Free Tokens Are a Queue, Not a Wallet

Free tokens look like a discount. In operations terms, they are usually a queue. The difference will decide whether your weekend stays free as well.

Every free-capacity offer hides the same trade: you pay with time instead of money. Requests wait for a slot. Slots arrive in bursts. When the burst ends, your SDK backs off, retries, and waits again. The bill stays at zero. The wall clock does not.

I keep coming back to this because of the current DEV debates about benchmark scores — the ones asking whether a number measures the model or the harness it runs in. Cost has the same twin problem. Accuracy answers "can it answer?" Throughput, retries, and queue wait answer "can it answer before my user leaves?" Both questions matter. Most of us measure only the first.

This is where MonkeyCode's offer becomes worth an ops conversation, not a marketing one. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The open-source project provides free model access — at the time of writing, 10 million tokens — along with a free server option for running it yourself. Notice what I am not doing: quoting a benchmark. I have not run one, and you should not trust a number in a blog post anyway. Quotas change. Queues change. Terms change. What you can trust is a measurement you take yourself against the actual endpoint.

The workflow has two steps. First, a single-shot latency check with curl, so you know the endpoint is even alive before you load it:

curl -w "total=%{time_total}s\n" -o /dev/null \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"your-model","messages":[{"role":"user","content":"Say ok"}],"max_tokens":8}' \
  https://your-endpoint/v1/chat/completions

Second, a small probe that replays the load pattern you actually care about. You need four numbers — p50 latency, p95 latency, retry count, and behavior at modest concurrency. The script below measures all of them against any chat-completions-style JSON API, MonkeyCode's free tier included:

#!/usr/bin/env python3
"""Cost-ops probe: measure queueing, retries, and latency shape."""
import json
import time
import urllib.error
import urllib.request
from concurrent.futures import ThreadPoolExecutor

ENDPOINT = "https://your-endpoint/v1/chat/completions"
API_KEY = "your-key"
MODEL = "your-model"
REQUESTS = 20
CONCURRENCY = 4
MAX_RETRIES = 4

def call_once(i):
    body = json.dumps({
        "model": MODEL,
        "messages": [{"role": "user", "content": "Reply with the word ok."}],
        "max_tokens": 8,
    }).encode()
    req = urllib.request.Request(ENDPOINT, data=body, headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    })
    t0 = time.perf_counter()
    retries = 0
    while True:
        try:
            with urllib.request.urlopen(req, timeout=30) as resp:
                json.load(resp)
            return {"ok": True, "latency": time.perf_counter() - t0,
                    "retries": retries}
        except urllib.error.HTTPError as e:
            if e.code == 429 and retries < MAX_RETRIES:
                retries += 1
                time.sleep(2 ** retries)
                continue
            return {"ok": False, "status": e.code,
                    "latency": time.perf_counter() - t0, "retries": retries}
        except urllib.error.URLError:
            return {"ok": False, "status": "network",
                    "latency": time.perf_counter() - t0, "retries": retries}

with ThreadPoolExecutor(max_workers=CONCURRENCY) as pool:
    results = list(pool.map(call_once, range(REQUESTS)))

ok = sorted(r["latency"] for r in results if r["ok"])
p50 = ok[len(ok) // 2] if ok else float("nan")
p95 = ok[int(len(ok) * 0.95)] if ok else float("nan")
wait = sum(2 ** r["retries"] for r in results if r["retries"])
print(f"requests={REQUESTS} concurrency={CONCURRENCY}")
print(f"ok={len(ok)} failed={len(results) - len(ok)}")
print(f"p50={p50:.2f}s p95={p95:.2f}s")
print(f"retries={sum(r['retries'] for r in results)} backoff_wait={wait:.0f}s")

Run it:

python3 probe.py

Then read the shape, not the average. If p95 sits at three times p50, your requests are queueing somewhere. If retries are non-zero, part of your "free" call cost is sleep. If failures appear at concurrency four, that capacity is a shared parking lot where everyone else is circling too. Pick a sample size that mirrors your real traffic; twenty requests at concurrency four is a smoke test, not a load test. If you expect one hundred concurrent users, raise both numbers and run it overnight.

Here is the failure mode I see most often. A team wires a free endpoint into its agent loop because each call is tiny. Then the agent calls the model four times per user turn, and every call races the same rate limit. The first call succeeds, the second hits 429, the retry sleeps long enough that the whole turn times out. Token cost: zero. User-visible failure: total. A single measurement misleads you; you have to measure the loop.

Interpretation is where the decision lives. Free tokens are the right bet for jobs that tolerate waiting: nightly evaluation runs, dataset labeling, embedding backfills, smoke tests that may retry for an hour. A batch job that finishes at 3 a.m. instead of 2 a.m. costs you nothing. A user chat that pauses for eight seconds costs you a user.

That is the whole thesis. Free capacity is not a wallet you spend; it is a queue you join. When the value of your workload depends on arrival time — interactive chat, tool-calling agents, anything on a user request path — free tokens are the wrong bet, and a paid lane is the honest price. When the value depends on completion rather than arrival, free capacity is hard to beat, and MonkeyCode's 10-million-token access plus its free server is a sensible place to park that workload. You can stand the server up yourself, wire it to your batch pipeline, and read real numbers before spending an hour of engineering time.

One more ops caveat. Free capacity is a moving target in a way paid capacity is not. A quota can shrink, a queue can lengthen, and a generous tier can change direction with the project. Treat 10 million tokens as today's snapshot, not next quarter's contract. If your pipeline cannot survive a sudden change in access, it should not sit on free capacity at all.

Who should not use this approach? Anyone with a hard latency SLO. Any pipeline where a retry triggers expensive recomputation downstream. Any team that cannot absorb a quota change without warning. And, honestly, anyone whose engineering hour costs more than the token bill would — optimizing a twenty-cent invoice for two hours is not cost ops, it is a hobby.

The fair way to decide is the probe above. Clone MonkeyCode's repo, point its free server at your workload, and let p95 make the call instead of a README. If the number fits your tolerance, use it. If it does not, you just avoided a debugging session. That avoided session was the real cost all along.