whatmodel.sh / decision / Ryotide Qwen v0.3.1 (decision readout)

Ryotide Qwen v0.3.1 (decision readout)

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Ryotide Qwen v0.3.1 (decision readout) is a source-available decision implementation from csabag. Its published size is 4B parameters.

Catalogue identity notes

Exact Decision Index tested engine ryotide-qwen-v0.3.1; reported model Ryotide Qwen v0.3.1. Type: inference technique; technique: autoregressive. Implementation: https://github.com/csabag/ryotide. 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.

47.1±1.2#229 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Ryotide Qwen v0.3.1 (decision readout), out of 357 with a score.

#	model	score	where	released
229	Sieve 2B v3 Sthānika AI	48±1	model files
230	Lev1 3.7B theaviv	47±1	model files	2026-10-04
231	Ryotide Qwen v0.3.1 (decision readout) csabag	47±1	source code
232	decider-2b v10 (JevBench mini) Mapika	47±2	source code
233	Jev-Style 2B v4 chaoliangUNSW	47±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #169 of 250 · 8.4% of its score · 6 tests

Beats LiquidAI d1-3B, vLLM-SR Decision 2.0 Nox 4B, Decider 4B here, though they rank higher overall. Trails JevK5 Lite, Gevva E2B, 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: BFCL (case exact accuracy) submitted	91.1% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 81/246	very easy · avg 92%	▇ 3.0%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	65.2% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 80/236	easy · avg 70%	▇ 2.5%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	65.3% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 81/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	60.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 64/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	6.8% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 62/243	medium · avg 37%	▇ 0.8%	sharp · at its level · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	77.0% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #202 of 258 · 5.8% of its score · 7 tests

Beats Solar Decide Flash here, though they rank higher overall. Trails Bosun v3.1 0.6B LoRA, Mola-68M, Bosun v3.1 1.7B LoRA 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	40.3% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 98/246	medium · avg 38%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	38.5% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 55/246	medium · avg 47%	▇ 1.9%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	62.4% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 67/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	72.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 137/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	81.0% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 67/238	easy · avg 83%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	72.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 48/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	51.6% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #137 of 256 · 24% of its score · 9 tests

Beats Solar Decide, Winnow-12B, JoshuaSP diffgemma (decision readout) here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy) submitted	87.0% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 119/246	very easy · avg 85%	▇▇▇ 7.2%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	67.5% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 66/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	56.6% ✎	▁▂▃▅▅█▂····▁ beats 94/246	medium · avg 60%	▇▇ 4.9%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	42.2% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 100/246	medium · avg 44%	▇ 1.9%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	65.2% ✎	▁▁▁▁▁▁▁▂▃▇▇█ beats 59/246	easy · avg 71%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	43.8% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 52/246	medium · avg 62%	▇ 0.9%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	20.7% ✎	▁··▂▃▃▅▇█▇▃▃ beats 113/246	hard · avg 22%	▇ 0.8%	noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	66.3% ✎	▁···▁▁▁▁▃▆█▃ beats 36/246	easy · avg 84%	▇ 0.8%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	52.1% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 57/246	easy · avg 66%	▇ 0.8%	at its level · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #125 of 247 · 8.5% of its score · 6 tests

Beats Bobcat 1.3 (31B), JEV-35B, OneJev1.6-4B 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	62.3% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 118/246	medium · avg 61%	▇ 3.7%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	60.8% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 112/246	medium · avg 60%	▇ 1.7%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	87.8% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 122/246	easy · avg 83%	▇ 1.4%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	57.1% ✎	▁········▁▇█ beats 70/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	2.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 54/237	hard · avg 21%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	44.8% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 132/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #146 of 263 · 46% of its score · 14 tests

Beats Winnow-12B (DecisionBench b6ac22b0), StartLux-Decision v1.1 2B, Standard One 8B SH (v4) here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	47.6% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 103/246	medium · avg 46%	▇▇▇ 8.7%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	75.8% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 125/246	easy · avg 70%	▇▇▇ 7.4%	sharp · at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	94.5% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 135/246	very easy · avg 90%	▇▇▇ 6.3%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	57.7% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 79/246	easy · avg 66%	▇▇▇ 6.3%	at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.1% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 130/246	very easy · avg 96%	▇▇ 3.9%	sharp · maxed out · noisy
Decision Index 0.4.1: WinoGrande (accuracy) submitted	70.3% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 86/246	easy · avg 78%	▇ 3.4%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	25.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 56/246	medium · avg 45%	▇ 2.9%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	41.8% ✎	▂▆▅▅▆█▆▃▁··▁ beats 126/246	medium · avg 39%	▇ 2.6%	blunt · 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	53.9% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 53/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	43.5% ✎	██▄▃▃▄▁▁···▁ beats 78/246	medium · avg 58%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	7.3% ✎	▁▁▁▁▁▃▁▂▅█▂▁ beats 38/246	hard · avg 22%	▇ 1.0%	typical
Decision Index 0.4.1: CLadder (accuracy) submitted	60.6% ✎	▁███▆▅▃▃▁▁▁▁ beats 67/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	13.8% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 98/246	hard · avg 16%	▇ 0.5%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	10.4% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 45/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 7.1% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	36.0400 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 81/247	avg 41.206 points	▇▇ 4.6%	blunt
Decision Index 0.4.1: private new domains (equated points)	25.9000 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 88/247	avg 38.861 points	▇ 2.5%	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/csabag ↗

$ 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		= 47.1

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	77.0%	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]	51.6%	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