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.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
standard-one
this-that
Fine-tuned from
mistralai/ministral-3-8b-instruct-2512-bf16
mapika/decider
License
apache-2.0
mit
Availability
Open weights + hosted API
Open weights
Hosted by
Standard Thinking
—
Input price
—
—
Decision accuracy
71.1%
87.8%
Calibration error
—
—
Valid action rate
—
—
Median latency
25.8 ms
31.4 ms
p95 latency
41.9 ms
—
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 this-that-model?
standard-one is from Standard Thinking and this-that-model from FLock.io. standard-one has open weights and a hosted API; this-that-model has open weights you can download and run. Both answer choice, score and noul questions. Only standard-one answers classify and route. this-that-model is the smaller model, at 1.9B parameters to 8.0B. standard-one is licensed apache-2.0; this-that-model, mit.
Which is more accurate, standard-one or this-that-model?
They report on different suites — standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2), this-that-model 87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, standard-one or this-that-model?
standard-one: Hosted, price not published, or free to self-host. this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, standard-one or this-that-model?
By their publishers’ figures, standard-one answers in about 25.8 ms at the median and this-that-model in about 31.4 ms — measured on different hardware, so treat it as a rough guide.
Can I run standard-one or this-that-model locally?
Yes, both: systemone pull standard-thinking/standard-one and systemone pull flock-io/this-that-model download the weights.
