Berget AI's System One model for gating agent commands: a LoRA adapter and a fine-tuned joint schema head on Cloudflare's Clef-Flash that answer noul, choice and score questions over a state in one forward pass. Trained on Swedish and English operations decisions.
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
noul, choice, score
choice, score, noul, classify, route
Architecture
clef
decider
Fine-tuned from
cloudflare/clef-flash
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
97.0%
75.2%
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 bev and decider?
bev is from Berget AI and decider from Mapika. Both have open weights you can download and run. Both answer noul, choice and score questions. Only decider answers classify and route. decider is the smaller model, at 1.9B parameters to 9.0B.
Which is more accurate, bev or decider?
They report on different suites — bev 97.0% on Berget held-out risk split (16,902 questions; same operations-traffic corpora as training), decider 75.2% on Decider regression set, 28 held-out tasks (decider-2b v11) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bev or decider?
bev: Free (open weights). decider: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bev or decider locally?
Yes, both: systemone pull berget-ai/bev and systemone pull mapika/decider download the weights.
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Evaluation suite
Berget held-out risk split (16,902 questions; same operations-traffic corpora as training)
