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.
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
simple-jev
this-that
Fine-tuned from
google/gemma-4-26b-a4b-it
mapika/decider
License
apache-2.0
mit
Availability
Open weights + hosted API
Open weights
Hosted by
Featherless AI
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Input price
—
—
Decision accuracy
—
87.8%
Calibration error
—
—
Valid action rate
—
—
Median latency
—
31.4 ms
p95 latency
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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 simple-jev and this-that-model?
simple-jev is from Featherless AI and this-that-model from FLock.io. simple-jev 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 simple-jev answers classify and route. simple-jev is licensed apache-2.0; this-that-model, mit.
Which is more accurate, simple-jev or this-that-model?
Only this-that-model publishes an accuracy figure (87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark)); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, simple-jev or this-that-model?
simple-jev: 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.
Can I run simple-jev or this-that-model locally?
Yes, both: systemone pull featherless-ai/simple-jev and systemone pull flock-io/this-that-model download the weights.
