The first System One model. Reads a state, answers typed Choice, Score and Noul questions in one call with calibrated probabilities, and generates no text. Closed weights, served by TypeSafe AI.
Metask Lab's calibrated decision model in 16 languages. Qwen3.5-4B with a merged LoRA trained with the Nimble candidate-logit objective; one forward pass and a softmax over at most 26 answer-letter logits, with one temperature per question type.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
jev
metask-jev
Fine-tuned from
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qwen/qwen3.5-4b
License
proprietary
apache-2.0
Availability
Hosted API
Open weights
Hosted by
TypeSafe AI
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Input price
$0.042/MTok
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Decision accuracy
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80.1%
Calibration error
—
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Valid action rate
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Median latency
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62.8 ms
p95 latency
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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 jev and metask-jev?
jev is from TypeSafe AI and metask-jev from Metask Lab. jev is only available as a hosted API; metask-jev has open weights you can download and run. Both answer choice, score, noul, classify and route questions. jev reads up to 32K tokens of state, against 4K tokens for metask-jev. jev is licensed proprietary; metask-jev, apache-2.0.
Which is more accurate, jev or metask-jev?
Only metask-jev publishes an accuracy figure (80.1% on JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run); jev does not, so there is no comparison to make without your own test.
Which is cheaper, jev or metask-jev?
jev: $0.042 / $0 per 1M. metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jev or metask-jev locally?
metask-jev yes — systemone pull metask-lab/metask-jev downloads its weights. The other is only served as a hosted API.
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JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
