Token speed fails to predict real task time or cost, a 13-model probe finds
According-Floor5177 · reddit · 2026-07-23
A Reddit user argues that tokens per second is a poor proxy for how fast or cheaply a model finishes a real task.
- In one probe across 13 models, KAT-Coder was third in token output speed but finished tenth because it spent 5,536 tokens, while GPT-5.5 used only 1,777.
- The same probe found provider variance: Kimi K2.7 ranged from 223 tok/s on Together to 28 tok/s on DeepInfra, and GLM-5.2 landed on six different providers in six runs.
- The takeaway is that wall-clock time and end-to-end cost matter more than raw generation speed, and provider routing can materially change results.
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