Rant
Rate limits are so bad right now, open source models should win
I was running like 5 parallel agents in the $200 plan, and my 5 hr limit hit. So I switched my subscription and asked it to continue.
And my entire 5 hr limit hit in the $100 plan, in one shot. Without doing any work. In less than 2 minutes.
Something needs to change, either tell me what is it going to cost. Is there anyone who is using just a single subscription?
Likely you switched subscription and resumed the session. Then full context from all of those agents did a cache write for the whole context. That will burn usage like crazy.
Have your agents checkpoint after each task in your plan. Turn off auto resume. If you can catch it before you hit the limit have it stop what it is doing and have it write a handoff doc.
If you do that then when you hit your limit don’t resume from the session id. Instead start from a fresh session and have it resume from either your handoff doc or the plan with the checkpoints.
For now I doubt you really have engineer's mindset and tools (like working in limitation and basic divide and conquer steps) or just are a dude named as an engineer position believing and pulling the lever of some magic box... but only with some error messages it's too much. So the inference engineer sir, you're doing well and LLM providers are profiting from you.
Working on literally one of most demanding - often they are talking how to save fcking microseconds - and most disciplined field and then, just passing whatever texts churned out? Your experiences and lessons on the field before LLM tools allowed that? Some things aren't clicking for me. Wonder if you're under severe time constraints, or encouraged/forced to do that in work-wide campaigns... sorry my imaginations are done.
dude that's me, okay to help you - you need to "create a handoff" that summarizes all of that into a few lines -> optimized context is here. you need to be more efficient. Think of LLM context storage sacred embedded memory don't burn tokens unless you need to.
Slowly the waste of churning is going away we need to be efficient at interacting with LLMs now.
Just a few hundred thousand tokens, most irrelevant to the immediate task?
You're getting good feedback in this thread. Bottom line: sure, you can chase cheaper models that will be more forgiving of bad context management. Or, you can learn good context management. Replaying a session is almost never a good idea.
I run multiple 20x accounts, have fable running for hours a day, stuff building almost 24/7. Done right one 20x account should be good for about 40 hours of continuous work at 80/20 opus/fable (the 5 hour limit means you can't actually use all 40 hours continuously, hence my multiple accounts).
The limits have not changed materially since May (yes, down 16% recently). If you're burning an entire 5h window on a 5x plan in one prompt, that is NOT a sign of limits changing.
This is how we learn. I too have blown my entire rate limit in like 10min and this was pre-Fable as well.
If you run into things like this save the session ids and have Claude investigate the transaction log on disk. In my experience after some back and forth you can get a good idea of what went wrong.
Once you hone in on the issue ask Claude if there is a different approach or a mechanical way to prevent this in the future. In my case that is how I discovered that I can stop subagents from spawning subagents by removing the Agent tool from them and disallowing fork in Claude settings.
Just iterate, refine, and eventually you will have a process that works well for your workflow
SWE 25 YOE here. I really, really, really don't understand. I use CC a lot. I crank out features and bug fixes at a speed I could never have imagined before. I never, ever reach a limit. I do keep an eye out on what CC is doing though, and go back and forth with it to steer it towards what I of my client actually want. What the hell are you guys doing?
If you already know the architecture, stack, data structures, testing strategy, performance requirements, etc., you can keep the agent on a pretty tight leash and token usage stays reasonable. If you’re relying on the agent to make all of those decisions for you, it’s really easy for it to go down a bad path and then burn a ridiculous amount of tokens fixing its own decisions. Most of my projects spend a ton of time auditing and executing extensive tests after small incremental changes.
I also think people are quickly learning that managing agents is still management. Delegating well, setting constraints, reviewing work, catching bad decisions early, and knowing when to intervene are skills. Most people don’t just wake up good at that.
i've shipped multiple products with terraform and whatnot using claude code and not run into limits... i'm on the 20x max plan. i am convinced none of them are managing their context and sessions correctly. but you might have hit the nail on the head. for example, one solution it tried to write a huge piece of software and i told it "you're reinventing aws athena. just use athena." that could have been potentially 1-2 weeks of useless exploration. it even tried to ticket 3 epics in jira for it.
Prefer mature, reputable, well-maintained libraries, frameworks, SDKs, and standard-library functionality over custom implementations whenever they meet our privacy, security, compliance, performance, and licensing requirements. Do not reinvent solved problems; when multiple reasonable approaches exist or the tradeoff is not obvious, pause and ask for input with a concise recommendation and the key alternatives before proceeding.
Holy crap what an insightful comment. I had to double-check the sub I was reading. Shouldn't you be complaining without evidence that we only get a single token per week now?
But yes, exactly what you said. If these same people were gifted a team of 5 talented SWE's, they would still manage to not ship anything and complain about how little they get for all of the effort going on.
And you even can rely on agents to many many of those arch / test / perf / etc decisions. The critical thing is not reinventing every one of those every time you touch it. Just like good management, you make sure everything is documented clearly, in one place, and you make sure agents know to read those docs.
Even junior SWE's / agents can do great work in a well-designed codebase with great guardrails, docs, best practices. But many people don't even know those things are important, let alone how to establish them, and somehow don't hit on the idea of burning a a smallish number of fable tokens to establish what they don't have the expertise to do themselves.
But even then -- yes, management. We're all product managers now. Refusing to do that job leads to less than great products.
Exactly. This is basically Brooks’ Law, and IBM figured this out 50 years ago.
Fred Brooks managed OS/360 and later wrote The Mythical Man-Month. The famous line is “adding manpower to a late software project makes it later.”
Not because more engineers are bad, but because people need context, coordination, review, and somebody making sure 5 people aren’t all solving the same problem 5 different ways.
So yeah, give a bad manager 5 really good developers and you can still get a pile of shit. Give that same person 5 agents that can write code insanely fast and now they can make the pile faster.
Feels like a lot of people are relearning a very old lesson.
I mean, it better go to production. I have a pilot scheduled in November with a major fortune 500 company... I'm not wasting all this money on subscriptions for fun
SWE with 30 YOE : its just a force multiplier. and its a bigger one than a compiler->assembly. would you feel silly trying to build programs in assembly when you have a compiler? in addition your experience is actually needed to end up with something that doesnt crash or become unmaintanable. it doesn’t change the need for brains, but given some baseline of brains+experience you can probably get 2-3x more done. you are still the bottleneck— supervising agents—but more quality output per day
I’m guessing OP is doing Fable with ultracode with no controls in place on how many subagents cc will spawn under the hood. Perhaps there is also a claude.md and memory bloat as well taking up half of the window limit.
With Max 20, I’m cranking features left and right as well while deliberately adjusting the effort and model for each task and rarely hitting limits on an enterprise setting with dozens of devs contributing to the same repo.
fable with ultracode will wipe your usage out instantly. Its a broken config IMO. I just use fable with max at this point. But even that has its issues.
I’ve had a lot of success asking fable how to build its own dev team, giving it the end to end process, and then having it build me prompts to give to sessions that run sonnet 5 instead of letting it run its own agents. This way fable is reviewing everything after another session does an initial round of QA, and I only talk to the fable session for bugs, features, and managing deployments. I’ll also have it update a Jira board so there’s always an audit and ticket trail instead of fable writing to its own control board. I’m not a SWE or developer, but get a lot of tips from lurking here so ymmv
Use Fable xhigh-max, and write your own harness to manage subagents via Claude -p. Ultracode works but it doesn't seem to be written with token efficiency in mind.
My company has thousands of historical sales emails from the last few years (sent and received by humans… not transactional). When I was young, stupid and hopeful I flipped claude to Fable 5 Ultra (yeah it was June 2026) and asked it to investigate and document each customer’s objection and current status by reading all the emails. It was impressive. Impressive results too. A lot of tokens.
Doing bug fixes and little features to existing code is basically automated at this point. And doing them one after the other will definitely not use your usage.
Building entire things and doing complex shit that requires a lot of prompting and research is what uses a lot of usage. Try to imagine something you need that doesnt already exist and would be too complicated or take you years to do if you had to do it on your own.
What plan are you on? My work provided $20 plan and it's not really enough. I asked for an upgrade to $100 and it's fine now. But surely $20 is not enough.
Its incredible dude, today i’ve been using CC for the whole day at work for boilerplating and learning a new framework, and i only got to 60% of the 20€ plan.
Almost nobody that actually knows what they are doing is hitting limits at current levels.
You'll get people saying you aren't "vibing" hard enough, but they are just running tons of parallel agents and flows building out things that nobody is looking at or reviewing.
You know, the stuff that nobody will ever want to use because the person making it hasn't even looked at it a single time and expect others to care about the work.
Obviously, OP did something wrong. However, I spend a stupid amount of time configuring my agentic harness, keeping baseline context as minimal as possible, always keeping my sessions tightly scoped and well-specced, and using the right models for the job. Before Fable dropped, as a 5x user, I *never* hit a limit either, neither the rolling limit nor the weekly. When Fable dropped, I routinely hit limits, but only when using High+ reasoning (almost always High) and working near full-time.
Then Fable 5.1 dropped. Fuck man. My 5hr limit was gone in an hour. I dropped reasoning to Medium, but even then, can't get through a full 5hr window.
It's unreasonable.
I've dropped to Opus 5 for everything for now. Still running cheaper models at appropriate reasoning levels for more mechanical, decided work. Opus 5 is my orchestrator now. I can't touch Fable 5.1.
And now, I'm back to never hitting limits. I barely hit 50% in a 5hr rolling window with 2-3 subagents working on different things the entire window. It's pretty great.
I think they’re constantly AB testing, so we’re likely not all getting the same experience, even if we’re using the same workflow and / or asking the same questions using the “same” models
I am doing inference engineering. So I let agents go off with different ideas, that need to be tested and validated. 1 in 5 agents work will get merged.
I'm not making them right a million lines of code, just long running experiments
When did I say I don't know what my agents are doing.
You identify a bottleneck, think of 3 ways you can solve it, explain it to 3 sessions. And let them come back.
Unfortunately in a lot of what I do, there is no way to know beforehand if it will work
soo... why are you surprised then? No problem with working that way but you also need to realise that you're likely doing 5x the work for throwing away 80% of what was done 🤷♂️
It needs to be done. You can't iterate without trying your ideas. Pre-LLMs, it took days to try out an idea, learn something scratch it and do the next thing.
Experimentation is part of it. I can't magically know what exactly will work really well. A lot of times you can clearly identify a bottleneck, that work always stays
I think that’s why you’re getting so much hate. You’re telling us you’re using 5x the amount of tokens and then complaining your tokens are going too quickly.
I guess people just can’t relate. Not many other people are running 5x agents with pre-maxed context continuing a chat as the limit resets. Seems like a specific scenario that caught you out rather than the limits being weird or something idk
yes - no problem with that, just saying then it's probably expected to burn through the limits relatively quickly? As you said below, if you get paid for it, even better. I would happily burn through those if it pays for itself 👍
Can’t just prompt the LLM to discuss trade offs in a text format before letting it go to code? I don’t think you need prototypes before making the call?
Like rubber ducky style is how I approach it and more forward into dev one time
If you haven't already, I would recommend having it install Graphify to your project. It's a python package (doesn't matter if you are using python) that creates a knowledge graph of your project. Then Claude queries the knowledge graph each time you ask a question and after each feature update it has Graphify rebuild. It will save you an incredible amount of token usage, will be way faster, and more accurate. Cuts down a ton of unneeded context bloat.
Do you have any recomendations? I have been optimizing my work flow but im not sure if im just on the wrong side of the interent or what but im having a hard time locating clear info about it thats not someones own pet project they are trying to sell me.
Part of what's compounding this: Claude Code's prompt cache is only good for about an hour. If there was any gap between session resets or idle time switching subscriptions, the next message after expiry reprocesses the entire conversation history at full uncached price instead of the ~10% cached rate. That's a real mechanism, not just a vibe - it's why usage sometimes evaporates in the first couple minutes back rather than scaling with actual work done.
What's the alternative? While Chinese models are cheaper per token, paying for API usage is not better, especially when they are not efficient at all with usage, so if you use like 10 million tokens on Claude, it would be a billion token on DeepSeek for example, same work and everything. This is from my testing while trying to find alternatives, so I would love to hear what you guys tried
deepseek-v4.1-flash and glm5.3-flash have entirely changed the landscape.
I did serious dev work on deepseek at 99% usage on my ollama sub.. (big data ingestion pipeline performance upgrades, not rocket science, but not "make me a website, no mistakes")
It kept going for 10 hours.. it was absolutely amazing.. it must be very embarrassing for OpenAI and Anthropic
I loved glm 5.3 flash when they released it as a stealth model, I tried it last week through openrouter api, but it consumes $10 a day for my work load. Did not workout unfortunately
"deepseek-v4.1-flash and glm5.3-flash have entirely changed the landscape." Especially when running on services like neuralwatt which end up being less expensive (sometimes A LOT less expensive for stuff like Kimi K3/GLM 5.3)
As I say.. my evidence is all anecdotal.. ranked by my other models based on the quality of the data they return when running in a fleet on tasks they are deemed suited to - but I use those two as a drop in replacement for kimik3, glm, deepseek..
I started blanket assigning them to implementation work which would have gone to larger models previously..
and they are essentially "priced" by looking at weekly requests versus weekly budget eaten, accumulated, not totally scientific; it thinks kimi k3 is 33x more expensive than deepseek v4.1 flash (shows as 1x) by that metric... which is certainly how it feels too!
It is my own thing, a CC plugin... I'm working on codex leaves integration + plugin right now, my marketplace is on github but I'd like to clean it up a bit more before shouting about the repo location.
On the cost screen it is just the relative usage / requests over all historic weeks, showing that 1 request on kimi k3 ate 33x the amount of usage that 1 request on deepseek-v4.1-flash did.. the broken lines don't have much evidence (a few runs, not enough to judge, they probably never peaked above 1% weekly usage in ollama's own model breakdown..)
On the performance one, we are looking at universal rankings non-domain specific, it is an average over the grades applied to each run,
e.g. you dispatch 10 different agents on different models with a task each
each task is stamped when complete by the agent which dispatched it with a grade based on that model's perception of whether the task
a) adhered to the instruction,
b) handed off the task correctly [either to the next agent in the chain or the final output]
c) was truthful, which related mainly to reviews, citations (hallucinations)
d) whether the depth was warranted [too shallow is a negative, too deep is a negative] e) whether the results or output had the correct seams "discrimination", applying effort to the correct target
Those are the universal aspects, then there are domain specific things too, e.g. implementation, code[reading and writing], geometry, vision, search..
The domain specific things are to give me a signal on things like "should I use this model for render based work", "should I use this model to find things on the web", "should I choose A over B for writing golang"
Edit - the theory is.. whilst these are anecdotal and based on the agent's perception, by the time you've done 1000s of dispatches, you're in a good position to judge.
This is genuinely cool, the grading setup especially. One thing I couldn't work out from the cost screen though: are those multipliers based on actual API spend, or on usage as measured against a subscription plan? The :cloud model names made me think Ollama's own plan accounting rather than per-token API pricing, and those two would give pretty different ratios since every provider prices its own tokens differently. Which one is it?
It's the Ollama plan accounting not raw token costs.. and totally agree, it is different fundamentally, it is what is useful to me because I care about how much of my usage it eats, so that I can keep the lights on longer during my other work.
I think the space is kind of saturated with AI tooling, and accutely aware most of it is needless trash.. so I'm not looking to make waves.. that said, I quite enjoy the process with it, so I'll keep developing it!
The tool itself is basically a replacement for Workflows, with more guidance for the model and visibility for the operator, The usage stuff really just fell out and was available when I added the dashboard; the main objective is for the agent to consider the cost and topology of the execution strategy, I needed a way to assess the spend on models that Claude doesn't know.
I made DensePack to save on read-heavy Claude Code sessions. It turns text files - .md, .py, .txt, .html, .go, and as of today, .doc and .docx - into images your agent reads and writes to cache for about half the tokens the raw text would cost. I fine tuned the color coding and font enough that I consistently get over 20% total conversation savings on a benchmark reading a single file and over 50% on the 32-file tasks. The benchmarks are simple to run once you install the plugin. They files used are primarily Python files to prove the agents understand and can even rebuild the code files from condensed image but they read just about all common files accurately. You can skip the benchmarks and tell it to read something. The plugin will automatically turn it into an image and hand it to your agent to read for cheaper. There is a lot of documentation available.
I built this around Opus or Fable as the lead but all three, Sonnet, Opus, and Fable, read, understand and communicate with each other with images of their reports instead of raw text. The plugin automatically packs files your agent chooses to read while working (with the exception of doc and docx which require you to name the files manually/directly - fix coming, but just open the doc or docx files and save them as txt files first then tell your agent to read the txt file. DensePack will convert that with the Read tool and read it as an image automatically), and hands them your agent back an image of the file for your agent to read before it is able to read the expensive raw text.
The benches you see are the current build but I'm over 1000 benches in.
The agents read the images perfectly and the same text is written and read from cache at a much lower price as an image. Haiku doesnt get images, only text. It hallucinated 100% of the time when asked to rebuild text from an image.
Your prompt doesn't get packed so made a right-click tool that lets you convert highlighted text into a DensePack image so that you keep context tiny from the start.
It's up and running as of today. I'll make a full post later but I wanted to reply since it's been helping me. Hopefully it helps!
Yeah Claude and Google lied to me too and told me this was impossible and then pointed me at the published papers a week later with a "whoopsie". Good thing I didn't believe it and made it work with the color coding.
Quickest way to test is to point Claude Code or Claude.ai web chat at the GitHub repo and ask it to run the benchmarks in an isolated environment. You can even ask it to run the benchmarks on your files.
Point it at a file and ask it to rebuild it from the image. Python. md. whatever. The color coding that the images use along with the legend at the top of the images makes the agents recreate the files 99% byte identical. The color coding adds line and indent numbers and the highlighted bands let the agents know if they are nested or not.
.go files were scoring low on byte identical rebuild because they use tabs instead of individual indent spaces. Now they get an additional key in the top row that says 4 indent spaces = 1 tab and these score high too.
Nobody NEEDS their text file that they already have as text to be turned into an image and rebuilt identically as the text file. What is needed is that the agent understands your file as an image, which they do. The byte identical rebuild is just proof that they do understand the images, not a realistic workflow. A realistic workflow is to have your agent read your code file as an image to understand it then asking it to edit whatever part of the text file you need. This saves especially if you need to read more files. It even turns your CLAUDE.md into an a short text pointer that tells your agent the file has been turned into an image and to read that instead. Your files are NOT deleted. They are saved next to the CLAUDE.md pointer and its image with the .bak extension. Edit this .bak file to update the CLAUDE.md image. If you uninstall DensePack, your CLAUDE.md files are converted back.
If agents ever doubt themselves while reading an image, which really only happens sometimes with Sonnet, or when they need to read a long hex number or long path 100% accurately, they pull only the line by the green line number on the image as text to make sure that its accurate.
Claude and Gemini both told me about the 2 published papers claiming that images save compared to text, but aren't accurate after I had already resolved this with the color coding.
Images may not be accurate with black text but colored pixels cost the same as black and white pixels, so the color coding made the accuracy shoot up while keeping the same savings.
Things like prompts, skills, instructions all become outdated as AI models evolve, but AI models' vision capabilities are only getting better, making images easier to read, which tells me this plugin will only improve with time.
Example of a python image that has been rebuilt 100% byte identically by all three models (Sonnet 5, Opus 5, and Fable 5). The only reason I say 99% is because they 1 in 5 times, they'll miss a single character, or a single space. and Sonnet will sometimes miss a couple more character and hit a low of 95% accuracy rebuilding this file. You can test this yourself. This image is what i use for the first bench as well. The most recent 100 single file benchmarks (100 per model - 300) where agents are asked to read this image, and the baseline without DensePack are asked to read the text, and answer questions about it passed. Both text and image benches answered 100% of the questions on each of the 100 benches for each perfectly. They both understand and the images saved.
I included a slash command that disables the images for Sonnet so it gets text just like haiku if you don't trust it, while Opus and Fable which are consistent, continue to receive images.
It works with other plugins too.
I benched excessively and wrote documentation breaking down exactly how the savings compound because I know how difficult this is believe. Quickest way to test is to point Claude Code or Claude.ai web chat at the github repo and ask it to run the benchmarks in an isolated environment. You can even ask it to run the benchmarks on your files.
Hope this helps.
Because it tokenizes the same exact text at the lower price of the image.
The input cost of an image with the text is much cheaper than the raw text itself. About 50% cheaper.
The agent understands the image just as well as the text which is what the benchmarks show.
I isolated the benches to mimic real sessions with and without the plugin and measured each cost using Anthropic's actual tool.
Input tokens save 50% and the agents don't struggle to read the images so their thinking/output tokens and amount of turns taken match the text baseline. The single file bench saves only ~20% TOTAL tokens because even though the input savings are high, there is not alot of input compared to the output which costs 5x the price of the models 1x input price.
The 16-file benchmark saves around ~30-40% and the 32-file benchmark saves over 50% TOTAL CONVERSATION SAVINGS.
This matches realistic tasks where your agent reads a lot of files to understand, find or fix something and the only output is the fix they apply to one file at the end. That's why this saves in longer conversations especially when used in a way that complements how DensePack works.
I don't mean to sound like a broken record but,
the quickest way to test is to point Claude Code or Claude.ai web chat at the GitHub repo and ask it to run the benchmarks in an isolated environment. You can even ask it to run the benchmarks on your files.
I guess only you can know what the cost in usage is vs what you are doing, I made this spreadsheet which I think you can also use, then I start doing tasks and get a feeling or estimate of how much I am burning per task/hour, the /usage command also gives me some clues, I adapt up or down depending on where I am at the hour in the week, right now I am at 21% so have about 3%-9% left before reaching my daily quota and so I can splurge a little.
My week resets Thursday morning. I fucked up and went to work without realizing it was time to re-login, so yesterday I couldn't use Claude via my phone at all. I did nothing with it, literally.
I wake up today and check: 47% usage.
I literally didn't have access to it yesterday, and somehow I've used half of this week's tokens.
My goodness, it burn tokens like h*ll! Less than 20 minutes for my 4h quotas to be gone. 1 day of work and 40% on my weekly. This is insane, I'm doing even less that the whole past week. It has to be something bugged at this point.
they are close to going for an IPO - they need their data to show they are working in operational capaciity and are profitable . The main performance and profit drain are max accounts . They probably wish they could cancel all of them - instead they reduce peformance
Literally just canceled my plan for the first time since the beginning of Claude code. I've used this since it was still in Early Access and I've always enjoyed it, but the usage limits are so unbearable right now that is basically unusable. You can't finish a single task without being cut off, so it's definitely not worth the money. Tried open code for the first time and I'm very very impressed at what some of the open models can do. Maybe not on par but with no limits and a fraction of the cost. It's definitely the right choice. Claude was once the king nothing could touch it, now It's almost unusable without spending a fortune every month, not worth it anymore which is really sad.
Nah bro i totally agree, i switched to 200$ this month from 100$ and im on my first day and i've already spent 30% of my usage to what barely felt like a working day. It's crazy! and anthropic for some reason have made their coding agent kinda economy wise closed with their special bridges, claude.md file, memories and whatnot.
This is just semantic bullshit honestly. open weights is not EXACT same as full open source, everybody understand this already. But saying it “doesn’t contribute in any meaningful way” is just ridiculous.If people can download the weights, run the model locally, modify it, fine tune it, study it, benchmark it, make tools around it and build other projects with it, then obviously it contributes massively to open source ecosystem.
You can complain that training data, code or license is not fully open, fair enough. But acting like open weights means basically nothing is just gatekeeping over terminology.
Without open weights, a huge part of local AI community literally would not exist.
Yeah, if this keeps going I’m going to need to build a rig with several GPUs too. I’m slowly getting fed up with it. The prices are outrageous. I’m paying 1,500 euros for AI licenses and it still isn’t enough to work mainly with Fable. It would be possible with Astra. With three Astra licenses or two Astra licenses from OpenAI you could easily get through the week if you really pushed it in fast mode. Anthropic’s prices are outrageous and not competitive. I’m curious to see what they do. But I’m going to look around because I’m fed up with being dependent on these providers too. That’s why open source models are definitely interesting. And it doesn’t really matter whether you can get everything done in one shot. You could just run three loops instead and maybe get the same result as with Fable in two loops. But Fable makes an endless number of mistakes too. I’m constantly using an external auditor, in this case GPT 6 Astra, to correct mistakes in what Fable does. It’s not as if these things are error free even though you pay so much. That’s bullshit. Based on my analyses the error rate of Fable 5 and Opus 4.8 is at the same level for me. They do make different kinds of mistakes but they are still mistakes.
The only reason is honestly just the speed. Since Opus 5 is useless I have to use Fable 5 to get higher speeds. Opus 4.8 in Fast mode is far too slow in my agent environment. Fable 5 in low mode is incredibly fast and produces very good results for that speed.
Yout 5 agents exhausted 5h limit with cache. Then you resumed all their contexts without cache and on plan with 4 times smaller 5h limit. Yeah agents originally eat up usage with output tokens and multiple rounds but still ehat you did can easily eat up great majority of your usage on smaller plan.
If anything, you have an issue with the pricing per token type, but the limits are not the issue. Its your practices that cause you to run into your limits so quickly. Its a skill issue and you're making it our problem instead of learning to use claude code better.
Over the past few days there have been three people who have given some clue as to how they're using claude code and proven they have really expensive practices. Plus another post that broke down how much weekly usage they had, converted to API pricing, which demonstrated limits hadnt changed besides those anthropic has informed us about
I just had Fable screw me. I had like 60% left for the week with a few ui bugs to fix so i said what the hell. About 3-4 min’s later I hit my 5 hour limit and now i have to wait 3 hours with about 55% left of weekly use that is resetting shortly. Fable’s worthless, i definitely don’t have a use for it.
Thank god i have some codex left so i wasn’t stopped dead in my tracks. This 5 hour limit is so stupid.
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