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.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
cygnet
mira
Fine-tuned from
google/gemma-4-12b-it
jhu-clsp/mmbert-small
License
mit
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
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—
Decision accuracy
87.9%
74.5%
Calibration error
—
0.025
Valid action rate
—
100.0%
Median latency
50 ms
30 ms
p95 latency
—
43 ms
Evaluation suite
JevBench public set (231 items), JevBench CLI
s1-decision-bench
Latest version
2026.09
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 cygnet and mira?
cygnet is from Blockbrain Labs and mira from SAGEA. Both have open weights you can download and run. Both answer choice, score and noul questions. Only mira answers classify and route. cygnet is licensed mit; mira, apache-2.0.
Which is more accurate, cygnet or mira?
They report on different suites — cygnet 87.9% on JevBench public set (231 items), JevBench CLI, mira 74.5% on s1-decision-bench — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, cygnet or mira?
cygnet: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, cygnet or mira?
By their publishers’ figures, mira answers in about 30 ms at the median and cygnet in about 50 ms — measured on different hardware, so treat it as a rough guide.
Can I run cygnet or mira locally?
Yes, both: systemone pull blockbrain-labs/cygnet and systemone pull sagea/mira download the weights.