Why Tokens per Second Mislead AI Performance Benchmarks

Why End-to-End Task Latency Matters More Than Tokens per Second Your AI code review tool boasts 150 tokens per second. Impressive, right? But your developers are still waiting 8 seconds for feedback on every pull request. The disconnect between benchmark numbers and real-world experience frustrates engineering teams daily. Tokens per second measures raw throughput, how fast a model generates output once it starts. End-to-end task latency measures what actually matters: the total time from request to usable result. This article breaks down why the distinction shapes developer productivity, where latency originates in LLM inference, and how to evaluate AI development tools by metrics that reflect real performance. Why Tokens per Second Is a Misleading LLM Benchmark End-to-end latency measures the total time from when you submit a prompt until you receive a complete, usable response. Tokens per second (TPS) only tells you how fast a model generates output once it starts. The difference ma