The cheapest model is not the one with the lowest price per token. It is the one whose failure modes you can afford, and for agent workloads that makes hosting a break-even problem, not a benchmark problem. This article gives you a three-variable framework — volume, failure cost, and operational time — plus a runnable script that computes the winner from your own numbers.
I will use MonkeyCode as the managed-free example. It is an open-source project whose current offering includes free model access and a free server option, which makes it a useful stand-in for the whole category of free endpoints. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Two properties make it a fair test case. The free model access removes the marginal cost of inference, which is the dominant line item in most agent bills. The free server option removes the control-plane cost, so the remaining question is whether the free tier's constraints — allowance, rate limits, latency — fit your workload. Because the project is open source, you can inspect the code paths that consume tokens before you commit anything to it.
The timing is not accidental. Agent loops are token-hungry: a single task with five tool calls can burn fifty thousand tokens, and a retry multiplies that. Teams keep choosing a hosting option from a price sheet, then discover the real cost in the first incident review. Cost-per-token benchmarks tell you the rate; they do not tell you the bill. The fix is to model the workload before you pick the platform.
