A Field Guide to Stopping a Free Model Tier

Free model access is cheapest at the front of a pipeline and most expensive at the end. That asymmetry is the whole story. The cutoff where a free tier stops paying off is measurable, and it almost never lands where a team's intuition puts it.

The bill is not the failure mode. Attention is. A free endpoint that returns plausible output nobody asked for converts a zero-dollar invoice into a review queue, and review queues are the most expensive thing a small team owns. So the useful work is not finding a free tier — it is writing down, in numbers, the point at which you stop sending it work.

The two availability claims, stated plainly

MonkeyCode is an open-source project that offers free model access and a free server option. The operator also quotes a free introductory allowance on the order of ten million tokens, plus the server, as current terms rather than permanent constants. Treat both as something to re-read before you budget against them; the harness below deliberately assumes no particular model, quota, or machine shape.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Everything past this paragraph stands on its own if the product disappears tomorrow. That is the test any free tier should be held to, including this one.

What to measure instead of gut feel

Three numbers carry most of the decision. The first is accepted-change yield: outputs that pass your existing acceptance check per 100k tokens spent. The second is retry tax: total attempts divided by accepted attempts. The third is p95 seconds, because a free tier's latency profile decides whether it can sit anywhere near a human.

Yield answers "is this cheap?" Retry tax answers "is this cheap after I pay for the retries?" p95 answers "is this usable by a person waiting?" All three come from the same probe run.

Each task in the manifest carries a prompt and an acceptance command you already trust. No new test framework, no golden files written by a model.

{"id":"T-014","cwd":"./repo","prompt":"Add a null guard to parse_invoice and explain the change.","out_path":"out/T-014.diff","accept":"git apply --check out/T-014.diff"}

# tier_probe.py — run against YOUR task set. Prints measurements, not benchmarks.
import json, os, subprocess, time
from openai import OpenAI

client = OpenAI(base_url=os.environ["TIER_BASE_URL"],
                api_key=os.environ["TIER_API_KEY"])

def attempt(task, model):
    t0 = time.monotonic()
    r = client.chat.completions.create(
        model=model, temperature=0,
        messages=[{"role": "user", "content": task["prompt"]}],
    )
    dt = time.monotonic() - t0
    with open(task["out_path"], "w") as fh:
        fh.write(r.choices[0].message.content or "")
    ok = subprocess.run(task["accept"], shell=True, cwd=task["cwd"],
                        capture_output=True).returncode == 0
    return {"tokens": r.usage.total_tokens, "seconds": dt, "accepted": ok}

def score(rows):
    tokens = sum(r["tokens"] for r in rows)
    accepted = sum(r["accepted"] for r in rows)
    wait = sorted(r["seconds"] for r in rows)
    p95 = wait[max(int(len(wait) * 0.95) - 1, 0)]
    return {
        "tokens": tokens,
        "accepted": accepted,
        "yield_per_100k": round(accepted / (tokens / 100_000), 2) if tokens else 0.0,
        "retry_tax": round(len(rows) / accepted, 2) if accepted else float("inf"),
        "p95_seconds": round(p95, 2),
    }

def verdict(m):
    if m["yield_per_100k"] < 1.0 or m["retry_tax"] > 2.0:
        return "DROP"
    if m["yield_per_100k"] < 3.0 or m["p95_seconds"] > 8.0:
        return "CAP"
    return "KEEP"

pip install openai
export TIER_BASE_URL="https://your-free-endpoint/v1"
export TIER_API_KEY="..."
python tier_probe.py --manifest tasks.jsonl | tee probe.json

Twenty tasks is enough to get a signal. Two hundred tasks is enough to argue about it. The thresholds in verdict are starting points, not law — move them once you know what a good week looks like on your own repo.

Reading the verdict

A CAP result is the normal outcome. Most free tiers are worth a capped slice of a pipeline and nothing else. The discipline is refusing to let a capped tier drift upward because it had one good afternoon.

Red flags worth naming out loud

The first flag is a human blocking on the response. If the probe's p95 exceeds your interaction budget by more than double, no amount of quality rescues the placement — the endpoint has told you it belongs behind a queue, not behind a button.

The second flag is state you cannot rebuild. A free server is excellent for restartable, stateless bursts and terrible for anything holding a lock, a cursor, or a half-finished migration. If a process restart loses work that only exists in memory, that work does not belong there.

The third flag is output that becomes a record. Release notes, migration scripts, compliance text, and customer-facing copy have a durability requirement that a free tier's terms cannot promise. Use it to draft; never to sign.

The fourth flag is retry amplification inside your own code. A retry loop you own multiplies token spend silently, and free tiers make that loop feel affordable right up until the queue is full of duplicate work.

# Cheap guard: refuse before the client is even constructed.
import os
def tier_allowed(workload: str) -> bool:
    return workload in {"draft", "scaffold", "rename", "summarize-internal"}

if tier_allowed(os.environ.get("WORKLOAD", "")):
    client = OpenAI(base_url=os.environ["TIER_BASE_URL"],
                    api_key=os.environ["TIER_API_KEY"])
else:
    raise RuntimeError("workload not cleared for the free tier")

Better alternatives, flag by flag

For latency, the alternative is not a faster free model — it is a paid tier for the click path and an async reviewer for everything else. For determinism, the alternative is often no model at all: a codemod, a linter rule, or a typed schema solver will beat any endpoint on volume and correctness. For state, the alternative is a small managed database or a durable queue you pay for, and it is usually cheaper than the incident.

For durability, the alternative is a paid tier with a written guarantee, or a human signoff that the free output was only an input.

That leaves a genuinely good slot: the wide pass. Cheap, high-volume, discard-heavy work where most outputs get thrown away and the few that survive earn a human look. Free model access is well suited to that slot, and the free server is well suited to running it in bursts.

Exit criteria that live in CI

Write the exit down where it can fire without permission. A probe that only runs when someone remembers is a probe that will still be green six months after the tier stopped earning its place.

python tier_probe.py --manifest tasks.jsonl > probe.json
python - <<'PY'
import json, sys
m = json.load(open("probe.json"))
ok = m["yield_per_100k"] >= 1.0 and m["retry_tax"] <= 2.0
print(m, "PASS" if ok else "EXIT")
sys.exit(0 if ok else 1)
PY

A red build on that step is the exit criteria doing its job. Move the workload, keep the harness, re-run it against whatever replaces the tier.

Who should not use this

Teams without acceptance checks should not start here — without a pass/fail signal, yield is a feeling, and feelings drift toward whatever the last output looked like. Teams handling regulated or personal data without a written agreement should not send prompts to a free tier at all. Teams with a hard sub-second latency SLO should keep it off the interactive path permanently. And teams that need durable storage should not treat a free server as a database, however convenient the price is.

If you want to run the probe before deciding anything, MonkeyCode's free model access and free server option make the first run cheap — and the harness above will tell you, in numbers, when to stop.