Price per 1M tokens is meaningless
TLDR ● Covered by 2 sources
Different AI models use different tokenizers, so the same text consumes different numbers of tokens across models—for example, this article required 160 tokens in GPT-4o but 200 in GPT-4, making per-token price comparisons unreliable. DeepSeek V4 Pro costs $0.04–$0.05 per benchmark task despite appearing cheaper per token, while Claude Sonnet 5 performs worse than Claude Opus 4.8 yet costs more per completed task due to lower token efficiency. Companies selecting AI models based solely on per-token pricing will make poor decisions and end up paying more for worse performance, since actual token efficiency and output quality vary significantly across models.
Why it matters
When evaluating AI models, you need to consider the actual cost per task rather than just price per token to avoid inferior performance at higher costs.
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