A 0.6B replica of the System One idea trained on game environments. A Qwen3-0.6B backbone with an attention-based Choice head that scores a dynamic candidate set for Maze, Snake, ViZDoom and position prediction, shipped with its full training pipeline.
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, noul, score
choice, score, noul, classify, route
Architecture
nanojev
openthai
Fine-tuned from
qwen/qwen3-0.6b
qwen/qwen3.5-0.8b-base
License
mit
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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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 nanojev and openthai-systemone?
nanojev is from TianyuCodings and openthai-systemone from iApp Technology. Both have open weights you can download and run. Both answer choice, noul and score questions. Only openthai-systemone answers classify and route. nanojev is the smaller model, at 600M parameters to 800M. nanojev is licensed mit; openthai-systemone, apache-2.0.
Which is more accurate, nanojev or openthai-systemone?
Only openthai-systemone publishes an accuracy figure (74.3% on Bespoke public subsets (13, macro average)); nanojev does not, so there is no comparison to make without your own test.
Which is cheaper, nanojev or openthai-systemone?
nanojev: Free (open weights). openthai-systemone: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run nanojev or openthai-systemone locally?
Yes, both: systemone pull tianyu-codings/nanojev and systemone pull iapp-technology/openthai-systemone download the weights.
