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Jevfire Qwen3.8-27B (decision readout)

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Jevfire Qwen3.8-27B (decision readout) is a source-available decision implementation from kikoncuo. Its published size is 27.78B parameters.

Catalogue identity notes

Exact Decision Index tested engine jevfire-uncapped; reported model Jevfire Qwen3.8-27B. Type: inference technique; technique: autoregressive. Published checkpoint/base reference: Qwen/Qwen3.8-27B-FP8. Implementation: https://github.com/kikoncuo/jevfire. 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.

54.4±1.2#166 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Jevfire Qwen3.8-27B (decision readout), out of 357 with a score.

#	model	score	where	released
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
170	Solomon v1.1 Doccy Health	54±1	model files	2026-09-21

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #108 of 250 · 7.5% of its score · 6 tests

Beats Senova-4B v2, SynACK Decide 26B-A4B v7, Tern-35B-A3B here, though they rank higher overall. Trails OneJev1.6-0.8B, TokenRhythm NeoHorse-Jev-4B, mmastrac 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	84.1% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 140/236	easy · avg 70%	▇ 2.3%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	95.6% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 139/246	very easy · avg 92%	▇ 2.0%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	47.7% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 129/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	77.5% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 130/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	64.9% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 128/246	medium · avg 60%	▇ 1.0%	blunt · at its level
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▁▁▁·▁▁▁▁▃▃█▅ #117 of 258 · 6.4% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, Quyet-1.0-Large, deck31b (Gemma 4 31B) (decision readout) 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	49.2% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 220/246	medium · avg 38%	▇ 2.4%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	53.5% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 138/246	medium · avg 47%	▇ 2.2%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	71.2% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 105/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	74.4% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 149/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	81.3% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 68/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	75.2% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 65/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	65.0% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #122 of 256 · 23% of its score · 9 tests

Beats garum (Gemma 4 31B), Shisa DE-2.1 (repeat), garas (Gemma 4 31B) here, though they rank higher overall. Trails vLLM-SR d3-nano (2B), Decider 4B, jiwo 4B v2 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	75.2% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 100/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	90.5% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 132/246	very easy · avg 85%	▇▇ 5.4%	sharp
Decision Index 0.4.1: ANLI (macro-F1) submitted	63.5% ✎	▁▂▃▅▅█▂····▁ beats 117/246	medium · avg 60%	▇▇ 5.3%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	53.2% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 147/246	medium · avg 44%	▇ 2.3%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	22.4% ✎	▁··▂▃▃▅▇█▇▃▃ beats 124/246	hard · avg 22%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	75.5% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 121/246	easy · avg 71%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	60.8% ✎	▁▁▁▂▅▄▄▃▄▆█▄ beats 109/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	80.3% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 179/246	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	80.3% ✎	▁···▁▁▁▁▃▆█▃ beats 70/246	easy · avg 84%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #58 of 247 · 9.3% of its score · 6 tests

Beats OneJev1.6-27B, mogan-decision-31B-it, Cerno 27B here, though they rank higher overall. Trails Kas 4B, baselabs joey-1 (2B) 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	68.8% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 144/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	57.8% ✎	▁·▁▃▂▃▅█▄▂▁▁ beats 76/246	medium · avg 60%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	94.9% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 189/246	easy · avg 83%	▇ 1.2%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	60.5% ✎	▁········▁▇█ beats 105/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	13.2% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 113/237	hard · avg 21%	▇ 0.8%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	39.5% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 57/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #112 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	57.3% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 145/246	medium · avg 46%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	79.7% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 155/246	easy · avg 70%	▇▇▇ 7.1%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	68.0% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 121/246	easy · avg 66%	▇▇▇ 6.4%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	96.0% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 162/246	very easy · avg 90%	▇▇ 4.1%	sharp · maxed out
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	33.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 88/246	medium · avg 45%	▇ 3.4%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	74.5% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 107/246	easy · avg 78%	▇ 3.2%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	46.9% ✎	▂▆▅▅▆█▆▃▁··▁ beats 167/246	medium · avg 39%	▇ 3.1%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.5% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 155/246	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	27.5% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 121/246	hard · avg 22%	▇ 1.3%	at its level
Decision Index 0.4.1: MuSR (accuracy) submitted	59.4% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 127/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	53.7% ✎	██▄▃▃▄▁▁···▁ beats 127/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	61.8% ✎	▁███▆▅▃▃▁▁▁▁ beats 71/246	easy · avg 71%	▇ 1.0%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	17.3% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 137/246	hard · avg 16%	▇ 0.6%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	12.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 112/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.1% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	46.3500 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 126/247	avg 41.206 points	▇▇ 5.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	41.4600 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 135/247	avg 38.861 points	▇ 2.9%	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/kikoncuo ↗

$ 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]	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]	65.0%	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

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