We evaluate 13 LLMs on generating, solving, simulating, and diagnosing novice programming misconceptions on code-tracing problems. While frontier models reliably perform these tasks, small models ($\le$14B) struggle with dynamic execution. Notably, code-tuned models fail during misconception simulation by reverting to correct execution. Finally, misconception diagnosis is significantly easier in True/False formats than in open-ended generation tasks.

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