From tokens per dollar to tasks per dollar

Nobody wants tokens. You want tickets answered, documents summarized, invoices read or code reviewed. Tokens per dollar is a step toward the figure that matters: how many of those a dollar gets done.

Two models with the same tokens per dollar can differ widely in tasks per dollar, if one needs fewer tokens or fewer attempts.

Run the task for real, a few dozen times. For each run, record the input tokens and the output tokens from the API response. Include every call the task makes, not only the last one.

Price the average run: input tokens at the input price, output tokens at the output price. That is the cost of one task. One dollar divided by it is tasks per dollar.

Classifying a message is small and input-heavy: a short text in, a label out. Summarizing a document is large and input-heavy. Answering from retrieved passages sits between the two.

Writing a product description is output-heavy. An agent run is the largest of all, because every step resends the growing history, and the input compounds.

Three things move it more than a change of host. A smaller model, where it can do the task. A shorter prompt, with less history and fewer retrieved passages. And fewer retries, which means a model and a prompt that get it right the first time.

On 12 Nov 2026, the lowest listed input price for GLM 5.3 Flash was $0.04 per million tokens. For GLM 5.3 it was $0.07 per million tokens. For an input-heavy task, that gap is most of the gap in tasks per dollar.

Runcrate bills by the token from one prepaid balance. Run a batch of one task on one model and the drop in the balance, divided by the runs, is the cost of a task. Repeat on another of its 125 models with the same client. Runcrate lists GLM 5.3 Flash at $0.158 per million input tokens.

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