<p><em>Originally published on <a href="https://aideazz.hashnode.dev/multi-model-llm-routing-why-76-of-your-inference-shouldnt-touch-gpt-4" rel="noopener noreferrer">AIdeazz</a> —
<h1> I Stopped Paying GPT-4 for Simple Queries </h1> <p>You know that feeling when you send "What's 2+2?" to GPT-4 and watch $0.03 vanish?</p> <p>I was burning through my OpenAI
<p>57% of organizations estimate their data isn't AI-ready. General-purpose LLMs handle broad tasks well but hallucinate on specialized queries, miss domain jargon, and can't
<p>In many enterprise workflows, classification sounds simple.</p> <p>An email arrives.<br /><br /> A ticket is created.<br /><br /> A request needs to be routed.</p> <p>At first
<p>In the first eight weeks of 2026, ten major open-weight LLM architectures were released.</p> <p>GLM-5 matched GPT-5.2 and Claude Opus 4.6 on benchmarks. Step 3.5 Flash
<p>Your AI system isn't going to crash. It's going to drift.</p> <p>A recommendation engine making 1.4 model calls instead of 1. A retrieval pipeline fetching 5 chunks instead of
<p>Every time your LangGraph agent sees "check nginx logs", it might call a different tool.</p> <p>That's not an exaggeration. LangGraph's routing is driven by an LLM prompt — and
<h1> Local Models vs Cloud Models — The AI Advantage Is Moving From Access to Infrastructure </h1> <h2> Core thesis </h2> <p>AI is no longer new.</p> <p>The question isn't
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