> For a fair comparison you need to look at the total cost, because 4.7 produces significantly fewer output tokens than 4.6
Does it? Anthropic's own announcement says that for the same "effort level" 4.7 does more thinking (i.e uses more output tokens) than 4.6, and they've also increased the default effort level from 4.6 high to 4.7 xhigh.
I'm not sure what dominates the cost for a typical mix of agentic coding tasks - input tokens or output ones, but if you are working on an existing project rather than a brand new one, then file input has to be a significant factor and preliminary testing says that the new tokenizer is typically generating 40% or so more tokens for the exact same input.
I really have to wonder how much of 4.7's increase in benchmark scores over 4.6 is because the model is actually better trained for these cases, or just because it is using more tokens - more compute and thinking steps - to generate the output. It has to be a mix of the two.
Does it? Anthropic's own announcement says that for the same "effort level" 4.7 does more thinking (i.e uses more output tokens) than 4.6, and they've also increased the default effort level from 4.6 high to 4.7 xhigh.
I'm not sure what dominates the cost for a typical mix of agentic coding tasks - input tokens or output ones, but if you are working on an existing project rather than a brand new one, then file input has to be a significant factor and preliminary testing says that the new tokenizer is typically generating 40% or so more tokens for the exact same input.
I really have to wonder how much of 4.7's increase in benchmark scores over 4.6 is because the model is actually better trained for these cases, or just because it is using more tokens - more compute and thinking steps - to generate the output. It has to be a mix of the two.