whatmodel.sh / decision / Trio-Spark DI (9B)

Trio-Spark DI (9B)

+ contribute

Trio-Spark DI (9B) is an open-weights decision model from MachineFi Labs. Its published size is 8.95B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Exact Decision Index tested engine trio-spark-di; reported model Trio-Spark DI (9B). Type: full fine-tune; technique: autoregressive. 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.

56.7±1.3#138 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Trio-Spark DI (9B), out of 357 with a score.

#	model	score	where	released
138	Cerno 4B v1.1 apertixai	57±1	model files	2026-10-19
139	jqv (DecisionBench 9216db57) Octalab-Inc	57±2	model files
140	Trio-Spark DI (9B) MachineFi Labs	57±1	request access
141	openjev (DecisionBench ec4ff3df) razorback16	57±2	model files
142	matilda-jev-v1.5 (27B) Maincode	57±4	model files	2026-10-09

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #162 of 250 · 7.3% of its score · 6 tests

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: API-Bank (accuracy) submitted	85.0% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 158/236	easy · avg 70%	▇ 2.2%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	91.6% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 86/246	very easy · avg 92%	▇ 1.7%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	63.6% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 153/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	89.3% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 208/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	62.8% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 99/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.6% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #92 of 258 · 6.7% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, Decider 31B (NVFP4), Jev 1.13 here, though they rank higher overall. Trails Jev-Style 2B v4, Sieve 2B v3, StartLux-Decision v1.1 0.8B 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	38.2% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 85/246	medium · avg 38%	▇ 2.5%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	55.5% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 160/246	medium · avg 47%	▇ 2.3%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	86.3% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 197/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	64.8% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 104/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	87.2% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 94/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	85.5% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 135/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	84.3% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #77 of 256 · 23% of its score · 9 tests

Beats Turion Jev 31B, AJev lm6q (Gemma 4 31B), oenogarum (Gemma 4 31B) here, though they rank higher overall. Trails Sieve 4B, Amber 4B, Decider 35B-A3B 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	77.2% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 118/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: ANLI (macro-F1) submitted	69.1% ✎	▁▂▃▅▅█▂····▁ beats 149/246	medium · avg 60%	▇▇ 5.4%	at its level
Decision Index 0.4.1: HellaSwag (accuracy) submitted	94.2% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 176/246	very easy · avg 85%	▇▇ 4.8%	sharp
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	49.5% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 130/246	medium · avg 44%	▇ 2.4%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	33.9% ✎	▁··▂▃▃▅▇█▇▃▃ beats 179/246	hard · avg 22%	▇ 1.2%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	75.8% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 124/246	easy · avg 71%	▇ 1.1%	noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	77.1% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 184/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	76.0% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 134/246	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	86.0% ✎	▁···▁▁▁▁▃▆█▃ beats 105/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #132 of 247 · 10% of its score · 6 tests

Beats OneJev1.6-4B, Nace.AI Drex v1.5, Cerno 4B v1.1 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	66.3% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 135/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	64.4% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 195/246	medium · avg 60%	▇ 2.0%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	86.5% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 112/246	easy · avg 83%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	61.7% ✎	▁········▁▇█ beats 123/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	61.4% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 177/237	hard · avg 21%	▇ 1.0%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	41.7% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 91/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #116 of 263 · 45% of its score · 14 tests

Beats OneJev1.6-4B, Mica v0.5 4B, Winnow-12B (DecisionBench b6ac22b0) here, though they rank higher overall. Trails PurpleMIST-Flash-1.0, Jevfire Qwen3.8-27B (decision readout), JevK5 9B v0.3.3 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	53.5% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 127/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	77.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 135/246	easy · avg 70%	▇▇▇ 6.9%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	69.5% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 132/246	easy · avg 66%	▇▇▇ 6.3%	at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	53.4% ✎	▁··▄█▆▆▆▅▅▃▁ beats 144/246	medium · avg 45%	▇ 3.6%	sharp · at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.1% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 142/246	very easy · avg 90%	▇ 3.5%	sharp · maxed out
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	45.9% ✎	▂▆▅▅▆█▆▃▁··▁ beats 158/246	medium · avg 39%	▇ 3.3%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	86.7% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 174/246	easy · avg 78%	▇ 3.1%	typical
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.2% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 138/246	very easy · avg 96%	▇ 1.7%	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	20.4% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 77/246	hard · avg 22%	▇ 1.5%	at its level
Decision Index 0.4.1: MuSR (accuracy) submitted	58.2% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 112/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	61.2% ✎	██▄▃▃▄▁▁···▁ beats 146/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	88.7% ✎	▁███▆▅▃▃▁▁▁▁ beats 216/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	19.4% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 162/246	hard · avg 16%	▇ 0.7%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 65/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.5% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	50.0000 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 146/247	avg 41.206 points	▇▇ 5.5%	blunt
Decision Index 0.4.1: private new domains (equated points)	38.9800 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 120/247	avg 38.861 points	▇ 3.0%	blunt

$ where to run it

Request access to model files ↗
Gated files: the publisher requires access approval. 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/MachineFi ↗

$ 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		= 56.7

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.6%	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]	84.3%	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