whatmodel.sh / decision / matilda-jev-v1.5 (27B)
matilda-jev-v1.5 (27B)
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matilda-jev-v1.5 (27B) is an open-weights decision model from Maincode, released 9 October 2026.
Catalogue identity notes
Exact Decision Index tested engine matilda-jev-v1.5; reported model matilda-jev-v1.5 (27B). Type: None; technique: None. Comparison entry; no current run command, API price or speed inferred from the benchmark.
Model information sources
$ how it compares
The decision models ranked either side of matilda-jev-v1.5 (27B), out of 357 with a score, then more from Maincode.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 140 |  Trio-Spark DI (9B) | 57±1 | request access | ||
| 141 | openjev (DecisionBench ec4ff3df) | 57±2 | model files | ||
| 142 | matilda-jev-v1.5 (27B) | 57±4 | model files | ||
| 143 |  Jebadiah 9B v2.1 (T1e) | 57±1 | local | ||
| 144 |  CoCo-Decision-4B 2.4 | 57±1 | model files | ||
| more from Maincode | |||||
| 31 | Matilda Decision v1 | 68±2 | model files | ||
see all decision models · find the ones that fit your machine
$ what it's good at
vision▁·▃▂▃▃▇▄▄█▄▃ #43 of 79 · 95% of its score · 12 tests
Beats Surogate Rune 26B-A4B v3, Clef-omni here, though they rank higher overall. Trails JPT-9B Qwen3.5-9B LoRA, Solomon v1.1 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 | 63.8200 points ✎ | ▁·▁▁▃▁▄▄▃▅█▂ beats 17/61 | 12% |
| Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted | 27.2100 points ✎ | ▁▁▅▁▄▁▄█▄▃▇▇ beats 21/61 | 11% |
| Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted | 79.7500 points ✎ | ▁·▁▁▁▁·▂▁▃▅█ beats 31/61 | 10% |
| Decision Index Vision 0.4: private same skills (points) | 58.6700 points ✎ | ▁▁▃▃▁▂▄▆▃▃▅█ beats 36/61 | 10% |
| Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted | 36.8500 points ✎ | ▆▃▃▆▄▄▇██▁▆▆ beats 30/61 | 8.8% |
| Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted | 58.9400 points ✎ | ▁·▂▂▁▃▃▅▅▃▂█ beats 27/61 | 8.0% |
| Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted | 75.9400 points ✎ | ▂▁▁▁▁▃▁▅▅▆██ beats 39/61 | 7.8% |
| Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted | 77.0500 points ✎ | ▁·▁▂▃▁▃▁▂▂▅█ beats 9/61 | 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 | 37.1900 points ✎ | ▂▄▇▆█▄▄▇▂··▁ beats 53/61 | 6.2% |
| Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted | 53.2800 points ✎ | ▁···▁▂▂▂█▄▂▃ beats 27/61 | 5.9% |
| Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted | 76.4400 points ✎ | ▁▁▁▁▂▁▁▁▄█▃▃ beats 43/61 | 4.7% |
| Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted | 90.3800 points ✎ | ▁··▁▁▁▁▁▂▄█▁ beats 23/61 | 3.4% |
$ 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/Maincode ↗
$ 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 | = 56.6 |
$ 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