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.
A typed-decision layer for Google's DiffusionGemma, from David Villalón at Maisa AI. It compiles a request into a small answer canvas, runs one denoising read on patched vLLM and reads the probabilities of the allowed labels, for text, images and images offered as options.
Decides
choice, noul, score, classify, route
choice, score, noul
Architecture
nimble
djev
Fine-tuned from
qwen/qwen3.5-9b
google/diffusiongemma-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights + hosted API
Hosted by
Bespoke Labs
Maisa
Input price
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$0.035/MTok
Decision accuracy
90.1%
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Calibration error
0.054
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Valid action rate
—
—
Median latency
106 ms
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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 bespoke-nimble-9b and djev?
bespoke-nimble-9b is from Bespoke Labs and djev from Maisa. Both have open weights and a hosted API. Both answer choice, noul and score questions. Only bespoke-nimble-9b answers classify and route. bespoke-nimble-9b is the smaller model, at 9.0B parameters to 26B.
Which is more accurate, bespoke-nimble-9b or djev?
Only bespoke-nimble-9b publishes an accuracy figure (90.1% on Bespoke held-out set (324 examples)); djev does not, so there is no comparison to make without your own test.
Which is cheaper, bespoke-nimble-9b or djev?
bespoke-nimble-9b: Hosted, price not published, or free to self-host. djev: $0.035 / $0 per 1M, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run bespoke-nimble-9b or djev locally?
Yes, both: systemone pull bespoke-labs/bespoke-nimble-9b and systemone pull maisa/djev download the weights.
