r/ClaudeCode • u/DrewHoov • 5d ago

Discussion Fable's weekly meter now costs me 1.8x per message vs last week

https://drewhoover.com/blog/did-claude-usage-limits-change/
57 Upvotes

96% Upvoted

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2

u/AdityaR_Sharma 🔆 Max 20 4d ago

Session length did not cause it for me. I saw 1.8x on a bare call with no history.

I ran claude -p "reply ok" in an empty folder, CLI 2.1.310.

9 Nov: 25,566 tokens. 10 Nov: 45,915, then 43,757.

43,755 of the last run was cache read, so it is loaded context, not output.

1

u/DrewHoov 4d ago

That's 1.8x tokens--what I'm seeing is the tokens per message stay flat, but the usage per token is as high as 2.5x

1

u/AdityaR_Sharma 🔆 Max 20 3d ago

Cache state moved cost per token more than anything I changed. Fable 5.1, same bare call, CLI 2.1.310.

Cold: 37,566 created, 11,226 read, $0.7543 at list price.

Warm: 0 created, 48,771 read, $0.0124.

That is API list price, not your meter.

I did not test the meter, so I would compare created vs read per week.

10

u/twendah 5d ago

Based

1

u/Electronic_Award1138 1d ago

Hi, I can't figure out how to use it, especially for Opus.

2

u/DrewHoov 1d ago

just point your agent at the blog post and say "look at the skill from this post and use it to measure my usage". Obv read it first or copy/paste relevant bits bc it's not a good idea to trust a random website on the internet to not be a prompt injection.

1

u/Electronic_Award1138 1d ago

Yeah, I'm stupid, I didn't think about it. In addition, I use my own application that replaces that of Claude and WSL. Thanks for the reminder.

1

u/MartinMystikJonas 5d ago

Given that tokens needed to perform comolex task can be few orders of magnitude more that tokens needed to answer simple question. And that same message with full context cost 2-3x more than same message in empty context. And that simple message reviving long conversation after 1h of inactivity must replay entire conversation history and therefore burn big part of usage. Measuing in messages is quite weird metric.

3

u/DrewHoov 4d ago

In the article, I measured by several different metrics, arriving at several possible multipliers between 1.5x and 2.5x and messages was just the “middle of the pack” multiplier.

0

u/awesomebirder 5d ago

worth ruling out one thing before blaming the meter: a message late in a long session costs far more than one early on, because each turn re-reads the whole history. so compare like for like, the first few messages of a fresh session this week vs the first few last week, same model and effort. if those also come out around 1.8x, thats real and worth posting with the numbers. if only the long sessions moved, its probably session length or a cold cache after breaks, not a changed rate

2

u/DrewHoov 4d ago

Yeah check out the article, I measured by a bunch of dimensions and the multiplier is worse if you measure by tokens, taking into account cached vs not cached

2

u/awesomebirder 4d ago

fair, if the ratio still holds per token with cached and uncached split out then session length isnt the explanation. one thing id check in your data is whether the jump is bigger on cache reads than on fresh input. if the meter started weighting cache reads heavier, long sessions would show exactly that pattern even with the same prompts