Pick an AI Model the Way You Pick a Dependency

Every week a new model appears, and every week the claims get louder. Faster. Smarter. Cheaper. You could spend a month reading benchmarks. You could also spend an afternoon running your own controlled test. The second option produces evidence you actually trust.

A few months ago I watched a team choose an AI assistant the same way people choose a restaurant: by the longest menu. They listed five models, skimmed the feature tables, and picked the one with the most impressive sounding name. Two weeks later they discovered the model was excellent at generating boilerplate and terrible at refactoring legacy code. The menu had told them nothing about their specific codebase.

That experience pushed me toward a different habit. Before adopting any AI coding tool, I run a small evaluation against real tasks from our repository. I don't need expensive infrastructure for this. I just need access to a couple of providers and a disposable machine. That is exactly where MonkeyCode's free models and free server become useful. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

MonkeyCode is an open-source project that offers a free tier with access to several free models, plus a free server where you can execute those models without touching your laptop. The exact list of models and the server specifications change over time, so you should check the repository before relying on them. But the core idea remains the same: a low-cost environment for running your own model comparison.

I've been using their free server for exactly this kind of experiment. It gives me a clean Linux box, a shell, and enough room to run a small test script. The free models handle the inference. My job is only to define the task, collect the outputs, and score the results. That is the whole method.

The One-Question Evaluation

Before you compare anything, decide what you actually care about. For most teams, the question sounds like this: If I give the model a failing test and a buggy function, can it produce a patch that makes the test pass without wrecking the rest of the suite?

That question is narrow, measurable, and close to real work. It ignores marketing metrics like MMLU or HumanEval. It focuses on the one behavior that will annoy you daily if it fails.

Once you have the question, you need a set of tasks. Take five real bugs from your repository's history. Not imaginary problems. Real commits that fixed actual issues. For each bug, prepare a clean checkout of the parent commit, the failing test, and a short problem statement. The statement should be neutral, without hints about the correct patch.

One Script to Rule Them All

Here is the evaluation runner I use. It is intentionally short, because complexity hides bias. The script assumes your model endpoints are compatible with the OpenAI chat completions format, which is common among free model providers.

#!/usr/bin/env python3
import json, os, sys, time
import urllib.request

# Configuration: fill these from your MonkeyCode free server env
API_BASE = os.environ.get("API_BASE", "http://localhost:8080/v1")
MODELS = ["model-a", "model-b"]  # replace with the free model names from MonkeyCode docs

TASK_DIR = sys.argv[1] if len(sys.argv) > 1 else "./tasks"

def run_model(model, task_prompt):
    payload = {
        "model": model,
        "messages": [{"role": "user", "content": task_prompt}],
        "max_tokens": 2000,
        "temperature": 0.1,
    }
    data = json.dumps(payload).encode()
    req = urllib.request.Request(
        f"{API_BASE}/chat/completions",
        data=data,
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req, timeout=120) as resp:
        body = json.loads(resp.read())
    return body["choices"][0]["message"]["content"]

def score_patch(model, task_dir):
    # Read task files: prompt.txt, parent.diff, test_snippet.py
    prompt = open(os.path.join(task_dir, "prompt.txt")).read()
    patch = run_model(model, prompt)
    # Apply patch to a fresh worktree, run test, return pass/fail
    # This part is intentionally left to your environment.
    # A complete version includes git apply + test runner.
    with open(f"output.{model}.txt", "a") as f:
        f.write(f"{task_dir}: {patch}\n")
    return None  # in production, return True/False

for model in MODELS:
    for root, _, files in os.walk(TASK_DIR):
        if "prompt.txt" not in files:
            continue
        print(f"Testing {model} on {root}")
        try:
            score_patch(model, root)
        except Exception as e:
            print(f"  error: {e}")
        time.sleep(1)  # respect rate limits

The script does the honest work. It sends a prompt, receives a patch, and records the raw output. The scoring loop remains unrolled because every environment has a different way to apply patches and run tests. That is fine. The point is to keep the experiment transparent.

The Decision Table

After you run the script against two free models, you will end up with a table that looks like this. Fill it with your own results.

Model	Tests passed (out of 5)	Unrelated files changed	Time to finish	Verdict
Model A	4	0	12 min	Choose A for this task
Model B	2	3	18 min	Reject B unless no alternative

This table condenses the entire experiment into four numbers. The best model is not the one with the highest raw score. It is the one that passes tests, keeps the diff clean, and does not waste your free server hours. A model that fixes two bugs but rewrites half your codebase is worse than a model that fixes one bug with a two-line change.

Why the Free Server Matters

Running this evaluation on your own laptop sounds easy until the agent starts spawning processes, installing dependencies, and modifying files you forgot to commit. The free server removes that fear. You get a clean, disposable environment where an experiment can go wrong without any consequence. If the model deletes a directory, you only lose a temporary machine.

The free models matter because they let you run this test without a corporate credit card. You can compare two or three options, gather real evidence, and only then decide whether a paid model is worth the upgrade. That is a better decision path than trusting a blog post written by someone who has never seen your repository.

Honest Limits

This method has clear boundaries. The free models on MonkeyCode can change, disappear, or throttle without notice. Your results are a snapshot, not a permanent verdict. A model that wins on your five bugs may lose on a different codebase. The free server also has resource limits; do not treat it as a production environment.

You should also avoid this approach if your evaluation needs to be legally defensible, such as for a client deliverable or a security audit. A quick script with unrolled scoring logic is not a certification. It is a decision-making tool for a two-hour afternoon.

The Real Takeaway

Choosing an AI model is no different from choosing a database or a caching layer. You read the docs, you write a load test, and you measure against your own workload. Benchmarks from vendors are menus. Your repository is the kitchen. The only way to know if the dish works is to cook it.

MonkeyCode's free models and free server give every team a kitchen that costs nothing. Spend one afternoon cooking. The evidence you collect will save you much more than the price of a paid subscription. And if you discover the free option is good enough, you have just saved your team a recurring bill. That is my favorite kind of experiment: one where the conclusion is free.