At 2:47 AM, a batch job died. The log showed one line: rate_limit_exceeded. The developer had trusted a free AI server with 4,000 tasks. Task 1,312 failed. The retry loop made it worse.
This is the real failure mode of free tiers. They do not fail loudly. They fail quietly, in the middle of the night.
Free model access is a budget, not a benchmark. A landing page cannot show latency percentiles. It cannot show schema drift rates. Only a probe can.
The target: MonkeyCode's free tier
MonkeyCode is an open-source AI coding project. Its current free tier includes 10 million tokens of model access. It also offers a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The offering details are operator-supplied. The evaluation method below is the reproducible part. Anyone can run it against any endpoint.
Every week, a new model drops. Every week, a team routes real work to it. The free tier is the easiest on-ramp. It is also the easiest way to learn bad habits.
Why a probe beats a demo
A demo shows one happy path. A probe shows the distribution. The difference matters for agents. Agents make many calls. Each call is a chance to fail.
Three failure modes break agent workloads most often:
- Rate limits: the server says "slow down" mid-batch.
- Timeouts: the call hangs past your patience threshold.
- Malformed tool JSON: the model returns arguments that do not parse.
A paid API hides these behind support. A free tier hides them behind a signup page. The probe exposes them.
The harness
The script below uses the OpenAI-compatible API. It sends a small task set repeatedly. It records latency, token usage, and tool-call validity. It prints a JSON report.
# probe_free_server.py
# A 15-minute smoke probe for a free AI server.
# Usage:
# export MONKEYCODE_API_KEY=your_key
# export MONKEYCODE_BASE_URL=https://your-endpoint/v1
# export MONKEYCODE_MODEL=current-free-model
# python probe_free_server.py --tasks 30
import argparse
import json
import os
import time
from collections import Counter
from openai import OpenAI
TOOL = {
"type": "function",
"function": {
"name": "apply_edit",
"description": "Apply a precise edit to a file.",
"parameters": {
"type": "object",
"properties": {
"path": {"type": "string"},
"old_string": {"type": "string"},
"new_string": {"type": "string"},
},
"required": ["path", "old_string", "new_string"],
},
},
}
TASKS = [
"Return a JSON object with keys: title, severity, assignee.",
"Extract all URLs from: 'see https://a.dev and http://b.io now'.",
"Summarize this bug in one sentence: 'login fails on Safari 17'.",
]
def classify(exc: Exception) -> str:
text = str(exc).lower()
if "429" in text or "rate limit" in text:
return "rate_limit"
if "timeout" in text or "timed out" in text:
return "timeout"
if any(code in text for code in ("500", "502", "503", "504")) or "server error" in text:
return "server_error"
return "other"
def run_once(client: OpenAI, model: str, task: str) -> dict:
start = time.monotonic()
try:
resp = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": task}],
tools=[TOOL],
tool_choice="auto",
max_tokens=512,
temperature=0.0,
)
choice = resp.choices[0]
calls = choice.message.tool_calls or []
valid = 0
for call in calls:
try:
json.loads(call.function.arguments)
valid += 1
except json.JSONDecodeError:
pass
return {
"ok": True,
"latency_s": round(time.monotonic() - start, 2),
"tool_calls": len(calls),
"valid_tool_calls": valid,
"finish": choice.finish_reason,
"prompt_tokens": resp.usage.prompt_tokens,
"completion_tokens": resp.usage.completion_tokens,
}
except Exception as exc: # noqa: BLE001
return {
"ok": False,
"latency_s": round(time.monotonic() - start, 2),
"error": classify(exc),
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--tasks", type=int, default=30)
args = parser.parse_args()
client = OpenAI(
base_url=os.environ["MONKEYCODE_BASE_URL"],
api_key=os.environ["MONKEYCODE_API_KEY"],
timeout=60.0,
)
model = os.environ["MONKEYCODE_MODEL"]
results = [run_once(client, model, TASKS[i % len(TASKS)]) for i in range(args.tasks)]
ok = [r for r in results if r["ok"]]
latencies = sorted(r["latency_s"] for r in ok)
errors = Counter(r["error"] for r in results if not r["ok"])
total_tokens = sum(r["prompt_tokens"] + r["completion_tokens"] for r in ok)
report = {
"total_tasks": args.tasks,
"success": len(ok),
"success_rate": round(len(ok) / args.tasks, 3),
"p50_latency_s": latencies[len(latencies) // 2] if latencies else None,
"p95_latency_s": latencies[min(len(latencies) - 1, int(len(latencies) * 0.95))] if latencies else None,
"error_counts": dict(errors),
"total_tokens_burned": total_tokens,
"tokens_per_successful_task": round(total_tokens / len(ok), 1) if ok else None,
"valid_tool_call_rate": round(
sum(r["valid_tool_calls"] for r in ok) / max(sum(r["tool_calls"] for r in ok), 1), 3
),
}
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
Run it like this:
export MONKEYCODE_API_KEY=your_key
export MONKEYCODE_BASE_URL=https://your-endpoint/v1
export MONKEYCODE_MODEL=current-free-model
python probe_free_server.py --tasks 30
Thirty tasks take about 15 minutes. That is enough for a smoke signal. For a firmer signal, run 100 tasks across two hours.
A sample report
A reference run produces output like this. The values are illustrative. Your run will differ.
{
"total_tasks": 30,
"success": 27,
"success_rate": 0.9,
"p50_latency_s": 4.2,
"p95_latency_s": 18.7,
"error_counts": {"timeout": 2, "rate_limit": 1},
"total_tokens_burned": 61200,
"tokens_per_successful_task": 2266.7,
"valid_tool_call_rate": 0.963
}
Reading: 90% success, p95 at 18.7 seconds. The server is stable but slow. Interactive tools would feel sluggish. A background batch agent would be fine. Token burn is 2,267 per task. The 10 million budget covers about 4,400 tasks.
Reading the report
The report answers four questions. Each answer maps to a workload decision.
| Measured result | What it means | Safe workload |
|---|---|---|
| Success rate ≥ 95%, p95 < 5s | Healthy endpoint | Interactive agent tools |
| p95 between 5s and 30s | Slow but stable | Batch jobs, background agents |
| p95 > 30s or timeouts > 10% | Shared server saturated | Offline batch with retries only |
| Rate limits > 5% | Concurrency or quota cap | Reduce parallelism, add backoff |
| Valid tool calls < 95% | Schema drift | Add a validator before the executor |
The table is a gate, not a grade. A failing row does not disqualify the server. It disqualifies the workload.
Token math is deterministic
The harness reports tokens per successful task. That number drives budget math. Suppose one task burns 2,000 tokens. Ten million tokens divide into 5,000 tasks. Suppose a task burns 8,000 tokens. The same budget covers 1,250 tasks.
The marketing number is 10 million. The useful number is tokens per task. Measure it. Then compute your own ceiling.
Where the free tier breaks
Free servers are shared. Shared means noisy. Noisy means tail latency. Tail latency kills interactive agents. Batch jobs tolerate it. That is the core trade-off.
Expect three concrete breakages:
- Long context inflates token burn. A 4,000-token input doubles the cost of every task.
- Retries compound the burn. A failed call still consumes tokens. A retry consumes more.
- Tool calls are the weakest link. Free models often return valid prose and invalid JSON.
Extending the probe
The default task set is minimal. Replace it with your own workload. Use real prompts from your agent. Use the real tool schemas your agent calls. The probe becomes a regression test.
Run it weekly. Track the report over time. Free tiers drift. Latency creeps. Success rates sag. A weekly run catches the drift before users do.
Limitations of this approach
This probe measures availability, not quality. It does not grade code correctness. It does not measure reasoning depth. Use it as a pre-flight check, not a full benchmark.
Free-tier terms change. The 10 million token figure is current as of 2026-08-25. Verify it before you depend on it. The endpoint URL and model name change too. Read the current docs.
Who should not use this approach:
- Teams with hard SLAs.
- Customer-facing agents.
- Production billing or auth flows.
Those workloads need reserved capacity. A free tier is not a promise. It is an experiment.
Conclusion
Free AI servers are a constraint. Constraints are measurable. The probe above turns a vague promise into a concrete report. Run it against MonkeyCode's free tier. Read the numbers. Then decide if the workload fits.
The free tier is live. The probe is the fastest way to test it against your own tasks.
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