A 144M-parameter System One model on the multilingual mmBERT-small encoder. Takes a state, a question and 2 to 20 options and returns one decision with probabilities for choice, score and yes/no questions, on a CPU.
Featherless AI's open server that turns a stock Hugging Face language model into a typed-decision endpoint. It reads the next-token logits for each question and builds the Choice, Score or Noul answer itself; no classifier head is trained and no JSON is generated.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
julia
simple-jev
Fine-tuned from
jhu-clsp/mmbert-small
google/gemma-4-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
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Featherless AI
Input price
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Decision accuracy
73.2%
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Calibration error
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Valid action rate
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Median latency
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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 julia-1 and simple-jev?
julia-1 is from Supersonic Labs and simple-jev from Featherless AI. julia-1 has open weights you can download and run; simple-jev has open weights and a hosted API. Both answer choice, score, noul, classify and route questions.
Which is more accurate, julia-1 or simple-jev?
Only julia-1 publishes an accuracy figure (73.2% on typed-decisions test set (400 cases, 2,000 questions)); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, julia-1 or simple-jev?
julia-1: Free (open weights). simple-jev: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run julia-1 or simple-jev locally?
Yes, both: systemone pull supersonic-labs/julia-1 and systemone pull featherless-ai/simple-jev download the weights.
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Evaluation suite
typed-decisions test set (400 cases, 2,000 questions)
