Selecting the Cheapest AI Model Does Not Guarantee the Cheapest Execution Cost

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Developers building AI agents often assume routing tasks to lower-cost models will keep expenses predictable, but actual execution costs frequently diverge from those estimates. A single agent run can span multiple model calls across providers like Anthropic, OpenAI, and Groq, each with different per-token pricing for input and output. Fallbacks triggered by rate limits or latency spikes can silently multiply costs — for example, switching from Haiku to Sonnet for the same task raises input costs nearly fourfold. Unpredictable output lengths compound the problem further, since a verbose model response can cost ten times more than a brief one without any visible warning. Experts recommend logging the actual model selected and real token counts after each call, then computing costs at execution time using published per-million-token rates to replace guesswork with measurable data.

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