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AnyJev L0 · Gemma-4-31B (decision readout)

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AnyJev L0 · Gemma-4-31B (decision readout) is a source-available decision implementation from MorrisZJ. Its published size is 31.27B parameters.

Catalogue identity notes

Exact Decision Index tested engine anyjev-l0-gemma-4-31b; reported model AnyJev L0 · Gemma-4-31B. Type: inference technique; technique: autoregressive. Implementation: https://github.com/MorrisZJ/anyjev-l0-gemma. Comparison entry; no current run command, API price or speed inferred from the benchmark.

Model information sources

· Parameter count ↗

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

63.8±1.4#67 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of AnyJev L0 · Gemma-4-31B (decision readout), out of 357 with a score.

#	model	score	where	released
65	bosun-v3.1-0.6b (DecisionBench aaa9dd06) Hanno Labs	64±2	model files	2026-09-22
66	Bobcat 1.3 (31B) sanghwa-na	64±1	model files	2026-10-11
67	AnyJev L0 · Gemma-4-31B (decision readout) MorrisZJ	64±1	source code
68	OneJev1.6-9B OmniJev	64±1	model files	2026-10-17
69	JEV-27B-VL AutoTrust AI Lab	64±4	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #19 of 250 · 6.2% of its score · 6 tests

Beats Jeffan-31b-1710, Sieve 31B v3, Fastino GLiDE 1.2 here, though they rank higher overall. Trails Solomon v1.1, Clef-flash, Clef-omni 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	85.0% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 162/236	easy · avg 70%	▇ 1.9%	sharp · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	64.8% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 159/243	medium · avg 37%	▇ 1.1%	sharp · at its level · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	98.0% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 233/246	very easy · avg 92%	▇ 1.1%	sharp · maxed out · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	61.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 75/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: When2Call (accuracy) submitted	80.9% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 147/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	80.0% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #109 of 258 · 7.4% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, Quyet-1.0-Large here, though they rank higher overall. Trails vLLM-SR vela3-fast (Vela 3.0, 0.4B), crystal-0.8b, Jev-Style 2B v4 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	45.4% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 159/246	medium · avg 38%	▇ 3.0%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	54.1% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 144/246	medium · avg 47%	▇ 2.6%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	75.5% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 124/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	87.8% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 204/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	91.5% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 148/238	easy · avg 83%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	79.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 85/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	1.5% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #106 of 256 · 22% of its score · 9 tests

Beats 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: ANLI (macro-F1) submitted	74.0% ✎	▁▂▃▅▅█▂····▁ beats 190/246	medium · avg 60%	▇▇ 5.5%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	83.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 193/246	easy · avg 74%	▇▇ 5.2%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	92.4% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 145/246	very easy · avg 85%	▇ 3.4%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	54.1% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 152/246	medium · avg 44%	▇ 2.7%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	21.4% ✎	▁··▂▃▃▅▇█▇▃▃ beats 119/246	hard · avg 22%	▇ 1.7%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	74.1% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 162/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	74.9% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 116/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	78.8% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 165/246	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	93.5% ✎	▁···▁▁▁▁▃▆█▃ beats 207/246	easy · avg 84%	▇ 0.5%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▃▁▁ #13 of 247 · 10% of its score · 6 tests

Beats Jeffan-31b-1710, KnowLine 31B Gen1, vLLM-SR d3-turbo v1.1 (27B) here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	75.8% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 198/246	medium · avg 61%	▇▇ 4.1%	typical
Decision Index 0.4.1: Humicroedit (accuracy) submitted	65.3% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 209/246	medium · avg 60%	▇ 2.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	33.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 152/237	hard · avg 21%	▇ 1.3%	sharp · at its level · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	66.6% ✎	▁········▁▇█ beats 219/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	98.2% ✎	▁█▄▄▅█▆▅▄▃▁▁ beats 235/246	easy · avg 83%	▇ 0.9%	sharp · maxed out · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	48.7% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 176/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #49 of 263 · 43% of its score · 14 tests

Beats OneJev1.6-27B, baselabs elephant-1 (9B), StartLux-Decision v1.1 9B here, though they rank higher overall. Trails Solar Decide, Clef-flash, Clef-omni 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	70.6% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 212/246	medium · avg 46%	▇▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	86.8% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 206/246	easy · avg 70%	▇▇ 6.2%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	75.9% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 164/246	easy · avg 66%	▇▇ 6.1%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	52.5% ✎	▂▆▅▅▆█▆▃▁··▁ beats 220/246	medium · avg 39%	▇▇ 3.9%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	75.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 209/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	85.4% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 164/246	easy · avg 78%	▇ 2.8%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	97.2% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 209/246	very easy · avg 90%	▇ 2.2%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	29.7% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 140/246	hard · avg 22%	▇ 2.0%	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	66.0% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 186/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	80.5% ✎	██▄▃▃▄▁▁···▁ beats 200/246	medium · avg 58%	▇ 1.2%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	76.9% ✎	▁███▆▅▃▃▁▁▁▁ beats 165/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	99.1% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 211/246	very easy · avg 96%	▇ 0.9%	sharp · maxed out · noisy
Decision Index 0.4.1: ChessBench (accuracy) submitted	24.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 214/246	hard · avg 16%	▇ 0.9%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	14.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 174/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 10% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	58.7600 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 202/247	avg 41.206 points	▇▇▇ 6.5%	blunt
Decision Index 0.4.1: private new domains (equated points)	70.9900 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 215/247	avg 38.861 points	▇ 3.6%	blunt

$ where to run it

Implementation and setup instructions ↗
Published implementation and setup documentation verified. This entry describes a decision readout or evaluated configuration, not necessarily separate trained weights. Follow the referenced version and model settings; hardware compatibility has not been verified. Source · checked 2026-10-31.

Model repository and documentation: github.com/MorrisZJ ↗

$ 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		= 63.8

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	80.0%	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]	1.5%	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