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Why Meta, Amazon, and Microsoft killed their token use leaderboards.

We have all seen the posts by now. Technical and non-technical people alike are celebrating how they use AI agents to maximize their code output. They share the number of lines generated, pull requests (PRs) opened, tasks completed, and tokens consumed. The numbers are oftentimes enormous, which makes them easy to celebrate.

The era of “tokenmaxxing” has taken this one step further. Once token usage appears on a dashboard, it is only a short time before teams compare it, leaders reward it, and engineers begin optimizing for it.

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Most large language model (LLM) inference framework comparisons begin with a leaderboard. One framework posts the highest tokens per second on a standard benchmark, and that number quietly becomes the reason a team adopts it.

The trouble is that the conditions that produce a clean benchmark result rarely resemble the conditions a model faces in production.

Synthetic benchmarks tend to use fixed prompt lengths, steady request rates, and a single model on familiar hardware. Production traffic does none of that.

This article is written for engineering leaders who are choosing an inference framework and want a way to reason about that choice beyond the headline numbers.

It covers why a benchmark winner can underperform once real traffic arrives, three tradeoff axes that usually decide the outcome, and a practical evaluation process you can run before you commit.