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DecisionTune 1.0

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DecisionTune 1.0 is an open-weights decision model from DecisionTune. Its published size is 0.39B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine decisiontune-1.0; reported model DecisionTune 1.0. Type: full fine-tune; technique: encoder. 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.

30.6±1.0#303 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of DecisionTune 1.0, out of 357 with a score.

#	model	score	where	RAM
303	vera-spark scar-ai	31±1	model files
304	vLLM-SR Decision 2.0 Eos 0.8B vLLM Semantic Router	31±1	local	~5.8GB
305	DecisionTune 1.0 DecisionTune	31±1	model files
306	Together Tev1 Qwen3.5-0.8B Together	31±1	model files
307	Dinah-1 Lukitaduarte	30±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▃▃▄▆██▆ #203 of 250 · 10% of its score · 6 tests

Beats Cam1, mini-jev Qwen3-4B (decision readout), InternLM Intern-Decision Qwen3.5-2B here, though they rank higher overall. Trails JevK5 Lite, Mola-68M, Crivo-68M 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	80.5% ✎	▁▂▁▁▁▃▂▄▅▆█▁ beats 46/246	very easy · avg 92%	▇▇ 5.9%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	46.3% ✎	▁▁▁▁▁▁▁▃▃▂█▂ beats 43/236	easy · avg 70%	▇ 2.0%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	69.0% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 93/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	56.9% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 47/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	0.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 19/243	medium · avg 37%	▇ 0.2%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	54.6% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #153 of 258 · 4.6% of its score · 7 tests

Beats Quyet-1.0-Large, Decider chat · Qwen3.6-27B (decision readout), Solar Decide here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	50.0% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 107/246	medium · avg 47%	▇ 1.3%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	18.4% ✎	▁▁▁▁▃▁▁▂▂▄▇█ beats 15/246	medium · avg 38%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	91.9% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 154/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	64.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 79/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.1% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 27/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	92.2% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 216/238	easy · avg 79%	▇ 0.4%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	60.6% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #154 of 256 · 24% of its score · 9 tests

Beats PurpleMIST-Flash-1.0, Hopper 12B (trained), Jev-Omni 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	83.9% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 103/246	very easy · avg 85%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	49.0% ✎	▁▁▁▃▂▃▃▄▇▇█▄ beats 14/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	29.7% ✎	▁▃▃▅▅█▂····▁ beats 14/246	medium · avg 60%	▇▇ 3.8%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	20.8% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 25/246	medium · avg 44%	▇ 1.2%	typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	82.1% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 182/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	84.9% ✎	▁···▁▁▁▁▃▆█▃ beats 98/246	easy · avg 84%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	36.0% ✎	▁▁▁▃▅▄▄▂▄▆█▄ beats 17/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	65.7% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 80/246	easy · avg 66%	▇ 0.6%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ACOS (per-review F1) submitted	6.3% ✎	▁··▂▃▃▅▇█▇▃▃ beats 32/246	hard · avg 22%	▇ 0.3%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #196 of 247 · 7.1% of its score · 6 tests

Beats Kev 9B v2, vLLM-SR d3-nano (2B), Valen-4B here, though they rank higher overall. Trails Qwen-2.5-1B-RLCD (decision readout), Bekko System One v0 68M, Crivo-17M 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	25.0% ✎	▁▃▂▃▃▃▇▅█▅▃▁ beats 10/246	medium · avg 61%	▇ 2.9%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	59.9% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 51/246	easy · avg 83%	▇ 1.8%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	46.7% ✎	▁·▃▃▂▂▅█▄▂▁▁ beats 4/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	49.9% ✎	▁········▁▇█ beats 13/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	37.2% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 32/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	1.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 31/237	hard · avg 21%	▇ 0.1%	sharp · too hard for it · noisy

knowledge & reasoning▁▂▃▂▂▃▃▅▅▆█▂ #241 of 263 · 49% of its score · 14 tests

Beats Amber 0.4B here, though they rank higher overall. Trails laya (DecisionBench 1c5edc17), JevK5 Lite, LFM2.5-2.6B-RLCD here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	60.1% ✎	▁▁▃▁▂▂▂▂▃▆█▅ beats 22/246	very easy · avg 96%	▇▇▇▇▇ 12%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	44.6% ✎	▂▂▃▂▂▂▁▃▄▆█▆ beats 24/246	very easy · avg 90%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	35.2% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 22/246	easy · avg 70%	▇▇▇ 6.5%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	37.5% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 25/246	easy · avg 66%	▇▇ 5.2%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	12.6% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 6/246	medium · avg 46%	▇▇ 4.7%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	71.9% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 97/246	easy · avg 78%	▇ 3.5%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	22.4% ✎	▃▆▅▅▆█▆▃▁··▁ beats 6/246	medium · avg 39%	▇ 1.7%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	19.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 28/246	medium · avg 45%	▇ 1.5%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	50.0% ✎	▁▁▃█▅▄▁▁▁▁▁▁ beats 31/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	72.4% ✎	▁███▆▅▃▃▁▁▁▁ beats 141/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	40.7% ✎	██▄▃▃▄▁▁···▁ beats 62/246	medium · avg 58%	▇ 0.7%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	3.0% ✎	▁▁▁▃▁▁▁▂▅█▂▁ beats 25/246	hard · avg 22%	▇ 0.5%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	7.0% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 3/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	13.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 165/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.6% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	21.3200 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 40/247	avg 41.206 points	▇ 3.6%	blunt
Decision Index 0.4.1: private new domains (equated points)	11.0700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 25/247	avg 38.861 points	▇ 2.0%	blunt

$ where to run it

Download model files ↗
Published model files verified. 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/decision-tune ↗

$ how the score was worked out · Decision Index 0.4.1 100%

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	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 30.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	54.6%	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]	60.6%	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