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.
Standard Thinking's decision model. Ministral 3 8B with a merged LoRA, served through stock SGLang and an open jev-adapter that exposes /v1/systemone and scores the supplied options in one forward pass with per-type temperatures.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
metask-jev
standard-one
Fine-tuned from
qwen/qwen3.5-4b
mistralai/ministral-3-8b-instruct-2512-bf16
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
—
Standard Thinking
Input price
—
—
Decision accuracy
80.1%
71.1%
Calibration error
—
—
Valid action rate
—
—
Median latency
62.8 ms
25.8 ms
p95 latency
—
41.9 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 metask-jev and standard-one?
metask-jev is from Metask Lab and standard-one from Standard Thinking. metask-jev has open weights you can download and run; standard-one has open weights and a hosted API. Both answer choice, score, noul, classify and route questions. standard-one reads up to 8K tokens of state, against 4K tokens for metask-jev. metask-jev is the smaller model, at 4.5B parameters to 8.0B.
Which is more accurate, metask-jev or standard-one?
They report on different suites — metask-jev 80.1% on JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run, standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, metask-jev or standard-one?
metask-jev: Free (open weights). standard-one: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Which is faster, metask-jev or standard-one?
By their publishers’ figures, standard-one answers in about 25.8 ms at the median and metask-jev in about 62.8 ms — measured on different hardware, so treat it as a rough guide.
Can I run metask-jev or standard-one locally?
Yes, both: systemone pull metask-lab/metask-jev and systemone pull standard-thinking/standard-one download the weights.
Evaluation suite
JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
