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.
Omar (kouhxp)'s CPU decision model: a Qwen3.5-0.8B fine-tune shipped as GGUF for llama.cpp that answers yes/no, choice and score questions with a probability per option plus a 'none of these' reject probability, served by a local Jev-style HTTP runtime.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
decider
gutsy
Fine-tuned from
qwen/qwen3.5-2b-base
qwen/qwen3.5-0.8b
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%
73.2%
Calibration error
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Valid action rate
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Median latency
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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 decider and gutsy?
decider is from Mapika and gutsy from Omar (kouhxp). Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. decider reads up to 32K tokens of state, against 8K tokens for gutsy. gutsy is the smaller model, at 800M parameters to 1.9B.
Which is more accurate, decider or gutsy?
They report on different suites — decider 75.2% on Decider regression set, 28 held-out tasks (decider-2b v11), gutsy 73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, decider or gutsy?
decider: Free (open weights). gutsy: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decider or gutsy locally?
Yes, both: systemone pull mapika/decider and systemone pull kouhxp/gutsy download the weights.
