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.
A Gemma 4 12B fine-tune for typed decisions that also chats and reads images. Ships as GGUF for llama.cpp, holds a 64K context on a 16 GB GPU, and serves /v1/systemone next to /v1/chat/completions.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
simple-jev
winnow
Fine-tuned from
google/gemma-4-26b-a4b-it
google/gemma-4-12b-it
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Featherless AI
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Input price
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Decision accuracy
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85.7%
Calibration error
—
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Valid action rate
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Median latency
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143 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 simple-jev and winnow?
simple-jev is from Featherless AI and winnow from EldanRing. simple-jev has open weights and a hosted API; winnow has open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, simple-jev or winnow?
Only winnow publishes an accuracy figure (85.7% on JevBench public subset (231 items), Q8_0); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, simple-jev or winnow?
simple-jev: Hosted, price not published, or free to self-host. winnow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run simple-jev or winnow locally?
Yes, both: systemone pull featherless-ai/simple-jev and systemone pull eldanring/winnow download the weights.
