OneJev1.6-4B

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OneJev1.6-4B is an open-weights decision model from OmniJev, released 17 October 2026. Its published size is 4.54B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine onejev1.6-4b; reported model OneJev1.6-4B. 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.

59.6±1.3#114 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of OneJev1.6-4B, out of 357 with a score, then more from OmniJev.

#	model	score	where	released
113	JEV-27B AutoTrust AI Lab	60±1	model files
114	simple-jev · Qwen3.8-27B (featherless) (decision readout) featherless-ai	60±1	source code
115	OneJev1.6-4B OmniJev	60±1	model files	2026-10-17
116	Eikos-27B-FP8 Qwen3.8-27B LoRA caiovicentino1	59±1	model files
117	JADE theunnecessarythings	59±1	model files	2026-10-01
	more from OmniJev
23	OneJev1.6-27B OmniJev	69±2	model files	2026-10-17
58	OneJev1.5-27B OmniJev	65±4	model files	2026-10-13
68	OneJev1.6-9B OmniJev	64±1	model files	2026-10-17

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #18 of 250 · 6.8% of its score · 6 tests

Beats Jeffan-31b-1710, Sieve 31B v3, Fastino GLiDE 1.2 here, though they rank higher overall. Trails Solomon v1.1, 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: API-Bank (accuracy) submitted	89.0% ✎	▁▁▁▁▁▁▁▂▃▂█▃ beats 226/236	easy · avg 70%	▇ 2.1%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	97.9% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 226/246	very easy · avg 92%	▇ 1.5%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	65.9% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 164/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	88.4% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 200/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	71.0% ✎	▁▁▁▁▁·▁▁▁▄█▃ beats 238/246	medium · avg 60%	▇ 1.0%	blunt · at its level
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▁▁▁·▁▁▁▁▃▃█▅ #136 of 258 · 7.0% of its score · 7 tests

Beats Quyet-1.0-Large here, though they rank higher overall. Trails vLLM-SR vela3-fast (Vela 3.0, 0.4B), crystal-0.8b, Kas 4B 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	39.2% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 95/246	medium · avg 38%	▇ 2.7%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	51.9% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 117/246	medium · avg 47%	▇ 2.4%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	86.9% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 207/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	90.6% ✎	█▅▅▄▃▃▁▁▁▁·▁ beats 215/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	95.8% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 215/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	90.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 180/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	67.1% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #91 of 256 · 22% of its score · 9 tests

Beats Turion Jev 31B, AJev lm6q (Gemma 4 31B), FlexDM · Gemma-4-31B (decision readout) here, though they rank higher overall. Trails Decider 4B, Sieve 4B, Amber 4B 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	68.1% ✎	▁▂▃▅▅█▂····▁ beats 144/246	medium · avg 60%	▇▇ 5.4%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	80.2% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 153/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	93.3% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 161/246	very easy · avg 85%	▇▇ 4.2%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	48.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 126/246	medium · avg 44%	▇ 2.5%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	36.2% ✎	▁··▂▃▃▅▇█▇▃▃ beats 188/246	hard · avg 22%	▇ 1.4%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	80.7% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 232/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	84.5% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 219/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	83.4% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 214/246	easy · avg 66%	▇ 0.8%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	93.3% ✎	▁···▁▁▁▁▃▆█▃ beats 198/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #149 of 247 · 10% of its score · 6 tests

Trails Crivo-32M v2, Crivo-68M, Crivo-150M v2 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	70.8% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 160/246	medium · avg 61%	▇▇ 4.1%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	64.1% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 193/246	medium · avg 60%	▇ 2.1%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	60.9% ✎	▁········▁▇█ beats 111/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	79.4% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 217/237	hard · avg 21%	▇ 1.1%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	82.4% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 98/246	easy · avg 83%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	60.4% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 208/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #124 of 263 · 44% of its score · 14 tests

Trails PurpleMIST-Flash-1.0, JPT-9B Qwen3.5-9B LoRA, Sieve 4B 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	52.0% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 122/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	74.4% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 117/246	easy · avg 70%	▇▇▇ 6.6%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	82.8% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 205/246	easy · avg 66%	▇▇ 6.2%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	56.3% ✎	▁··▄█▆▆▆▅▅▃▁ beats 151/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	44.4% ✎	▂▆▅▅▆█▆▃▁··▁ beats 146/246	medium · avg 39%	▇ 3.5%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	83.2% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 145/246	easy · avg 78%	▇ 3.0%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	93.7% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 120/246	very easy · avg 90%	▇ 2.9%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	38.9% ✎	▁▁▁▁▁▁▁▂▅█▃▁ beats 220/246	hard · avg 22%	▇ 1.7%	at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	97.8% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 118/246	very easy · avg 96%	▇ 1.3%	sharp · maxed out · noisy
Decision Index 0.4.1: MuSR (accuracy) submitted	88.8% ✎	▁▁▂█▅▄▁▁▁▁▃▁ beats 243/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	49.5% ✎	██▄▃▃▄▁▁···▁ beats 106/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	90.8% ✎	▁███▆▅▃▃▁▁▁▁ beats 224/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	21.6% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 180/246	hard · avg 16%	▇ 0.8%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.2% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 69/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 9.1% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	55.3700 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 171/247	avg 41.206 points	▇▇ 5.9%	blunt
Decision Index 0.4.1: private new domains (equated points)	39.1500 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 121/247	avg 38.861 points	▇ 3.2%	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		= 59.6

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]	67.1%	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