Token Economics: Five Structural Leaks Between LLM Pricing Pages and Your Actual Bill

Short summary

Five structural leaks cause LLM bills to run 40-65% higher than naive pricing-page estimates: output-to-input token ratios (up to 2.9x spread), tokenizer variance across providers, unconfigured prompt caching (24% of bill), unused batch processing discounts, and retry overhead. The article provides concrete cost tables per workload type and provider, with actionable fixes for each leak including caching configuration and tokenizer benchmarking.

• •Output-to-input token ratio is the largest cost variable — up to 2.9x spread across workloads
• •Prompt caching alone can save 24% of total LLM bill but most teams never configure it
• •Tokenizer variance means cheaper per-token prices can actually cost more after tokenization

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