19 models · checked 20 September 2026
Decision models under 500M parameters
Small enough to run on a CPU next to whatever is making the decision. This is where the cost argument for the whole category lives.
laya
convaiinnovations
The most-liked open-weight decision model of the post-Jev wave, a 421M ModernBERT-large encoder. The base release of a three-model family.
421M · apache-2.0
Community cardlaya-multilingual
convaiinnovations
The only multilingual decision model we have found, a 322M encoder on mmBERT. Also the one our English-only test set cannot say anything useful about.
322M · apache-2.0
Community cardlaya-typed-decisions
convaiinnovations
The Laya variant tuned on the typed-decisions benchmark, and the only open model whose card publishes head-to-head numbers against Jev. One of those numbers is the reason we started measuring.
421M · apache-2.0
Community cardopen-jev-deberta-v3-large
com-kotobalabs
A DeBERTa-v3-large encoder positioned as an open reimplementation of Jev. The highest ratio of likes to downloads in the directory, which is usually what a credible claim looks like early.
434M · microsoft/deberta-v3-large · apache-2.0
Community cardLFM2.5-350M-RLCD
notnotsamuel
A typed-decision model from notnotsamuel, 354M parameters. Licensed other, check before commercial use.
354M · other
Community cardrlcd-modernbert-151m
heman10x
The smallest decision model we track, at 151M parameters on a GLiClass/ModernBERT encoder. If it holds accuracy, it changes what this class of model costs to run.
151M · knowledgator/gliclass-modern-base-v2.0 · apache-2.0
Community cardmodernbert-ja-310m-jev
argos1111
modernbert-ja-310m fine-tuned to return typed decisions, 315M parameters. Licensed cc-by-sa-4.0, check before commercial use.
315M · sbintuitions/modernbert-ja-310m · cc-by-sa-4.0
Community cardlaya-mlx
aac6fef
laya fine-tuned to return typed decisions, 421M parameters.
421M · convaiinnovations/laya · apache-2.0
Community cardlaya-vision-smolvlm-256m
thaitea
SmolVLM-256M-Instruct fine-tuned to return typed decisions, 237M parameters. Licensed cc-by-nc-sa-4.0, check before commercial use.
237M · HuggingFaceTB/SmolVLM-256M-Instruct · cc-by-nc-sa-4.0
Community cardjev-schema-scorer-deberta-v3-large
mobarmg
deberta-v3-large fine-tuned to return typed decisions, 435M parameters.
435M · microsoft/deberta-v3-large · mit
Community cardlaya-multilingual-mlx
aac6fef
laya-multilingual fine-tuned to return typed decisions, 322M parameters.
322M · convaiinnovations/laya-multilingual · apache-2.0
Community cardlaya-grounded
Luni
laya fine-tuned to return typed decisions, 421M parameters. Licensed cc-by-nc-4.0, check before commercial use.
421M · convaiinnovations/laya · cc-by-nc-4.0
Community cardsystemone-lite-0.5b
dwidlee
A 0.5B decision model on Qwen2.5, the only entry here built on a previous-generation base. Real downloads, zero likes.
494M · Qwen/Qwen2.5-0.5B-Instruct · apache-2.0
Community cardsystem-one-mini
DavidHatley
distilbert-base-uncased fine-tuned to return typed decisions, 69M parameters.
69M · distilbert/distilbert-base-uncased · apache-2.0
Community cardbert4jev
ukung
deberta-v3-large fine-tuned to return typed decisions, 434M parameters.
434M · microsoft/deberta-v3-large · apache-2.0
Community cardsystem-one-distilled
shreyanbr
deberta-v3-xsmall-zeroshot-v1.1-all-33 fine-tuned to return typed decisions, 71M parameters.
71M · MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33 · apache-2.0
Community cardsystem-one-gold
shreyanbr
deberta-v3-xsmall-zeroshot-v1.1-all-33 fine-tuned to return typed decisions, 71M parameters.
71M · MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33 · apache-2.0
Community cardsystem-one-zeroshot
shreyanbr
deberta-v3-xsmall-zeroshot-v1.1-all-33 fine-tuned to return typed decisions, 71M parameters.
71M · MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33 · apache-2.0
Community cardlaya-typed-decisions-mlx
aac6fef
laya-typed-decisions fine-tuned to return typed decisions, 421M parameters.
421M · convaiinnovations/laya-typed-decisions · apache-2.0
Find out what your data actually supports
We turn your historical decisions into a frozen test set, then tell you which model, which threshold, and how much of it you can safely automate.