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.
An open System One model from Interfaze AI. A rank-32 LoRA on Qwen3.5-4B reads yes/no, choice and score answers from the logits of one forward pass, returns calibrated probabilities and serves the /v1/systemone API.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
julia
lev
Fine-tuned from
jhu-clsp/mmbert-small
qwen/qwen3.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
73.2%
68.9%
Calibration error
—
0.115
Valid action rate
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Median latency
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69 ms
p95 latency
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Evaluation suite
typed-decisions test set (400 cases, 2,000 questions)
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 julia-1 and lev?
julia-1 is from Supersonic Labs and lev from Interfaze AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. julia-1 is the smaller model, at 144M parameters to 4.0B.
Which is more accurate, julia-1 or lev?
They report on different suites — julia-1 73.2% on typed-decisions test set (400 cases, 2,000 questions), lev 68.9% on S1Bench (13 public subsets, macro) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, julia-1 or lev?
julia-1: Free (open weights). lev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run julia-1 or lev locally?
Yes, both: systemone pull supersonic-labs/julia-1 and systemone pull interfaze-ai/lev download the weights.
