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.
Uprelic's hosted decision API, run on GPUs in Paris. Answers noul, choice and score questions about text, JSON and up to 8 images, with a probability for every option and no generated text, on the same request shape as Jev. No weights; base model and size not disclosed.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
lumma-fev
strom
Fine-tuned from
frontiersmind/lumma-0.6b-base
—
License
apache-2.0
proprietary
Availability
Open weights
Hosted API
Hosted by
—
Uprelic
Input price
—
$0.042/MTok
Decision accuracy
64.0%
87.0%
Calibration error
—
0.033
Valid action rate
—
—
Median latency
45.8 ms
136 ms
p95 latency
—
—
Evaluation suite
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 strom?
lumma-fev is from FrontiersMind and strom from Uprelic. lumma-fev has open weights you can download and run; strom is only available as a hosted API. Both answer choice, score and noul questions. Only strom answers classify and route. strom reads up to 32K tokens of state, against 8K tokens for lumma-fev. lumma-fev is licensed apache-2.0; strom, proprietary.
Which is more accurate, lumma-fev or strom?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), strom 87.0% on JevBench public set (231 items), the maker's production run of Strom 1.0.7 on 1 Oct 2026 (Open-Jev harness format) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or strom?
lumma-fev: Free (open weights). strom: $0.042 / $0 per 1M. Open weights cost nothing per call beyond your own hardware.
Which is faster, lumma-fev or strom?
By their publishers’ figures, lumma-fev answers in about 45.8 ms at the median and strom in about 136 ms — measured on different hardware, so treat it as a rough guide.
Can I run lumma-fev or strom locally?
lumma-fev yes — systemone pull frontiersmind/lumma-fev downloads its weights. The other is only served as a hosted API.
typed-decisions (maker's table; split not stated)
JevBench public set (231 items), the maker's production run of Strom 1.0.7 on 1 Oct 2026 (Open-Jev harness format)
