10 Million Free Tokens: A Token-Budget Field Test on a Free Server

A teammate received a free AI coding allowance last month, but it was exhausted within days while performing repeated full-file rewrites. Most developers struggle to estimate token costs for a code refactor, and this article provides a reproducible method for planning a 10-million-token allowance. MonkeyCode, an open-source AI coding project, offers free model access and a free server, making it suitable for testing the token budget.

The article outlines the process using a Python harness called `budget_harness.py` that logs every request to a CSV file. The model used in the experiment is a 4-characters-per-token heuristic, which provides a stable budgeting estimate. The harness measures latency and status for each task.

The test suite consists of five types of tasks: generation, debugging, test-writing, refactoring, and full-file rewriting. These tasks represent realistic scenarios for AI coding assistance. The average token cost for each task type varies significantly, with refactors costing 2.5 times more than fresh generation tasks.

The article then presents the results of running 50 tasks on the free server, with ten repetitions for each task type. The test measured wall time, success rate, and median latency. The results showed that the free server performs well on short, focused tasks such as generations and debug sessions. However, it struggles with long completions and rapid sequential calls, leading to timeouts and rejections.

The experiment demonstrates that a 10-million-token allowance can support a wide range of tasks, from simple code generation to complex refactoring. By understanding the token cost of each task type and planning accordingly, developers can make the most efficient use of their AI coding allowance.