A few weeks of scrolling DEV will convince you of two things at once: AI coding assistants are everywhere, and almost nobody agrees on how to judge them. Benchmarks screenshots get argued about in comment threads, vibes-based reviews contradict each other, and the loudest opinions usually come from people testing different things than you actually do.
The only evaluation that matters is the one you run on your kind of work. This article is a checklist for doing that cheaply — using free model access and a free hosted server so the experiment costs you an afternoon instead of a subscription — plus a decision table for interpreting what you find.
Why free tiers are the right evaluation sandbox
Paying for a tool before you know it fits your workflow creates a subtle bias: you've already spent money, so you rationalize keeping it. Free access removes the sunk-cost pressure and lets you be genuinely critical.
Two things make a free option actually useful for evaluation rather than just a demo:
- Real model access, not a crippled preview — you need the same model you'd use in production, or your test tells you nothing.
- A hosted server option — if you have to provision infrastructure just to run the experiment, the friction kills it. A free server means you can go from "I wonder if this works" to "here are my results" in one sitting.
One place this combination currently exists is MonkeyCode, which offers free model access and a free server option for running it.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
I'm treating it here as the evaluation sandbox, not the verdict — everything below works the same way on any provider with a comparable free tier, and the whole point is that you generate the data.
The evaluation battery: five prompts, no cherry-picking
The failure mode of most tool reviews is selective testing — running the one prompt you know will look good. Instead, pick five prompts in advance that map to your real work, and commit to reporting all of them, including the embarrassing failures.
A battery I'd suggest as a starting template (swap in your own stack and problem shapes):
| # | Task type | Why it's in the battery |
|---|---|---|
| 1 | Generate a small utility function from a spec | Baseline correctness |
| 2 | Explain a confusing error message from your codebase | Real-world debugging, not textbook problems |
| 3 | Refactor a ~50-line function with a constraint (e.g., "no new dependencies") | Tests instruction-following, not just generation |
| 4 | Write a unit test for existing code | Tests whether it understands intent, not just syntax |
| 5 | Ask about an API/library detail and check the answer against official docs | Hallucination probe |
Rule: decide the five prompts before you touch the tool. If you design tests after seeing outputs, you're fitting the exam to the student.
Score with a rubric, not a vibe
"Felt pretty good" is not data. Score each response 0–2 on three axes:
- Correctness (0 = wrong, 1 = right direction but broken, 2 = works/runs or verified accurate)
- Constraint-following (did it respect the limits you set?)
- Time saved (0 = you'd have been faster without it, 2 = genuinely faster)
Maximum 6 points per prompt, 30 total. The absolute number matters less than where the failures cluster. A tool that aces generation but hallucinates APIs tells you to use it for scaffolding and never trust it for library details — that's an actionable workflow decision.
A minimal logging script
If you want latency numbers alongside quality scores, here's a template you can adapt. It's intentionally boring — the point is reproducibility, not sophistication:
import time, json, datetime
LOG = "eval_log.jsonl"
def run_eval(prompt_id, prompt_text, call_model):
"""call_model: your function that sends a prompt and returns the response text."""
start = time.perf_counter()
response = call_model(prompt_text)
elapsed = time.perf_counter() - start
record = {
"ts": datetime.datetime.utcnow().isoformat(),
"prompt_id": prompt_id,
"prompt": prompt_text,
"response": response,
"latency_s": round(elapsed, 2),
# fill these in manually after reviewing the response:
"score_correctness": None,
"score_constraints": None,
"score_time_saved": None,
"notes": ""
}
with open(LOG, "a") as f:
f.write(json.dumps(record) + "\n")
return record
Two deliberate design choices:
- Scores are filled in by hand, not auto-judged. Letting a model grade itself or its competitor is circular. You are the rubric.
- Everything is appended to a JSONL log. If you rerun the battery next month on a new model or an updated version, you can diff the logs and see what actually changed.
Keep the prompt text in the log verbatim. "I asked something about sorting" is not reproducible; the exact string is.
Interpreting results: a decision table
| Pattern in your results | What it means | What to do |
|---|---|---|
| High scores on generation, low on API facts (prompt 5) | Fluent but unreliable on specifics | Use for scaffolding; verify every API claim against docs |
| Good on greenfield, bad on your real codebase (prompt 2) | Weak context handling | Only feed it isolated, self-contained problems |
| Fails constraint tests (prompt 3) | Doesn't follow instructions reliably | Not ready for automated pipelines; fine for interactive drafting |
| Consistently fast but mediocre scores | Speed isn't your bottleneck | Latency is a tiebreaker, not a reason to adopt |
| Low scores everywhere | Wrong tool for your work | Drop it. The experiment did its job — it saved you from a bad commitment |
That last row is important: a "no" is a successful evaluation. The cost of the test was an afternoon; the cost of skipping it is months of working around a tool that doesn't fit.
Limitations and honest caveats
- Sample size of five prompts is small. This catches gross mismatches, not subtle ones. Treat it as a screen, not a certification.
- Free tiers change. Model availability, rate limits, and server terms on any platform can shift without notice. Re-run the battery if the tier you're on changes, and don't build critical infrastructure on a free server without an exit plan.
-
You are the noisy variable. Your scoring will drift between sessions. Score everything in one sitting if you can, and write your rubric decisions in the
notesfield. - Latency on a free server is not production latency. Use it for relative comparison only.
Who should skip this approach
- If your work is dominated by one narrow, well-benchmarked task (say, competitive programming), a public leaderboard for that specific task is more informative than your own five prompts.
- If you handle sensitive or proprietary code, don't paste it into any hosted evaluation sandbox — free or paid — until you've read the data-handling terms. Adapt the battery to synthetic or sanitized code instead.
- If you need a procurement-grade answer for a team of fifty, an afternoon of solo testing won't survive scrutiny. This checklist is for individual developers making a personal tooling call.
Closing thought
The healthiest posture toward AI coding tools right now isn't enthusiasm or skepticism — it's measurement on your own terms. Free access makes that measurement cheap enough to be honest. Run the battery, log the failures, and let your own log file argue with the hype cycle.
If you want a zero-cost place to run this exact battery, the free model access and free server from MonkeyCode are one option to start with — but whatever sandbox you pick, the checklist above is the part worth keeping.
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