whatmodel.sh / decision / simple-jev · Qwen3.8-27B (featherless) (decision readout)

simple-jev · Qwen3.8-27B (featherless) (decision readout)

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simple-jev · Qwen3.8-27B (featherless) (decision readout) is a source-available decision implementation from featherless-ai. Its published size is 27.78B parameters.

Catalogue identity notes

Exact Decision Index tested engine simple-jev-qwen3.8-27b; reported model simple-jev · Qwen3.8-27B (featherless). Type: inference technique; technique: autoregressive. Published checkpoint/base reference: Qwen/Qwen3.8-27B. Implementation: https://github.com/featherless-ai/simple-jev. 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.

59.6±1.3#113 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of simple-jev · Qwen3.8-27B (featherless) (decision readout), out of 357 with a score, then more from featherless-ai.

#	model	score	where	released
112	openjev-sglang (DecisionBench 1355db6a) ekzhang	60±2	model files
113	JEV-27B AutoTrust AI Lab	60±1	model files
114	simple-jev · Qwen3.8-27B (featherless) (decision readout) featherless-ai	60±1	source code
115	OneJev1.6-4B OmniJev	60±1	model files	2026-10-17
116	Eikos-27B-FP8 Qwen3.8-27B LoRA caiovicentino1	59±1	model files
	more from featherless-ai
120	simplejev-qwen3.8-27b (DecisionBench 1d4bf0f2) featherless-ai	59±2	model files
136	simplejev-qwen3.6-35b-a3b (DecisionBench 995ad96e) featherless-ai	58±2	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #72 of 250 · 6.9% of its score · 6 tests

Beats Sieve 27B v2, Darwin-27B-ZTC, allec-oavg (Gemma 4 31B) here, though they rank higher overall. Trails baselabs joey-1 (2B), Hopper 12B (trained), Solomon v1.1 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	85.4% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 179/236	easy · avg 70%	▇ 2.1%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	96.8% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 176/246	very easy · avg 92%	▇ 1.5%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	64.8% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 162/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	75.0% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 112/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	68.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 200/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.8% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #69 of 258 · 7.0% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, oenogarum (Gemma 4 31B), Decider 31B (NVFP4) here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Standard One 8B SH (v4), StartLux-Decision v1.1 2B 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	49.7% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 229/246	medium · avg 38%	▇ 2.7%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	57.4% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 184/246	medium · avg 47%	▇ 2.4%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	74.6% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 121/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	84.7% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 188/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	88.1% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 102/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	79.5% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 90/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	41.9% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #95 of 256 · 22% of its score · 9 tests

Beats Turion Jev 31B, FlexDM · Gemma-4-31B (decision readout), 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	69.2% ✎	▁▂▃▅▅█▂····▁ beats 150/246	medium · avg 60%	▇▇ 5.4%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	83.7% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 198/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	93.1% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 158/246	very easy · avg 85%	▇▇ 4.2%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	60.4% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 186/246	medium · avg 44%	▇ 2.5%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	35.6% ✎	▁··▂▃▃▅▇█▇▃▃ beats 185/246	hard · avg 22%	▇ 1.4%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	71.9% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 147/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	77.5% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 138/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	72.3% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 111/246	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	88.5% ✎	▁···▁▁▁▁▃▆█▃ beats 129/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #37 of 247 · 10% of its score · 6 tests

Beats Jeffan-31b-1710, vLLM-SR d3-turbo v1.1 (27B), Nace.AI Drex Max v1 (27B) here, though they rank higher overall. Trails Kas 4B, baselabs joey-1 (2B), Xor 1.2 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 145/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.3% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 129/246	medium · avg 60%	▇ 2.1%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	63.6% ✎	▁········▁▇█ beats 158/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	28.9% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 149/237	hard · avg 21%	▇ 1.1%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	96.1% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 211/246	easy · avg 83%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	41.0% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 84/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #92 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, Xor 1.2 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	59.8% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 155/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	84.0% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 178/246	easy · avg 70%	▇▇▇ 6.6%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	70.8% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 138/246	easy · avg 66%	▇▇▇ 6.3%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	36.2% ✎	▁··▄█▆▆▆▅▅▃▁ beats 96/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	48.5% ✎	▂▆▅▅▆█▆▃▁··▁ beats 185/246	medium · avg 39%	▇ 3.5%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	84.5% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 155/246	easy · avg 78%	▇ 3.0%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	96.8% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 186/246	very easy · avg 90%	▇ 2.9%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	32.1% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 172/246	hard · avg 22%	▇ 1.7%	at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	99.2% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 227/246	very easy · avg 96%	▇ 1.3%	sharp · maxed out · noisy
Decision Index 0.4.1: MuSR (accuracy) submitted	63.2% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 161/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	62.3% ✎	██▄▃▃▄▁▁···▁ beats 147/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	71.0% ✎	▁███▆▅▃▃▁▁▁▁ beats 130/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	17.4% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 140/246	hard · avg 16%	▇ 0.8%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	12.4% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 126/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 9.1% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	55.0500 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 168/247	avg 41.206 points	▇▇ 5.9%	blunt
Decision Index 0.4.1: private new domains (equated points)	59.9000 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 185/247	avg 38.861 points	▇ 3.2%	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/featherless-ai ↗

$ 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		= 59.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.8%	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]	41.9%	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