Why the codebase is your biggest context bill, and why output compression doesn't fix it

When people think about reducing AI coding costs, they usually think about the model's output: fewer words, shorter explanations, tighter responses. It's intuitive. The AI talks a lot. Make it talk less.

The problem is that output is the wrong target.

Paul Kinlan analysed OpenRouter's programming traffic using their public usage rankings and a reproducible script against the OpenRouter API. What he found:

"Real-world data from OpenRouter's programming category shows 93.4% input tokens, 2.5% reasoning tokens, and just 4.0% output tokens. It's almost entirely input."

That stat surfaced in a 471-point Hacker News thread discussing output-compression techniques. Even if you silenced the model completely, you'd be optimising 4% of the problem.