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.
Featherless AI's open server that turns a stock Hugging Face language model into a typed-decision endpoint. It reads the next-token logits for each question and builds the Choice, Score or Noul answer itself; no classifier head is trained and no JSON is generated.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
lumma-fev
simple-jev
Fine-tuned from
frontiersmind/lumma-0.6b-base
google/gemma-4-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
—
Featherless AI
Input price
—
—
Decision accuracy
64.0%
—
Calibration error
—
—
Valid action rate
—
—
Median latency
45.8 ms
—
p95 latency
—
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 simple-jev?
lumma-fev is from FrontiersMind and simple-jev from Featherless AI. lumma-fev has open weights you can download and run; simple-jev has open weights and a hosted API. Both answer choice, score and noul questions. Only simple-jev answers classify and route.
Which is more accurate, lumma-fev or simple-jev?
Only lumma-fev publishes an accuracy figure (64.0% on typed-decisions (maker's table; split not stated)); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, lumma-fev or simple-jev?
lumma-fev: Free (open weights). simple-jev: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run lumma-fev or simple-jev locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull featherless-ai/simple-jev download the weights.
