Free model access is not a discount. It is a queue you pay for with wall-clock time, and wall-clock time has a salary.
The platform debates of the last week asked what developers do while AI codes, and whether AI promoted everyone to reviewer. The sharper question sits one layer down: what does your program do while the token queue fills? If the answer is "retry, then retry again," your cost model is missing the most expensive line item — your own attention.
When you accept free capacity, you accept three budgets at once: tokens, latency, and attention. Most teams model the first and ignore the other two. Then a batch job that should take forty minutes takes six hours because every retry re-enters the same queue, the same concurrency cap, the same unlucky afternoon. The tokens were free. Your evening was not.
I have been poking at MonkeyCode, an open-source project that offers free model access and a free server slot. The operator's free tier as of 2026-08-29 includes ten million tokens and a server you do not pay for. Treat that as a snapshot, not a law — quotas change without a blog post, so check the repo before you build on it. What makes the project useful here is not the handout. It is that the free tier hands you a real token meter and a real queue to test your assumptions against. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The assumption I see most often is straight multiplication: calls times tokens per call equals price. That math omits retries, queueing, and the human watching a progress bar. So I built a small discrete-event simulator, no dependencies, around one question: what is the probability that a workload finishes within a wall-clock budget on shared free capacity?
#!/usr/bin/env python3
"""Queue bet: should this workload run on free capacity?
Models Poisson arrivals, token consumption, retries, and a wall-clock
budget. Prints the probability of finishing in time.
"""
import argparse
import random
def simulate(arrivals, tokens_per_call, free_tokens, workers,
retry_delay, budget, sims=3000):
hits = 0
for _ in range(sims):
events = []
t = 0.0
for _ in range(arrivals):
t += random.expovariate(2.0) # 2 arrivals/sec average
events.append(t)
tokens = free_tokens
free_at = [0.0] * workers
finished = True
for arrive in events:
start = max(arrive, min(free_at))
retries = 0
while tokens < tokens_per_call and retries < 4:
start += retry_delay
retries += 1
tokens = free_tokens
if retries == 4:
finished = False
break
finish = start + tokens_per_call / 1500.0
tokens -= tokens_per_call
free_at[free_at.index(min(free_at))] = finish
if finish > budget:
finished = False
break
if finished:
hits += 1
return hits / sims
def main():
p = argparse.ArgumentParser()
p.add_argument("--arrivals", type=int, default=120)
p.add_argument("--tokens-per-call", type=int, default=4000)
p.add_argument("--free-tokens", type=int, default=10_000_000)
p.add_argument("--workers", type=int, default=2)
p.add_argument("--retry-delay", type=float, default=2.0)
p.add_argument("--budget", type=float, default=300.0)
a = p.parse_args()
p_ok = simulate(a.arrivals, a.tokens_per_call, a.free_tokens,
a.workers, a.retry_delay, a.budget)
print(f"P(finish within {a.budget:.0f}s) = {p_ok:.2f}")
if __name__ == "__main__":
main()
The model is deliberately crude: arrivals are Poisson, each worker serves tokens at a fixed rate, and any call that cannot get tokens retries after a delay and then re-checks the meter. It is not a benchmark. It is a decision aid. Change one number and you will watch the probability collapse.
A typical run looks like this (the exact numbers vary with the random seed; the shape does not):
$ python3 queue_bet.py --arrivals 120 --tokens-per-call 4000 --budget 300
P(finish within 300s) = 0.52
$ python3 queue_bet.py --arrivals 120 --tokens-per-call 4000 --budget 900
P(finish within 900s) = 0.91
At a five-minute deadline, the job fails roughly half the time. At fifteen minutes it mostly lands, but the tail still stretches past the point where a human stopped checking.
This is where the cost-ops decision lives. You are not choosing between free and paid tokens. You are choosing between uncertain wall-clock and certain wall-clock. Paid capacity wins the instant the expected hours of waiting exceed the price of a few million tokens, and for a small batch that happens sooner than most people think. The crossover is even faster when your time is the expensive, interruptible kind.
So I turned the whole thing into a gate rather than a feeling: fix a deadline, run three thousand simulations, and accept free capacity only when the finish probability clears 0.95. Below that, schedule the job off-peak, buy a small burst of paid calls, or split it into chunks that fit the concurrency instead of hoping the queue is kind. Free capacity is the right bet for exploration, prototypes, background syncs, and load-test garbage you are happy to lose. It is the wrong bet for anything with a hard deadline, a real-time user, or a revenue path, because retries compound and the queue does not care about your demo.
One trick worth stealing from our earlier failure-chasing: treat the free tier as a chaos box. When the queue stalls, do not curse it. Measure your recovery time, your retry budget, your alert delay. A free queue that fails is not a bug report; it is a free load test with production-shaped traffic.
Limitations are real. The simulator assumes Poisson arrivals and a fixed service rate; real queues have diurnal waves and hidden concurrency caps. The ten-million-token figure is an operator snapshot, not a contract, and a free server is never an SLA. If your workload cannot tolerate a lost batch, free capacity should not carry it. That is the whole point of the gate.
Now about that promotion you got last week. AI did not promote you to reviewer. It promoted you to queue manager. The moment you accept that job, the math above stops being academic. It becomes the difference between a pleasant evening and a six-hour wait for a job that should have taken forty minutes.
The script is short enough to read in one coffee break, and the debt it removes is the one that actually compounds: guessing. If you want a real token meter plus a free place to run this against reality, the MonkeyCode repo is a fork away. Bring your own deadline.
