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.
An embedding model turned decision model. Qwen3-Embedding-4B with a merged LoRA, driven by the open JevEmbed toolkit, which scores Choice, Score and Noul questions from embedding similarity and returns a distribution without generating text.
Decides
choice, score, noul, classify, route
choice, score, noul, classify
Architecture
intern-decision
jevembed
Fine-tuned from
qwen/qwen3.5-4b
qwen/qwen3-embedding-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%
85.9%
Calibration error
—
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Valid action rate
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Median latency
44 ms
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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 jevembed?
intern-decision is from InternLM (Shanghai AI Laboratory) and jevembed from HIT-TMG (Lychee Team). Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only intern-decision answers route. intern-decision reads up to 8K tokens of state, against 1K tokens for jevembed. jevembed is the smaller model, at 4.0B parameters to 4.5B.
Which is more accurate, intern-decision or jevembed?
They report on different suites — intern-decision 80.6% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, intern-decision or jevembed?
intern-decision: Free (open weights). jevembed: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run intern-decision or jevembed locally?
Yes, both: systemone pull internlm/intern-decision and systemone pull hit-tmg/jevembed download the weights.
Evaluation suite
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions)
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
