Updated 2 November 2026
Alternatives to laya
Grouped by the reason you would actually switch, not by a leaderboard position that does not exist yet.
laya 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.
47 of the 344 projects in our directory reference laya, so there is working code to read before you commit either way. Full detail on the model itself is on its page.
Choosing between them
Every group below is built from facts you can check on the model’s own card: licence, format, architecture and size. To see which candidate holds up against laya on your traffic, a private benchmark runs them side by side on your own historical decisions.
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.
JEV-27B-VL
Qwen3.8-27B fine-tuned to return typed decisions, 28B parameters.
JEV-35B
Qwen3.5-35B-A3B fine-tuned to return typed decisions, 36B parameters.
Qwen-2.5-1B-RLCD
Qwen2.5-1.5B-Instruct fine-tuned to return typed decisions.
JEV-27B
Qwen3.8-27B fine-tuned to return typed decisions, 27B parameters.
AutoTrust-JEV-27B-v1.1
AutoTrust-JEV-27B-v1.1, an open-weight model published to return typed decisions, 27B parameters.
Jev-Omni
gemma-4-12B-it fine-tuned to return typed decisions.
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.
Qwen-2.5-1B-RLCD
Qwen2.5-1.5B-Instruct fine-tuned to return typed decisions.
jev
jev, an open-weight model published to return typed decisions, 882M parameters.
raya
laya fine-tuned to return typed decisions, 322M parameters.
Bonsai-JEV
Ternary-Bonsai-2-27B-gguf fine-tuned to return typed decisions, 27B parameters.
wolf-defender-jev
mmBERT-small fine-tuned to return typed decisions, 151M parameters.
archai-jev-zagreus-0.4b-ita
zagreus-0.4B-ita fine-tuned to return typed decisions, 438M parameters.
Smaller and cheaper to run
Fewer parameters, so cheaper per decision at volume. Whether they give up accuracy for it is unmeasured.
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.
embeddinggemma-2-ft-typed-decision
embeddinggemma-2 fine-tuned to return typed decisions, 272M parameters.
laya-vision-smolvlm-256m
SmolVLM-256M-Instruct fine-tuned to return typed decisions, 237M parameters. Licensed cc-by-nc-sa-4.0, check before commercial use.
LFM2.5-350M-RLCD
A typed-decision model from notnotsamuel, 354M parameters. Licensed other, check before commercial use.
mini-Jev
Qwen3-0.6B fine-tuned to return typed decisions.
modernbert-ja-310m-jev
modernbert-ja-310m fine-tuned to return typed decisions, 315M parameters. Licensed cc-by-sa-4.0, check before commercial use.
Compare them head to head
laya vs Jev
Side by side on licence, architecture, size and what has been measured.
laya vs JEV-27B-VL
Side by side on licence, architecture, size and what has been measured.
laya vs JEV-35B
Side by side on licence, architecture, size and what has been measured.
laya vs Qwen-2.5-1B-RLCD
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.