Your Free AI Token Allowance Is a Budget, Not a Gift

A token allowance is a budget with real economics, and teams that treat it that way finish whole sprints on the free tier while others run dry mid-week. This article covers a two-week experiment in which I metered every request that went through the free models and the free server of an open-source coding assistant called MonkeyCode, because the workflow is the point, not the hype. The outcome was a small estimation script, two workflow rules, and a burn rate per task that predicted the week's exhaustion before the week ended.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

At the time of writing, MonkeyCode offers free model access, a free hosted server option, and a token allowance that the project README lists at ten million tokens. Those terms have changed before and will change again, so check the repository before you build any plan on the exact figure. What mattered for this experiment was not the precise number but the habit of recording a consumption estimate for every task, the way you log cloud spend before the bill arrives.

Context resends are the silent tax

The leak that most token budgets ignore is silent context resending. You ask a coding agent to review one changed file, and the runtime quietly re-reads the surrounding modules to rebuild state it already has in memory. Over a week of small reviews, that invisible resending can consume more tokens than the actual changes you asked for.

The remedy is not a bigger allowance; it is a short state file that lets the next task start from a summary instead of a full re-read. After every task, write at most five lines to STATE.md covering what changed, what is still failing, and what comes next, and the agent should read that file before touching the tree again. The discipline feels over-engineered on day one, and it pays for itself by day three.

The estimator that made the budget visible

The estimator below is deliberately crude, because precision is not the goal; the goal is to make every task produce a number that gets logged somewhere. It assumes roughly four characters per token, which is a common rule of thumb, and it records the exit code, the elapsed time, and separate prompt and completion estimates into one JSONL file. That file becomes the audit trail for every decision you make about the agent's budget.

#!/usr/bin/env python3
# meter.py - rough per-task token accounting for coding-agent batches.
# Estimate: ~4 characters per token. Real API usage will vary.
import json
import subprocess
import sys
import time

CHARS_PER_TOKEN = 4.0
LOG_PATH = 'token-budget.ndjson'


def estimate_tokens(text: str) -> int:
    return max(1, int(len(text) / CHARS_PER_TOKEN))


def run_task(label: str, command: str) -> dict:
    started_at = time.strftime('%Y-%m-%dT%H:%M:%S')
    start = time.monotonic()
    result = subprocess.run(command, shell=True, capture_output=True, text=True)
    elapsed = time.monotonic() - start
    entry = {
        'task': label,
        'started_at': started_at,
        'elapsed_seconds': round(elapsed, 2),
        'exit_code': result.returncode,
        'estimated_prompt_tokens': estimate_tokens(len(result.args)),
        'estimated_completion_tokens': estimate_tokens(
            len(result.stdout) + len(result.stderr)
        ),
    }
    entry['estimated_total_tokens'] = (
        entry['estimated_prompt_tokens'] + entry['estimated_completion_tokens']
    )
    with open(LOG_PATH, 'a') as fh:
        fh.write(json.dumps(entry) + chr(10))
    return entry


if __name__ == '__main__':
    label = sys.argv[1]
    command = sys.argv[2]
    print(json.dumps(run_task(label, command), indent=2))

python3 meter.py review-auth 'your-agent-cli run --task review-auth'
python3 meter.py diff-types  'your-agent-cli run --task diff-types'
python3 meter.py add-logger 'your-agent-cli run --task add-logger'

I ran that pattern twice a day for two weeks, and the first log already showed something predictable and still annoying: the assistant kept restating context it had already been given. The state file from the previous section fixed that leak within a couple of days, and the daily totals dropped accordingly. The second discovery was retries, the quietest kind of waste because retries look like progress.

Two failures in that period, an unstubbed network call and a missing fixture, each produced several repeated attempts, and every attempt re-sent the same large context with a slightly different prompt. A one-line rule stopped that category: never repeat a command that produced the same failure twice, because the next move must be a change in approach, not a louder echo. That rule alone cut the most expensive block of the whole experiment.

A handoff guide for free models

The decision table below is the handoff guide that came out of those two weeks, and it is calibrated to free models rather than to an imaginary infinite quota. Small mechanical fixes are always fine, mid-size tasks are fine with scripted verification, but anything that approaches a cross-cutting refactor should go back to a human reviewer. The threshold that mattered most was not model quality but the token price of a wrong guess.

Task shape	Rough token class	Verdict for free models
Typo, lint, and comment fixes	under 5k	always fine
Dependency bump with a green test	5k to 20k	fine, verify by script
Test generation for one small module	20k to 60k	fine, review every diff
Cross-cutting refactor or API redesign	100k and up	hand to a human

When the free tier should not be used

This workflow is not for every team. A repository containing secrets, or an organization whose compliance rules require data to stay inside its own boundary, should not point a hosted free server at its codebase even when the meter looks clean. Anyone whose tasks regularly exceed what a session can hold should look elsewhere, because no estimation script fixes a context window that is simply too small.

The number that predicted the week's budget was not the token total; it was the burn rate per task, and once that rate became visible the experiment stopped being about any single tool and started being about the workflow around it. Meter your next sprint the same way, and if you try the script against the MonkeyCode free server, check the README first because the terms keep moving. The most useful comment you can leave is your own burn rate per task, because real numbers beat any endorsement.