OpenAI Uses GPT-5.6 Sol to Optimize Its Own Inference Efficiency

OpenAI has applied its GPT-5.6 Sol model to optimize its own running efficiency post-deployment. This recursive self-optimization resulted in a 20% reduction in serving costs and a 15% or greater improvement in token-generation speed. This achievement demonstrates the practical viability of recursive self-optimization, where AI models are used to improve their own underlying code and infrastructure. Lowering serving costs and increasing speed are critical for scaling large language models to support massive user bases economically. The efficiency gains were achieved through production GPU kernel improvements, which lowered serving costs, and enhanced speculative decoding, which boosted token-generation speed. These optimizations compound across OpenAI's technology stack to deliver better performance at various cost-intelligence points.