Standard Thinking's decision model. Ministral 3 8B with a merged LoRA, served through stock SGLang and an open jev-adapter that exposes /v1/systemone and scores the supplied options in one forward pass with per-type temperatures.
Juspay's open decision model. Qwen3.6-35B-A3B with a merged rank-16 LoRA, shipped with a pinned SGLang serving bundle that answers Choice, Score and Noul questions over text, images or a video through a TypeSafe-compatible /v1/systemone API.
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 standard-one and xor?
standard-one is from Standard Thinking and xor from Juspay. standard-one has open weights and a hosted API; xor has open weights you can download and run. Both answer choice, score, noul, classify and route questions. standard-one is the smaller model, at 8.0B parameters to 35B.
Which is more accurate, standard-one or xor?
They report on different suites — standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2), xor 90.0% on JevBench public set (231 items), maker's self-run of Xor 1.2 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, standard-one or xor?
standard-one: Hosted, price not published, or free to self-host. xor: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, standard-one or xor?
By their publishers’ figures, standard-one answers in about 25.8 ms at the median and xor in about 69 ms — measured on different hardware, so treat it as a rough guide.
Can I run standard-one or xor locally?
Yes, both: systemone pull standard-thinking/standard-one and systemone pull juspay/xor download the weights.
JevBench public set (231 items), maker's self-run of Xor 1.2
