A typed-decision layer for Google's DiffusionGemma, from David Villalón at Maisa AI. It compiles a request into a small answer canvas, runs one denoising read on patched vLLM and reads the probabilities of the allowed labels, for text, images and images offered as options.
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
choice, score, noul, classify, route
Architecture
djev
metask-jev
Fine-tuned from
google/diffusiongemma-26b-a4b-it
qwen/qwen3.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Maisa
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Input price
$0.035/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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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 djev and metask-jev?
djev is from Maisa and metask-jev from Metask Lab. djev has open weights and a hosted API; metask-jev has open weights you can download and run. Both answer choice, score and noul questions. Only metask-jev answers classify and route. metask-jev is the smaller model, at 4.5B parameters to 26B.
Which is more accurate, djev 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); djev does not, so there is no comparison to make without your own test.
Which is cheaper, djev or metask-jev?
djev: $0.035 / $0 per 1M, or free to self-host. metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run djev or metask-jev locally?
Yes, both: systemone pull maisa/djev and systemone pull metask-lab/metask-jev download the weights.
Evaluation suite
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JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run