Why the 'cheapest' AI model may cost more depending on your workload

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Choosing the most cost-effective AI model depends not on leaderboard rankings but on the ratio of input to output tokens a specific workload generates. Models price input and output tokens differently, and that gap varies significantly — for example, Grok 4.3 charges only twice as much for output as input, while others charge five to six times more. A document classification task heavily skewed toward input tokens favors Claude Haiku 4.5 by 17%, whereas a code generation task with far more output tokens makes Grok 4.3 42% cheaper. Reliability also affects true cost: a model with a 20% failure rate that reroutes failed calls to a pricier fallback can erase its apparent savings entirely. Developers are advised to measure their own token ratios across real requests before comparing model prices.

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