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.
A 4B decision model fine-tuned from Qwen3.5-4B-Base. A state and typed questions (choice, yes/no, score) go in; a temperature-calibrated probability for every option comes out of one pass, through a self-hosted /v1/systemone-compatible server. Apache-2.0 weights and code.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
decider
wald
Fine-tuned from
qwen/qwen3.5-2b-base
qwen/qwen3.5-4b-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
75.2%
88.3%
Calibration error
—
0.045
Valid action rate
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Median latency
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33 ms
p95 latency
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168 ms
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 decider and wald?
decider is from Mapika and wald from ORG2 AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. wald reads up to 128K tokens of state, against 32K tokens for decider. decider is the smaller model, at 1.9B parameters to 4.2B.
Which is more accurate, decider or wald?
They report on different suites — decider 75.2% on Decider regression set, 28 held-out tasks (decider-2b v11), wald 88.3% on JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, decider or wald?
decider: Free (open weights). wald: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decider or wald locally?
Yes, both: systemone pull mapika/decider and systemone pull org2ai/wald download the weights.
