{"slug": "free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit", "title": "Free AI Tiers Fail Differently. Run a Budget Burn-Down Before You Commit.", "summary": "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.", "body_md": "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.\n\nAI 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.\n\nMonkeyCode 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.\n\nTen 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.\n\nThe useful number is tokens per passing task. It combines cost, quality, and reliability into one figure. Lower is better. Stable is better than fast.\n\nThe burn-down test uses a fixed task set. Each task has a test assertion. Each task runs three times. Variance matters more than averages.\n\nThe harness is endpoint-agnostic. It needs one adapter function. That function returns text and token counts. Everything else is standard Python.\n\n``` python\n# burn_down.py — tokens per passing task for any AI coding endpoint\nimport json\nimport subprocess\nimport sys\nimport time\nfrom pathlib import Path\n\n# Adapter: replace with your provider's completion call.\ndef complete(prompt: str, system: str) -> dict:\n    \"\"\"Return {'text': str, 'prompt_tokens': int, 'completion_tokens': int}.\"\"\"\n    raise NotImplementedError(\"Plug in your provider SDK here.\")\n\nTASKS = [\n    {\n        \"id\": \"reverse_string\",\n        \"prompt\": \"Write a Python function reverse_string(s: str) -> str.\",\n        \"test\": \"assert reverse_string('abc') == 'cba'\",\n    },\n    {\n        \"id\": \"fizzbuzz\",\n        \"prompt\": \"Write a Python function fizzbuzz(n: int) -> list[str].\",\n        \"test\": \"assert fizzbuzz(5) == ['1', '2', 'Fizz', '4', 'Buzz']\",\n    },\n    {\n        \"id\": \"dedupe\",\n        \"prompt\": \"Write a Python function dedupe(xs: list[int]) -> list[int] preserving order.\",\n        \"test\": \"assert dedupe([1, 2, 1, 3, 2]) == [1, 2, 3]\",\n    },\n]\n\ndef run_one(task: dict, runs: int = 3) -> dict:\n    results = []\n    for _ in range(runs):\n        started = time.monotonic()\n        out = complete(task[\"prompt\"], \"You are a Python expert. Return only code.\")\n        code = out[\"text\"].strip()\n        if code.startswith(\"```\n\npython\"):\n            code = code.removeprefix(\"\n\n``` python\").strip()\n        if code.startswith(\"```\n\n\"):\n            code = code.removeprefix(\"\n\n```\").strip()\n        if code.endswith(\"```\n\n\"):\n            code = code.removesuffix(\"\n\n```\").strip()\n        Path(\"solution.py\").write_text(code + \"\\n\\n\" + task[\"test\"])\n        proc = subprocess.run(\n            [sys.executable, \"solution.py\"],\n            capture_output=True,\n            text=True,\n            timeout=30,\n        )\n        results.append({\n            \"passed\": proc.returncode == 0,\n            \"prompt_tokens\": out[\"prompt_tokens\"],\n            \"completion_tokens\": out[\"completion_tokens\"],\n            \"latency_s\": round(time.monotonic() - started, 2),\n        })\n    return {\"id\": task[\"id\"], \"results\": results}\n\ndef summarize(data: list[dict]) -> None:\n    total = sum(\n        r[\"prompt_tokens\"] + r[\"completion_tokens\"]\n        for task in data\n        for r in task[\"results\"]\n    )\n    passed = sum(r[\"passed\"] for task in data for r in task[\"results\"])\n    runs = len(data) * len(data[0][\"results\"])\n    per_pass = total / max(passed, 1)\n    print(json.dumps({\n        \"total_tokens\": total,\n        \"pass_rate\": round(passed / runs, 2),\n        \"tokens_per_passing_task\": round(per_pass),\n    }, indent=2))\n\nif __name__ == \"__main__\":\n    data = [run_one(t) for t in TASKS]\n    summarize(data)\n```\n\nRun it from a clean directory:\n\n```\npython burn_down.py\n```\n\nMost providers expose an OpenAI-compatible chat endpoint. The adapter below is pseudocode. It shows the required shape, not a specific SDK.\n\n``` python\n# adapter_example.py — pseudocode, not production code\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"YOUR_ENDPOINT\", api_key=\"YOUR_KEY\")\n\ndef complete(prompt: str, system: str) -> dict:\n    resp = client.chat.completions.create(\n        model=\"YOUR_MODEL\",\n        messages=[\n            {\"role\": \"system\", \"content\": system},\n            {\"role\": \"user\", \"content\": prompt},\n        ],\n    )\n    return {\n        \"text\": resp.choices[0].message.content,\n        \"prompt_tokens\": resp.usage.prompt_tokens,\n        \"completion_tokens\": resp.usage.completion_tokens,\n    }\n```\n\nExtend the task list to ten entries. Small functions are enough. The goal is signal, not coverage.\n\nThe 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.\n\nNow 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.\n\nUse this decision table:\n\n| Tokens per passing task | Pass rate | Verdict |\n|---|---|---|\n| < 5,000 | > 80% | Adopt for daily work |\n| 5,000–15,000 | 60–80% | Hybrid: free tier for simple tasks |\n| > 15,000 | < 60% | Reject; debugging time exceeds savings |\n\nCheck four failure modes. Each one can change the verdict.\n\nThis 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.\n\nA 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.\n\nRun the harness against MonkeyCode's free tier. Then decide with numbers, not marketing.\n\nMonkeyCode provides free models that can run this workflow.", "url": "https://wpnews.pro/news/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit", "canonical_source": "https://dev.to/gitrs_5994/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit-d4j", "published_at": "2026-09-04 11:02:54+00:00", "updated_at": "2026-09-04 11:25:21.985535+00:00", "lang": "en", "topics": ["developer-tools", "ai-tools", "ai-products", "artificial-intelligence"], "entities": ["MonkeyCode"], "alternates": {"html": "https://wpnews.pro/news/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit", "markdown": "https://wpnews.pro/news/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit.md", "text": "https://wpnews.pro/news/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit.txt", "jsonld": "https://wpnews.pro/news/free-ai-tiers-fail-differently-run-a-budget-burn-down-before-you-commit.jsonld"}}