5 ms·
For a 56.7 score on the Artificial Intelligence Index, GPT 5.5 used 22m output tokens. For a score of 57, Opus 4.7 used 111m output tokens. The efficiency gap
by louiereederson 6mo ago
For a 56.7 score on the Artificial Intelligence Index, GPT 5.5 used 22m output tokens. For a score of 57, Opus 4.7 used 111m output tokens.
The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?
- swyx 6mo agowhy would chip affect token quantity. this is all models.
- louiereederson 6mo agoChip costs strongly impact the economics of model serving. It is entirely plausible to me that Opus 4.7 is designed to consume more tokens in order to artificially reduce the API cost/token, thereby obscuring the true operating cost of the model. I agree though, I chose poor phrasing originally. Better to say that GB200 vs Tranium could contribute to the efficiency differential.
- itemize123 6mo agoprobably the wrong take - they are arm racing to a better model. it's not enshittification era for models just yet
- fiatpandas 6mo agoModels are still in arms race mode, but harnesses and subscription strategy are tiptoeing into their enshittification era.
- karmasimida 6mo agoChips doesn’t impact output quality in this magnitude
- ChrisGreenHeur 6mo agoTrue, but the qualifying the power played a large part. Most likely nuclear power for this high quality token efficiency.
- dist-epoch 6mo agoIf it's a new pretrain, the token embeddings could be wider - you can pack more info into a token making it's way through the system. Like Chinese versus English - you need fewer Chinese characters to say something than if you write that in English. So this model internally could be thinking in much more expressive embeddings.
- AtNightWeCode 6mo agoYou need to compare total cost. Token count is irrelevant.