A Five-Minute Probe Before You Point a Side Project at a Free Model Endpoint

Every model release seems to arrive with a free tier now. The problem isn't finding a free endpoint; it's deciding whether that endpoint can survive the same constraints your side project already has: a small token budget, a latency ceiling, and a clean failure when something goes wrong.

A quota page will tell you how many tokens you are allowed to want. It won't tell you whether the endpoint reports usage, fails fast, or blows past your p95 latency. Before changing a single environment variable, I want a smaller test.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode is an open-source project whose current outreach mentions free model access, a 30M-token trial, and a free server option. I treat all of those as claims to verify, not as production guarantees.

The probe below sends one pinned prompt, records the response, latency, usage, and failure shape, then exits with a pass or fail code. It uses an OpenAI-compatible request shape because that is the most common gateway pattern among model providers. If your endpoint uses a different schema, swap the request body without changing the decision fields.

#!/usr/bin/env python3
'''One-shot canary for a free model endpoint.

Usage:
  FREE_ENDPOINT_URL=https://endpoint.example/v1 FREE_API_KEY=trial-key FREE_MODEL=model-name python free_endpoint_probe.py --budget-tokens 1200 --max-latency-ms 3500
'''
import json
import os
import sys
import time
from urllib import error, request


def main():
    base = os.environ['FREE_ENDPOINT_URL'].rstrip('/')
    api_key = os.environ['FREE_API_KEY']
    model = os.environ.get('FREE_MODEL', 'default')
    budget_tokens = int(sys.argv[sys.argv.index('--budget-tokens') + 1])
    max_latency_ms = int(sys.argv[sys.argv.index('--max-latency-ms') + 1])

    payload = {
        'model': model,
        'messages': [
            {
                'role': 'user',
                'content': chr(10).join([
                    'Summarize this TODO list in three bullets:',
                    '- fix timeout',
                    '- add retry',
                    '- ship',
                ]),
            }
        ],
        'temperature': 0,
        'max_tokens': 128,
    }

    req = request.Request(
        base + '/chat/completions',
        data=json.dumps(payload).encode(),
        headers={
            'Authorization': 'Bearer ' + api_key,
            'Content-Type': 'application/json',
        },
        method='POST',
    )

    started = time.perf_counter()
    try:
        with request.urlopen(req, timeout=20) as resp:
            body = json.loads(resp.read().decode())
            latency_ms = int((time.perf_counter() - started) * 1000)
            usage = body.get('usage', {})
            total_tokens = usage.get('total_tokens', 0)
            result = {
                'ok': True,
                'latency_ms': latency_ms,
                'prompt_tokens': usage.get('prompt_tokens'),
                'completion_tokens': usage.get('completion_tokens'),
                'total_tokens': total_tokens,
                'first_chars': body['choices'][0]['message']['content'][:120],
                'flags': {
                    'latency_budget': latency_ms <= max_latency_ms,
                    'token_budget': total_tokens <= budget_tokens,
                    'usage_reported': total_tokens > 0,
                },
            }
            print(json.dumps(result, indent=2))
            return 0 if all(result['flags'].values()) else 2
    except error.HTTPError as e:
        print(json.dumps({'ok': False, 'http_status': e.code, 'body': e.read().decode()[:200]}, indent=2))
        return 3
    except Exception as e:
        print(json.dumps({'ok': False, 'error': type(e).__name__, 'detail': str(e)[:200]}, indent=2))
        return 4


if __name__ == '__main__':
    main()

Run it once before you change production config:

FREE_ENDPOINT_URL=https://your-endpoint.example/v1 FREE_API_KEY=your-trial-key FREE_MODEL=your-model-name python free_endpoint_probe.py --budget-tokens 1200 --max-latency-ms 3500

A useful output looks like this:

{
  "ok": true,
  "latency_ms": 1840,
  "prompt_tokens": 34,
  "completion_tokens": 42,
  "total_tokens": 76,
  "first_chars": "- Fix the timeout first\n- Add a retry with backoff\n- Ship after both",
  "flags": {
    "latency_budget": true,
    "token_budget": true,
    "usage_reported": true
  }
}

The exit code matters more than the text: 0 for all flags pass, 2 for a completed call that failed a budget, 3 for an HTTP failure, and 4 for a hang or unwrapped error.

Decision table

The server option is a separate claim from the model endpoint. A model endpoint answers "can I call a model for free." A server option answers "can I run a small worker or webhook without a VPS." Do not let a passing model probe convince you the server option is also safe. For that, run a second canary: deploy a tiny worker, hit it from a cold start, and record server_cold_start_ms separately.

Where this breaks down

• The probe only tests one prompt size. It does not catch rate limits, throughput ceilings, or concurrency behavior.
• The OpenAI-compatible shape is an assumption. Some providers move /chat/completions or change the auth header.
• Free-tier quotas and model names can change without notice. Pin the endpoint, model name, and last-known-good result in your repo, not in a chat thread.
• A shared free server often has cold starts and no SLA. If your sync request path depends on it, keep the paid endpoint as a fallback.

Who should not use this approach

• If you are sending customer PII, credentials, or production logs, do not route them to an unvetted free endpoint just because the probe passed.
• If latency is revenue, a free shared endpoint is not a good replacement for your paid sync path.
• If the 30M-token trial is the only thing between your app and a bill, build the fallback before you celebrate.
• If you need guaranteed capacity, predictable memory, or a long-running server process, a free server option is not a substitute for infrastructure you control.

The probe JSON is already useful, but the missing field for the free server option is server_cold_start_ms. Model response time and server boot time fail in different ways, and a solo project needs to know which one is broken before midnight. If you run this against MonkeyCode's free model access and free server option, share the probe JSON plus your p95 latency—not just a screenshot of the quota page.