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Measuring Claude 4.7's tokenizer costs

claudecodecamp.com

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Re: Measuring Claude 4.7's tokenizer costs

#161

Post by bisonbear »

namnnumbr wrote: post #56

The title is a misdirection. The token counts may be higher, but the cost-per-task may not be for a given intelligence level. Need to wait to see Artificial Analysis' Intelligence Index run for this, or some other independent per-task cost analysis. The final calculation assumes that Opus 4.7 uses the exact same trajectory + reasoning output as Opus 4.6. I have not verified, but I assume it not to be the case, given…

yep, ran a controlled experiment on 28 tasks comparing old opus 4.6 vs new opus 4.6 vs 4.7, and found that 4.7 is comparable in cost to old 4.6, and ~20% more expensive then new 4.6 (because new 4.6 is thinking less)

https://www.stet.sh/blog/opus-4-7-zod

Re: Measuring Claude 4.7's tokenizer costs

#162

Post by saltyoldman »

I was sort of hoping that the peak is something like $15 per hour of vibe help (yes I know some of you burn $15 in 12milliseconds), and that you can have last year's best or the current "nano/small" model at $1 per hour.

But it looks like it's just creeping up. Probably because we're paying for construction, not just inference right now.

Re: Measuring Claude 4.7's tokenizer costs

#164

Post by rambojohnson »

_fat_santa wrote: post #62

A question I've been asking alot lately (really since the release of GPT-5.3) is "do I really need the more powerful model"? I think a big issue with the industry right now is it's constantly chasing higher performing models and that comes at the cost of everything else. What I would love to see in the next few years is all these frontier AI labs go from just trying to create the most powerful model at any cost to ac…

[dead]

Re: Measuring Claude 4.7's tokenizer costs

#166

Post by JohnMakin »

30% more token use, but even by their benchmarks, don't appear to have any real big successes there, and some regressions. What's the point? It doesn't do any better on the suite of obedience/compliance tests I've written for 4.6, and in some tests, got worse, despite their claim there it is better. Anecdotally, it was gobbling so many tokens on even the simplest queries I immediately shut it off and went back to 4.5.

Why release this?

Re: Measuring Claude 4.7's tokenizer costs

#167

Post by WarmWash »

arcanemachiner wrote: post #81
WarmWash wrote: post #72

Earlier quoted context omitted.

The problem is that people equate money to power and power to evil. So no matter what, if you do something lots of people like (and hence compensate you for), you will be evil. It's a very interesting quirk of human intuition.

A reasonable conclusion, considering that money and power seem to have their own gravity, so people with more of both end up getting even more of both, and vice versa. Can't blame someone who comes to such a conclusion about money and power.

The unreasonable part automatically labeling power as evil.

Re: Measuring Claude 4.7's tokenizer costs

#169

Post by Bridged7756 »

aray07 wrote: post #43
_pdp_ wrote: post #29

IMHO there is a point where incremental model quality will hit diminishing returns. It is like comparing an 8K display to a 16K display because at normal viewing distance, the difference is imperceptible, but 16K comes at significant premium. The same applies to intelligence. Sure, some users might register a meaningful bump, but if 99% can't tell the difference in their day-to-day work, does it matter? A 20-30% cost…

yeah thats is my biggest issue - im okay with paying 20-30% more but what is the ROI? i dont see an equivalent improvement in performance. Anthropic hasnt published any data around what these improvements are - just some vague “better instruction following"

Its enshittificating real fast. They'll just keep releasing model after model, more expensive than the last, marginal gains, but touted as "the next thing". Evangelists will say that they're afraid, it's the future, in 6 months it's all over. Anthropic will keep astroturfing on Reddit. CEOs will make even more outlandish claims.

You raised a good point, what's a good metric for LLM performance? There's surely all the benchmarks out there, but aren't they one and done? Usually at release? What keeps checking the performance of those models. At this point it's just by feel. People say models have been dumbed down, and that's it.

I think the actual future is open source models. Problem is, they don't have the huge marketing budget Anthropic or OpenAI does.

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