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.
A Thai-and-English System One model. Qwen3.5-0.8B continued-pretrained on about 5B Thai tokens, its language-model head replaced by a 256-way slot head (slot 255 abstains), trained on 2–3M decision examples and temperature-calibrated per question type.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
lev
openthai
Fine-tuned from
qwen/qwen3.5-4b
qwen/qwen3.5-0.8b-base
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
68.9%
74.3%
Calibration error
0.115
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Valid action rate
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Median latency
69 ms
40 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 lev and openthai-systemone?
lev is from Interfaze AI and openthai-systemone from iApp Technology. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. openthai-systemone is the smaller model, at 800M parameters to 4.0B.
Which is more accurate, lev or openthai-systemone?
They report on different suites — lev 68.9% on S1Bench (13 public subsets, macro), openthai-systemone 74.3% on Bespoke public subsets (13, macro average) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lev or openthai-systemone?
lev: Free (open weights). openthai-systemone: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, lev or openthai-systemone?
By their publishers’ figures, openthai-systemone answers in about 40 ms at the median and lev in about 69 ms — measured on different hardware, so treat it as a rough guide.
Can I run lev or openthai-systemone locally?
Yes, both: systemone pull interfaze-ai/lev and systemone pull iapp-technology/openthai-systemone download the weights.
