whatmodel.sh / decision / GEV-26B-Decide

GEV-26B-Decide

+ contribute

GEV-26B-Decide is an open-weights decision model from AutoTrust AI Lab. Its published size is 25.8B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jev-gemma4-26b-a4b; reported model GEV-26B-Decide. 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.

61.3±1.3#98 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of GEV-26B-Decide, out of 357 with a score, then more from AutoTrust AI Lab.

#	model	score	where	released
96	Lumma-fev-27B FrontiersMind	61±1	model files
97	vLLM-SR Decision 2.0 Vega 27B vLLM Semantic Router	61±1	model files
98	GEV-26B-Decide AutoTrust AI Lab	61±1	model files
99	litjev (DecisionBench 1d4bf0f2) zhengxuyu	61±2	model files
100	Surogate Rune 26B-A4B v3 Surogate	61±1	request access
	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▁▃▁▁▁▂▃▄▆██▆ #127 of 250 · 6.6% of its score · 6 tests

Beats Tern-35B-A3B here, though they rank higher overall. Trails OneJev1.6-0.8B, TokenRhythm NeoHorse-Jev-4B, razorback16 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: API-Bank (accuracy) submitted	84.3% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 143/236	easy · avg 70%	▇ 2.0%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	94.6% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 123/246	very easy · avg 92%	▇ 1.3%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	21.6% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 91/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	67.1% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 184/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: When2Call (accuracy) submitted	85.7% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 175/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.9% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #29 of 258 · 7.2% of its score · 7 tests

Beats Perplexity Decider v1.1 (27B), H2O Lightning 31B v1.1, AutoTrust-JEV-27B v1.1 here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	46.0% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 167/246	medium · avg 38%	▇ 2.8%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	60.8% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 219/246	medium · avg 47%	▇ 2.5%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	87.9% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 215/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	78.9% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 168/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	93.0% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 176/238	easy · avg 83%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	88.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 162/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	32.0% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #39 of 256 · 22% of its score · 9 tests

Beats Ainvest-Decision-27B v1.0, H2O Lightning 31B v1.1, Turion Jev 31B here, though they rank higher overall. Trails Decider 35B-A3B, Sieve 9B v2, Clef-flash here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ANLI (macro-F1) submitted	70.3% ✎	▁▂▃▅▅█▂····▁ beats 163/246	medium · avg 60%	▇▇ 5.5%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	77.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 122/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	96.4% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 215/246	very easy · avg 85%	▇▇ 3.8%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	60.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 185/246	medium · avg 44%	▇ 2.6%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	24.5% ✎	▁··▂▃▃▅▇█▇▃▃ beats 143/246	hard · avg 22%	▇ 1.5%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	60.9% ✎	▁▁▁▂▅▄▄▃▄▆█▄ beats 111/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	83.1% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 197/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	84.1% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 224/246	easy · avg 66%	▇ 0.8%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	88.9% ✎	▁···▁▁▁▁▃▆█▃ beats 137/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #103 of 247 · 10% of its score · 6 tests

Beats Bobcat 1.3 (31B), Nace.AI Drex v2.0 (35B-A3B), decisio 0.6.0 (Gemma 4 31B) (decision readout) 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	82.0% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 231/246	medium · avg 61%	▇▇ 4.1%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	63.0% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 175/246	medium · avg 60%	▇ 2.1%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	63.2% ✎	▁········▁▇█ beats 149/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	19.7% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 129/237	hard · avg 21%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	90.3% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 141/246	easy · avg 83%	▇ 1.0%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	60.8% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 212/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #82 of 263 · 44% of its score · 14 tests

Beats StartLux-Decision v1.1 4B, KnowLine 4B Gen5.5, Jeffan-4B-2010 here, though they rank higher overall. Trails Solar Decide, Clef-flash, Decider chat · Qwen3.6-27B (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	65.2% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 181/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	83.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 172/246	easy · avg 70%	▇▇▇ 6.4%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	75.2% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 158/246	easy · avg 66%	▇▇ 6.2%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	65.0% ✎	▁··▄█▆▆▆▅▅▃▁ beats 173/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	43.9% ✎	▂▆▅▅▆█▆▃▁··▁ beats 143/246	medium · avg 39%	▇ 3.7%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	91.0% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 206/246	easy · avg 78%	▇ 2.9%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	96.6% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 177/246	very easy · avg 90%	▇ 2.6%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	34.4% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 197/246	hard · avg 22%	▇ 1.8%	at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	66.1% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 190/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.6% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 160/246	very easy · avg 96%	▇ 1.2%	sharp · maxed out · noisy
Decision Index 0.4.1: CRUXEval (accuracy) submitted	67.2% ✎	██▄▃▃▄▁▁···▁ beats 160/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	71.6% ✎	▁███▆▅▃▃▁▁▁▁ beats 133/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	24.0% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 212/246	hard · avg 16%	▇ 0.8%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	8.6% ✎	▁▁▃▃▆█▇▅▅▃▁▁ beats 8/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 9.5% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	54.5800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 167/247	avg 41.206 points	▇▇ 6.1%	blunt
Decision Index 0.4.1: private new domains (equated points)	51.8800 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 163/247	avg 38.861 points	▇ 3.4%	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%

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		= 61.3

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.9%	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]	32.0%	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