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.
Typed decisions from Google's frozen Gemma-4-12B-it, with no fine-tuning. A small shim over unmodified vLLM letters the options, reads the model's own probability for each letter at one answer position and applies one calibration temperature; one output token per decision.
Decides
choice, noul, score, classify, route
choice, score, noul
Architecture
nimble
cygnet
Fine-tuned from
qwen/qwen3.5-9b
google/gemma-4-12b-it
License
apache-2.0
mit
Availability
Open weights + hosted API
Open weights
Hosted by
Bespoke Labs
—
Input price
—
—
Decision accuracy
90.1%
87.9%
Calibration error
0.054
—
Valid action rate
—
—
Median latency
106 ms
50 ms
p95 latency
—
—
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 cygnet?
bespoke-nimble-9b is from Bespoke Labs and cygnet from Blockbrain Labs. bespoke-nimble-9b has open weights and a hosted API; cygnet has open weights you can download and run. Both answer choice, noul and score questions. Only bespoke-nimble-9b answers classify and route. cygnet reads up to 16K tokens of state, against 8K tokens for bespoke-nimble-9b. bespoke-nimble-9b is the smaller model, at 9.0B parameters to 12B. bespoke-nimble-9b is licensed apache-2.0; cygnet, mit.
Which is more accurate, bespoke-nimble-9b or cygnet?
They report on different suites — bespoke-nimble-9b 90.1% on Bespoke held-out set (324 examples), cygnet 87.9% on JevBench public set (231 items), JevBench CLI — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bespoke-nimble-9b or cygnet?
bespoke-nimble-9b: Hosted, price not published, or free to self-host. cygnet: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, bespoke-nimble-9b or cygnet?
By their publishers’ figures, cygnet answers in about 50 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 cygnet locally?
Yes, both: systemone pull bespoke-labs/bespoke-nimble-9b and systemone pull blockbrain-labs/cygnet download the weights.
