AI BriefingDanggeun: Cache hit rate drops to 2.3% after GPT-5.6 swap, increasing LLM costs by 3.2x
·2026.10.21 18:28
Key point
Danggeun reduced costs to one-fifth by adding explicit breakpoints after costs increased 3.2x due to automatic cache rule changes following the transition to GPT-5.6.
Details
- After switching models to GPT-5.6, the cache hit rate dropped from 85.8% to 2.3%, increasing the cost per request by 3.2x
- Models after GPT-5.6 changed the automatic cache breakpoint from the previous 2,048-token interval to the end of the most recent message, causing caching to fail in requests where fixed instructions and variable inputs are mixed
- Adding an explicit breakpoint (prompt_cache_breakpoint) at the boundary between the fixed portion and variable inputs, and adjusting the prompt structure, restored the hit rate to around 95% and reduced costs by approximately one-fifth
- The minimum prompt cache length, retention time, and write markup policies differ across OpenAI, Anthropic, and Gemini; cache hit rates and costs should be monitored as operational metrics when switching models
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