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LLM Prompt Caching Varies Widely, Marker Crucial for Some Models

A study on LLM prompt caching in production revealed significant variations in hit rates across different models and providers, ranging from 0% to 91%. The research highlighted the importance of a specific `cache_control` marker for certain models like Gemini 3.1 Flash Lite, which otherwise showed no caching benefits. Additionally, the minimum prompt length required for caching to engage was found to be crucial, with shorter prompts failing to utilize the feature. AI

IMPACT Optimizing LLM infrastructure can significantly reduce costs and latency, improving user experience and operational efficiency.

RANK_REASON The item details a technical investigation into LLM caching mechanisms and performance, presenting empirical data and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · sm1ck ·

    We Measured LLM Prompt Caching in Production — Same Prompt, 0% to 91% Hit Rates

    <p>We run an AI companion bot. Every chat turn, the model sees the same ~5K-token prefix — character persona, content-tier rules, formatting guardrails, a memory blob — plus one new user line. Without caching, we pay for those 5K input tokens <em>every single turn</em>. So we tur…