How enabling two settings tripled our scores on the ARC-AGI-3 benchmark
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Enabling two specific API settings resulted in a threefold increase in GPT-5.6's scores on the ARC-AGI-3 benchmark; this significant performance boost will likely impact the optimization strategies for LLMs and agents in production, as it demonstrates that tweaking API settings can substantially improve model efficiency and accuracy.
Enabling retained reasoning and compaction tripled GPT-5.6 scores on ARC-AGI-3 while improving efficiency. For production agents, this means API configuration can materially change benchmark and task performance without a model swap, so long-running reasoning workloads should preserve intermediate reasoning state and compact context instead of repeatedly restarting or truncating it.