A 149.6M ModernBERT-base cross-encoder with a scalar head that picks an agent's next action or tool from labels and descriptions you supply. Each option is scored against the state and a softmax ranks the set; no text is generated. Experimental preview, not calibrated.
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, rank, route
choice, score, noul, classify, route
Architecture
modernjev
wald
Fine-tuned from
answerdotai/modernbert-base
qwen/qwen3.5-4b-base
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
73.4%
88.3%
Calibration error
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0.045
Valid action rate
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Median latency
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33 ms
p95 latency
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168 ms
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 modernjev-decide and wald?
modernjev-decide is from Maziyar Panahi and wald from ORG2 AI. Both have open weights you can download and run. Both answer choice and route questions. Only modernjev-decide answers rank. Only wald answers score, noul and classify. wald reads up to 128K tokens of state, against 4K tokens for modernjev-decide. modernjev-decide is the smaller model, at 150M parameters to 4.2B.
Which is more accurate, modernjev-decide or wald?
They report on different suites — modernjev-decide 73.4% on AgentToolDecisions-180K held-out test, next action type (1,158 decisions, 3 choices; the maker's split), 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, modernjev-decide or wald?
modernjev-decide: Free (open weights). wald: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run modernjev-decide or wald locally?
Yes, both: systemone pull maziyar-panahi/modernjev-decide and systemone pull org2ai/wald download the weights.
Evaluation suite
AgentToolDecisions-180K held-out test, next action type (1,158 decisions, 3 choices; the maker's split)
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
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Dockerfile, LICENSE, NOTICE, model-files, reference, server
