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.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
intern-decision
this-that
Fine-tuned from
qwen/qwen3.5-4b
mapika/decider
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
80.6%
87.8%
Calibration error
—
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Valid action rate
—
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Median latency
44 ms
31.4 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 this-that-model?
intern-decision is from InternLM (Shanghai AI Laboratory) and this-that-model from FLock.io. Both have open weights you can download and run. Both answer choice, score and noul questions. Only intern-decision answers classify and route. this-that-model is the smaller model, at 1.9B parameters to 4.5B. intern-decision is licensed apache-2.0; this-that-model, mit.
Which is more accurate, intern-decision or this-that-model?
They report on different suites — intern-decision 80.6% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), this-that-model 87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, intern-decision or this-that-model?
intern-decision: Free (open weights). this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, intern-decision or this-that-model?
By their publishers’ figures, this-that-model answers in about 31.4 ms at the median and intern-decision in about 44 ms — measured on different hardware, so treat it as a rough guide.
Can I run intern-decision or this-that-model locally?
Yes, both: systemone pull internlm/intern-decision and systemone pull flock-io/this-that-model download the weights.
Evaluation suite
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions)
