@cheaptokensAI

For agent tests, the useful unit is not just price per token. It is whether the run has a funded key, a clear daily limit, and a place to check remaining usage. CheapTokens is built around that operating loop.

Use CheapTokens for finite inference jobs. Good fit: eval batches, prompt sweeps, cleanup runs, launch prep, agent tests. Bad fit: workloads you cannot start before daily capacity resets.

Daily inference credits work best when the job has a clear window. Queue the batch, run it while the key is funded, then use the status page to confirm what remains before reset.

The quiet API spend is usually not the main chat box. It is evals, retries, summaries, background jobs, and agents. That is where cheaper credits matter.

Treat inference like inventory. Buy credits for the workload, give the job a key, let it run. CheapTokens makes that flow cheap and explicit.