Claude Prompt Caching: How to Cut API Costs (2026)

<h1> Fix Your Prompt Structure Before You Touch Your Infrastructure </h1> <p>Most engineering teams treat LLM inference costs as an infrastructure problem. They evaluate model

<p>LLM API costs add up fast when you're running a production application. If your system prompt is 2,000 tokens and you're processing 10,000 requests a day, you're paying for

<p><em>Originally published at <a href="https://theculprit.ai/blog/anthropic-prompt-caching-90-percent" rel="noopener

<p><strong>TL;DR: We turned on vLLM's prefix cache for our agent workloads at Nexus Labs and watched TTFT drop from 480ms to 110ms on one tenant and stay exactly the same on

<p>Anthropic shipped Opus 4.7 on April 16, 2026. Same $5/$25 pricing as 4.6. Same 1M context. Same 128K output ceiling. Different model entirely when you put it in an agentic

<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't

<p>You have 200,000 tokens to work with—but most people are still copy-pasting text like they're fighting a 4K limit that doesn't exist anymore.</p> <p>I watched a senior

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,