whatmodel.sh / decision / OneJev1.5-27B

OneJev1.5-27B is an open-weights decision model from OmniJev, released 13 October 2026. Its published size is 27.36B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine onejev1.5-27b; reported model OneJev1.5-27B. 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.

64.9±3.5#58 of 355 decision modelsfrom 12 benchmark results across 1 source

$ how it compares

The decision models ranked either side of OneJev1.5-27B, out of 357 with a score, then more from OmniJev.

#modelscorewhereRAMreleased
56Mercury Decide (paid endpoint) Inception65±1api price unknown
57Jebadiah 27B v2.1 (T1e) frontier-infra65±1local~69GB2026-10
58OneJev1.5-27B OmniJev65±4model files2026-10-13
59jev-brain · Gemma-4-31B (decision readout) wcole365±1source code
60Aleria Judge 1.1.0 (Gemma 4 31B) (decision readout) Decision Index submission: aleria-judge-1.1.065±4source code2026-10-14
more from OmniJev
23OneJev1.6-27B OmniJev69±2model files2026-10-17
68OneJev1.6-9B OmniJev64±1model files2026-10-17
107OneJev1.5-9B OmniJev61±4model files2026-10-13

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

$ what it's good at

vision▁·▃▂▃▃▇▄▄█▄▃ #26 of 79 · 95% of its score · 12 tests

Beats StartLux-Decision v1.1 9B, RSI-Jev v6.1 VL 27B (effort auto), StartLux-Decision v1.1 4B here, though they rank higher overall. Trails Clef-flash, Perplexity Decider Flash v1.2 (4B), Mercury Decide (free endpoint) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted83.9700 points ✎▁·▁▁▃▁▄▄▃▅█▂ beats 48/61avg 63.423 points▇▇▇▇▇ 12%blunt
Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted40.8600 points ✎▁▁▅▁▄▁▄█▄▃▇▇ beats 44/61avg 22.797 points▇▇▇▇ 11%blunt
Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted83.0000 points ✎▁·▁▁▁▁·▂▁▃▅█ beats 40/61avg 66.987 points▇▇▇▇ 10%blunt
Decision Index Vision 0.4: private same skills (points)62.1500 points ✎▁▁▃▃▁▂▄▆▃▃▅█ beats 41/61avg 48.067 points▇▇▇▇ 10%blunt
Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted58.2900 points ✎▆▃▃▆▄▄▇██▁▆▆ beats 61/61avg 28.443 points▇▇▇▇ 8.8%blunt
Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted67.5400 points ✎▁·▂▂▁▃▃▅▅▃▂█ beats 43/61avg 53.651 points▇▇▇ 8.0%blunt
Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted80.4500 points ✎▂▁▁▁▁▃▁▅▅▆██ beats 54/61avg 62.137 points▇▇▇ 7.8%blunt
Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted95.6300 points ✎▁·▁▂▁▁▃▁▂▂▅█ beats 40/61avg 83.859 points▇▇ 6.2%blunt
4 more tests
testresultvs the others in its rundifficultycountswhy
Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted24.9200 points ✎▂▄▇▆█▄▄▇▂··▁ beats 33/61avg 19.676 points▇▇ 6.2%blunt
Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted75.1900 points ✎▁···▁▂▂▂█▄▂▃ beats 59/61avg 44.994 points▇▇ 5.9%blunt
Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted77.5000 points ✎▁▁▁▁▂▁▁▁▄█▃▃ beats 51/61avg 67.662 points▇▇ 4.7%blunt
Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted93.3900 points ✎▁··▁▁▁▁▁▂▄█▁ beats 59/61avg 85.184 points▇ 3.4%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 Vision 0.4 95%

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

sourceresultsshare of the scorelicence
Decision Index Vision 0.4 · submitted; independent execution unverified12CC BY 4.0
prior · pull toward the middle
weighted average= 64.9

$ improve this page

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 · sources and licences · json