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PurpleMIST-Flash-1.0

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PurpleMIST-Flash-1.0 is an open-weights decision model from Olaverse Lab. Its published size is 7.94B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine purplemist-flash-1.0; reported model PurpleMIST-Flash-1.0. Type: LoRA + head; 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.

53.2±1.2#180 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of PurpleMIST-Flash-1.0, out of 357 with a score.

#	model	score	where	released
180	SemIf (DecisionBench 1f2dea3e) TheoLeeCJ	54±2	source code
181	RSI-Jev v6.1 VL 4B shgao	53±1	model files	2026-10-07
182	PurpleMIST-Flash-1.0 Olaverse Lab	53±1	model files
183	JevAny-Qwen-4B (direct token) (JevBench mini) SimpleJev	53±2	source code
184	decider-2b (DecisionBench fa996cea) Mapika	53±2	model files	2026-09-19

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #140 of 250 · 7.7% of its score · 6 tests

Beats Cerno 4B v1.1, Trio-Spark DI (9B), Decider 35B-A3B here, though they rank higher overall. Trails JevK5 Lite, Amber 0.4B, Crivo-1B 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	75.4% ✎	▁▁▁▁▁▁▁▂▃▃█▂ beats 104/236	easy · avg 70%	▇ 2.4%	sharp · at its level · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	92.9% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 106/246	very easy · avg 92%	▇ 2.1%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	29.5% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 104/243	medium · avg 37%	▇ 1.1%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	73.1% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 106/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	65.1% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 136/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.9% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #152 of 258 · 6.3% 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. Trails Amber 0.4B, Saina Helm 2 Omni (0.8B), jdi-ptr14-0.8b 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	34.8% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 70/246	medium · avg 38%	▇ 2.3%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	49.7% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 105/246	medium · avg 47%	▇ 2.2%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	67.9% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 91/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	62.4% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 95/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	80.6% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 66/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	73.8% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 55/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	57.3% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #168 of 256 · 23% of its score · 9 tests

Trails DecisionTune 1.0, Decider 2B, vLLM-SR d3-lite (0.8B) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy) submitted	80.8% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 84/246	very easy · avg 85%	▇▇ 5.7%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	77.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 120/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: ANLI (macro-F1) submitted	67.2% ✎	▁▂▃▅▅█▂····▁ beats 139/246	medium · avg 60%	▇▇ 5.2%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	46.0% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 113/246	medium · avg 44%	▇ 2.2%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	32.9% ✎	▁··▂▃▃▅▇█▇▃▃ beats 175/246	hard · avg 22%	▇ 1.1%	noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	76.4% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 131/246	easy · avg 71%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	64.2% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 116/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	67.9% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 90/246	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	90.8% ✎	▁···▁▁▁▁▃▆█▃ beats 160/246	easy · avg 84%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #172 of 247 · 9.2% of its score · 6 tests

Trails Crivo-17M, Crivo-32M v2, Crivo-68M 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	65.5% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 133/246	medium · avg 61%	▇▇ 3.9%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	54.7% ✎	▁·▁▃▃▂▅█▄▂▁▁ beats 55/246	medium · avg 60%	▇ 1.8%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	73.6% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 74/246	easy · avg 83%	▇ 1.2%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	60.8% ✎	▁········▁▇█ beats 110/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	14.4% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 117/237	hard · avg 21%	▇ 0.8%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	36.5% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 27/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #110 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.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	59.4% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 153/246	medium · avg 46%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	79.3% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 149/246	easy · avg 70%	▇▇▇ 7.2%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	68.6% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 123/246	easy · avg 66%	▇▇▇ 6.4%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.5% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 150/246	very easy · avg 90%	▇▇ 4.4%	sharp · maxed out
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	61.4% ✎	▁··▄█▆▆▆▅▅▃▁ beats 164/246	medium · avg 45%	▇ 3.4%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	79.9% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 126/246	easy · avg 78%	▇ 3.2%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	41.3% ✎	▂▆▅▅▆█▆▃▁··▁ beats 121/246	medium · avg 39%	▇ 3.0%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.2% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 140/246	very easy · avg 96%	▇ 2.3%	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	31.7% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 166/246	hard · avg 22%	▇ 1.3%	at its level
Decision Index 0.4.1: MuSR (accuracy) submitted	58.0% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 105/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	58.6% ✎	██▄▃▃▄▁▁···▁ beats 139/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	67.3% ✎	▁███▆▅▃▃▁▁▁▁ beats 113/246	easy · avg 71%	▇ 1.0%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	12.1% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 83/246	hard · avg 16%	▇ 0.6%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	8.0% ✎	▁▃▂▃▆█▇▅▅▃▁▁ beats 4/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.0% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	47.7400 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 135/247	avg 41.206 points	▇▇ 5.1%	blunt
Decision Index 0.4.1: private new domains (equated points)	47.2300 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 150/247	avg 38.861 points	▇ 2.8%	blunt

$ where to run it

Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. 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/olaverse ↗

$ 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		= 53.2

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.9%	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]	57.3%	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