OpenAI researchers demonstrated that two specific API configuration settings—enabling reasoning retention and compaction—tripled performance scores on the ARC-AGI-3 benchmark, a standard measure of general reasoning ability. These settings preserve the model's intermediate reasoning steps and compress them more efficiently, allowing for better handling of complex multi-step problems.

The finding suggests that out-of-the-box model performance often underutilizes available capabilities. Organizations using GPT-5.6 for reasoning-heavy tasks like data analysis, strategy development, or complex code generation may be leaving significant performance gains on the table.

What This Means for Your Business

Teams deploying GPT-5.6 for analytical work should audit their API configurations and test these settings immediately. The potential for 3x performance improvement on reasoning tasks could translate to faster analysis cycles and more accurate outputs without switching models or upgrading infrastructure.