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JEV-27B-VL
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JEV-27B-VL is an open-weights decision model from AutoTrust AI Lab. Its published size is 27.8B parameters. Licence: apache-2.0.
Catalogue identity notes
Exact Decision Index tested engine jev-27b-vl; reported model JEV-27B-VL. Type: LoRA; 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.
$ how it compares
The decision models ranked either side of JEV-27B-VL, out of 357 with a score, then more from AutoTrust AI Lab.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 67 | AnyJev L0 · Gemma-4-31B (decision readout) | 64±1 | source code | ||
| 68 |  OneJev1.6-9B | 64±1 | model files | ||
| 69 | JEV-27B-VL | 64±4 | model files | ||
| 70 | KnowLine 35B-A3B Gen1 | 64±4 | model files | ||
| 71 | jev-vision-27b | 64±4 | model files | ||
| more from AutoTrust AI Lab | |||||
| 18 | AutoTrust-JEV-27B v1.1 | 70±1 | model files | ||
| 83 | JEV-35B | 63±1 | model files | ||
| 98 | GEV-26B-Decide | 61±1 | model files | ||
see all decision models · find the ones that fit your machine
$ what it's good at
vision▁·▃▂▃▃▇▄▄█▄▃ #28 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.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted | 78.6200 points ✎ | ▁·▁▁▃▁▄▄▃▅█▂ beats 37/61 | 12% |
| Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted | 40.1300 points ✎ | ▁▁▅▁▄▁▄█▄▃▇▇ beats 42/61 | 11% |
| Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted | 84.5000 points ✎ | ▁·▁▁▁▁·▂▁▃▅█ beats 47/61 | 10% |
| Decision Index Vision 0.4: private same skills (points) | 66.4000 points ✎ | ▁▁▃▃▁▂▄▆▃▃▅█ beats 51/61 | 10% |
| Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted | 40.3100 points ✎ | ▆▃▃▆▄▄▇██▁▆▆ beats 36/61 | 8.8% |
| Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted | 72.2500 points ✎ | ▁·▂▂▁▃▃▅▅▃▂█ beats 49/61 | 8.0% |
| Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted | 79.7000 points ✎ | ▂▁▁▁▁▃▁▅▅▆██ beats 50/61 | 7.8% |
| Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted | 96.7200 points ✎ | ▁·▁▂▁▁▃▁▂▂▅█ beats 44/61 | 6.2% |
4 more tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted | 31.0600 points ✎ | ▂▄▇▆█▄▄▇▂··▁ beats 43/61 | 6.2% |
| Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted | 63.2700 points ✎ | ▁···▁▂▂▂█▄▂▃ beats 48/61 | 5.9% |
| Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted | 76.8500 points ✎ | ▁▁▁▁▂▁▁▁▄█▃▃ beats 47/61 | 4.7% |
| Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted | 92.0500 points ✎ | ▁··▁▁▁▁▁▂▄█▁ beats 38/61 | 3.4% |
$ 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/autotrust ↗
$ 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
| source | results | share of the score |
|---|---|---|
 Decision Index Vision 0.4 · submitted; independent execution unverified | 12 | 95% |
| prior · pull toward the middle | 5% | |
| weighted average | = 63.6 |
$ 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