OneJev1.6-27B

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OneJev1.6-27B is an open-weights decision model from OmniJev, released 17 October 2026. Its published size is 27.36B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine onejev1.6-27b; reported model OneJev1.6-27B. 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

Release date ↗ · Weights licence ↗ · Parameter count ↗

Publisher parameter count; includes the served backbone, not just adapter size.

69.2±1.5#23 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of OneJev1.6-27B, out of 357 with a score, then more from OmniJev.

#	model	score	where	released
21	d1-ultra Liquid AI	69±2	api price unknown	2026-10-15
22	KnowLine 26B-A4B Gen2.5 PelaAI	69±2	model files	2026-10-22
23	OneJev1.6-27B OmniJev	69±2	model files	2026-10-17
24	baselabs elephant-1 (9B) baselabs	69±1	model files	2026-10-21
25	vLLM-SR d3 (27B) vLLM Semantic Router	69±1	model files
	more from OmniJev
58	OneJev1.5-27B OmniJev	65±4	model files	2026-10-13
68	OneJev1.6-9B OmniJev	64±1	model files	2026-10-17
107	OneJev1.5-9B OmniJev	61±4	model files	2026-10-13

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #8 of 250 · 5.4% of its score · 6 tests

Beats Jeffan-31b-1710, Sieve 31B v3, Fastino GLiDE 1.2 here, though they rank higher overall. Trails Clef-flash, Clef-omni, Clef 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	89.6% ✎	▁▁▁▁▁▁▁▂▃▂█▃ beats 232/236	easy · avg 70%	▇ 1.6%	sharp · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	72.1% ✎	▁▁▁▁▁·▁▁▁▄█▃ beats 243/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	79.5% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 188/243	medium · avg 37%	▇ 0.9%	sharp · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	92.4% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 241/246	easy · avg 72%	▇ 0.9%	noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	98.2% ✎	▁▂▁▁▁▂▂▄▅▆█▃ beats 239/246	very easy · avg 92%	▇ 0.8%	sharp · maxed out · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	80.0% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #48 of 258 · 8.1% of its score · 7 tests

Beats H2O Lightning 31B v1.1, AutoTrust-JEV-27B v1.1, MISA AI 2.0 Decision Plus · oak 1.1 (31B) 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	44.1% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 139/246	medium · avg 38%	▇ 3.4%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	58.7% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 201/246	medium · avg 47%	▇ 2.9%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	88.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 228/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	96.1% ✎	█▅▅▄▃▂▁▃▁▁·▁ beats 233/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	90.9% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 196/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	96.1% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 221/238	easy · avg 83%	▇ 0.3%	sharp · maxed out · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	67.2% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #26 of 256 · 21% of its score · 9 tests

Beats KnowLine 31B Gen1, Ainvest-Decision-27B v1.0, WeMM-JEV-27B here, though they rank higher overall. Trails Clef-flash, Clef-omni, JEV-35B 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	77.7% ✎	▁▂▃▅▅█▂····▁ beats 221/246	medium · avg 60%	▇▇ 5.5%	typical
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	87.4% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 240/246	easy · avg 74%	▇▇ 5.1%	typical
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	63.7% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 204/246	medium · avg 44%	▇ 2.9%	at its level
Decision Index 0.4.1: HellaSwag (accuracy) submitted	96.8% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 221/246	very easy · avg 85%	▇ 2.5%	sharp · maxed out
Decision Index 0.4.1: ACOS (per-review F1) submitted	36.0% ✎	▁··▂▃▃▅▇█▇▃▃ beats 187/246	hard · avg 22%	▇ 2.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	81.9% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 240/246	medium · avg 62%	▇ 1.1%	typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	84.8% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 226/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	84.9% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 233/246	easy · avg 66%	▇ 0.7%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	94.9% ✎	▁···▁▁▁▁▃▆█▃ beats 232/246	easy · avg 84%	▇ 0.5%	maxed out · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #61 of 247 · 10% of its score · 6 tests

Trails Crivo-1B, Kas 4B, baselabs joey-1 (2B) 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	79.5% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 215/246	medium · avg 61%	▇▇ 4.0%	typical
Decision Index 0.4.1: Humicroedit (accuracy) submitted	64.8% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 203/246	medium · avg 60%	▇ 2.5%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	67.5% ✎	▁········▁▇█ beats 230/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	82.3% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 231/237	hard · avg 21%	▇ 1.4%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	94.2% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 184/246	easy · avg 83%	▇ 0.8%	sharp · maxed out · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	63.5% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 233/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #61 of 263 · 42% of its score · 14 tests

Trails Solar Decide, Clef-flash, Clef-omni 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	66.4% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 188/246	medium · avg 46%	▇▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	90.0% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 236/246	easy · avg 66%	▇▇ 5.7%	typical
Decision Index 0.4.1: MMLU (accuracy) submitted	84.6% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 185/246	easy · avg 70%	▇▇ 5.5%	sharp
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	52.5% ✎	▂▆▅▅▆█▆▃▁··▁ beats 218/246	medium · avg 39%	▇▇ 4.4%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	74.6% ✎	▁··▄█▆▆▆▅▅▃▁ beats 201/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	92.3% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 214/246	easy · avg 78%	▇ 2.6%	typical
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	42.1% ✎	▁▁▁▁▁▁▁▂▅█▃▁ beats 230/246	hard · avg 22%	▇ 2.4%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	96.6% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 175/246	very easy · avg 90%	▇ 1.5%	sharp · maxed out

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	92.0% ✎	▁▁▂█▅▄▁▁▁▁▁▃ beats 246/246	medium · avg 60%	▇ 1.5%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	77.9% ✎	██▄▃▃▄▁▁···▁ beats 190/246	medium · avg 58%	▇ 1.2%	noisy
Decision Index 0.4.1: ChessBench (accuracy) submitted	22.2% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 191/246	hard · avg 16%	▇ 1.1%	blunt
Decision Index 0.4.1: CLadder (accuracy) submitted	94.1% ✎	▁███▆▅▃▃▁▃▁▁ beats 233/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.7% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 162/246	very easy · avg 96%	▇ 0.6%	sharp · maxed out · noisy
Decision Index 0.4.1: HLE (accuracy) submitted	11.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 86/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 11% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	62.1500 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 220/247	avg 41.206 points	▇▇▇ 7.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	60.4300 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 188/247	avg 38.861 points	▇▇ 4.0%	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/OmniJev ↗

$ 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		= 69.2

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	80.0%	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]	67.2%	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