Shanghai AI Laboratory's multimodal decision model. A fine-tune of Qwen3.5-4B's language backbone (vision tower frozen) that answers up to 16 Choice, Score and Noul questions about a state and up to eight images in one forward pass, with a fitted calibration temperature.
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
intern-decision
metask-jev
Fine-tuned from
qwen/qwen3.5-4b
qwen/qwen3.5-4b
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
80.6%
80.1%
Calibration error
—
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Valid action rate
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Median latency
44 ms
62.8 ms
p95 latency
44.6 ms
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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 intern-decision and metask-jev?
intern-decision is from InternLM (Shanghai AI Laboratory) and metask-jev from Metask Lab. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. intern-decision reads up to 8K tokens of state, against 4K tokens for metask-jev.
Which is more accurate, intern-decision or metask-jev?
They report on different suites — intern-decision 80.6% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), metask-jev 80.1% on JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, intern-decision or metask-jev?
intern-decision: Free (open weights). metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, intern-decision or metask-jev?
By their publishers’ figures, intern-decision answers in about 44 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 intern-decision or metask-jev locally?
Yes, both: systemone pull internlm/intern-decision and systemone pull metask-lab/metask-jev download the weights.
Evaluation suite
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions)
JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
