How do you arrive at that number? I find it hard to make sense of this ad hoc, given that the total token cost is not very interesting; it's token efficiency we care about.
> prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session for standard, batch, and flex.
which is basically maxxed out quickly. So there is 2x (the first lever)
Then there is the /fast mode, which they state costs 2x more (for 1.5x speedup)
And then there is the model base price ($2.50 vs $1.75), well yeah thats 42% increase. It is in fact a 5.7x total increase of token cost in fast mode and large context. (Sorry for the confusion, I thought it was 8x because I thought gpt-5.3-codex was $1.25)
(After a day of usage, I am relatively certain in practice this does not end up being a 5.7x cost increase or anything close to that, though I am still fairly unclear on what that computation is worth to begin with, given that I am entirely fine with the model using the least amount of tokens possible to get the job done)
1. it's 1.5x , it's quite fast for the level of thinking it has
2. no if you are on subscription, it's the same, at 20$ codex 5.4 xhigh provide way more than 20$ opus thinking ( this one instead really can burn 33% with 1 request, try to compare then on same tasks ) also 8x .. ??? if you need 1M token for a special tasks doesn't hit /fast and vice-versa , the higher price doesn't apply on subscription too..
3. false, i'm on pro , so 10x the base , always on /fast (no 1M), and often 2 parallel instances working.. hardly can use 2% (=20% of 5h limit , in 1h of work ( about 15/20 req/hour) ) , claude is way worse on that imo
1. Fast mode ain't that fast
2. Large context * Fast * Higher Model Base Price = 8x increase over gpt-5.3-codex
3. I burnt 33% of my 5h limit (ChatGPT Business Subscription) with a prompt that took 2 minutes to complete.