Tokens and Credits are different thinks get some GPT 5.5 pimp 
The project instructions were so clear that the model didn’t need to ask questions. It would run the tests, write the .md files, and then move on to the next slice. So it was completely natural for it to work for that long.
Give the model clear goals and a solid roadmap. I remember spending around 5–6 hours just creating .md files to build that roadmap.
Yeah, I get the distinction you are making. Tokens and credits are not literally the same unit. Tokens are the usage meter, credits are the spend balance. But in practical API use, they are directly tied together. The more tokens you burn, the faster credits disappear. The exact conversion depends on model, input versus output, cached versus uncached context, and effort level.
My point was about real production usage on high effort workloads. At that level, token volume turns into spend very quickly, even when caching helps.