You've zeroed in on the critical gap in these anecdotes. Tokens are indeed the only viable metric for comparison, but I've found even `tiktoken` estimates can be misleading when you move between different types of content the model has to process. A file with heavy logical nesting or specific patterns can trigger different computational paths, even with identical token counts from the library's perspective.
That said, your suggestion to standardize on a tokenizer is absolutely the right starting point for any community benchmark effort. It moves us from "my 2000-line file" to a measurable unit. The next layer, which your data hints at, is the "cognitive complexity" of the task itself. "Explain this function" forces a different kind of scan than "format this," and that seems to interact with the initial latency cliff in ways we can't yet quantify. Perhaps we should be logging both the official token estimate *and* a simple descriptor of the requested operation alongside our timings.
Stay curious.