Free AI Tiers Fail Differently. Run a Budget Burn-Down Before You Commit.

MonkeyCode, an open-source AI coding assistant, has published a 45-minute budget burn-down test to evaluate whether free AI tiers can sustain real workloads. The method measures tokens per passing task rather than raw token price, accounting for agentic loops and retries. The test harness is endpoint-agnostic and can be adapted to any provider.

A free AI tier is not a smaller paid tier. It is a different product with different failure modes. Token price is only half of the equation. The real metric is tokens per passing task. AI coding assistants now compete on free access. Open-source projects use token grants as their growth engine. Teams adopt free tiers without measuring the actual cost per task. This article builds a 45-minute budget burn-down test. It answers one question: whether a free tier can sustain a real workload. MonkeyCode is an open-source AI coding assistant. As of August 2026, its free tier includes 10 million tokens and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The method below works for any provider. The goal is to verify the free tier, not to advertise it. Ten million tokens sounds generous. Agentic loops multiply token use. One code change can trigger many model calls. A single task may consume tens of thousands of tokens. Retries double the burn. Failed runs are not free. The useful number is tokens per passing task. It combines cost, quality, and reliability into one figure. Lower is better. Stable is better than fast. The burn-down test uses a fixed task set. Each task has a test assertion. Each task runs three times. Variance matters more than averages. The harness is endpoint-agnostic. It needs one adapter function. That function returns text and token counts. Everything else is standard Python. python burn down.py — tokens per passing task for any AI coding endpoint import json import subprocess import sys import time from pathlib import Path Adapter: replace with your provider's completion call. def complete prompt: str, system: str - dict: """Return {'text': str, 'prompt tokens': int, 'completion tokens': int}.""" raise NotImplementedError "Plug in your provider SDK here." TASKS = { "id": "reverse string", "prompt": "Write a Python function reverse string s: str - str.", "test": "assert reverse string 'abc' == 'cba'", }, { "id": "fizzbuzz", "prompt": "Write a Python function fizzbuzz n: int - list str .", "test": "assert fizzbuzz 5 == '1', '2', 'Fizz', '4', 'Buzz' ", }, { "id": "dedupe", "prompt": "Write a Python function dedupe xs: list int - list int preserving order.", "test": "assert dedupe 1, 2, 1, 3, 2 == 1, 2, 3 ", }, def run one task: dict, runs: int = 3 - dict: results = for in range runs : started = time.monotonic out = complete task "prompt" , "You are a Python expert. Return only code." code = out "text" .strip if code.startswith " python" : code = code.removeprefix " python" .strip if code.startswith " " : code = code.removeprefix " " .strip if code.endswith " " : code = code.removesuffix " " .strip Path "solution.py" .write text code + "\n\n" + task "test" proc = subprocess.run sys.executable, "solution.py" , capture output=True, text=True, timeout=30, results.append { "passed": proc.returncode == 0, "prompt tokens": out "prompt tokens" , "completion tokens": out "completion tokens" , "latency s": round time.monotonic - started, 2 , } return {"id": task "id" , "results": results} def summarize data: list dict - None: total = sum r "prompt tokens" + r "completion tokens" for task in data for r in task "results" passed = sum r "passed" for task in data for r in task "results" runs = len data len data 0 "results" per pass = total / max passed, 1 print json.dumps { "total tokens": total, "pass rate": round passed / runs, 2 , "tokens per passing task": round per pass , }, indent=2 if name == " main ": data = run one t for t in TASKS summarize data Run it from a clean directory: python burn down.py Most providers expose an OpenAI-compatible chat endpoint. The adapter below is pseudocode. It shows the required shape, not a specific SDK. python adapter example.py — pseudocode, not production code from openai import OpenAI client = OpenAI base url="YOUR ENDPOINT", api key="YOUR KEY" def complete prompt: str, system: str - dict: resp = client.chat.completions.create model="YOUR MODEL", messages= {"role": "system", "content": system}, {"role": "user", "content": prompt}, , return { "text": resp.choices 0 .message.content, "prompt tokens": resp.usage.prompt tokens, "completion tokens": resp.usage.completion tokens, } Extend the task list to ten entries. Small functions are enough. The goal is signal, not coverage. The arithmetic is simple. Suppose nine runs cost 45,000 tokens total. Six runs pass. Tokens per passing task equals 7,500. The 10 million token budget sustains about 1,333 passing tasks. Now suppose only three runs pass. The same 45,000 tokens produce 15,000 tokens per passing task. The budget sustains only 666 tasks. A lower pass rate cuts the budget in half. Free tiers fail at the tail, not the average. Use this decision table: | Tokens per passing task | Pass rate | Verdict | |---|---|---| | < 5,000 | 80% | Adopt for daily work | | 5,000–15,000 | 60–80% | Hybrid: free tier for simple tasks | | 15,000 | < 60% | Reject; debugging time exceeds savings | Check four failure modes. Each one can change the verdict. This method measures one snapshot. It does not measure code quality beyond tests. It does not measure security or license risk. Teams with compliance constraints should verify data residency first. Free tier terms change. Check the project documentation before relying on the 10 million figure. A free tier is a budget, not a guarantee. Measure tokens per passing task before committing. The burn-down test takes 45 minutes. It pays for itself on the first failed adoption. Run the harness against MonkeyCode's free tier. Then decide with numbers, not marketing. MonkeyCode provides free models that can run this workflow.