Free tiers and free model access are everywhere right now, and that's genuinely useful — but it creates a new problem: how do you compare models you haven't paid for, without burning a weekend on vibes-based testing?
Most people evaluate a coding model the same way: paste one prompt, skim the output, decide it "feels smart" or "feels dumb." That's not an evaluation. That's a coin flip with extra steps. The model that impresses you on a greenfield snippet might fall apart on a messy legacy refactor, and the one that bored you might be the most reliable at writing tests.
This post lays out a small, reproducible harness you can run against any free model endpoint in under an hour, plus a scoring rubric that survives your own mood. It works whether you're comparing hosted free tiers, local models, or a mix of both.
The core idea: fixed tasks, fixed prompts, fixed rubric
The entire method rests on three constraints:
- The same tasks for every model, stored as files — never retyped from memory.
- The same prompt template, so differences in output come from the model, not from how you happened to phrase things at 11pm.
- A written rubric you fill in before reading any model's name attached to an output, if you can manage it.
That's it. No benchmark suite, no GPU cluster. Just discipline, packaged as a script.
The task set
Pick five tasks that mirror your actual work. Here's a starter set that covers the failure modes I see people hit most often:
| # | Task type | What it reveals |
|---|---|---|
| 1 | Greenfield function with a spec doc | Spec fidelity, hallucinated APIs |
| 2 | Bug fix in an unfamiliar file | Reading comprehension, minimal diffs |
| 3 | Refactor with a constraint ("no behavior change") | Whether it respects invariants |
| 4 | Test generation for existing code | Edge-case awareness |
| 5 | "This code is slow, why?" explanation | Reasoning vs. pattern-matching |
Task 2 and 3 are where weak models get exposed. Greenfield generation is the easiest thing to fake; surgical edits are not.
The harness
Save each task as a directory with an input/ (the code) and a prompt.md. Then a small runner applies the same prompt to each model endpoint and writes outputs to separate folders so you can score them blind:
#!/usr/bin/env python3
"""Minimal model-eval runner. Bring your own endpoint config."""
import json, subprocess, sys
from pathlib import Path
MODELS = {
"model_a": {"cmd": ["python", "clients/client_a.py"]},
"model_b": {"cmd": ["python", "clients/client_b.py"]},
# Add or remove freely. Each client reads a prompt on stdin,
# prints the model's raw response on stdout.
}
def run_model(client_cmd: list[str], prompt: str) -> str:
result = subprocess.run(
client_cmd, input=prompt, capture_output=True, text=True, timeout=300
)
if result.returncode != 0:
return f"[ERROR] {result.stderr.strip()}"
return result.stdout
def main(task_dir: str):
task = Path(task_dir)
prompt = (task / "prompt.md").read_text()
code_context = "\n\n".join(
f"### {p.name}\n{p.read_text()}" for p in sorted((task / "input").glob("*"))
)
full_prompt = f"{prompt}\n\n## Code\n{code_context}"
for name, cfg in MODELS.items():
out_dir = task / "outputs" / name
out_dir.mkdir(parents=True, exist_ok=True)
response = run_model(cfg["cmd"], full_prompt)
(out_dir / "response.md").write_text(response)
print(f"{name}: done ({len(response)} chars)")
if __name__ == "__main__":
main(sys.argv[1])
The important part isn't the Python — it's that the prompt is assembled identically for every model, and outputs land in folders you can review without knowing which is which.
For task 2 and 3, add one more step: apply the model's suggested diff and run the project's existing test suite. A response that reads beautifully but breaks pytest scores zero. That single check eliminates most "looks right" false positives.
The rubric
Score each output 0–2 on four axes, written down before you check which model produced it:
- Correctness: does it run / pass existing tests? (0 = broken, 1 = partially, 2 = yes)
- Minimality: did it touch only what it needed to?
- Spec fidelity: did it follow the constraints in the prompt, or quietly drop one?
- Explanation quality: when asked why, is the reasoning verifiable?
Max score per task is 8; five tasks means 40 points per model. Below ~24, the model will cost you more time than it saves on real work.
Where free access fits in
This harness is exactly the situation where free model access is most valuable: you want breadth of comparison without committing budget to a model you haven't validated on your tasks.
Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode currently offers free model access plus a free server option, which makes it a reasonable candidate to slot into the MODELS table above as one of the endpoints under test — especially if you'd rather not run everything locally. The honest way to treat it, though, is the same as every other candidate: run it through the five tasks blind and let the rubric decide. If it scores well on your task mix, keep it; if it doesn't, the free tier cost you nothing but an hour.
If you want to try this, the lowest-friction starting point is running the harness against two or three free endpoints on a single task type you care about most, then expanding from there.
Limitations and who shouldn't use this
- Sample size is tiny. Five tasks tell you about your workflow, not about the model in general. Don't publish your rubric scores as a universal benchmark — they aren't one.
- Blind scoring is hard to maintain. If you wrote the client config, you'll recognize output styles. Recruit a teammate to shuffle folders if rigor matters.
- Latency and reliability aren't measured here. A model that scores 38/40 but times out every third request is a production liability. Measure that separately over a longer window.
- Free tiers change. Availability, rate limits, and which models are included can shift without notice. Re-run the harness before you build anything load-bearing on a free option.
If you're choosing a model for a large team with compliance or data-residency requirements, this process is a starting filter at best — you still need the procurement-grade evaluation. And if your work is mostly one-off scripts you'll never revisit, the hour of setup may genuinely not pay for itself.
Takeaway
Free model access is only useful if you can tell the good outputs from the fluent ones. Fix the tasks, fix the prompts, score blind, run the existing tests. An hour of structure beats a week of vibes.
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