On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, which dwarfs the difference in per-token pricing.
The token differential varies a lot by task, so it's hard to give a reliable rule of thumb (I'm guessing it's usually going to be well below ~5x), but hope this shows that price per task is not a linear function of price per token, as different models use different token vocabularies and different amounts of tokens.
We have raised per-token prices for our last couple models, but we've also made them a lot more efficient for the same capability level.
Agreed. Would be great if everyone starts reporting cost per task alongside eval scores, especially in a world where you can spend arbitrary test-time compute. This is one thing I like about the Artificial Analysis website - they include cost to run alongside their eval scores: https://artificialanalysis.ai/
Their subscription subscribers will see/feel the difference irregardless, API pricing is hopefully read by devs that know about token efficiency and effort.
yes but as far as i know gpt tokenizer is about the same as opus 4.6's, where 4.7 is seeing something in the ballpark of a 30% increase. this should still be cheaper even disregarding the concerns around 4.7 thinking burning tokens
Yep, it's more expensive per token.
However, I do want to emphasize that this is per token, not per task.
If we look at Opus 4.7, it uses smaller tokens (1-1.35x more than Opus 4.6) and it was also trained to think longer. https://www.anthropic.com/news/claude-opus-4-7
On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, which dwarfs the difference in per-token pricing.
The token differential varies a lot by task, so it's hard to give a reliable rule of thumb (I'm guessing it's usually going to be well below ~5x), but hope this shows that price per task is not a linear function of price per token, as different models use different token vocabularies and different amounts of tokens.
We have raised per-token prices for our last couple models, but we've also made them a lot more efficient for the same capability level.
(I work at OpenAI.)
I don't have anything to add, but I like how you guys are actually sending people to communicate in Hacker News. Brilliant.
you're one of them?
Maybe a good idea to be more explicit about this -- maybe a cost analysis benchmark would be a nice accompaniment.
This kind of thing keeps popping up each time a new model is released and I don't think people are aware that token efficiency can change.
Agreed. Would be great if everyone starts reporting cost per task alongside eval scores, especially in a world where you can spend arbitrary test-time compute. This is one thing I like about the Artificial Analysis website - they include cost to run alongside their eval scores: https://artificialanalysis.ai/
Their subscription subscribers will see/feel the difference irregardless, API pricing is hopefully read by devs that know about token efficiency and effort.
yes but as far as i know gpt tokenizer is about the same as opus 4.6's, where 4.7 is seeing something in the ballpark of a 30% increase. this should still be cheaper even disregarding the concerns around 4.7 thinking burning tokens
That pricing is extremely spicy, wow.
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