This article outlines four practical configuration steps to reduce token consumption when using Claude Opus 5.5: setting reasoning effort to medium, applying prompt audits, defining stop-and-ask rules in CLAUDE.md, and moving task lists out of conversation context into a separate TASKS.md file.
This article reveals three major sources of waste in AI agent workflows — redundant context recomputation, outdated prompt patterns, and misallocated reasoning effort — and provides a systematic optimization framework with caching strategies, prompt auditing, effort calibration, and holdout-set validation, demonstrating 50–73% cost reductions without performance loss across benchmarks.
The article dissects Sebastian Raschka’s “Controlling Reasoning Effort in LLMs”, explains GPT‑5.6’s multi‑level effort settings, clarifies the notion of reasoning models, outlines training vs. inference scaling, details RLVR recipes, and compares the post‑training formulas of six open‑source flagship LLMs.
