Two specific API settings tripled performance scores for GPT-5.6 on the ARC-AGI-3 benchmark. By retaining reasoning chains and enabling compaction, the model achieved higher efficiency. This optimization proves that prompt-level configuration outweighs raw scale for abstract reasoning. Practitioners should prioritize these settings to unlock latent capabilities in complex logic tasks.