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.
AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
intern-decision
blocks-of-experts
Fine-tuned from
qwen/qwen3.5-4b
qwen/qwen3.8-27b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
80.6%
88.7%
Calibration error
—
—
Valid action rate
—
—
Median latency
44 ms
137 ms
p95 latency
44.6 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 intern-decision and jev-27b?
intern-decision is from InternLM (Shanghai AI Laboratory) and jev-27b from AutoTrust AI Lab. Both have open weights you can download and run. Both answer choice, score and noul questions. Only intern-decision answers classify and route. intern-decision is the smaller model, at 4.5B parameters to 27B.
Which is more accurate, intern-decision or jev-27b?
They report on different suites — intern-decision 80.6% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), jev-27b 88.7% on JevBench public set (231 items), family-macro score, maker's run — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, intern-decision or jev-27b?
intern-decision: Free (open weights). jev-27b: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, intern-decision or jev-27b?
By their publishers’ figures, intern-decision answers in about 44 ms at the median and jev-27b in about 137 ms — measured on different hardware, so treat it as a rough guide.
Can I run intern-decision or jev-27b locally?
Yes, both: systemone pull internlm/intern-decision and systemone pull autotrust-ai/jev-27b download the weights.
Evaluation suite
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions)
JevBench public set (231 items), family-macro score, maker's run
