JEV-9B

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JEV-9B is an open-weights decision model from AutoTrust AI Lab. Its published size is 9.65B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jev-9b; reported model JEV-9B. Type: full fine-tune; 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.

51.6±1.2#191 of 355 decision modelsfrom 43 benchmark results across 2 sources

$ how it compares

The decision models ranked either side of JEV-9B, out of 357 with a score, then more from AutoTrust AI Lab.

#	model	score	where	RAM	released
191	QEV-2B ken-jo	52±4	model files
192	Jebadiah 4B v2 (Ollaya Q8_0) frontier-infra	52±3	local	~5.5GB	2026-10
193	JEV-9B AutoTrust AI Lab	52±1	model files
194	Aplomb 1 EmpirioLabs AI	52±1	request access
195	Shisa DE-2 (repeat) Shisa.AI	51±4	model files
	more from AutoTrust AI Lab
18	AutoTrust-JEV-27B v1.1 AutoTrust AI Lab	70±1	model files		2026-10-17
69	JEV-27B-VL AutoTrust AI Lab	64±4	model files
83	JEV-35B AutoTrust AI Lab	63±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #146 of 250 · 7.8% of its score · 6 tests

Beats Cerno 4B v1.1, Trio-Spark DI (9B), Decider 35B-A3B here, though they rank higher overall. Trails JevK5 Lite, Amber 0.4B, Crivo-1B 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	62.4% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 76/236	easy · avg 70%	▇ 2.4%	sharp · at its level · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	92.6% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 103/246	very easy · avg 92%	▇ 2.3%	sharp · maxed out · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	77.5% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 128/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	26.1% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 100/243	medium · avg 37%	▇ 1.0%	sharp · at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	62.7% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 96/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.7% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #167 of 258 · 6.9% of its score · 8 tests

Beats Solar Decide, decisio 0.4.0 (Qwen3.6-35B-A3B) (decision readout), JevK5 9B v0.3.3 here, though they rank higher overall. Trails Bosun v3.1 1.7B LoRA, Dinah-1, DecisionTune 1.0 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	44.2% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 140/246	medium · avg 38%	▇ 2.2%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	46.2% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 88/246	medium · avg 47%	▇ 2.1%	at its level
GEO Citation · listwise Spearman (October 2026)	0.5157 spearman-rho ✎	▃···▃······█ beats 1/4	avg 0.256 spearman-rho	▇ 0.8%	blunt
Decision Index 0.4.1: HoVer (accuracy) submitted	64.7% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 78/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	60.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 85/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	79.0% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 61/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	72.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 49/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	62.6% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #143 of 256 · 23% of its score · 9 tests

Beats Solar Decide, JoshuaSP diffgemma (decision readout), PurpleMIST-Flash-1.0 here, though they rank higher overall. Trails Decider 2B, Ryotide Qwen v0.3.1 (decision readout), Sieve 2B v3 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	84.8% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 112/246	very easy · avg 85%	▇▇ 6.0%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	79.7% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 151/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: ANLI (macro-F1) submitted	67.6% ✎	▁▂▃▅▅█▂····▁ beats 141/246	medium · avg 60%	▇▇ 5.1%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	34.6% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 71/246	medium · avg 44%	▇ 2.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	70.7% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 78/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	42.3% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 38/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	18.0% ✎	▁··▂▃▃▅▇█▇▃▃ beats 98/246	hard · avg 22%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	66.2% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 83/246	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	76.2% ✎	▁···▁▁▁▁▃▆█▃ beats 61/246	easy · avg 84%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #140 of 247 · 8.9% of its score · 6 tests

Beats OneJev1.6-4B, Nace.AI Drex v1.5, Jegue 0.1 (9B) here, though they rank higher overall. Trails Crivo-68M, Crivo-150M v2, vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B) 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	60.4% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 105/246	medium · avg 61%	▇▇ 3.9%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	60.5% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 105/246	medium · avg 60%	▇ 1.8%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	85.1% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 107/246	easy · avg 83%	▇ 1.3%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	61.6% ✎	▁········▁▇█ beats 120/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	20.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 131/237	hard · avg 21%	▇ 0.7%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	43.3% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 111/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #128 of 263 · 45% 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) 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	53.3% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 126/246	medium · avg 46%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	76.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 130/246	easy · avg 70%	▇▇▇ 7.2%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	67.3% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 118/246	easy · avg 66%	▇▇▇ 6.3%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	94.3% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 130/246	very easy · avg 90%	▇▇ 4.8%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	74.4% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 105/246	easy · avg 78%	▇ 3.2%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	48.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 138/246	medium · avg 45%	▇ 3.2%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	39.3% ✎	▂▆▅▅▆█▆▃▁··▁ beats 111/246	medium · avg 39%	▇ 2.9%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.3% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 146/246	very easy · avg 96%	▇ 2.7%	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	33.6% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 190/246	hard · avg 22%	▇ 1.2%	typical
Decision Index 0.4.1: MuSR (accuracy) submitted	60.2% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 136/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	51.2% ✎	██▄▃▃▄▁▁···▁ beats 116/246	medium · avg 58%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	65.6% ✎	▁███▆▅▃▃▁▁▁▁ beats 99/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	12.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 87/246	hard · avg 16%	▇ 0.6%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	7.0% ✎	▃▁▂▃▆█▇▅▅▃▁▁ beats 1/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 7.6% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	42.2700 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 111/247	avg 41.206 points	▇▇ 4.9%	blunt
Decision Index 0.4.1: private new domains (equated points)	41.9800 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 137/247	avg 38.861 points	▇ 2.7%	blunt

$ 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 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/autotrust ↗

$ how the score was worked out · Decision Index 0.4.1 99%, Citead GEO Citation Benchmark… 1%

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
Citead GEO Citation Benchmark (October 2026)	1	▇ 1%	CC BY-SA 4.0
prior · pull toward the middle		▇ 1%
weighted average		= 51.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.7%	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]	62.6%	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