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Compare how different models handle the same prompt and why they diverge
I want to understand behavioral differences between models on the same prompt, not just which output I personally prefer. Given this prompt: prompt and outputs from models_list:
1. For each pair of models that diverge meaningfully, identify the specific instruction or ambiguity in the prompt that each model resolved differently, don't just describe the outputs as 'different in style'.
2. Distinguish divergence that's a genuine capability difference (one model got a factual/logical detail wrong the other got right) from divergence that's a preference/interpretation difference (both are defensible readings of an underspecified instruction).
3. For preference/interpretation divergences, propose the specific prompt change that would make the instruction unambiguous enough that both models would converge, and state your confidence that the fix would actually work versus just being a reasonable guess.
4. Note if any model appears to have ignored an explicit instruction entirely (not just interpreted it loosely) since that's a different, more concerning category than reasonable interpretive divergence.
5. Give a practical recommendation: if I need consistent behavior across these models, what's the minimum prompt change needed, versus what divergence I should just accept as inherent.
Prompt: prompt
Outputs by model: outputs_by_model