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

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. Why token price is a trap 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 experiment design 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. Fix the task set. Ten small coding tasks with assertions. Freeze the environment. Same repo, same endpoint, same temperature. Record three numbers per run. Prompt tokens, completion tokens, pass or fail. Repeat each task three times. Compute the pass rate. Divide total tokens by passing runs. That is the burn-down number. The harness The harness is endpoint-agnostic. It needs one adapter function. That function returns text and token counts. Everything else is standard 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 The provider adapter Most providers expose an OpenAI-compatible chat endpoint. The adapter below is pseudocode. It shows the required shape, not a specific SDK. # 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. Reading the numbers 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 Where free tiers break Check four failure modes. Each one can change the verdict. Long context. Large repos inflate prompt tokens. Every call carries the full context. A 10M budget shrinks fast. Retry loops. Agentic tools retry failed steps. Each retry adds full context cost. Failed runs consume the budget. Queueing. Shared free capacity can spike latency. Measure p95 latency, not the median. Slow responses waste developer time. Policy drift. Free quotas change. Pin the measurement date. Re-run the burn-down monthly. Limitations 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. Conclusion 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.