Turn a Free Token Quota Into a Rejection Rate Before You Merge the Route

30 million free tokens sounds like capacity. The number that actually changes your architecture is how many of those tokens you can pull per minute before the endpoint starts returning 429s, resetting connections, or silently truncating a completion. This article walks through a token-aware load test that turns a free-tier quota into two engineering numbers: rejections per second and p95 latency at a fixed token rate.

MonkeyCode is an open-source project that, according to operator-supplied materials, currently offers a free model endpoint and a free server option with a reported 30 million-token allocation. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I did not verify the current quota on the project page, so treat the 30 million figure as a claim to test, not a fact to import into your capacity plan. The method below works for any hosted model endpoint.

Measure rejection, not tokens

A token quota is a budget, not a throughput guarantee. A 30M-token allocation tells you how much you can spend, but it says nothing about how fast you can spend it. Three separate limits determine whether a free route can serve real traffic:

Signal	What it measures	Why it matters
Token quota	Total tokens allowed over a period	Predicts monthly cost, not latency
Request rate limit	Requests per minute or second	Determines concurrency ceiling
Token accounting	How prompt and completion tokens are counted	Changes cost per request and overage risk
Failure mode	429 vs timeout vs dropped connection	Decides retry and fallback behavior

A useful load test targets the last two rows. You do not need to consume the entire 30M allocation to learn what happens at the boundary; you need to hold a fixed token rate and observe when the endpoint starts rejecting work.

A token-aware load generator

The following Python harness uses httpx and a configurable token estimate. Replace the endpoint, key, and model string with the values from the current project page. Run it for five to fifteen minutes so you can see steady-state behavior rather than a cold-start spike.

import asyncio
import os
import time
import httpx

ENDPOINT = os.getenv("MODEL_ENDPOINT", "https://your-endpoint.example/v1/chat/completions")
API_KEY = os.getenv("MODEL_API_KEY", "")
MODEL = os.getenv("MODEL_NAME", "replace-me")
CONCURRENCY = 8
TARGET_TOKENS_PER_REQUEST = 240

async def one_request(client, sem):
    async with sem:
        payload = {
            "model": MODEL,
            "messages": [
                {"role": "user", "content": "x" * 400}
            ],
            "max_tokens": 80,
        }
        started = time.monotonic()
        try:
            resp = await client.post(
                ENDPOINT,
                headers={"Authorization": f"Bearer {API_KEY}"},
                json=payload,
                timeout=30.0,
            )
            latency = time.monotonic() - started
            body = resp.text
            status = resp.status_code
            prompt_tokens = len(payload["messages"][0]["content"]) // 4
            completion_tokens = min(len(body) // 4, 80)
            total_tokens = prompt_tokens + completion_tokens
            print(f"{status} latency={latency:.2f}s tokens={total_tokens}")
            return status, latency, total_tokens
        except Exception as exc:
            latency = time.monotonic() - started
            print(f"ERR {type(exc).__name__} latency={latency:.2f}s")
            return "ERR", latency, 0

async def main():
    sem = asyncio.Semaphore(CONCURRENCY)
    async with httpx.AsyncClient() as client:
        tasks = [one_request(client, sem) for _ in range(200)]
        results = await asyncio.gather(*tasks)
    statuses = [r[0] for r in results]
    latencies = [r[1] for r in results]
    total_tokens = sum(r[2] for r in results)
    print(f"statuses={ {s: statuses.count(s) for s in set(statuses)} }")
    print(f"total_tokens={total_tokens} mean_latency={sum(latencies)/len(latencies):.2f}s")
    print(f"p95_latency={sorted(latencies)[int(0.95*len(latencies))]:.2f}s")

if __name__ == "__main__":
    asyncio.run(main())

This is not a benchmark of MonkeyCode; it is a probe you can run against the endpoint you intend to use. The token estimate is deliberately coarse because provider tokenizers differ. For a stricter test, replace the // 4 heuristic with the provider's tokenizer so your rejection count aligns with billable usage.

Decision table and limits

Use the measured rejection rate to decide whether the free server belongs in development, staging, or nowhere near production.

Observed behavior	Safe use	Unsafe use
Rejection rate < 1% at target token rate	Dev/staging shadow route, internal demos	User-facing traffic without a paid fallback
Rejection rate 1-10%	Background batch jobs with retries	Synchronous request path
Intermittent timeouts or dropped connections	Canary experiments with strict deadlines	Workloads with strict SLOs
Token accounting is opaque or changes daily	Cost estimation only	Budget forecasting

The free server option becomes most useful as a shadow tier: send a copy of non-critical traffic, compare outputs and failure modes against your primary route, and keep the paid route as the fallback. That mirrors the shadow-contract pattern, but the missing piece is usually rate-limit measurement, which is what this harness adds.

Do not use the approach if you are handling regulated data, need guaranteed tail latency, or cannot tolerate a promotional quota changing before you finish your test. A free allocation is a starting condition, not a service-level agreement. Check the project page for the current token amount and server terms before relying on either. If you run the harness, share the failure state you hit first: 429, timeout, or token accounting that did not match the request you sent.