Hanno Labs' small calibrated decision model. A LoRA on Qwen3-1.7B plus trained decision-token embeddings with stable slots, returning the full distribution over up to 255 caller-defined choices and a null slot for Choice, Score and Noul questions.
Juspay's open decision model. Qwen3.6-35B-A3B with a merged rank-16 LoRA, shipped with a pinned SGLang serving bundle that answers Choice, Score and Noul questions over text, images or a video through a TypeSafe-compatible /v1/systemone API.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
bosun
xor
Fine-tuned from
qwen/qwen3-1.7b
qwen/qwen3.6-35b-a3b
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
84.9%
90.0%
Calibration error
0.050
0.073
Valid action rate
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—
Median latency
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69 ms
p95 latency
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162 ms
Evaluation suite
DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families)
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 bosun and xor?
bosun is from Hanno Labs and xor from Juspay. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. bosun is the smaller model, at 2.0B parameters to 35B.
Which is more accurate, bosun or xor?
They report on different suites — bosun 84.9% on DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families), xor 90.0% on JevBench public set (231 items), maker's self-run of Xor 1.2 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bosun or xor?
bosun: Free (open weights). xor: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bosun or xor locally?
Yes, both: systemone pull hanno-labs/bosun and systemone pull juspay/xor download the weights.
JevBench public set (231 items), maker's self-run of Xor 1.2
