Free Tokens, Real Queues: Measure What Your LLM Calls Actually Cost

Free tokens are not free. They are a queue you join with your time, your retries, and your patience. Before you route any real workload through a free model endpoint, measure what the queue actually costs you.

The same mistake shows up in batch jobs all the time. Someone finds a free tier with a generous allowance, points their pipeline at it, and celebrates the zero on the invoice. Then the rate limiter answers the first burst with 429s, the client retries with exponential backoff, and the "free" job runs three times longer than the paid one would have. The invoice is still zero. The clock is not.

MonkeyCode is an open-source project that pairs free model access with a free server option — the kind of offer that looks unbeatable until you count the wall-clock. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I am not here to sell you on it. I am here to give you a way to test whether its free capacity is actually cheap for your workload.

The claim, as of this writing, is simple: a free allowance of 10 million tokens, plus a server you do not pay for. Whether that is a good deal depends entirely on your traffic pattern. A free allowance is a budget, not a guarantee. You are sharing the endpoint with everyone else who found the same deal, and their bursts become your latency.

So stop guessing. Measure.

Here is a small batch runner that keeps a ledger of everything that matters: wall time, retries, and tokens. It treats the endpoint as a black box, because that is what it is.

import json
import time
from openai import OpenAI

client = OpenAI(base_url="YOUR_ENDPOINT", api_key="YOUR_KEY")

def run_batch(prompts, model, max_retries=4):
    ledger = []
    wall_start = time.monotonic()

    for i, prompt in enumerate(prompts):
        attempts = 0
        t0 = time.monotonic()
        while True:
            try:
                resp = client.chat.completions.create(
                    model=model,
                    messages=[{"role": "user", "content": prompt}],
                )
                ledger.append({
                    "index": i,
                    "attempts": attempts + 1,
                    "wall_ms": round((time.monotonic() - t0) * 1000, 1),
                    "tokens": resp.usage.total_tokens,
                })
                break
            except Exception:
                attempts += 1
                if attempts >= max_retries:
                    raise
                time.sleep(min(2 ** attempts, 30))

    total_wall = time.monotonic() - wall_start
    tokens = sum(entry["tokens"] for entry in ledger)
    retries = sum(entry["attempts"] - 1 for entry in ledger)

    print(json.dumps({
        "items": len(ledger),
        "total_wall_s": round(total_wall, 1),
        "total_tokens": tokens,
        "tokens_per_s": round(tokens / total_wall, 1),
        "retries": retries,
        "retry_rate": round(retries / len(ledger), 3),
    }, indent=2))

Feed it a representative sample of your real prompts, not a toy. Fifty documents that look like your production traffic are worth more than five hundred that do not. Run the same sample against the free endpoint and against whatever you pay for today, then compare the two JSON summaries.

The number that matters is tokens per second, not tokens per dollar. If the free endpoint gives you, say, 40 tokens per second and your paid one gives you 400, then a job that costs you nothing in money costs you ten times longer in time. For a one-off script, that trade is fine. For a pipeline that runs every night, it is a tax you pay forever.

Retries deserve their own line in the ledger. Treat a retry rate above five percent as a warning sign: it usually means you are the queue, not a user of it. Watch how retries cluster. If they all happen in the first minute of the run, you are bursting too hard, and a simple rate limiter on your side will flatten the curve. If they are spread evenly, the endpoint is simply saturated, and no client-side trick will save you.

Run the same batch at three different times of day. Free endpoints have rush hours, and the ledger will show them as clearly as a traffic report. If your job can move to a quiet window, you just found free capacity that behaves like paid capacity.

There is one thing this script cannot tell you, and you should be honest about it. Wall time includes network hops, server queueing, and generation, all mixed together. You cannot separate queueing from generation with a plain client, so treat tokens per second as a composite number. It is still the right number to compare, because your pipeline experiences all of it as one delay.

When is free capacity the wrong bet? Three situations come to mind. First, interactive work: if a human is waiting on the response, a queue is a bug, not a bargain. Second, sustained traffic: a free allowance is a shared resource, and shared resources degrade exactly when you need them most — during your peak. Third, anything with a deadline: batch jobs that must finish before a downstream step starts should not depend on a queue you do not control.

Who should not use this approach at all? Teams with hard service-level agreements, real-time features, or workloads that already saturate a free tier. If your retry rate is climbing week over week, the answer is not a better client; it is a paid endpoint or a self-hosted model. Free capacity is an experiment, not an architecture.

The honest way to evaluate MonkeyCode, or any free tier, is to run the ledger and read the numbers. If the free server and the 10-million-token allowance keep your tokens per second high enough for your actual workload, you have found a genuinely cheap option. If not, you have found a cheap way to learn what your time is worth. Either result is useful, and it costs you nothing but a few minutes of measurement.