▇ 1.9%	blunt
Benchmark Heaven jevbench v1.6.1: calibration (points)	87.0445 points ✎	▂······▂▆▂██ beats 6/13	avg 70.133 points	▇ 0.5%	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/akhilaaa3 ↗

$ how the score was worked out · Decision Index 0.4.1 86%, Decision Index Vision 0.4 10%, Benchmark Heaven (own decisio… 3%

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	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 86%	CC BY 4.0
Decision Index Vision 0.4 · submitted; independent execution unverified	12	▇▇▇ 10%	CC BY 4.0
Benchmark Heaven (own decision evaluations)	4	▇ 3%	CC BY 4.0
prior · pull toward the middle		▇ 1%
weighted average		= 50.9

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	77.6%	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]	50.4%	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