A typed-decision layer for Google's DiffusionGemma, from David Villalón at Maisa AI. It compiles a request into a small answer canvas, runs one denoising read on patched vLLM and reads the probabilities of the allowed labels, for text, images and images offered as options.
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.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
djev
neohorse
Fine-tuned from
google/diffusiongemma-26b-a4b-it
tokenrhythm/neohorse-1-4b
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Maisa
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Input price
$0.035/MTok
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Decision accuracy
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75.3%
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 djev and neohorse-jev?
djev is from Maisa and neohorse-jev from TokenRhythm. djev has open weights and a hosted API; neohorse-jev has open weights you can download and run. Both answer choice, score and noul questions. Only neohorse-jev answers classify and route. neohorse-jev is the smaller model, at 4.0B parameters to 26B.
Which is more accurate, djev or neohorse-jev?
Only neohorse-jev publishes an accuracy figure (75.3% on JevBench public set (231 items), vLLM, maker's run); djev does not, so there is no comparison to make without your own test.
Which is cheaper, djev or neohorse-jev?
djev: $0.035 / $0 per 1M, or free to self-host. neohorse-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run djev or neohorse-jev locally?
Yes, both: systemone pull maisa/djev and systemone pull tokenrhythm/neohorse-jev download the weights.
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Evaluation suite
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JevBench public set (231 items), vLLM, maker's run