FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
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.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
lumma-fev
standard-one
Fine-tuned from
frontiersmind/lumma-0.6b-base
mistralai/ministral-3-8b-instruct-2512-bf16
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
—
Standard Thinking
Input price
—
—
Decision accuracy
64.0%
71.1%
Calibration error
—
—
Valid action rate
—
—
Median latency
45.8 ms
25.8 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 lumma-fev and standard-one?
lumma-fev is from FrontiersMind and standard-one from Standard Thinking. lumma-fev has open weights you can download and run; standard-one has open weights and a hosted API. Both answer choice, score and noul questions. Only standard-one answers classify and route. lumma-fev is the smaller model, at 649M parameters to 8.0B.
Which is more accurate, lumma-fev or standard-one?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or standard-one?
lumma-fev: Free (open weights). standard-one: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Which is faster, lumma-fev or standard-one?
By their publishers’ figures, standard-one answers in about 25.8 ms at the median and lumma-fev in about 45.8 ms — measured on different hardware, so treat it as a rough guide.
Can I run lumma-fev or standard-one locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull standard-thinking/standard-one download the weights.
