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Measuring Claude 4.7's tokenizer costs

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Re: Measuring Claude 4.7's tokenizer costs

#361

Post by alex_sf »

lbreakjai wrote: post #281
fragmede wrote: post #200

Earlier quoted context omitted.

That post doesn't address the human factor of cost, and I don't mean that in a good way. Even if AI costs more than a human, it's tireless, doesn't need holidays, is never going to have to go to HR for sexual harassment issues, won't show up hungover or need an advance to pay for a dying relative's surgery. It can be turned on and off with the flip of a switch. Hire 30 today, fire 25 of them next week. Spin another 5…

The cost to hire a human is highly predictable. The cost of AI isn't. I, as a human, need food and shelter, which puts a ceiling to my bargaining power. I can't withdraw my labour indefinitely. The power dynamics are also vastly against me. I represent a fraction of my employer's labour, but my employer represents 100% of my income. That dynamic is totally inverted with AI. You are a rounding error on their revenue s…

The same way companies already deal with any cost.

Re: Measuring Claude 4.7's tokenizer costs

#362

Post by Aurornis »

KronisLV wrote: post #264
Aurornis wrote: post #157

Earlier quoted context omitted.

> It's not really clear whether Opus 4.5+ represent a level shift on this frontier or just inhabits place on that curve which delivers higher performance, but at rapidly diminishing returns to inference cost. I think we're reaching the point where more developers need to start right-sizing the model and effort level to the task. It was easy to get comfortable with using the best model at the highest setting for every…

> It was easy to get comfortable with using the best model at the highest setting for everything for a while, but as the models continue to scale and reasoning token budgets grow, that's no longer a safe default unless you have unlimited budgets. For a while I used Cerebras Code for 50 USD a month with them running a GLM model and giving you millions of tokens per day . It did a lot of heavy lifting in a software mig…

Good points. I was speaking from a position of using an LLM in a pair programming style where I'm interactive with each request.

For handing work off to an LLM in large chunks, picking the best model available is the only way to go right now.

Re: Measuring Claude 4.7's tokenizer costs

#363

Post by ianm218 »

scuff3d wrote: post #352
hyraki wrote: post #315

Earlier quoted context omitted.

Yes 200 as a business expense is really not that bad. But a hobby is hard to justify.

It's not gonna stay that way. Token cost is being massively subsidized right now. Prices will have to start increasing at some point.

This is hard to say definitively. The new Nvidia Vera Rubin chips are 35-50x more efficient on a FLOPS/ megawatt basis. TPU/ ASICS/ AMD chips are making similar less dramatic strides.

So a service ran at a loss now could be high margin on new chips in a year. We also don’t really know that they are losing money on the 200/ month subscriptions just that they are compute constrained.

If prices increase might be because of a supply crunch than due to unit economics.

Re: Measuring Claude 4.7's tokenizer costs

#364

Post by Aurornis »

speedgoose wrote: post #103

The "multiplier" on Github Copilot went from 3 to 7.5. Nice to see that it is actually only 20-30% and Microsoft wanting to lose money slightly slower. https://docs.github.com/fr/copilot/reference/ai-models/suppo...

This article is only about the tokenizer. It doesn't measure the number of tokens needed for each request, which could be higher or lower overall.

Re: Measuring Claude 4.7's tokenizer costs

#365

Post by aaronblohowiak »

amelius wrote: post #345
louiereederson wrote: post #10

LLMs exist on a logaritmhic performance/cost frontier. It's not really clear whether Opus 4.5+ represent a level shift on this frontier or just inhabits place on that curve which delivers higher performance, but at rapidly diminishing returns to inference cost. To me, it is hard to reject this hypothesis today. The fact that Anthropic is rapidly trying to increase price may betray the fact that their recent lead is a…

Once they implement their models directly in silicon, the cost will come down and the speed will go up. See Taalas.

taalas is amazing. id gladly spend 5-15k on something that matched that performance with opus 4.6 quality

Re: Measuring Claude 4.7's tokenizer costs

#366

Post by mh- »

piker wrote: post #216
fragmede wrote: post #200

Earlier quoted context omitted.

That post doesn't address the human factor of cost, and I don't mean that in a good way. Even if AI costs more than a human, it's tireless, doesn't need holidays, is never going to have to go to HR for sexual harassment issues, won't show up hungover or need an advance to pay for a dying relative's surgery. It can be turned on and off with the flip of a switch. Hire 30 today, fire 25 of them next week. Spin another 5…

That was a great promise before the models starting becoming "moody" due to their proprietors arbitrarily modifying their performance capabilities and defaults without transparency or recourse.

I still haven't seen any statistically sound data supporting that this is happening on the API (per-token pricing.)

If you've got something to share I'd love to see it.

Re: Measuring Claude 4.7's tokenizer costs

#367

Post by foobar10000 »

criemen wrote: post #296
iainmerrick wrote: post #292

Earlier quoted context omitted.

That sounds very plausible. But it implies they could offer even higher performance models at much higher costs if they chose to; and presumably they would if there were customers willing to pay. Is that the case? Surely there are a decent number of customers who’d be willing to pay more, much more, to get the very best LLMs possible. Like, Apple computers are already quite pricey -- $1000 or $2000 or so for a decent…

> Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train? I guess at the time that was GPT-4.5. I don't think people used it a lot because it was crazy expensive, and not that much better than the rest of the crop.

The issue is not better - it’s better _AND_ fast enough. An agentic loop is essentially [think,verify] in a loop - i.e. [t1,v1,t2,v2,t3,v3,…] A model that does [t1,t2,t3,t4] in 40 minutes, if verify takes 10 min, will most likely do MUCH worse that a model that does t1 (decently worse) in 10 mins, v1 in 10 mins, t2 now based on t1 and v1 in 10 mins, v2 in 10 mins, etc..

So, for agentic workflows - ones where the model gets feedback from tools, etc…, fast enough is important.

Re: Measuring Claude 4.7's tokenizer costs

#368

Post by phainopepla2 »

slopinthebag wrote: post #314
Someone1234 wrote: post #280

Earlier quoted context omitted.

Can you link to a source for anything you're claiming?

https://github.blog/changelog/2026-04-16-claude-opus-4-7-is-... > Over the coming weeks, Opus 4.7 will replace Opus 4.5 and Opus 4.6 in the model picker for Copilot Pro+. > This model is launching with a 7.5× premium request multiplier as part of promotional pricing until April 30th TBF, it's a rumour that they are switching to per-token price in May, but it's from an insider (apparently), and seeing how good of a de…

The per-request pricing is ridiculous (in a good way, for the user). You can get so much done on a single prompt if you build the right workflow. I'm sure they'll change it soon

Re: Measuring Claude 4.7's tokenizer costs

#370

Post by epistasis »

apelapan wrote: post #286
epistasis wrote: post #257

Anybody else having problem getting Opus 4.7 to write code? I had it pick up a month-old project, some small one off scripts that I want to modify, and it refused to even touch the code. So far it costs a lot less, because I'm not going to be using it.

On the contrary, I threw a multi-threading optimization task on it, that 4.5 and 4.6 have been pretty useless at handling. 4.7 bested my hand-tuned solution by almost 2x on first attempt. This was what I thought was my best moat as a senior dev. No other model has been able to come close to the throughput I could achieve on my own before. Might be a fluke of course, and they've picked up a few patterns in training th…

Good to hear! My experience with code and 4.7 is still "I won't touch your python scripts because of my malware system instruction." With other chats the tool usage is through the roof with Opus 4.7 with mediocre results after much longer latency. I'll try again in a few days...
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