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.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
neohorse
this-that
Fine-tuned from
tokenrhythm/neohorse-1-4b
mapika/decider
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
75.3%
87.8%
Calibration error
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Valid action rate
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Median latency
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31.4 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 neohorse-jev and this-that-model?
neohorse-jev is from TokenRhythm and this-that-model from FLock.io. Both have open weights you can download and run. Both answer choice, score and noul questions. Only neohorse-jev answers classify and route. this-that-model is the smaller model, at 1.9B parameters to 4.0B. neohorse-jev is licensed apache-2.0; this-that-model, mit.
Which is more accurate, neohorse-jev or this-that-model?
They report on different suites — neohorse-jev 75.3% on JevBench public set (231 items), vLLM, maker's run, this-that-model 87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, neohorse-jev or this-that-model?
neohorse-jev: Free (open weights). this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run neohorse-jev or this-that-model locally?
Yes, both: systemone pull tokenrhythm/neohorse-jev and systemone pull flock-io/this-that-model download the weights.
Evaluation suite
JevBench public set (231 items), vLLM, maker's run
