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The cheapest per-token model is not reliably the cheapest bill, and the most capable one is not reliably worth it. A short decision procedure based on the shape of your workload rather than on benchmark scores.

A context window is a hard ceiling on input and output combined, and it varies by a factor of sixty across current models. Here is what each one actually holds, and why filling it is rarely the goal.

A forecast you can defend takes four numbers and about ten minutes. The estimates that go badly wrong usually miss the same three things — and none of them appear on a pricing page.

The gap between the cheapest and most expensive model on an identical request is roughly 58x. Here is the arithmetic on a realistic workload, and why the cheapest per-token price is not always the cheapest bill.

The same amount of information can cost two or three times as much when it arrives as code instead of English. The reason is in how tokenizers were built, and most of the overhead is avoidable.

Most prompts carry 20–40% padding that contributes nothing to the answer. These are the edits that shrink the bill while leaving the output intact.

Every provider ships its own tokenizer, so the same paragraph can cost meaningfully more on one model than another. Here's what actually drives the number.