[dudeinhawaii]

I just ran some numbers and it works out if you're a prolific user.

Over 9 days I would have spent roughly $63 dollars on Codex with 11.5M input tokens plus 141M cached input tokens and 1.3M output tokens.

That roughly mirrors the $100-200/wk in API spending that drove me to the subscription.

  | Category | Tokens | Rate (/1M) | Estimated Cost |
  |---|---:|---:|---:|
  | Input (uncached) | 11,568,331 | $1.75 | $20.24 |
  | Cached input | 141,566,720 | $0.175 | $24.77 |
  | Output | 1,301,078 | $14.00 | $18.22 |
  | Total | 154,436,129 | — | $63.23 |

BUT... like a typical gym user. This is a 30/d window and I only used it for 9 days, $63 worth. OpenAI kept the other $137.

It makes sense though for heavy use.

[edg5000]

My hopes are on harness engineering allowing cheaper (but still large) models to shine. I'm evaluating DeepSeek because it would allow insane agent armies. Although DeepSeek charges for thinking tokens, something easy to overlook.

DeepSeek has the tendency to think... a lot!. Without a good harness I can't evaluate it well; time will tell.

OpenAI doesn't; it's embedded into the price, I think.

Cheap = we can run 10x the workloads, bigger imagination = innovation. Maybe 10 dumb agents in a loop can beat 1 Opus? Haha.