I Built a 5-Task Free Coding Model Gauntlet (Instead of Arguing About Leaderboards)

I spent a Sunday night pasting the same broken function into four chat windows.

Four different answers came back. One of them actually caught the bug. I had no idea why, because I had no notes, no clean test, and no way to repeat the comparison. That was the moment I stopped trusting leaderboard screenshots and started relying on something far more boring: a tiny gauntlet I can run on my own machine.

DeepSeek-V4-Pro-0813 and Grok 4.6 are all over my feed right now. I won't pretend I have hard numbers on either. I've been burned enough times by copied benchmark charts that I only care about whether a model passes five small coding tasks reproducibly, on an endpoint I can afford.

The leaderboard problem

Most leaderboards measure something I don't need. They're trained on huge, polished benchmark sets, not the actual small functions I ask models to write at midnight. A model can look brilliant in public numbers and still fumble a simple merge function when it has to return only code.

So I built a five-task Python gauntlet.

What the harness does

The script:

• sends a prompt to any OpenAI-compatible endpoint,
• asks the model to return only Python code,
• runs that code in a separate subprocess,
• records a pass or fail for each assertion,
• prints JSON I can diff between runs or models.

No model names are baked in. No private data. No benchmark claims.

Install the client if you don't have it:

pip install openai

Then save this as gauntlet.py:

# gauntlet.py
# toy harness: run model output in a fresh subprocess; do not run on your bare host.

import json
import os
import subprocess
import sys
import time

from openai import OpenAI

TASKS = [
    {
        'name': 'only_evens',
        'prompt': 'Write a Python function only_evens(numbers) that returns a new list with only even integers, preserving order. Return only Python code.',
        'test': 'assert only_evens([1, 2, 3, 4, 0]) == [2, 4, 0]; assert only_evens([]) == []',
    },
    {
        'name': 'merge_sorted',
        'prompt': 'Write a Python function merge_sorted(a, b) that merges two sorted lists into one sorted list. Return only Python code.',
        'test': 'assert merge_sorted([1, 3], [2, 4]) == [1, 2, 3, 4]; assert merge_sorted([], [1]) == [1]',
    },
    {
        'name': 'flatten_once',
        'prompt': 'Write a Python function flatten_once(nested) that takes a list of lists and returns one flat list. Return only Python code.',
        'test': 'assert flatten_once([[1, 2], [3], []]) == [1, 2, 3]; assert flatten_once([]) == []',
    },
    {
        'name': 'clamp',
        'prompt': 'Write a Python function clamp(value, low, high) that returns low if value is below low, high if value is above high, otherwise value. Return only Python code.',
        'test': 'assert clamp(12, 0, 10) == 10; assert clamp(-1, 0, 10) == 0; assert clamp(5, 0, 10) == 5',
    },
]

def run_one(task, code):
    runner = code + chr(10) + task['test'] + chr(10)
    env = dict(os.environ)
    env['PYTHONIOENCODING'] = 'utf-8'
    result = subprocess.run(
        [sys.executable, '-c', runner],
        capture_output=True,
        text=True,
        timeout=5,
        env=env,
    )
    return result.returncode == 0, (result.stderr or result.stdout).strip()

def evaluate(endpoint, key, model):
    client = OpenAI(base_url=endpoint, api_key=key)
    report = []
    for task in TASKS:
        started = time.time()
        response = client.chat.completions.create(
            model=model,
            messages=[{'role': 'user', 'content': task['prompt']}],
            temperature=0,
        )
        code = response.choices[0].message.content.strip()
        passed, note = run_one(task, code)
        report.append({
            'task': task['name'],
            'passed': passed,
            'note': note,
            'latency_s': round(time.time() - started, 2),
        })
    return report

if __name__ == '__main__':
    endpoint = sys.argv[1]
    key = sys.argv[2]
    model = sys.argv[3]
    print(json.dumps(evaluate(endpoint, key, model), indent=2))

Run it against whatever endpoint you have:

python gauntlet.py $FREE_ENDPOINT $FREE_KEY your-model

For me, that command is the whole point: the same script works for a local model server, a paid endpoint, or a free server slot.

Why I keep a free server slot

Local models are nice until the fan sounds like a launch sequence. A free server means I can run the same five tasks without touching my local GPU or my credit card. That matters when I'm just trying to decide whether a free model is worth a second look.

One of the slots I keep open is the free server route. MonkeyCode advertises free model access and a free server option, so I run the exact same script against that whenever I want a cheap first pass. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I treat those free tiers the way I treat any free tier: useful for first pass, potentially rate-limited, and definitely not a production guarantee.

How I read the results

Pattern	What I do next
4/5 or 5/5 on first try	Add two harder tasks from my own codebase, but redact them first
Fails only because it added explanation	The model usually wrote a summary instead of only code; retry with a stricter prompt
Passes locally but times out on a free endpoint	That's a rate-limit or server issue, not a model bug
5/5 but 4+ seconds per task	Fine for drafts; not for inline autocomplete

I don't treat 5/5 as a trophy. I treat it as permission to run a slower, more realistic test.

What this doesn't tell you

This is not a benchmark.

• The prompts are toy functions. They don't cover long context, tool use, or working across files.
• The harness executes model output in a subprocess. Don't run it on your bare laptop with unredacted code.
• It records one-shot latency, not throughput under load.
• A free endpoint can change limits or availability without warning.

So use this as a cheap filter, not a final judgment.

Who should skip this

Skip the free server part if you're handling proprietary customer data, regulated code, or a latency-critical app. In those cases, keep the harness idea but point it at a private endpoint or an isolated local sandbox, and treat the resulting JSON as internal evidence, not marketing.

The part nobody celebrates

Pass/fail isn't a verdict. It's a slot filter. I keep the winners in a small list, then run my personal regression suite before I let them touch anything real.

That two-step habit—tiny public gauntlet first, personal regression second—is what finally stopped me from swapping models on hype.

If you have a free endpoint you already use, run the five tasks and tell me which one surprised you. I'd rather see your JSON diff than another benchmark chart.