Benchmark a Free AI Coding Tier on a Cold Server

Benchmarking a free AI coding tier on a cold remote server produces a number that marketing screenshots rarely show. Warm local runs hide the two costs that matter most for daily coding, and those are first-token latency and variance across invocation. The workflow below uses a fixed Python harness, a small dataset, and a free server workspace so that every measurement has a reproducible home.

MonkeyCode is an open-source project that currently pairs free model access with a free server workspace, which makes it a convenient host for this method. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The exact token allowance and server conditions change over time, and the current documentation should be checked before any figure is quoted, including the operator's cited ten-million-token allowance. The value here is the methodology, not the number.

The dataset

A useful benchmark dataset is a contract, not a random collection. It should contain three prompt categories, and every prompt must have an explicit pass/fail condition so a human does not judge the output by feeling. The first category is deterministic generation, such as a function that finds the first non-repeated character. The second is regression repair, a short code snippet with a known bug and a test that must pass after the fix. The third is repository context, a recent commit from the reviewer's own project that requires matching the surrounding style.

Example dataset:

CASES = [
    {
        'name': 'first-non-repeated',
        'prompt': 'Write a Python function that returns the first non-repeated character in a string.',
        'check': 'first_non_repeated(',
        'timeout': 90
    },
    {
        'name': 'bug-repair',
        'prompt': 'Fix the bug in this function: def last(items): return items[-0]',
        'check': 'return items[-1]',
        'timeout': 90
    }
]

This is a skeleton, and the check field in a real run should call a compiler or a test runner. A substring check is enough for a smoke test, but it is not a proof of correctness.

The harness

The harness sends each prompt to an API endpoint and records two timestamps. The difference between the first streamed token and the stop event yields the two numbers that matter, first-token latency and total completion time. The script resets the conversation before every call, because a long context from an earlier prompt can silently change both numbers.

import json, os, time
from urllib.request import Request, urlopen

endpoint = os.environ['ENDPOINT']
model = os.environ['MODEL']
api_key = os.environ['API_KEY']
seed = 42

def run_case(case):
    payload = json.dumps({
        'model': model,
        'messages': [{'role': 'user', 'content': case['prompt']}],
        'temperature': 0.2,
        'seed': seed,
        'stream': True
    }).encode()
    request = Request(endpoint, data=payload, headers={'Authorization': 'Bearer ' + api_key})
    start = time.time()
    first_token = None
    total_text = ''
    for line in urlopen(request):
        line = line.decode().strip()
        if not line or not line.startswith('data:'):
            continue
        first_token = first_token or time.time() - start
        total_text += line
    return {
        'case': case['name'],
        'first_token_s': round(first_token, 3),
        'total_s': round(time.time() - start, 3),
        'pass': case['check'] in total_text
    }

This skeleton ignores retries, rate limits, and stream parsing, so treat it as an illustration and add those layers before trusting the results. The important habit is the timestamp placement, because the first token and the last token tell different stories.

Metrics and controls

One number is never enough. The harness should report median and p95 for first-token latency, total time, and a pass rate across three repeated runs. A timeout is a failure, an empty response is a failure, and a rate-limit response deserves its own label because it affects developer trust more than raw speed. Token consumption should be computed from the response body and divided by the free allowance to produce a cost per task, remembering that output tokens usually cost more than input tokens.

Every run needs the same seed, temperature, prompt order, and server session age. A seed does not guarantee identical output across every model, but it gives the report a repeatable starting point. A cold server is the correct control, because a warm server with cached context can inflate the results. The report should also state the region of the server and the date range, because free tiers change frequently.

Why a number is not marketing

Vendor benchmarks usually choose one ideal prompt, a warm connection, and an average across successes. A useful benchmark publishes the dataset, the raw timestamps, and the failure distribution, including the cold-start failures that the average hides. If a free tier fails one call out of ten, the interesting number is the failure, not the success rate, because developers will hit that failure many times per week.

Limitations

This method measures serving behavior and shallow correctness, not the quality of complex refactors. It also measures only one server endpoint from one network path, so the latency of a workspace in another region will differ. The right audience is a solo developer or a small team that wants an honest smoke test before reading another launch post. Teams with production requirements should collect real prompts from their own repositories and run the evaluation for a full week.

Developers who already maintain a golden set of prompts should use that set instead of this skeleton. The benchmark is a starting point, not a final verdict, and it should live in the repository as a script that anyone can rerun. The next time a vendor posts a screenshot, ask for the dataset and the p95, not the average. The free server from MonkeyCode makes this first run fast, and the open-source project's model access gives the benchmark a place to run without burning a personal account.