TokenRhythm's prefill-only decision model for agent workflows. Built on its NeoHorse-1-4B (a Qwen3.5-4B derivative), it predicts Choice, Noul and Score answers over application-defined options without generating text, with optional single-image input.
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
neohorse
simple-jev
Fine-tuned from
tokenrhythm/neohorse-1-4b
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
75.3%
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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
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 neohorse-jev and simple-jev?
neohorse-jev is from TokenRhythm and simple-jev from Featherless AI. neohorse-jev 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, neohorse-jev or simple-jev?
Only neohorse-jev publishes an accuracy figure (75.3% on JevBench public set (231 items), vLLM, maker's run); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, neohorse-jev or simple-jev?
neohorse-jev: 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 neohorse-jev or simple-jev locally?
Yes, both: systemone pull tokenrhythm/neohorse-jev and systemone pull featherless-ai/simple-jev download the weights.
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Evaluation suite
JevBench public set (231 items), vLLM, maker's run
