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.
FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
bosun
lumma-fev
Fine-tuned from
qwen/qwen3-1.7b
frontiersmind/lumma-0.6b-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
84.9%
64.0%
Calibration error
0.050
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Valid action rate
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Median latency
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45.8 ms
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 bosun and lumma-fev?
bosun is from Hanno Labs and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice, score and noul questions. Only bosun answers classify and route. lumma-fev is the smaller model, at 649M parameters to 2.0B.
Which is more accurate, bosun or lumma-fev?
They report on different suites — bosun 84.9% on DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families), lumma-fev 64.0% on typed-decisions (maker's table; split not stated) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bosun or lumma-fev?
bosun: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bosun or lumma-fev locally?
Yes, both: systemone pull hanno-labs/bosun and systemone pull frontiersmind/lumma-fev download the weights.
Evaluation suite
DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families)
