Checked 20 September 2026
Alternatives to cua-s1-forms
Grouped by the reason you would actually switch, not by a leaderboard position that does not exist yet.
cua-s1-forms is open weights already, so switching is about fit rather than about control.
The reasons to move are narrow: a licence you can ship, a size you can afford, a format you can deploy, or an architecture whose confidence you trust. Each group below is one of those reasons.
No project in our directory references cua-s1-forms directly, which is worth knowing: you would be early. Full detail on the model itself is on its page.
Licensed for commercial use
Apache-2.0 or MIT. Start here if this is going into a product, because licence rules a model out faster than any benchmark.
laya
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.
Qwen-2.5-1B-RLCD
Qwen2.5-1.5B-Instruct fine-tuned to return typed decisions.
laya-multilingual
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.
decider-2b
The most-downloaded open-weight decision model in the post-Jev wave. A Qwen3.5-2B decoder fine-tuned to emit typed decisions, and the largest member of a three-model family.
laya-typed-decisions
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.
open-jev-deberta-v3-large
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.
Ship in a runnable local format
GGUF, MLX, ONNX or Core ML builds exist, so you can run one without assembling a Python serving stack first.
Smaller and cheaper to run
Fewer parameters, so cheaper per decision at volume. Whether they give up accuracy for it is unmeasured.
laya
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.
laya-multilingual
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.
decider-2b
The most-downloaded open-weight decision model in the post-Jev wave. A Qwen3.5-2B decoder fine-tuned to emit typed decisions, and the largest member of a three-model family.
laya-typed-decisions
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.
open-jev-deberta-v3-large
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.
LFM2.5-350M-RLCD
A typed-decision model from notnotsamuel, 354M parameters. Licensed other, check before commercial use.
The honest caveat
None of these has been benchmarked against cua-s1-forms by anyone independent, including us. This page groups candidates by facts you can check. It cannot tell you which one holds up on your traffic, and anyone presenting a ranking here is guessing.
Compare them head to head
cua-s1-forms vs Jev
Side by side on licence, architecture, size and what has been measured.
cua-s1-forms vs laya
Side by side on licence, architecture, size and what has been measured.
cua-s1-forms vs Qwen-2.5-1B-RLCD
Side by side on licence, architecture, size and what has been measured.
cua-s1-forms vs laya-multilingual
Side by side on licence, architecture, size and what has been measured.
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.