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 144M-parameter System One model on the multilingual mmBERT-small encoder. Takes a state, a question and 2 to 20 options and returns one decision with probabilities for choice, score and yes/no questions, on a CPU.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
jev
julia
Fine-tuned from
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jhu-clsp/mmbert-small
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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73.2%
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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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 julia-1?
jev is from TypeSafe AI and julia-1 from Supersonic Labs. jev is only available as a hosted API; julia-1 has open weights you can download and run. Both answer choice, score, noul, classify and route questions. jev reads up to 32K tokens of state, against 8K tokens for julia-1. jev is licensed proprietary; julia-1, apache-2.0.
Which is more accurate, jev or julia-1?
Only julia-1 publishes an accuracy figure (73.2% on typed-decisions test set (400 cases, 2,000 questions)); jev does not, so there is no comparison to make without your own test.
Which is cheaper, jev or julia-1?
jev: $0.042 / $0 per 1M. julia-1: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jev or julia-1 locally?
julia-1 yes — systemone pull supersonic-labs/julia-1 downloads its weights. The other is only served as a hosted API.
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typed-decisions test set (400 cases, 2,000 questions)
