Compare how different models handle the same prompt and why they diverge

Model A and Model B diverge on how they handled 'list the top factors' with no number specified: Model A gave 3, Model B gave 7. This is a preference divergence (underspecified count), not a capability gap, both readings are defensible. Fix: specify 'exactly 4 factors' explicitly; confidence this resolves it is high since both models clearly can count and follow explicit numeric constraints, the divergence stems purely from the prompt's ambiguity, not a model limitation. Capability-difference divergence: on the calculation embedded in the task, Model C's arithmetic in step 3 is simply wrong (states 15% of 340 as 61 instead of 51), that's a factual error, not an interpretation choice, and worth flagging separately from the stylistic divergences above. Ignored-instruction flag: Model B's output doesn't include the requested citation format at all, despite the prompt explicitly requiring it in a labeled 'Output format' section; the other two models complied. This looks like an instruction being dropped rather than reasonably reinterpreted, worth testing again since it may be inconsistent rather than a stable model behavior. Recommendation: add the explicit count constraint (fixes the interpretation divergence), the arithmetic issue isn't prompt-fixable, it's a per-model reliability question, consider adding a verification step for numeric outputs regardless of which model you use.