Qwen3.5-4B with a merged rank-16 LoRA distilled from 17,408 teacher questions and 30,000 public training rows, read out as a temperature-scaled softmax over answer-letter logits. Also in 9B, 2B and GGUF.
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
jevk5
lev
Fine-tuned from
qwen/qwen3.5-4b
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
78.4%
68.9%
Calibration error
0.035
0.115
Valid action rate
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Median latency
13.2 ms
69 ms
p95 latency
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Evaluation suite
JevBench public hard tier
S1Bench (13 public subsets, macro)
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 jevk5 and lev?
jevk5 is from Alibi Serikbay and lev from Interfaze AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, jevk5 or lev?
They report on different suites — jevk5 78.4% on JevBench public hard tier, 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, jevk5 or lev?
jevk5: Free (open weights). lev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, jevk5 or lev?
By their publishers’ figures, jevk5 answers in about 13.2 ms at the median and lev in about 69 ms — measured on different hardware, so treat it as a rough guide.
Can I run jevk5 or lev locally?
Yes, both: systemone pull alibi-serikbay/jevk5 and systemone pull interfaze-ai/lev download the weights.