The Hidden 43% — How Teams Waste Half Their LLM API Budget

<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>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

<blockquote> <p>Your best feature may be destroying your margins, and your engineering team has no idea.</p> </blockquote> <p><strong>This article isn’t about AI as a productivity

<p>For the last few years the tech industry has quietly assumed this<br /><br /> AI is powerful almost free and it will eventually pay for itself </p> <p>Turns out that story is

<p>The price page hasn't moved. Five dollars per million input. Twenty-five per million output. Identical to Opus 4.6.</p> <p>Run the same Claude Code call on both, though, and

<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>At the International AI Summit in Dublin, OpenAI Chief Financial Officer Sarah Friar took the stage to announce that the lab is holding back a breakthrough model. According to

<p>How it works:</p> <p>Request arrives at an OpenAI-compatible endpoint<br /> Classifier detects task type + complexity<br /> Adaptive router picks the highest-scoring model for