An open Jev-style LoRA on Qwen3.5-9B from Bespoke Labs, trained on 2,676 contrastively curated examples to score the allowed answer tokens directly for enums, booleans and rubric levels. Recipe, data and a public benchmark suite are released with it.
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, noul, score, classify, route
choice, score, noul
Architecture
nimble
lumma-fev
Fine-tuned from
qwen/qwen3.5-9b
frontiersmind/lumma-0.6b-base
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Bespoke Labs
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Input price
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Decision accuracy
90.1%
64.0%
Calibration error
0.054
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Valid action rate
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Median latency
106 ms
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 bespoke-nimble-9b and lumma-fev?
bespoke-nimble-9b is from Bespoke Labs and lumma-fev from FrontiersMind. bespoke-nimble-9b has open weights and a hosted API; lumma-fev has open weights you can download and run. Both answer choice, noul and score questions. Only bespoke-nimble-9b answers classify and route. lumma-fev is the smaller model, at 649M parameters to 9.0B.
Which is more accurate, bespoke-nimble-9b or lumma-fev?
They report on different suites — bespoke-nimble-9b 90.1% on Bespoke held-out set (324 examples), 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, bespoke-nimble-9b or lumma-fev?
bespoke-nimble-9b: Hosted, price not published, or free to self-host. lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, bespoke-nimble-9b or lumma-fev?
By their publishers’ figures, lumma-fev answers in about 45.8 ms at the median and bespoke-nimble-9b in about 106 ms — measured on different hardware, so treat it as a rough guide.
Can I run bespoke-nimble-9b or lumma-fev locally?
Yes, both: systemone pull bespoke-labs/bespoke-nimble-9b and systemone pull frontiersmind/lumma-fev download the weights.
