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.
A 4B decision model fine-tuned from Qwen3.5-4B-Base. A state and typed questions (choice, yes/no, score) go in; a temperature-calibrated probability for every option comes out of one pass, through a self-hosted /v1/systemone-compatible server. Apache-2.0 weights and code.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
lumma-fev
wald
Fine-tuned from
frontiersmind/lumma-0.6b-base
qwen/qwen3.5-4b-base
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
64.0%
88.3%
Calibration error
—
0.045
Valid action rate
—
—
Median latency
45.8 ms
33 ms
p95 latency
—
168 ms
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 wald?
lumma-fev is from FrontiersMind and wald from ORG2 AI. Both have open weights you can download and run. Both answer choice, score and noul questions. Only wald answers classify and route. wald reads up to 128K tokens of state, against 8K tokens for lumma-fev. lumma-fev is the smaller model, at 649M parameters to 4.2B.
Which is more accurate, lumma-fev or wald?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), wald 88.3% on JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or wald?
lumma-fev: Free (open weights). wald: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, lumma-fev or wald?
By their publishers’ figures, wald answers in about 33 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 wald locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull org2ai/wald download the weights.
typed-decisions (maker's table; split not stated)
JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard
Latest version
2026.09
1.2.0
Variants
LICENSE, NOTICE
Dockerfile, LICENSE, NOTICE, model-files, reference, server
