An open Jev-style decision model by Andy Zhang: a LoRA adapter on Gemma 4 26B-A4B (also on Gemma 4 12B) that reads a state and returns a temperature-calibrated probability for every option of a yes/no, choice or score question in one forward pass.
A family of open System One models by Mark Marosi that softmax option-letter logits at an answer slot: decider-0.8b, 2b, 4b and 35b-a3b on Qwen3.5 bases, decider-12b on Gemma-4-12B-it, and training-free readouts of larger chat models. This page carries decider-2b v11.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
ajev
decider
Fine-tuned from
google/gemma-4-26b-a4b-it
qwen/qwen3.5-2b-base
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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75.2%
Calibration error
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Valid action rate
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Median latency
49 ms
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p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between ajev and decider?
ajev is from Andy Zhang and decider from Mapika. Both have open weights you can download and run. Both answer choice, score and noul questions. Only decider answers classify and route. decider is the smaller model, at 1.9B parameters to 26B.
Which is more accurate, ajev or decider?
Only decider publishes an accuracy figure (75.2% on Decider regression set, 28 held-out tasks (decider-2b v11)); ajev does not, so there is no comparison to make without your own test.
Which is cheaper, ajev or decider?
ajev: Free (open weights). decider: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run ajev or decider locally?
Yes, both: systemone pull andy-zhang/ajev and systemone pull mapika/decider download the weights.
