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.
Juspay's open decision model. Qwen3.6-35B-A3B with a merged rank-16 LoRA, shipped with a pinned SGLang serving bundle that answers Choice, Score and Noul questions over text, images or a video through a TypeSafe-compatible /v1/systemone API.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
simple-jev
xor
Fine-tuned from
google/gemma-4-26b-a4b-it
qwen/qwen3.6-35b-a3b
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
—
90.0%
Calibration error
—
0.073
Valid action rate
—
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Median latency
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69 ms
p95 latency
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162 ms
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 xor?
simple-jev is from Featherless AI and xor from Juspay. simple-jev has open weights and a hosted API; xor has open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, simple-jev or xor?
Only xor publishes an accuracy figure (90.0% on JevBench public set (231 items), maker's self-run of Xor 1.2); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, simple-jev or xor?
simple-jev: Hosted, price not published, or free to self-host. xor: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run simple-jev or xor locally?
Yes, both: systemone pull featherless-ai/simple-jev and systemone pull juspay/xor download the weights.
Evaluation suite
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JevBench public set (231 items), maker's self-run of Xor 1.2
