A pattern I see constantly in team chats: someone hits a frustrating bug, pastes it into the most expensive model they have access to, gets a mediocre answer, and concludes "AI coding tools don't work." The failure usually isn't the model. It's that there was no evaluation step at all — no baseline task, no comparison, no sense of which class of problem even needs a frontier model.
This article is a workflow for fixing that. The idea: before you route real work to paid inference, build a tiny personal benchmark of tasks from your codebase, run it against free model access first, and only escalate the tasks that genuinely fail on the free tier. For many teams, that's a minority of tasks.
Step 1: Build a five-task benchmark from your own repo
Generic benchmarks (HumanEval-style) tell you almost nothing about your daily work. Instead, pull five tasks from your last two weeks:
- One real bug you already solved (you know the correct answer — that's the point).
- One refactor you did under time pressure.
- One test-writing task for a function with edge cases.
- One "explain this code" task on a confusing legacy section.
- One small feature with a clear acceptance criterion.
Write each as a self-contained prompt with the relevant code pasted in. Keep them in a folder. This takes an hour and pays for itself immediately.
Step 2: Run the benchmark against free models first
Disclosure: This article was prepared as part of MonkeyCode's product outreach. I'm using it as the example here because its free model access and free server option map directly onto this workflow — you can run the benchmark without a billing decision, and host a comparison harness on the free server tier rather than on your laptop. The workflow itself works with any provider that offers free model access; swap the endpoint and nothing else changes.
The point of running free models first isn't frugality theater. It's measurement hygiene: if a free model solves a task, you never needed to pay for that task class. If it fails, you have a concrete failing case to bring to the paid model — which makes the paid model's output easier to judge too.
Step 3: Grade with a rubric, not vibes
For each task, score three things on a 0–2 scale:
- Correctness (0 = wrong, 1 = right idea/broken code, 2 = works)
- Locality (0 = rewrote unrelated code, 1 = touched more than needed, 2 = minimal diff)
- Explanation quality (0 = confident nonsense, 1 = vague, 2 = you'd learn something)
A free model scoring 10–12/30 across five tasks is still useful — for the tasks where it scored 2s. That's your routing table.
The artifact: a reproducible comparison harness
Here's a minimal script (proposal/pseudocode-level — adapt the endpoint and auth to your provider) that runs your benchmark folder against a model and logs structured results:
#!/usr/bin/env python3
"""Run a folder of task prompts against an LLM endpoint and log scores.
Usage: MODEL=<model-id> python bench.py ./tasks/
Fill in ENDPOINT and scoring after manual review of outputs.
"""
import json, os, sys, time
from pathlib import Path
import urllib.request
ENDPOINT = "https://your-provider.example/v1/chat/completions" # replace
API_KEY = os.environ.get("PROVIDER_API_KEY", "")
MODEL = os.environ["MODEL"]
def run_task(prompt: str) -> str:
body = json.dumps({
"model": MODEL,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.2, # keep it low; you want comparability, not creativity
}).encode()
req = urllib.request.Request(
ENDPOINT, data=body,
headers={"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"})
with urllib.request.urlopen(req, timeout=120) as r:
return json.loads(r.read())["choices"][0]["message"]["content"]
def main(task_dir: str):
results = []
for task_file in sorted(Path(task_dir).glob("*.md")):
prompt = task_file.read_text()
t0 = time.time()
output = run_task(prompt)
results.append({
"task": task_file.name,
"model": MODEL,
"latency_s": round(time.time() - t0, 2),
"output": output,
"scores": {"correctness": None, "locality": None, "explanation": None},
})
out = f"results_{MODEL.replace('/', '_')}.json"
Path(out).write_text(json.dumps(results, indent=2))
print(f"wrote {out} — now review outputs and fill in scores")
if __name__ == "__main__":
main(sys.argv[1] if len(sys.argv) > 1 else "./tasks")
Deliberate choices worth noting:
-
temperature: 0.2— you're comparing models, so reduce variance. Run each task twice if you want a crude stability check. - Scores are filled in by a human, not auto-graded. Auto-grading code tasks with another LLM at this stage just moves the uncertainty. For five tasks, manual review takes twenty minutes.
- Latency is logged because a slow-but-free model changes the calculus for interactive use vs. batch jobs.
Hosting this on a free server tier (rather than locally) matters if you want teammates to rerun the same benchmark, or if you want it on a schedule to detect model behavior drift over time.
Routing: a decision table
After one benchmark run, you can build a routing table like this (yours will differ — that's the point):
| Task class | Free model result | Route to |
|---|---|---|
| Boilerplate tests, obvious edge cases | Consistently correct | Free tier, always |
| "Explain this code" | Correct but shallow | Free tier for triage, escalate if confused |
| Refactors across >3 files | Loses track of constraints | Paid/frontier model |
| Subtle concurrency/state bug | Plausible but wrong fix | Paid model + you verify against the known answer |
| New small feature w/ clear spec | Mixed | Free tier for draft, human review gates merge |
The interesting finding in most versions of this exercise: the tasks that feel like they need the best model ("this bug was nasty") and the tasks that actually do ("the fix required reasoning across four files") overlap less than intuition suggests.
Limitations and who shouldn't do this
- Five tasks is a vibe check, not statistics. Treat results as routing hints, not benchmarks worth publishing. Expand the set before making budget decisions on it.
- Free tiers change. Model lineups, limits, and availability on any free offering shift over time. Don't hardcode model names into your routing; re-run the benchmark when the lineup changes. Verify what's currently available on the provider's own docs before building on it.
- Don't paste proprietary code into any third-party endpoint — free or paid — without clearing it with whoever owns your security policy. Sanitize or use synthetic reproductions of the bug instead.
- Skip this entirely if your work is dominated by one task class you already know needs frontier reasoning (e.g., large-scale architectural migration planning). The overhead of the benchmark isn't worth it when the answer is known.
- Also skip it if you can't review model output competently for the task class. A routing table built on unverifiable grades is worse than no routing table.
The actual takeaway
The expensive-model-first habit isn't a tooling problem, it's a missing-evaluation problem. An hour of benchmark construction and a free tier to run it against turns "which model should I use?" from a recurring argument into a table you update monthly. If you want a low-friction place to try this, MonkeyCode's free model access and free server option cover both halves of the setup — but the workflow stands on its own with whatever provider you already have.
What does your routing table look like? If you've run something similar, I'm curious which task classes surprised you.
Top comments (0)