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 396M ModernBERT-large cross-encoder that answers typed questions about a text or a JSON state: one or several options, scores, yes/no with "not stated", spans with evidence quotes, rankings and numeric bins, each with a confidence and an act-or-escalate signal.
Decides
choice, score, noul
choice, score, noul, rank, extract
Architecture
lumma-fev
solvi
Fine-tuned from
frontiersmind/lumma-0.6b-base
answerdotai/modernbert-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
64.0%
59.4%
Calibration error
—
0.210
Valid action rate
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Median latency
45.8 ms
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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 lumma-fev and solvi?
lumma-fev is from FrontiersMind and solvi from solvi. Both have open weights you can download and run. Both answer choice, score and noul questions. Only solvi answers rank and extract. lumma-fev reads up to 8K tokens of state, against 512 tokens for solvi. solvi is the smaller model, at 396M parameters to 649M.
Which is more accurate, lumma-fev or solvi?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), solvi 59.4% on Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or solvi?
lumma-fev: Free (open weights). solvi: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run lumma-fev or solvi locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull solvi-ai/solvi download the weights.
Evaluation suite
typed-decisions (maker's table; split not stated)
Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot
