A practical session on cutting AI costs without sacrificing quality. The speakers argue for measuring cost per completed task rather than per-token list prices, show how to right-size models with accuracy-versus-cost Pareto analysis, and walk through four levers: prompt caching, programmatic tool calling, reasoning effort, and batch or flex processing. Customer case studies from Perplexity, Notion, Clio, and Blitzy ground the advice.
This Build Hour focuses on "value maxing" with GPT-5.6, shifting from simply tracking token usage to measuring the actual outcomes and efficiency gained from AI. It covers how to select the right GPT-5.6 model (Sol, Terra, Luna) based on intelligence, latency, and cost, and provides practical strategies for optimizing cost-performance. Key topics include leveraging programmatic tool calling, prompt caching, persistent reasoning, and context compaction for API users, along with CodeX-specific tips. A customer spotlight on Ploy demonstrates real-world application, showcasing their migration to GPT-5.6 Sol, which resulted in 2.2x faster builds at 27% lower cost through advanced caching and tool optimization techniques.