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.
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
julia
openthai
Fine-tuned from
jhu-clsp/mmbert-small
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
73.2%
74.3%
Calibration error
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Valid action rate
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Median latency
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40 ms
p95 latency
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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 openthai-systemone?
julia-1 is from Supersonic Labs and openthai-systemone from iApp Technology. 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 800M.
Which is more accurate, julia-1 or openthai-systemone?
They report on different suites — julia-1 73.2% on typed-decisions test set (400 cases, 2,000 questions), 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, julia-1 or openthai-systemone?
julia-1: Free (open weights). openthai-systemone: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run julia-1 or openthai-systemone locally?
Yes, both: systemone pull supersonic-labs/julia-1 and systemone pull iapp-technology/openthai-systemone download the weights.
Evaluation suite
typed-decisions test set (400 cases, 2,000 questions)
