The first System One model. Reads a state, answers typed Choice, Score and Noul questions in one call with calibrated probabilities, and generates no text. Closed weights, served by TypeSafe AI.
A local, Jev-compatible decision model from Rizzo AI Academy. XHToken's Spark-X2.5-4B with a merged typed-decisions LoRA, run on llama.cpp; yes/no, choice and score questions share one prefill of the state and are read from the answer-letter logits. No text is generated.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
jev
rizzo-flow
Fine-tuned from
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xhtoken/spark-x2.5-4b
License
proprietary
apache-2.0
Availability
Hosted API
Open weights
Hosted by
TypeSafe AI
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Input price
$0.042/MTok
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Decision accuracy
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64.8%
Calibration error
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0.112
Valid action rate
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Median latency
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195 ms
p95 latency
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Evaluation suite
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 jev and rizzo-flow?
jev is from TypeSafe AI and rizzo-flow from Rizzo AI Academy. jev is only available as a hosted API; rizzo-flow has open weights you can download and run. Both answer choice, score, noul, classify and route questions. jev is licensed proprietary; rizzo-flow, apache-2.0.
Which is more accurate, jev or rizzo-flow?
Only rizzo-flow publishes an accuracy figure (64.8% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0); jev does not, so there is no comparison to make without your own test.
Which is cheaper, jev or rizzo-flow?
jev: $0.042 / $0 per 1M. rizzo-flow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jev or rizzo-flow locally?
rizzo-flow yes — systemone pull rizzo-ai-academy/rizzo-flow downloads its weights. The other is only served as a hosted API.
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LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0
