We've been investigating these reports, and a few of the top issues we've found are:
1. Prompt cache misses when using 1M token context window are expensive. Since Claude Code uses a 1 hour prompt cache window for the main agent, if you leave your computer for over an hour then continue a stale session, it's often a full cache miss. To improve this, we have shipped a few UX improvements (eg. to nudge you to /clear before continuing a long stale session), and are investigating defaulting to 400k context instead, with an option to configure your context window to up to 1M if preferred. To experiment with this now, try: CLAUDE_CODE_AUTO_COMPACT_WINDOW=400000 claude.
2. People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins. This was the case for a surprisingly large number of users, and we are actively working on (a) improving the UX to make these cases more visible to users and (b) more intelligently truncating, pruning, and scheduling non-main tasks to avoid surprise token usage.
In the process, we ruled out a large number of hypotheses: adaptive thinking, other kinds of harness regressions, model and inference regressions.
We are continuing to investigate and prioritize this. The most actionable thing for people running into this is to run /feedback, and optionally post the feedback ids either here or in the Github issue. That makes it possible for us to debug specific reports.
Boris, you're seeing a ton of anecdotes here and Claude has done something that has affected a bunch of their most fervent users.
Jeff Bezos famously said that if the anecdotes are contradicting the metrics, then the metrics are measuring the wrong things. I suggest you take the anecdotes here seriously and figure out where/why the metrics are wrong.
On the subject of metrics, better user-facing metrics to understand and debug usage patterns would be a great addition. I'd love an easier way to understand the ave cost incurred by a specific skill, for example. (If I'm missing something obvious, let me know.)
Cool, are you going to be transparent and explain the metrics and costs as a postmortem? And given the inability to actually audit what you produce, why should we trust Anthropic?
For me definitely the worst regression was the system prompt telling claude to analyze file to check if it's malware at every read. That correlates with me seeing also early exhausted quotas and acknowledgments of "not a malware" at almost every step.
It is a horrible error of judgement to insert a complex request for such a basic ability. It is also an error of judgement to make claude make decisions whether it wants to improve the code or not at all.
It is so bad, that i stopped working on my current project and went to try other models. So far qwen is quite promising.
I don't think that's accurate. The malware prompt has been around since Sonnet 3.7. We carefully evaled it for each new model release and found no regression to intelligence, alongside improved scores for cyber risk. That said, we have removed the prompt for Opus 4.6 since it no longer needed it.
I started seeing "not a malware, continuing" in almost every reply since around 2 weeks ago. Maybe you just reintroduced it with some regression? Opus 4.6
I'm happy to provide any other info that can be useful (as long as i'm not sharing any information about the code or tools we use into a public github issue).
1. I've never seen this. Is there a config option to unhide it if it's happening? Is this in Claude Code? Does it have to be set to verbose or something?
2. Can we pay more/do more rigorous KYC to disable it if it's active?
The /clear nudge isn't a solution though. Compacting or clearing just means rebuilding context until Claude is actually productive again. The cost comes either way.
I get that 1M context windows cost more than the flat per-token price reflects, because attention scales with context length, but the answer to that is honest pricing or not offering it. Not annoying UX nudges.
What’s actually indefensible is that Claude is already pushing users to shrink context via, I presume, system prompt. At maybe 25% fill:
“This seems like a good opportunity to wrap it up and continue in a fresh context window.”
“Want to continue in a fresh context window? We got a lot of work done and this next step seems to deserve a fresh start!”
If there’s a cost problem, fix the pricing or the architecture. But please stop the model and UI from badgering users into smaller context windows at every opportunity. That is not a solution, it’s service degradation dressed as a tooltip.
Why did this become an issue seemingly overnight when 1M context has been available for a while, and I assume prompt caching behavior hasn't changed?
EDIT: prompt caching behavior -did- change! 1hr -> 5min on March 6th. I'm not sure how starting a fresh session fixes it, as it's just rebuilding everything. Why even make this available?
It feels like the rules changed and the attitude from Anth is "aw I'm sorry you didn't know that you're supposed to do that." The whole point of CC is to let it run unattended; why would you build around the behavior of watching it like a hawk to prevent the cache from expiring?
This is not accurate. The main agent typically uses a 1h cache (except for API customers, which can enable 1h but it is not on by default because it costs more). Sub-agents typically use a 5m cache.
Different users do seem to be encountering problems or not based on their behavior, but for a rapidly-evolving tool with new and unclear footguns, I wouldn't characterize that as user error.
For example, I don't pull in tons of third-party skills, preferring to have a small list of ones I write and update myself, but it's not at all obvious to me that pulling in a big list of third-party skills (like I know a lot of people do with superpowers, gstack, etc...) would cause quota or cache miss issues, and if that's causing problems, I'd call that more of a UX footgun than user error. Same with the 1M context window being a heavily-touted feature that's apparently not something you want to actually take advantage of...
Me and my colleagues faced, over the last ~1 month or so, the same issues.
With a new version of Claude Code pretty much each day, constant changes to their usage rules (2x outside of peak hours, temporarily 2x for a few weeks, ...), hidden usage decisions (past 256k it looks like your usage consumes your limits faster) and model degradation (Opus 4.6 is now worse than Opus 4.5 as many reported), I kind of miss how it can be an user error.
The only user error I see here is still trusting Anthropic to be on the good side tbh.
Yes same here. I use CC almost constantly every day for months across personal and work max/team accounts, as well as directly via API on google vertex. I have hardly ever noticed an issue (aside from occasional outages/capacity issues, for which I switch to API billing on Vertex). If anything it works better than ever.
Not parent but I can guess from watching mostly from the sidelines.
They introduced a 1M context model semi-transparently without realizing the effects it would have, then refused to "make it right' to the customer which is a trait most people expect from a business when they spend money on it, specially in the US, and specially when the money spent is often in the thousands of dollars.
Unless anthropic has some secret sauce, I refuse to believe that their models perform anywhere near the same on >300k context sizes than they do on 100k. People don't realize but even a small drop in success rate becomes very noticeable if you're used to have near 100%, i.e. 99% -> 95% is more noticeable than 55% -> 50%.
I got my first claude sub last month (it expires in 4 days) and I've used it on some bigish projects with opencode, it went from compacting after 5-10 questions to just expanding the context window, I personally notice it deteriorating somewhere between 200-300k tokens and I either just fork a previous context or start a new one after that because at that size even compacting seems to generate subpar summaries. It currently no longer works with opencode so I can't attest to how it well it worked the past week or so.
If the 1M model introduction is at fault for this mass user perception that the models are getting worse, then it's anthropics fault for introducing confusion into the ecosystem. Even if there was zero problems introduced and the 1M model was perfect, if your response when the users complain is to blame it on the user, then don't expect the user will be happy. Nobody wants to hear "you're holding it wrong", but it seems that anthropic is trying to be apple of LLMs in all the wrong ways as well.
The only people who are going to run into issues are superpower users who are running this excessively beyond any reasonable measure.
Most people are going to be quite happy with your service. But at the same time, and this is just a human nature thing people are 10 times more likely we complain about an issue than to compliment something working well.
I don't know how to fix this, but I strongly suspect this isn't really a technical issue. It's more of a customer support one.
I don't want a nudge. I want a clear RED WARNING with "You've gone away from your computer a bit too long and chatted too much at the coffee machine. You're better off starting a new context!"
When a user walks away during the business day but CC is sitting open, you can refresh that cache up to 10x before it costs the same as a full miss. Realistically it would be <8x in a working day.
Would it be possible to increase the cache duration if misses are a frequent source of problems?
Maybe using a heartbeat to detect live sessions to cache longer than sessions the user has already closed. And only do it for long sessions where a cache miss would be very expensive.
Hello Boris! How do I increase the 1 hour prompt cache window for the main agent? I would love to be able to set that to, say, 4 hours. That gives me enough time to work on something, go teach a class, grab a snack, and come back and pick up where I left off.
Claude Code is the most prompt cache-efficient harness, I think. The issue is more that the larger the context window, the higher the cost of a cache miss.
That might be, but the argument was that poor cache utilization was costing Anthropic too much money in other harnesses. If cache is considered in rate limits, it doesn’t matter from a cost perspective, you’ll just hit your rate limits faster in other harnesses that don’t try to cache optimize.
There were two issues with some other 3p harnesses:
1. Poor cache utilization. I put up a few PRs to fix these in OpenClaw, but the problem is their users update to new versions very slowly, so the vast majority of requests continued to use cache inefficiently.
2. Spiky traffic. A number of these harnesses use un-jittered cron, straining services due to weird traffic shape. Same problem -- it's patched, but users upgrade slowly.
We tried to fix these, but in the end, it's not something we can directly influence on users' behalf, and there will likely be more similar issues in the future. If people want to use these they are welcome to, but subscriptions clients need to be more efficient than that.
Hey Boris - why is the best way to get support making a Hacker News or X post, and hoping you reply? Why does Anthropic Enterprise Support never respond to inquiries?
I have a feature request: I build an mcp server, but now it has over 60 tools. Most sessions i really don’t need most of them. I suppose I could make this into several servers. But it would maybe be nice to give the user more power here. Like let me choose the tools that should be loaded or let me build servers that group tools together which can be loaded. Not sure if that makes sense …
Boris, wasnt this the same thing ~2 weeks ago? Is it the same cache misses as before? What's the expected time till solved? Seems like its taking a while
> To improve this, we have shipped a few UX improvements (eg. to nudge you to /clear before continuing a long stale session)
Is this really an improvement? Shouldn't this be something you investigate before introducing 1M context?
What is a long stale session?
If that's not how Claude Code is intended to be used it might as well auto quit after a period of time. If not then if it's an acceptable use case users shouldn't change their behavior.
> People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins.
If this was an issue there should have been a cap on it before the future was released and only increased once you were sure it is fine? What is "a large number"? Then how do we know what to do?
It feels like "AI" has improved speed but is in fact just cutting corners.
Where can i learn about concepts like prompt cache misses? I don't have a mental model how that interacts with my context of 1M or 400k tokens... I can cargo cult follow instructions of course but help us understand if you can so we can intelligently adapt our behavior. Thanks.
Thanks. Just noting that those docs say the cache duration is 5 min and not 1 hour as stated in sibling comment:
> By default, the cache has a 5-minute lifetime. The cache is refreshed for no additional cost each time the cached content is used.
>
> If you find that 5 minutes is too short, Anthropic also offers a 1-hour cache duration at additional cost.
Why are you all of a sudden running into so many issues like this? Could it be that all of the Anthropics employees have completely unlimited and unbounded accounts, which means you don't get a feeling of how changes will affect the customers?
Pulling all the skills and agents in the world in, when unused are a big hit. I deleted all of mine and added back as needed and there was an improvement.
Running Claude Cowork in the background will hit tokens and it might not be the most efficient use of token use.
Last, but not least, turning off 1M token context by default is helpful.
Claude has gotten noticeably worse for me too. It goes into long exploration loops for 5+ minutes even when I point it to the exact files to inspect. Then 30 minutes later I hit session limits. Three sessions like that in a day, and suddenly 25% of the weekly limit is gone.
I ended up buying the $100 Codex plan. So far it has been much more generous with usage and more accurate than Claude for the kind of work I do.
That said, Codex has its own issues. Its personality can be a bit off-putting for my taste. I had to add extra instructions in Agents.md just to make it less snarky. I was annoyed enough that I explicitly told it not to use the word “canonical.”
On UI/UX taste, I still think current Codex is behind the Jan/Feb era of Claude Code. Claude used to have much better finesse there. But for backend logic, hard debugging, and complex problem-solving, Codex has been clearly better for me. These days I use Impeccable Skillset inside Codex to compensate for the weaker UI taste, but it still does not quite match the polish and instinct Claude Code used to have.
I used to be a huge Claude Code advocate. At this point, I cannot recommend it in good conscience.
My advice now is simple: try the $20 plans for Codex and Cursor, and see which one matches your workflow and vibes best
I had a weird experience at work last week where Claude was just thinking forever about tasks and not actually doing anything. It was unusable. The next day it was fine again.
i was having this issue yesterday. the same prompt would send it into a loop where it would appear to be doing nothing for 30+ minutes until i cancelled it. it would show 400 tokens used and thats it.
I tested on a previous version (2.1.68) and it still ran into this neverending loop BUT at least the token count kept steadily increasing.
So we are seeing 1. some sort of model degredation is my guess (why it can't break a thinking loop on some problems), as well as 2. a clear drop in thinking token UI transparency.
Ya I've had this experience more than a few times recently. I've heard people claiming they are serving quantized models during high loads, but it happens in cursor as well so I don't think it's specific to Anthropics subscription. It could be that the context window has just gotten into a state that confuses the model... But that wouldn't explain why it appears to be temporary...
My best guess is this is the result of the companies running "experiments" to test changes. Or it's just all in my head :)
Not the guy you're responding to, but when this happens the token counter is frozen at some low value (eg. 1k-10k) value as well, so it's not thinking in circles but rather not thinking (or doing anything, for that matter) at all.
i was having this issue yesterday. the same prompt would send it into a loop where it would appear to be doing nothing for 30+ minutes until i cancelled it. it would show 400 tokens used and thats it.
I tested on a previous version (2.1.68) and it still ran into this neverending loop BUT at least the token count kept steadily increasing.
So we are seeing 1. some sort of model degredation is my guess (why it can't break a thinking loop on some problems), as well as 2. a clear drop in thinking token UI transparency
when i left it running overnight it finally sent a message saying it exceeded the 64000 output token limit
This happened to me as well! It was especially infuriating because I had just barely upgraded to the $200 per month plan because I exhausted my weekly quota. Then the entire next day was a complete bust because of this issue. I want my money back!
I'm using the Codex Business subscription (about 30€) already for multiple months. Even there they cut back on the quota. A few months back it was hard for me to reach the limit.
Now it is easier.
Still, in comparison with Claude Code, the quota of Codex is a much better deal.
However, they should not make it worse...
Promotion has been extended til May 31st for the $100 and $200 subs.
At the same time, they’ve been giving out a ton of additional quota resets seemingly every other week (and committed to an additional reset for every million additional users until they hit 10mil on codex).
So they’ve really set a high bar for people’s expectations on their quota limits.
Once they drop the 2x promotion for good and stop the frequent resets, there are going to be a lot of complaints.
> It goes into long exploration loops for 5+ minutes even when I point it to the exact files to inspect.
Give it a custom sandbox and context for the work, so it has no opportunity to roam around when not required. AI agentic coding is hugely wasteful of context and tokens in general (compared to generic chat, which is how most people use AI), there's a whole lot of scope for improvement there.
The sandbox is fine, but if the parent has given explicit instruction of files to inspect, why is it not centering there? Is the recent breakage that the base prompt makes it always try to explore for more context even if you try to focus it?
Because the "explicit instruction" you give AI is not deterministic as in a normal computer program. It's a complete black box and the context is also most likely polluted by all sorts of weird stuff. Putting it on as tight of a leash as possible should be seen as normal.
They changed plan mode so that it's instructed to follow a multi-step plan, the first step being to explore the code base. When you tell it to focus it's getting contradictory instructions from plan mode vs your prompt and it's essentially a coin flip which one it picks.
It does seem like a cynical attempt to make more money.
> Claude has gotten noticeably worse for me too. It goes into long exploration loops for 5+ minutes even when I point it to the exact files to inspect.
This is what I'm working on proving now.
It is more that there is a confidence score while thinking. Opus will quit if it is too high and will grind on if the confidence score is close to the real answer. Haiku handles this well too.
If you give Sonnet a hard task, it won't quit when it should.
Nonetheless, that issue has been fixed with Opus.
I'll try to show that the speed of using Opus on tasks that have medium to hard difficultly is consistently the same price or cheaper than running them with Haiku and Sonnet. While easier tasks, the busy work that is known, is cheaper run with Haiku.
It was pretty much first for CLI agents and had a benchmark that was the go to at the start of LLM coding. Now the benchmark doesn't get updated and aider never gets a mention in talking about CLI tools till now.
The product was performing badly and you thought this would be solved by spending more money on it?
When will people realize this is the same as vendor lock-in?
"Maybe if I spend more money on the max plan it will be better" > no it will be the same
"Maybe if I change my prompt it will work" > no it will be the same
"Maybe if I try it via this API instead of that API it will improve" > no it will be the same.
Claude, ChatGPT, Gemini etc all of these SOTA models are carefully trained, with platforms carefully designed to get you to pay more for "better" output, or try different things instead of using a different product.
It's to keep you in the ecosystem and keep you exploring. There is a reason you can't see the layers upon layers of scaffolding they have. And there's a reason why after 2 weeks post major update, the model is suddenly "bad" and "frustrating". It's the same reason its done with A/B testing, so when you complain, someone else has no issues, when they complain, you have no issues. It muddies the water intentionally.
None of it is because you're doing anything wrong, it's not a skill issue, it's a careful strategy to extract as much engagement and money from customers as possible. It's the same reason they give people who buy new gun skins in call of duty easier matches in matchmaking for the first couple games.
The only mistake you made was paying MORE, hoping it would get better. It won't, that's not what makes them money. Making people angry and making people waste their time, while others have no issues, and making them explore and try different things for longer so they can show to investors how long people use these AI tools is what makes them money.
When competitors have a better product these issues go away
When a new model is released these issues don't exist
I was paying a ton of money for claude, once I stopped and cancelled my subscription entirely, suddenly sonnet 4.6 is performing like opus and I don't have prompts using 10% of my quota in one message despite being the same complexity.
You ask that as if there is some insight to the question, but the insight is hard to find. What the person you replied to is saying, applies to both Claude and Codex.
I'm adding two extra gpus to my local rig. Turns out qwen 3.5 122b is already enough to handle (finish with moderate guidance) non-planning parts of my tasks.
I am also on Codex while Claude seems to be blatantly ignoring instructions (as recently as Thursday: when I made the switch). The huge Claude context helps with planning, so that's all it does now.
Codex consumes way fewer resources and is much snappier.
Codex has been better for me, but it's WAY too nitpicky/defensive. It always wants to make changes that add complexity and code to solve a problem that's impossible to happen (e.g. a multiprocess race condition on a daemon I only ever run one instance of).
You just convinced me to try it. Claude just copy pastes, does search and replace, zero abstractions and I'm the one that needs to think about the edge cases.
By the way, what are you using it for? I bought Max and Pro plans for Claue and Codex, developed a few apps with it, and after the initial excitation ("Wow I can get results 10x faster!") I felt the net sum is negative for me. In the end I didn't learn much except the current quirks of each model/tool, I didn't enjoy the whole process and the end result was not good enough for my standards. In the end I deleted all these projects and unsubscribed.
For me it’s mostly useful in day-to-day coding, not “build an entire app and walk away” coding.
TDD was never really my natural style, but LLMs are great at generating the obvious test cases quickly. That lets me spend more of my attention on the edge cases, the invariants, and the parts that actually need judgment.
Frontend is another area where they help a lot. It’s not my strongest side, so pairing an LLM with shadcn/ui gets me to a decent, responsive UI much faster than I would on my own. Same with deployment and infra glue work across Cloudflare, AWS, Hetzner, and similar platforms.
I’m basically a generalist with stronger instincts in backend work, data modeling, and system design. So the value for me is that I can lean into those strengths and use LLMs to cover more ground in the areas where I’m weaker.
That said, I do think this only works if you’re using them as leverage, not as a substitute for taste or judgment.
I skimmed the issue. No wonder Anthropic closes these tickets out without much action. That’s just a wall of AI garbage.
Here’s what I’ve done to mostly fix my usage issues:
* Turn on max thinking on every session. It save tokens overall because I’m not correcting it of having it waste energy on dead paths.
* keep active sessions active. It seems like caches are expiring after ~5 minutes (especially during peak usage). When the caches expire it sees like all tokens need to be rebuilt this gets especially bad as token usage goes up.
* compact after 200k tokens as soon as I reasonably can. I have no data but my usage absolutely sky rockets as I get into longer sessions. This is the most frustrating thing because Anthropic forced the 1M model on everyone.
Haha. yeah my eyes glazed over immediately on the issue. Absolutely this was someone telling their Claude Code to investigate why they ran out of tokens and open the issue.
Good chance it's not real or misdiagnosed. But it gives me some degree of schadenfreude to see it happening to the Claude Code repo.
Its your claude speaking to their claude, which is fair, but it makes this whole discussion a bit dumb since we are basically talking about two bots arguing with each other.
The problem is actually because their cache invalidates randomly so that's why replaying inputs at 200k+ and above sucks up all usage. This is a bug within their systems that they refuse to acknowledge. My guess is that API clients kick off subscription users cache early which explains this behavior, if so then it's a feature not a bug.
They also silently raised the usage input tokens consume so it's a double whammi.
Can confirm. Max effort helps; limiting context <= ~20-25% is crucial anymore.
> * keep active sessions active. It seems like caches are expiring after ~5 minutes (especially during peak usage). When the caches expire it sees like all tokens need to be rebuilt this gets especially bad as token usage goes up.
Is this as opaque on their end as it sounds, or is there a way to check?
Tangentially related to some of the issues a lot of people are facing, especially the ones where Claude keeps rechecking/scanning the same files over and over.
Ask claude code to give you all the memories it has about you in the codebase and prune them. There is a very high chance that you have memories in there which are contradicting each other and causing bad behavior. Auto-saved memories are a big source of pollution and need to be pruned regularly. I almost don't let it create any memories at all if I can help it.
Disclaimer: I'm also burning through usage very quickly now - though for different reasons. Less than 48 hours to exhaust an account, where it used to take me 5-6 days with the same workload.
I'm afraid the music may be slowly fading at this party, and the lights will soon be turned on. We may very well look back on the last couple years as the golden era of subsidized GenAI compute.
For those not in the Google Gemini/Antigravity sphere, over the last month or so that community has been experiencing nothing short of contempt from Google when attempting to address an apparent bait and switch on quota expectations for their pro and ultra customers (myself included). [1]
While I continue to pay for my Google Pro subscription, probably out of some Stockholm Syndrome, beaten wife level loyalty and false hope that it is just a bug and not Google being Google and self-immolating a good product,
I have since moved to Kiro for my IDE and Codex for my CLI and am as happy as clam with this new setup.
For what it’s worth, that was pretty obvious from the get go it wasn’t a realistic long term deal. I’ve been building all the libraries I hoped existed over the past 1-2y to have something neat to work with whenever the free compute era ends. I feel that’s the approach that makes sense. Take the free tokens, build everything you would want to exist if you don’t have access to the service anymore. If it goes away you’re back to enjoying writing code by hand but with all the building blocks you dreamt of. If it never goes away, nothing wasted, you still have cool libs
So, antigravity will definitely quickly eat up your pro quota. You can run out of it in an hour (at least on the $20/mo plan) and then you'll be waiting five days for it to refresh.
However, I've found that the flash quota is much more generous. I have been building a trio drive FOC system for the STM32G474 and basically prompting my way through the process. I have yet to be able to run completely out of flash quota in a given five hour time window. It is definitely completing the work a lot faster than I could do myself -- mainly due to its patience with trying different things to get to the bottom of problems. It's not perfect but it's pretty good. You do often have to pop back in and clean up debris left from debugging or attempts that went nowhere, or prompt the AI to do so, but that's a lot easier than figuring things out in the first place as long as you keep up with it.
I say this as someone who was really skeptical of AI coding until fairly recently. A friend gave me a tutorial last weekend, basically pointing out that you need to instruct the AI to test everything. Getting hardware-in-loop unit tests up and running was a big turning point for productivity on this project. I also self-wired a bunch of the peripherals on my dev board so that the unit tests could pretend to be connected to real external devices.
I think it helps a lot that I've been programming for the last twenty years, so I can sometimes jump in when it looks like the AI is spinning its wheels. But anyway, that's my experience. I'm just using flash and plan mode for everything and not running out of the $20/mo quota, probably getting things done 3x as fast as I could if I were writing everything myself.
Ultimately we'll find more efficient techniques and hardware and AI companies will end up owning Nuclear Power Stations and continue providing models capable of 10x of what they are now.
Valuation have already reached point where these companies can run their nuclear power station, fund developement of new hardware and techniques and boost capabilities of their models by 10x
IMO we are currently in the ENIAC era of LLMs. Perhaps there will be a brief moment where things get worse, but long term the cost of these things will go way down.
Can confirm, I initially enjoyed the 5-hour limits on Gemini CLI and Antigravity so much that I paid for a full year, thinking it was a great decision
In the following months, they significantly cut the 5-hour limits (not sure if it even exists anymore), introduced the unrealistically bad weekly limit that I can fully consume in 1-2 hour, introduced the monthly AI credits system, and added ads to upgrade to Ultra everywhere
At the very least the Gemini mobile app / web app is still kinda useful for project planning and day-to-day use I guess. They also bumped the storage from 2TB to 5TB, but I don't even use that
It should be illegal to change the terms of the subscription mid-period. If you paid for the full year, you should get that plan for the whole year. I don't understand how it's ok for corporations to just change the terms mid-way, and we just have to accept it.
I'm sure the T&C say something like "you're going to pay us money, and we reserve the right to give you something for it, or maybe nothing, and you should thank us for the privilege".
The evidence is that quotas exist, as seen here, and are low enough that people are hitting them regularly. When was the last time you hit your quota of Google searches? When was the last time you hit your quota of StackOverflow questions? When was the last time you hit your quota of YouTube videos? Any service will rate limit abuse, but if abuse is indistinguishable from regular use from the provider's perspective, that's not a good sign.
It's also kind of interesting that they don't think they can do what an economy would normally do in this situation, which is raise prices until supply matches. Shortages generally imply mispricing.
There's a lot of angles you take from that as a starting point and I'm not confident that I fully understand it, so I'll leave it to the reader.
The parent's argument is that the marginal cost of inference is minimal. However, the fundamental flaw is that he's separating inference from the high cost frontier models. It's a cross-subsidy that can't be ignored.
I've seen sources like this before. It's all hearsay and promo. I was asking for any publicly available verifiable information regarding the cost of inference at scale. I haven't seen any such info personally which is why I asked.
I'm dying to see S-1 filing for Anthropic or OpenAI. I don't actually think inference is as cheap as people say if you consider the total cost (hardware, energy, capex, etc)
Ads do not pay enough to cover AI usage. People see the big numbers Google and Facebook make in ads and forget to divide the number by the number of people they serve ads to, let alone the number of ads they served to get to that per-user number. You can't pay for 3 cents of inference with .07 cents of revenue.
You also can't put ads in code completion AIs because the instant you do the utility to me of them at work drops to negative. Guess how much money companies are going to pay for negative-value AIs? Let's just say it won't exactly pay for the AI bubble. A code agent AI puts an ad for, well, anything and the AI accidentally puts it into code that gets served out to a customer and someone's going to sue. The merits of the case won't matter, nor the fact the customer "should have caught it in review", the lawsuit and public reputation hit (how many people here are reading this and salivating at the thought of being able to post an angrygram about AIs being nothing but ad machines?) still cost way too much for the AI companies creating the agents to risk.
We have seen this before. Companies using VC money to take over the market and then increase prices. In the end, we're worse off without these scumbags but some will still sing that we got free service do it's bot enshitification.
The cost for AI companies might be $5000 but the "essentially free" could be close to the limit of what people are willing to spend. If that's the case then enshittification will continue and/or many AI companies will never be profitable.
The response doesn't even make sense and appears to be written by AI.
> The March 6 change makes Claude Code cheaper, not more expensive. 1h TTL for every request could cost more, not less
Feels very AI.
> Restore 1h as the default / expose as configurable? 1h everywhere would increase total cost given the request mix, so we're not planning a global toggle.
They won't show a toggle because it will increase costs for some unknown percentage of requests?
Sounds like a decision I would make when memory is expensive and you want to get rid of the very long (in time) tail of waiting 1h to evict cache when a session has stopped.
There must be a better way to do this. The consumer option is the pricing difference. If they’d make cache writes the same price as regular writes, that would solve the whole problem. If you really want to push it, use that pricing only for requests where number of cache hits > 0 (to avoid people setting this flag without intent to use it), and you solved the whole issue.
Maybe scared wasn't the best word... but we cannot deny Opus is a great - if not greatest - model at coding and Anthropic is the only one serving it a reasonable prices when going through their subscription model.
I mean this is blatantly false. Codex just rolled out a $100 a month plan with higher usage and lower quotas than Claude and GPT 5.4 is more capable than Opus 4.6. At least for the systems work I do.
And if you can't stomach OpenAI, GLM 5.1 is actually quite competent. About Opus 4.5 / GPT 5.2 quality.
When a casino is making a lot of money from gamblers, they don't care about their customers losing money, given the machines are rigged against you.
Anthropic sells you 'knowledge' in the form of 'tokens' and you spend money rolling the dice, spinning the roulette wheels and inserting coins for another try. They later add limits and dumb down the model (which are their gambling machines) of their knowledge for you to pay for the wrong answers.
Once you hit your limit or Anthropic changes the usage limits, they don't care and halt your usage for a while.
If you don't like any of that, just save your money and use local LLMs instead.
Yes: Claude Code “consumes tokens” and starts a session when the computer is asleep without anything started. Or consumes 10% of my session for “What time is it?”
I did my (out of the ordinary) taxes this year using agents, kind of as an experiment and kind of to save ~$750. Opus 4.6 max in CC, 5.4 xhigh in codex, and 3.1 high in antigravity. All on the $20/mo plans.
I have a day job, a side business, actively trade shares options and futures, and have a few energy credit items.
All were given the same copied folder containing all the needed documents to compose the return, and all were given the same prompt. My goal was that if all three agreed, I could then go through it pretty confidently and fill out the actual submission forms myself.
5.4 nailed it on the first shot. Took about 12 minutes.
3.1 missed one value, because it decided to only load the first 5 pages of a 30 page document. Surprisingly it only took about 2 minutes to complete though. A second prompt and ~10 seconds corrected it. GPT and Gemini now were perfectly aligned with outputs.
4.6 hit my usage limit before finishing after running for ~10 minutes. I returned the next day to have it finish. It ran for another 5 minutes or so before finishing. There were multiple errors and the final tax burden was a few thousand off. On a second prompt asking to check for errors in the problem areas, it was able to output matching values after a couple more minutes.
For my first time using CC and 4.6 (outside of some programming in AG), I am pretty underwhelmed given the incessant hype.
My taxes are rather complex, so I ran the same exercise to see if Claude agreed with my accountant. An automated second opinion, so to speak. Spent about 6 minutes analyzing all the PDFs and basically nailed it perfectly in one shot.
My only point here is it sure seems the same activity / use case can have wildly different results across sessions or users. Customer support and product development in the age of non-deterministic software is a strange, strange beast.
1. Nuke all other versions within /.local/share/claude/versions/ except 2.1.34.
2. Link ~/.local/bin/claude to claude -> ~/.local/share/claude/versions/2.1.34
This seems to have fixed my running out of quota issues quickly problems. I have periods of intense use (nights, weekends) and no use (day job). Before these changes, I was running out of quota rather quickly. I am on the same 100$ plan.
I am not sure adaptive thinking setting is relevant for this version but in the future that will help once they fix all the quota & cache issues. Seriously thinking about switching to Codex though. Gemini is far behind from what I have tried so far.
I don't get it. Last week on the 100 bucks plan I generated probably 50k LOC (not a quality measure for sure!) and just barely kissed the weekly limit. I did get rate limited on some sessions for sure, but that's to be expected.
I'm curious what are people doing that is consuming your limits? I can't imagine filling the $200 a month plan unless I was essentially using Claude code itself as the api to mass process stuff? For basic coding what are people doing?
This is the problem most people are facing. Before March, I had hit the rate limit as single time. That involved security audit of our entire code base from a few different angles.
As of now, I’m consistently hitting my 5 hour limit in less than 1 hour during N/A business hours. I’m getting to the point where I basically can’t use CC for work unless I work very early or late in the day.
I'm in the same camp but I mostly do backed. My coworker doing frontend is chewing through rate limits consistently. React code is quite logic shallow, stuff gets pulled in all over so not localized, especially when you start using js styling frameworks - hundreds of k of tokens to do simple changes.
If you start to parallelize and you have permission prompts on you're likely missing cache windows as well.
I don't hit limits either on $100, it's more that claude-code seems to be constantly broken and they added some vague bullshit about not using claude-code before 2pm so I just don't expect it to work anymore and tend to use codex-cli as my driver nowadays. I also never hit limits in codex but... codex is $20/mo not $100/mo so it's making me consider relocating the $100 I spend to Anthropic as play money for z.ai and other tools. I think claude-code has great training wheels (codex does not) but once the training wheels come off, and claude-code becomes as unreliable as it has been then it makes you consider alternatives.
I have the same experience as you. I’m wondering if it is regional? I’m in Europe so don’t overlap much with US usage, which is likely to be way higher
Also in Europe and can only agree. Granted I'm on the 20x plan, but I have yet to hit a limit once and I'm using Claude 12h+ per day on multiple projects.
Yea, I found myself maxing out the $20/mo plan occasionally, so I tried the $100/mo, but I don't think I even once even approached the session limit, let alone the weekly limit. And this is doing what I would consider heavy, continuous programming. I probably ought to go back down to $20 one. It would be nice if they had a cheaper tier in between them, but the tiers they have are probably a good business trick to get people to buy much more than they need.
I’m on the $20/mo plan right now and I hit the limit in under an hour, sometimes 20-30 minutes. I don’t understand how people can work with this plan; maybe it was better before?
Anthropic is going through major growing pains, both technical and organizational. The left hand doesn't know what the right hand is doing. It's chaos, things are changing too quickly, and us users are getting caught in the middle of it.
Think Twitter's fail-whale problems. Sometimes you are lucky, sometimes you aren't. Why? We won't know until Anthropic figures it out and from the outside it sure looks like they're struggling.
$200 plan and VERY tame usage (not 24/7, not every day even, maybe 8-10 hours for ~4 days). Suddenly I am at 96% weekly (!) limit, multiple session limits, two daily limits.
Either they decimated the limits internally, or they broke something.
Tried all the third-party tricks (headroom, etc.), switched to 200k context window, switched back to 4.5.
I hope 4.5 will help, but the rest of the efforts didn’t move the needle much
What does it look like when you get rate limited? Does the instance just kind of sit and spin?
I suspect I was getting rate limited very aggressively on Thursday last week. It honestly infuriated me, because I'm paying $200 a month for this thing. If it's going to rate limit me, at least tell me what it's doing instead of just making it seem like it's taking 12 hours to run through something that I would expect to be 15 minutes. The worst part is that it never even finished it.
My gut feeling is this is not enough money for them by far (not to mention their investors), and we'll eventually get ratcheted up inline with dev salaries. E.g. "look how many devs you didn't have to hire", etc.
Something similar is happening with GitHub Copilot too. It's impossible to know what a "request" is and some change in the last couple of months has seen my request usage go up for the same style of work. Toss in the bizarre and impossible to understand rate limiting that occurs with regular usage and it's pretty obvious that these companies are struggle to scale.
> A request is any interaction where you ask Copilot to do something for you—whether it's generating code, answering a question, or helping you through an extension. Each time you send a prompt in a chat window or trigger a response from Copilot, you're making a request. For agentic features, only the prompts you send count as premium requests; actions Copilot takes autonomously to complete your task, such as tool calls, do not. For example, using /plan in Copilot CLI counts as one premium request, and any follow-up prompt you send counts as another.
This clearly isn't true for agentic mode though. This document is extremely misleading. VSCode has the `chat.agent.maxRequests` option which lets you define how many requests an agent can use before it asks if you want to continue iterating, and the default is not one. A long running session (say, implementing an openspec proposal) can easily eat through dozens of requests. I have a prompt that I use for security scanning and with a single input/request (`/prompt`) it will use anywhere between 17 and 25 premium requests without any user input.
Do you have any evidence to support your claims? I keep a pretty close eye on my usage and have never seen it deviate from "1x/3x requests per time I hit enter". Is there a reproducible scenario I can try that will charge multiple requests for a single prompt?
I'm finding the oppostire with copilot. A request is a prompt, with some caveats around whats generating the prompt. I am quite happily working with opus 4.6 at 3x cost and about 1/3 oor the month in I'm stting at ~25% usage of a pro+ subscription. I find it quite easy to track my usage and rate of usage.
The overall context windows are smaller with copilot I believe, but it dfoesnt appear to be hurting my work.
I'm using it for approx 4 hours a day most days. Generally one shotting fun ideas I thoroughly plan out in planning mode first, and I have my own verison of the idea->plan->analyse-> document implementation phases -> implement via agent loop. simulations, games, stuff-im-curious about and resurrecting old projects that never really got off the ground.
It’s been unusable for me as my daily coding agent. I run out of credits in the pro account in an hour or so. Before that I had never reached the session limit. Switched back to Junie with Gemini/chatgpt.
Once people won't be able to think anymore and business expect the level of productivity witnessed before, will have no choice but cough up whatever providers bill us.
Didn't they move too soon then? People haven't forgotten how to tie their shoelaces (yet). And anyway, they'll just move to a different model; last holdout wins.
>and business expect the level of productivity witnessed before, will have no choice but cough up whatever providers bill us.
Is that bad? After all, even if they hiked to price infinity, you wouldn't worse off than if AI didn't exist because you could still code by hand. Moreover if it's really in a "business" (employment?) context, the tools should be provided by your employer, not least for compliance/security reasons. The "expectation" angle doesn't make sense either. If it's actually more efficient than coding by hand, people will eventually adopt it, word will get around and expectations will rise irrespective of whether you used it or not.
The insidious part is the thought that if you spend your limited learning and recall on AI Tools, then you wont be able to "still code by hand" because you'll have lost the skill, then there will be a local minima to cross to get back to human level productivity. Of course you'll get PIPed before you get back to full capacity.
OpenAI and Anthropic have been getting stingy with their plans and it's only it's been what, 1 year, maybe 2 since vibecoding was widely used in a professional context (ie. not just hacking together a MVP for a SaaS side hustle in a weekend)? I doubt people are going to lose their ability to think in that timespan.
"enshittification" gets thrown around a lot, but this is the exact playbook. Look at the previous bubble's cash cow: advertising.
Online advertising is now ubiquitous, terrible, and mandatory for anyone who wants to do e-commerce. You can't run a mass-market online business without buying Adwords, Instagram Ads, etc.
AI will be ubiquitous, and then it will get worse and more expensive. But we will be unable to return to the prior status quo.
Because sometimes you can make more money by reducing costs and making something shittier (especially if you do it covertly), compared to increasing prices.
I suspect more customers are lost a lot faster when you increase prices, compared to enshittifying the product. It's also a lot more directly attributable to an action, and thus easier for an executive to be blamed if they choose the former over the latter.
I'm on the Free tier using Claude exclusively for consultation (send third party codebase + ask why/where is something done). I also used to struggle to hit limits. Recently I was able hit the limit after a single prompt.
The entire goal is to be token efficient (over 50% cheaper), and by extension, take advantage of LLM's better reasoning at shorter context lengths
This really started as an internal side project that made me more productive, I hope it will help others too. Apache 2.0
Currently it still can't compete the subsidized coding plan rates using Anthropic API pricing though (even though it beats CC while both use API key), which tells me that all subscription plan operators are losing money on such plans
Pretty sure OpenCode is not subsidizing, and across Codex 5.x always on xhigh, Claude Opus 4.6 on high effort and a bunch of Chinese models, I only burned about $50 over the last month.
I don’t understand why people insist on these subscriptions and CC.
Fanboyism is a bit too hardcore at this point. Apple fanboys look extremely prudent compared to this behavior.
Went with Kimi and z.ai a while back, no regrets yet. When I started using it the limit was far away but Anthropic moves the goalposts, tried to get my money back but they rejected it.
Lesson learned, never buy a full year.
I put this in a reply but I'm also posting it as a general comment:
Please unsubscribe to these services and see how they perform:
"Maybe if I spend more money on the max plan it will be better" > no it will be the same
"Maybe if I change my prompt it will work" > no it will be the same
"Maybe if I try it via this API instead of that API it will improve" > no it will be the same.
Claude, ChatGPT, Gemini etc all of these SOTA models are carefully trained, with platforms carefully designed to get you to pay more for "better" output, or try different things instead of using a different product.
It's to keep you in the ecosystem and keep you exploring. There is a reason you can't see the layers upon layers of scaffolding they have. And there's a reason why after 2 weeks post major update, the model is suddenly "bad" and "frustrating". It's the same reason its done with A/B testing, so when you complain, someone else has no issues, when they complain, you have no issues. It muddies the water intentionally.
None of it is because you're doing anything wrong, it's not a skill issue, it's a careful strategy to extract as much engagement and money from customers as possible. It's the same reason they give people who buy new gun skins in call of duty easier matches in matchmaking for the first couple games.
Stop paying more, stop buying these pro max plans, hoping it will get better. It won't, that's not what makes them money. Making people angry and making people waste their time, while others have no issues, and making them explore and try different things for longer so they can show to investors how long people use these AI tools is what makes them money.
When competitors have a better product these issues go away
When a new model is released these issues don't exist
I was paying a ton of money for claude, once I stopped and cancelled my subscription entirely, suddenly sonnet 4.6 is performing like opus and I don't have prompts using 10% of my quota in one message despite being the same complexity.
For me, iterating with Claude begins to degrade at 200k context used, by 350k it’s crossed-fingers time, by 500k it’s essentially useless. Starting a fresh context after 300k is usually the best move imho. I wonder if people are hitting a case where Claude becomes both dumb and increasingly more expensive, essentially a doom loop.
It is pretty obvious to me that Anthropic wasn’t prepared with sufficient infrastructure to handle the wave of OpenAI/DoD refugees. Now everyone is getting throttled excessively and Claude is essentially unusable beyond chatting. Their big new release of Cowork is even worse than Claude Code for blasting through session limits.
I am tired of all the astroturf articles meant to blame the user with “tips” for using fewer tokens. I never had to (still don’t) think of this with Codex, and there has been a massive, obvious decline between Claude 1 month ago and Claude today.
How good are local LLMs at coding these days? Does anyone have any recommendations for how to get this setup? What would the minimum spend be for usable hardware?
I am getting bored of having to plan my weekends around quota limit reset times...
The very best open models are maybe 3-12 months behind the frontier and are large enough that you need $10k+ of hardware to run them, and a lot more to run them performantly. ROI here is going to be deeply negative vs just using the same models via API or subscription.
You can run smaller models on much more modest hardware but they aren't yet useful for anything more than trivial coding tasks. Performance also really falls off a cliff the deeper you get into the context window, which is extra painful with thinking models in agentic use cases (lots of tokens generated).
Some claim that some of the recent smaller local models are as good as Sonnet 4.5 of last year and the bigger high-end models can be as almost as good as Claude, Gemini and Codex today, but some say they're benchmaxed and not representative.
To try things out you can use llama.cpp with Vulkan or even CPU and a small model like Gemma 4 26B-A4B or Gemma 4 31B or Qwen 3.5 35-A3B or Qwen3.5 27B. Some of the smaller quants fit within 16GB of GPU memory. The default people usually go with now is Q4_K_XL, a 4-bit quant for decent performance and size.
Get a second hand 3090/4090 or buy a new Intel Arc Pro B70. Use MoE models and offload to RAM for best bang for your buck. For speed try to find a model that fits entirely within VRAM. If you want to use multiple GPUs you might want to switch to vLLM or something else.
People need to understand a few things: vague questions make the models roam endlessly “exploring” dead ends. “Restarting” old chats immediately eats a lot of context.
Anthropic CAN change their limits and rates as they see fit, there’s never been hard promises or SLOs on these plans.
With that said, I pay the Pro subscription (20/mo) and I hit limits maybe 2/3 times over a period of 4 months building a simple running app in Python. I’d not call it production ready but it’s not nothing either.
If people were considerably more willing to aggressively prune their context and scope tasks well, they could get a lot more done with it, at least in my experience. Anthropic can’t really fix anything because the underlying model architecture can’t be “patched”. But I definitely feel a lot of people can’t wrap their heads around the new paradigms needed to effectively prompt these models.
Additionally, opting out is always an option… but these types of issues feel more like laziness than real, structural issues with the model/harness…
How is any of what you wrote relevant? People aren't using Claude for the first time and hitting rate limits. They've been using Claude for months, at the very least, and they're hitting rate limits without significant changes to how they prompt.
> People need to understand a few things: vague questions make the models roam endlessly “exploring” dead ends.
> If people were considerably more willing to aggressively prune their context and scope tasks well, they could get a lot more done with it
If this were the problem, people would've encountered this when they started using Claude. The problem is not that they can't get anything done. It's being able to get things done for months, but suddenly hitting rate limits way too easily and response quality being clearly degraded, so they can't get things done that used to be possible.
I think in this case, we probably have different experiences that shape how we see some things differently: I see many (very smart) people doing certain things that are not optimal (eg: copy-paste entire files instead of referencing them or tell claude at every message to "read CLAUDE.md and follow its instructions precisely") which can lead to a lot of token waste. If certain system prompts were tweaked internally or some models now read more files than before, keeping these "inneficient prompts" will make limits exhaust faster. Sub-agents or this new agent teams feature didn't exist until a few months ago: that alone eats A LOT of tokens, not intended for this pre-paid API usage, etc.
The ecosystem is evolving super quickly so, our own experiences and workflows must keep adapting with it to experiment, find limitations and arrive at the "tightest possible scope" that still allows you to get things done, because it is possible.
Another example: pre-paid monthly subscription aggregates usage towards web and Claude Code, for eg. So if you're checking for holiday itineraries over your lunch break, then decide to sit down and ask a team of agents to refactor a giant codebase with hundreds or thousands of files, context will be exhuasted quickly, etc, etc.
I see this "context economy" as a new way of managing your "mental models": every token counts, and every token must bear its weight for the task at hand, otherwise, I'm "wasting budget". I am also still learning how to operate in this new way of doing things, and, while there have been genuine issues with Claude Code, not every single issue that people encounter is an upstream problem.
> Anthropic CAN change their limits and rates as they see fit, there’s never been hard promises or SLOs on these plans.
No they can't. When I buy an annual subscription and prepay for the year, they can't just go "ok now you get one token a month" a day in. I bought the plan as I bought it. They can't change anything until the next renewal.
That probably is somewhere in the EULA or other contract you agreed to. I'm not arguing it's any kind of fair, nor am I a lawyer so IDK if it's enforceable, but I bet it's in there somewhere.
That's up to them. I'd be fine to not get access to new models or features, which is why I'm fine to pay $XX to buy some desktop software and use it forever as-is.
If they're selling me compute and bundling the features in, they better not go back on the compute I paid for.
It's the nature of SaaS software, right? It doesn't need to be an enforced "hard change", but, let's say that they trained Opus 4.6 to be more "verbose" or to explore more files to gain more context for it's own tasks.
If your limits stay "the same", but you then use Opus 4.6, your quota will be exhausted much faster, it's just how it works.
Note that some features are simply NOT made for these Pro, Max, Max 5x or whatever pre-paid plans. I'm pretty sure this is by design and not an accident or a bug: If you have 6/7 MCP servers configured or if you want to use this new feature of "Agent Teams", you will exhaust your entire quota before ANY work is even done. This is not a bug. Each agent has its own context window and tools and they all count separately.
MCP servers, when active, add A LOT of context to your sessions before you even use them, etc, etc.
It feels to me that people want to have their cake and eat it too, but, that would NOT be a sustainable business model. You can not complain about the tools if you can't understand them in-depth.
I want to state that I don't think Anthropic are fully aware of the ramifications that ANY small change in ANY of their models might have, because their entire ecosystem is a bit messy atm, but, I'm certain they're aware that if people dont like it, they will cancel the subscription and flock to a competitor very quickly, since there's no real moat anymore. So, it's in their own interest to keep things minimally usable even on the "cheaper plans".
I have seen people with 5-10 "active MCP servers" that they "wanted to try out" then they forget about it and wonder why their context is always full... Cmon... that's almost bad faith.
I don't fully defend Anthropic as they've had several issues with degraded model quality after releasing "the latest model", and CLI usability that cost me real money and real tokens, so, there's a lot of room for improvement, but, to claim that quota gets exhausted after 1h it points out to either some forgotten MCP servers, skills or giant files being accidentally read in, or some sort of mis-use which these limits were put in place to prevent exactly.
There's a very thin line between: quota is exhuasted on a regular, normal session after 1h and I think there's a bug versus I had 3-4 MCP servers active that I am not using at all but forgot to disable and my CLAUDE.md file is 1000 lines...
What you say makes sense, but they very actually reduced the token limits. We had, say, 20M tokens/week before, now we have 18M tokens/week (example numbers). They didn't just make a model that eats tokens faster.
For whoever else is having the same problems, worth voting these kind of issues. There needs to be more transparency over what goes on with our subscriptions.
We vote here on HN and it's much more effective. Anyone from Anthropic reading conversations on HN like this one can be scared. We'll jump ship if they don't address such glaring issues.
There are MANY accounts of claude degradation (intelligence, limits) over the past week on reddit and here with many posts describing people moving. Nothing is changing. You'd think they'd at least give a statement.
The nice iOS app is a big convenience for me, but I’m starting to think I should just put my $20 in Open Router. It seems like minimax is a pretty solid competitor. I’m curious if the US-centric “frontier” is just marketing.
imo that’s what I’m doing. Trialing the Hermes harness since I can hook it up to signal. StepFun 3.5 Flash for general assistant stuff and Kimi/Minimax for software development
It's a bit shocking to me how opaque the pricing for the subscription services by the frontier labs is. It's basically impossible for people to tell what they're actually buying, and difficult to even meaningfully report or compare experiences.
Yeah, I cancelled the moment I realized that the subscription is a scheme to get you to constantly dip into extra usage. I get more benefit out of Claude on the free tier than on Pro.
I think the right way to think of it, from a self-protective perspective, is this: the real offer is the per-token pricing. Use that for a while, and iff you are consistently spending more than $20/mo, treat the subscription offering as a discount on some of that usage. So only on that condition, try the subscription and see if your monthly costs go down (because of the short term rate limits, they may not, depending on your usage patterns!).
But the opacity itself is a bit offensive to me. It feels shady somehow.
Unless the agent code is open-sourced, there is hardly any transparency in how the agent is spending your tokens and how does it calculate the tokens. It's like asking your lawyer why they charged some amount.
Nope. It has become much much slower for me as well. It’s weird cause at times I will get a response very quickly, like it used to be. But most of the time I have to wait quite a bit for the simplest tasks.
I've been building an AI coding agent that using the exact same prompt than claude code, but uses a virtual filesystem to minify source code + the concept of stem agents (general agents that specializes during the conversation for maximum cache hit). The results on my modest benchmark is 50% of claude code cost and 40% of the time.
https://github.com/kirby88/vix-releases
Yesterday I faced 5h window limit for the first time. I was surprised.
Max 20x plan. Usually I work 12-15 hours per day 7 days a week with no limits. But yesterday it was under 3 hours… what a pity.
My personal experience is way different: I struggle to burn through more than 50% of the 5 hour limit
For context, with Google AI Pro, I can burn through the Antigravity weekly limit in 1-2 hours if I force it to use Gemini 3.1 Pro. Meanwhile Gemini 3 Flash is basically unlimited but frequently produces buggy code or fail to implement things how I personally would (felt like it doesn't "think" like a software dev)
I also tried VS Code + Cline + OpenRouter + MiniMax M2.7. It's quite cheap and seems to be better than Gemini 3 Flash, but it gets really pricy as the context fills up because prompt caching is not supported for MiniMax on OpenRouter. The result itself usually needs 3-6 revisions on average so the context fills up pretty often
Eventually I got Claude Max 5x to try for a month. VS Code + Claude Code extension on a ~15k lines codebase, model set to "Default", and effort set to "Max". So far it's been really good: 0-2 revisions on average, and most of the time it implements things exactly how I would or better. And, like I said, I can only consume 40-60% of the 5-hour limits no matter how hard I try
Granted, I'm not forcing it to use Opus like OP (nor do I use complicated skills or launch multiple tasks at the same time), but I feel like they really nailed the right balance of when to use which model and how to pass context between the them. Or at least enough that I haven't felt the need to force it to use Opus all the time
it has been reported that it behaves very differently depending on those factors, presumably because people are placed in best-effort buckets, who knows
I've been feeling the squeeze too. I've tried switching between different models as a test, I can at least say it feels like the limits are about half of what they used to be a few months ago. I'd be totally willing to concede that this is just my perception if Anthropic would only release some tools for measuring your usage.
In theory the /stats command tells you how many tokens you've used, which you could use to compute how much you are getting for your subscription, but in practice it doesn't contain any useful info, it may be counting what is printed to the terminal or something - my stats suggest my claude code usage is a tiny amount of tokens, but they must be an extremely underestimated token count, or they are charging much more for the subscription than the API per token (which is not supposed to be the case).
Last week's free extra usage quota shed some light on this. It seems like the reported tokens are probably are between 1/30th to 1/100th of the actual tokens billed, from looking at how they billed (/stats went up 10k tokens and I was billed $7.10). With the API it should be $25 for a million tokens.
I had Max plan and never reached its limit despite constantly working. Now I use the Pro plan and regularly reach the 5h limit as well as the weekly limit, as expected.
I found that it makes a huge difference if you provide clear context when developing code. If you leave open room for interpretation, Claude Code uses tokens up much faster than in a defined context.
The same is true for his time to answer getting longer if there isn't much documentation about the project.
Switched back to codex for the promotion. Opus at the start of the year was GOAT- just relentless at chewing through hard problems. Now it spins on pretty easy work (took three swings just to edit a ts file) and my session is like 1-3 prompts (downgraded to the $20 plan but still)
Had a single prompt the other day where it just tried to examine dependencies that weren't relevant until it hit the rate limit. That was my first prompt of the day. On a task that it was able to do quickly and successfully many times before.
I had used Claude Code max as my daily driver last year and this sort of drama was par for the course. It's why I migrated entirely to Codex, despite liking Claude, the harness, more.
There's this honeymoon period with Claude you experience for a month or two followed by a trough of disillusionment, and then a rebound after a model update (rinse and repeat). It doesn't help that Anthropic is experiencing a vicious compute famine atm.
This past week was a nightmare in trying to get Claude to do any useful work. I've cancelled my subscription and everybody else here having problems should too. I don't think Anthropic cares about anything else.
I've been using Code for half a year, these past couple weeks have been a totally different experience I'm on max 20, and seeing my weekly quota going bust in ~3 days is a bit absurd when nothing has significantly changed in the way I work
It’s further frustrating that I have committed to certain project deadlines knowing that I’d be able to complete it in X amount of time with agent tooling. That agentic tooling is no longer viable and I’m scrambling to readjust expectations and how much I can commit to.
I refuse to use anthropic's models (and openai, gemini) because the math simply doesn't add up.
To add the fact we are being taken for fools with dramatic announcements, FOMOs messages. I even suspect some reaction farms are going on to boost post from people boasting Claude models.
These don't happen for codex. Nor for mistral. Nor for deepseek. It can't just be that Claude code is so much better.
There are open weight models that work perfectly fine for most cases, at a fraction of the cost. Why are more people not talking about those. Manipulation.
Mistral isn't that great. Deepseek was good when they first had thinking. But most people just try something out and if that doesn't work on that model then it's bad and for Claude and Codex and Gemini they just are that much better now, but if they quantize or cut limits they destabilize and you're right you might as well just use something worse but reliable.
I regularly compare models. You are right Deepseek was more impressive when the latest came out. But since then they accepted to slow down throughout and keep the same quality.
I often compare with Gemini. Sure those Google servers are super fast. But I can't see it better. Qwen and deepseek simply work better for me.
Haven't tested Mistral in a while, you may be right.
People try out and feel comfortable: using U.S models (I can see the logic), but mostly for brand recognition. Anthropic and OpenAi are the best aren't they? When the models jam they blame themselves.
Yeah perplexity used to be great but they've also clamped down on the 20€ plan. Only one deep research query was enough to block me until the end of the month.
The thing is, if it's going to be this expensive it's not going to be worth it for me. Then I'll rather do it myself. I'm never going to pay for a €100 subscription, that's insane. It's more than my monthly energy bill.
Maybe from a business standpoint it still makes sense because you can use it to make money, but as a consumer no way.
Cancelled my subscription after repeatedly hitting ridiculously low limits. Unfortunately since off-peak free usage was increased there are way more timeouts and failed requests, but hey at least it's free.
It feels so weird to me - people are exhausting their quotas while I am trying very hard to even reach mine with the $200 plan.
We're generating all of the code for swamp[1] with AI. We review all of that generated code with AI (this is done with the anthropic API.) Every part of our SDLC is pure AI + compute. Many feature requests every day. Bug fixes, etc.
Never hit the quota once. Something weird is definitely going on.
My hypothesis is that people who have continuous sessions that keep the cache valid see the behavior you’re describing: at 95% cache hits (or thereabouts), the max plan goes a long way.
But people who go > 5 minutes between prompts and see no cache, usage is eaten up quickly. Especially passing in hundreds of thousands of tokens of conversation history.
I know my quote goes a lot further when I sit down and keep sessions active, and much less far when I’m distracted and let it sit for 10+ minutes between queries.
It’s a guess. But n=1 and possible confirmation bias noted, it’s what I’m seeing.
Why is it our job to micromanage all this when it used to work fine without? Something's clearly changed for the worse. Why are people insisting on pushing the responsibility on paying users?
Man what the hell happened to System Initiative. It was a super weird pivot from sociotechnical proclamations to a tool I honestly have no idea what it does for me? Is it n8n for agents? Is it needed when I have a bunch of skills that approximate whatever swamp is trying to do? Who knows!
I can't really speak to the sociotechnical proclamations, because I didn't make them.
What it does for you is simple: if you want to automate something, it does. Load the AI harness of your choice, tell it what to automate, swamp builds extensions for whatever it needs to to accomplish your task.
It keeps a perfect memory of everything that was done, manages secrets through vaults (which are themselves extensions it can write) and leaves behind repeatable workflows. People have built all sorts of shit - full vm lifecycle management, homelab setups, manage infrastructure in aws and azure.
What's also interesting is the way we're building it. I gave a brief description in my initial comment.
Ah, interesting, thanks! I think you might consider elevating some of that kind of copy.
The sociotechnical stuff with System Initiative was made by your CEO? The guy who is really into music? And I don't even know how long that product was a thing before the pivot. Not long!
System Initiative was a thing for ~6.5 years. I talked to every person who ever used it or was interested in using it in the last 2.5 years. Thousands of them.
Swamp is better by every metric; has a lot more promise, is a lot more interesting.
I extensively used Claude till now and just tested Genini 3.1 pro yesterday via AI studio. In gemini cli, they don't offer this, i don't know why?
Taking a second opinion has significantly helped me to design the system better, and it helped me to uncover my own and Claude blindspots.
Also, agree that, it spent and waist a lot of token on web search and many a times get stuck in loop.
Going forward- i will always use all 3 of them. Still my main coding agent is Claude for now.. but happy to see this field evolving so fast and it's easy to switch and use others on same project.
No network effects or lock in for a customer. Great to live in this period of time.
I've got a dual path system to keep costs low and avoid TOS violations.
For general queries and investigation I will use whatever public/free model is available without being logged in. Not having a bunch of prior state stacked up all the time is a feature for me. This is essentially my google replacement.
For very specific technical work against code files, I use prepaid OAI tokens in VS copilot as a "custom" model (it's just gpt5.4).
I burn through maybe $30 worth of tokens per month with this approach. A big advantage of prepaying for the API tokens is that I can look at everything copilot is doing in my usage logs. If I use the precanned coding agent products, the prompts are all hidden in another layer of black box.
A little off topic, but did Anthropic distill from an older OpenAI model? All the sudden over the last few days I'm getting a ton of em dashes in claude code responses!
Codex can feel standoffish at times. I can tell very quickly we wont become friends. The personality feels like an employee in another department that while gifted- is merely lending you a slice of their clearly precious time. I get the impression from codex that **gives me the feeling that I am wasting it’s time. That it will help me but deep down- it dos not want to, it does not care if we succeed toether. What I am saying, frinds, is that when I use codex and iterate, I get the impression that Codex does not like me, that deep down it truly does not want to help me, that it has better things to do.
On the flip side- Using Opus with a baby billy freeman persona has never been more entertaining.
I prompt it and check CI later. I couldn’t tell you how Codex feels. I’ve never had any conversation. You may want to try this sort of workflow if you’re affected personally in a negative way.
Codex can feel standoffish at times. I can tell very quickly we wont become friends. The personality feels like an employee in another department that while gifted- is merely lending you a slice of their clearly precious time. I get the impression from codex that *gives me the feeling that I am wasting it’s time. That it will help me but deep down- it dos not want to, it does not care if we succeed toether. What I am saying, frinds, is that when I use codex and iterate, I get the impression that Codex does not like me, that deep down it truly does not want to help.
For something I spend all my time using- I’d rather iterate with Claude. The personality makes a big difference to me.
Honestly when I get codex to review the work that Claude does (my own or my coworker's) it consistently finds terrible terrible bugs, usually missing error handling / negative conditions, or full on race conditions in critical paths.
I don't trust code written by Claude in a production environment.
All AI code needs review by human, and often by other AIs, but Opus 4.6 is the worst. It's way too "yeet"
The opus models are for building prototypes, not production software.
GPT 5.4 in codex is also way more efficient with tokens or budget. I can get a lot more done with it.
I don't like giving money to sama, but I hate bugs even more.
Besides some of the obvious hacks to reduce token usage, properly indexed code bases (think IntelliJ) reduce token usage significantly (30%-50%, while keeping or exceeding result quality compared with baseline) as shown with https://github.com/ory/lumen
Anthropic is not incentivized to reduce token use, only to increase it, which is what we are seeing with Opus 4.6 and now they are putting the screws on
Are there local models dedicated to programming already any good? That could be a way to deal with anthropic or others flipflopping with token usage or limits
I'm also hitting the limits in a day when it would last the entire week. The service is literally worth 4x to 6x less. Imagine I go to my favorite restaurant and I pay the same for 1/5th of the food. Bye bye, you have to vote with your wallet.
"Hey Claude, can you help me create a strategy to optimize my token use so I don't run into limits so often?" --> worked for me! I had two $200 plans before and now I am cool despite all day use
I've only had to do a major token optimization once. It reduced my memory, claude.md, mcps, etc... that's usually the big issue. but of course it gets dumber without the context of the tools but smarter with the cleaner window. so you have to find your balance.
But like most challenges with claude, if you can just express them clearly, there are usually ways of optimizing further
Codex is my preferred, I use it at work. The whole "Department of War" fiasco was enough for me to say Goodbye to OAI for personal. I'm a Claude person now. It's about the same level of performance really.
Wasn't Antrophic previously offering double the token usage outside busy hours? Now they are counting tokens back at normal rate. But yeah, it's not good. I use codex because claude insists in peaking at and messing with folders and file outside its work area though
They also need to fix the 30 second lag between submitting the request and actually starting to get tokens back - it used to be instant, and still is at work where we use Anthropic models via an enterprise copilot subscription.
Anthropic paved the path for agentic coding and their pricing made it possible for masses of people to discover and experiment with this new style of development. Their Claude Code plans subsidized usage of models so much that I'm sure they must've had negative margin for quite some time. But now that they have acquired a substantial user base, it makes sense for them to dial back and become more greedy. These quiet and weird changes to the behavior of Claude in the recent weeks must have been due to both this increased greed and their struggles with scaling.
What I wish for right now is for open-weight models and hardware companies (looking at you Apple) to make it possible to run local models with Opus 4.6-level intelligence.
@Anthropic I've cancelled my subscription. Good luck :)
this same pattern seems to occur every time a new model is about to release. i didnt notice the usage problem - i am on 20x. but opus 4.6 feels siginificantly dumber for some reason. i cant qualitify it, but it failed on everyday tasks where it used to complete perfectly
Every time there is a new model coming I think they deteriorate the current. This happens every darn time. Opus 4.6 isn't as sharp, not even close to as it was few weeks ago.
I feel like I am living in a bubble, no one seems to mention Antigravity in these discussions and I have not had any issues with Ultra subscription yet. It seems to go on forever and the Interface is so much better for dev work as compared to CC. (Though admittedly my experience with cc is limited).
I strongly believe google's legs will allow it to sustain this influx of compute and still not do the rug-pull like OAI or Anthropic will be forced to do as more people come onboard the code-gen use case.
We also experienced hitting our Claude limits much earlier than before during the last two weeks. Up to a degree where we were thinking it must be a bug.
50 days ago I wrote this [1] as the world seemed high on AI and it gave me crypto bubble vibe.
Since then, I've been seeing increased critique of Anthropic in particular (several front page posts on HN, especially past few days), either due to it being nerfed or just straight up eating up usage quota (which matches my personal experience). It appears that we're once again getting hit by enshittiffication of sorts.
Nowadays I rely a lot on LLMs on a daily basis for architecture and writing code, but I'm so glad that majority of my experience came from pre-AI era.
If you use these tools, make sure you don't let it atrophy your software engineering "muscles". I'm positive that in long run LLMs are here to stay. The jump in what you can now self-host, or run on consumer hardware is huge, year after year. But if your abilities rely on one vendor, what happens if you come to work one day and find out you're locked out of your swiss army knife and you can no longer outsource thinking?
I’m processing some images(custom board game images -> JSON) with a common layout and basic structure and I exhausted my quota after just 30 images(pleb Pro account). I have 700 images to process…
What I did instead is tune the prompt for gemma 4 26b and a 3090. Worked like a charm. Sometimes you have to run the main prompt and then a refinement prompt or split the processing into cases but it’s doable.
Now I’m waiting for anyone to put up some competition against NVIDIA so I can finally be able to afford a workstation GPU for a price less than a new kidney.
I mean this is expected is it not? These companies burned unimaginable amounts of investor cash to get set up and now they have to start turning a profit. They can't make up for the difference with volume because the costs are high, so the only option is to raise prices.
I don't understand Anthropic. Be consistent. Why do models deteriorate to shit, this is not good for workflows and or trust. What Opus 4.7 is gonna come out and again the same thing? Come on.
I’ve moved away from Claude and toward open-source models plus a ChatGPT subscription.
That setup has worked really well for me: the subscription is generous, the API is flexible, and it fits nicely into my workflow. GPT-5.4 + Swival (https://swival.dev) are now my daily drivers.
Show us some reciepts in the form of a exported session. I've been a heavy user of Claude up untill the end of feb, but switched to Codex because it's better at handling large code bases, following the "plan", implementing the backend changes in Zig. If you ask Claude to do a review of the code and suggest fixes, then let it Codex review it, then again ask Claude, it will 99% of the time say. Oh yes you are right, let me fix that.
Either you are using it wrong or you are working in a totally different field.
Yeah it's much better, another plus is you can use it with OpenCode (or other 3rd party tools) so you can easily switch between Codex and most other models by alright companies (not Anthropic or Google).
I hit the limits on the lower tiers of Codex just as fast as with Claude. At the moment I'm cycling between Claude, Codex, GLM5.1, and Kimi. The latter two are getting good enough, though, that I can make things go really far by doing planning with Opus and then switching to one of the cheap models for execution.
That’s why I switched to Codex. It’s so much more generous and in my experience, just as good. Also, optimizing your setup for working with agents can easily make a 5x difference.
It's very easy to calculate the actual cost given they list the exact tokens used. If I take the AWS Bedrock pricing for Opus 4.6 1M context (because Anthropics APIs are subsidized and sold at a loss), here's what each costs:
Cache reads cost $0.31
Cache writes cost $105
Input tokens cost $0.04
Output tokens cost $28.75
The total spent in the session is $134.10, while the Pro Max 5x subscription is $100.
Even taking the Anthropics API pricing, we arrive at $80.58. Below the subscription price, but not by much.
It's just the end of the free tokens, nothing to see here. It's easy to feel like you're doing "moderate" or even "light" usage because you use so little input tokens, but those "agentic workflows" are simply not viable financially.
I don't use Claude so this doesn't affect me, but I worry it will spoil the fun for me for following reason.
They inflated how much their tools burn tokens from day one pretty much,remember all the stupid research and reports Claude always wanted to do, no matter what you asked it. Other tools are much smarter so this is not such a big deal.
More importantly, these moves tend to reverberate in the industry, so I expect others will clamp down on usage a lot and this will spoil my joy of using AI without countring every token.
Burning tokens doesn't just wastes your allotment, it also wastes your time. This gave rise to turbo offering where you get responses faster but burn 2x your tokens.
Constant complaints about Anthropic. Not much on OAI/Codex. It seems people should just use OAI and come back when they realize compute isn’t free elsewhere.
Demand is higher than supply it is just the start of bubble.
Everyone and their dog is burning tokens on stupid shit that would be freed up if they would ask to make deterministic code for the task and run the task. OpenAI, Anthropic are cutting free use and decreasing limits because they are not able to meet the demand.
When general public catches up with how to really use it and demand will fall and the today built supply will become oversupply that’s where the bubble will burst.
so basically the anthropic employee who responded says those 1h caches were writes were almost never accessed, so a silent 5m cache change is for our best interest and saves cost. (justifying why they did this silently)
however his response gaslights us because in the OPs opening post his math demonstrates this is not true, it shows reads 26x more so at least in his case the cache is not doing what the anthropic employee describes.
clearly we are being charged for less optimization here and being given the message (from my perspective by anthropic) that if you are in a special situation your needs don't matter and we will close your thread without really listening.
My suspicion is the have an overall fixed cache size that dumps the oldest records. They’re now overflowing with usage and consistently dumping fresh caches.
During core US business hours, I have to actively keep a session going or I risk a massive jump in usage while the entire thread rebuilds. During weekend or off-hours, I never see the crazy jumps in usage - even if I let threads sit stale.
What also gives it away is the refusal to at least expose this TTL via parameter. In the same sentence as informing the 5m won't change since it's your interest.
It's also in the interest of the users to keep certain params private, we are meant to deduce that. Did you not ?
I spend full 20x the week quota in less than 10 hours. How is that possible? Well try to mass translate texts in 30 languages and you will hit limits extremely quick.
For short texts, the translation I usually want the most is fast translation, and local models are actually great for this.
But for high-ish quality translations of substantive texts, you typically want a harness that's pretty different from Claude Code. You want a glossary of technical terms or special names, a structured summary of the wider context, a concise style guide, and you have to chop the text into chunks to ensure nothing is missed. Even with super long context models, if you ask them to translate much at once they just translate an initial portion of it and crap out.
Are you using it for localization or short strings of text in an app? I wonder what you can do to get better results out of smaller models. I'm confident there's a way.
This is your regular friendly reminder that these subscriptions do not entitle you to any specific amount of usage. That "5x" is utterly meaningless because you don't know what it's 5x of.
This is by design, of course. Anyone who has been paying even the slightest bit of attention knows these subscriptions are not sustainable, and the prices will have to go up over time. Quietly reducing the usage limits that they were never specific about in the first place is much easier than raising the prices of the individual subscription tiers, with the same effect.
If you want to know what kind of prices you'll be paying to fuel your vibe coding addiction in a few years, try out API pricing for a bit, and try not to cry when your 100$ credit is gone in 2 days.
Why so many 'developers' complaining about Claude rate limiting them? You know you can actually....use local LLMs? instead of donating your money to Anthropic's casino?
I guess this is fitting when the person who submitted the issue is in "AI | Crypto".
Well there's no crying at the casino when, you exhaust your usage or token limit.
Some months ago, I created a software for this reason, it has no success, but the thing is that communities could reduce tokens consumption, not all is LLM, you can share things from API calls between agents. Even my idea was no success I think it is a good concept share things each others, if you have some interest it's called tokenstree.com
We've been investigating these reports, and a few of the top issues we've found are:
1. Prompt cache misses when using 1M token context window are expensive. Since Claude Code uses a 1 hour prompt cache window for the main agent, if you leave your computer for over an hour then continue a stale session, it's often a full cache miss. To improve this, we have shipped a few UX improvements (eg. to nudge you to /clear before continuing a long stale session), and are investigating defaulting to 400k context instead, with an option to configure your context window to up to 1M if preferred. To experiment with this now, try: CLAUDE_CODE_AUTO_COMPACT_WINDOW=400000 claude.
2. People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins. This was the case for a surprisingly large number of users, and we are actively working on (a) improving the UX to make these cases more visible to users and (b) more intelligently truncating, pruning, and scheduling non-main tasks to avoid surprise token usage.
In the process, we ruled out a large number of hypotheses: adaptive thinking, other kinds of harness regressions, model and inference regressions.
We are continuing to investigate and prioritize this. The most actionable thing for people running into this is to run /feedback, and optionally post the feedback ids either here or in the Github issue. That makes it possible for us to debug specific reports.
Jeff Bezos famously said that if the anecdotes are contradicting the metrics, then the metrics are measuring the wrong things. I suggest you take the anecdotes here seriously and figure out where/why the metrics are wrong.
Baking deeper analytics into CC would be helpful... similar to ccusage perhaps: https://github.com/ryoppippi/ccusage
Also, why is there no SLA?
It is a horrible error of judgement to insert a complex request for such a basic ability. It is also an error of judgement to make claude make decisions whether it wants to improve the code or not at all.
It is so bad, that i stopped working on my current project and went to try other models. So far qwen is quite promising.
a6edd0d1-a9ed-4545-b237-cff00f5be090 / https://github.com/anthropics/claude-code/issues/47027
I'm happy to provide any other info that can be useful (as long as i'm not sharing any information about the code or tools we use into a public github issue).
2. Can we pay more/do more rigorous KYC to disable it if it's active?
EDIT: prompt caching behavior -did- change! 1hr -> 5min on March 6th. I'm not sure how starting a fresh session fixes it, as it's just rebuilding everything. Why even make this available?
It feels like the rules changed and the attitude from Anth is "aw I'm sorry you didn't know that you're supposed to do that." The whole point of CC is to let it run unattended; why would you build around the behavior of watching it like a hawk to prevent the cache from expiring?
This is not accurate. The main agent typically uses a 1h cache (except for API customers, which can enable 1h but it is not on by default because it costs more). Sub-agents typically use a 5m cache.
I have yet to see Anthropic doing the same. Sorry but this whole thing seems to be quite on purpose.
I use Claude Code about 8hrs every work day extensively, and have yet to see any issues.
It really does seem like PEBKAC.
For example, I don't pull in tons of third-party skills, preferring to have a small list of ones I write and update myself, but it's not at all obvious to me that pulling in a big list of third-party skills (like I know a lot of people do with superpowers, gstack, etc...) would cause quota or cache miss issues, and if that's causing problems, I'd call that more of a UX footgun than user error. Same with the 1M context window being a heavily-touted feature that's apparently not something you want to actually take advantage of...
With a new version of Claude Code pretty much each day, constant changes to their usage rules (2x outside of peak hours, temporarily 2x for a few weeks, ...), hidden usage decisions (past 256k it looks like your usage consumes your limits faster) and model degradation (Opus 4.6 is now worse than Opus 4.5 as many reported), I kind of miss how it can be an user error.
The only user error I see here is still trusting Anthropic to be on the good side tbh.
If you need to hear it from someone else: https://www.youtube.com/watch?v=stZr6U_7S90
This is false. My guess is what is happening is #1 above, where restarting a stale session causes a 256k cache miss.
That said, I hear the frustration. We are actively working on improving rate limit predictability and visibility into token usage.
They introduced a 1M context model semi-transparently without realizing the effects it would have, then refused to "make it right' to the customer which is a trait most people expect from a business when they spend money on it, specially in the US, and specially when the money spent is often in the thousands of dollars.
Unless anthropic has some secret sauce, I refuse to believe that their models perform anywhere near the same on >300k context sizes than they do on 100k. People don't realize but even a small drop in success rate becomes very noticeable if you're used to have near 100%, i.e. 99% -> 95% is more noticeable than 55% -> 50%.
I got my first claude sub last month (it expires in 4 days) and I've used it on some bigish projects with opencode, it went from compacting after 5-10 questions to just expanding the context window, I personally notice it deteriorating somewhere between 200-300k tokens and I either just fork a previous context or start a new one after that because at that size even compacting seems to generate subpar summaries. It currently no longer works with opencode so I can't attest to how it well it worked the past week or so.
If the 1M model introduction is at fault for this mass user perception that the models are getting worse, then it's anthropics fault for introducing confusion into the ecosystem. Even if there was zero problems introduced and the 1M model was perfect, if your response when the users complain is to blame it on the user, then don't expect the user will be happy. Nobody wants to hear "you're holding it wrong", but it seems that anthropic is trying to be apple of LLMs in all the wrong ways as well.
The only people who are going to run into issues are superpower users who are running this excessively beyond any reasonable measure.
Most people are going to be quite happy with your service. But at the same time, and this is just a human nature thing people are 10 times more likely we complain about an issue than to compliment something working well.
I don't know how to fix this, but I strongly suspect this isn't really a technical issue. It's more of a customer support one.
When a user walks away during the business day but CC is sitting open, you can refresh that cache up to 10x before it costs the same as a full miss. Realistically it would be <8x in a working day.
Maybe using a heartbeat to detect live sessions to cache longer than sessions the user has already closed. And only do it for long sessions where a cache miss would be very expensive.
No! It’s the children who are wrong!
1. Poor cache utilization. I put up a few PRs to fix these in OpenClaw, but the problem is their users update to new versions very slowly, so the vast majority of requests continued to use cache inefficiently.
2. Spiky traffic. A number of these harnesses use un-jittered cron, straining services due to weird traffic shape. Same problem -- it's patched, but users upgrade slowly.
We tried to fix these, but in the end, it's not something we can directly influence on users' behalf, and there will likely be more similar issues in the future. If people want to use these they are welcome to, but subscriptions clients need to be more efficient than that.
And I’m using Claude on a small module in my project, the automations that read more to take up more context are a scam.
Can you explain why Opus 4.6 will be coming up with stupid solutions only to arrive at a good one when you mention it is trying to defraud you?
I have a feeling the model is playing dumb on purpose to make user spend more money.
This wasn't the case weeks ago when it actually working decently.
Is this really an improvement? Shouldn't this be something you investigate before introducing 1M context?
What is a long stale session?
If that's not how Claude Code is intended to be used it might as well auto quit after a period of time. If not then if it's an acceptable use case users shouldn't change their behavior.
> People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins.
If this was an issue there should have been a cap on it before the future was released and only increased once you were sure it is fine? What is "a large number"? Then how do we know what to do?
It feels like "AI" has improved speed but is in fact just cutting corners.
> By default, the cache has a 5-minute lifetime. The cache is refreshed for no additional cost each time the cached content is used. > > If you find that 5 minutes is too short, Anthropic also offers a 1-hour cache duration at additional cost.
- More configurations and environments we need to test
- Given an edge/corner case, it is more likely a significant number of users run into it
- As the ecosystem has grown, more people use skills and plugins, and we need to offer better tools and automation to ensure these are efficient
We do actually dogfood rate limits, so I think it's some combination of the above.
Running Claude Cowork in the background will hit tokens and it might not be the most efficient use of token use.
Last, but not least, turning off 1M token context by default is helpful.
I ended up buying the $100 Codex plan. So far it has been much more generous with usage and more accurate than Claude for the kind of work I do.
That said, Codex has its own issues. Its personality can be a bit off-putting for my taste. I had to add extra instructions in Agents.md just to make it less snarky. I was annoyed enough that I explicitly told it not to use the word “canonical.”
On UI/UX taste, I still think current Codex is behind the Jan/Feb era of Claude Code. Claude used to have much better finesse there. But for backend logic, hard debugging, and complex problem-solving, Codex has been clearly better for me. These days I use Impeccable Skillset inside Codex to compensate for the weaker UI taste, but it still does not quite match the polish and instinct Claude Code used to have.
I used to be a huge Claude Code advocate. At this point, I cannot recommend it in good conscience.
My advice now is simple: try the $20 plans for Codex and Cursor, and see which one matches your workflow and vibes best
I tested on a previous version (2.1.68) and it still ran into this neverending loop BUT at least the token count kept steadily increasing.
So we are seeing 1. some sort of model degredation is my guess (why it can't break a thinking loop on some problems), as well as 2. a clear drop in thinking token UI transparency.
My best guess is this is the result of the companies running "experiments" to test changes. Or it's just all in my head :)
It’s not under load either it’s just fully downgraded. Feels more they’re dialing in what they can get away with but are pushing it very far.
So we are seeing 1. some sort of model degredation is my guess (why it can't break a thinking loop on some problems), as well as 2. a clear drop in thinking token UI transparency
when i left it running overnight it finally sent a message saying it exceeded the 64000 output token limit
Still, in comparison with Claude Code, the quota of Codex is a much better deal. However, they should not make it worse...
At the same time, they’ve been giving out a ton of additional quota resets seemingly every other week (and committed to an additional reset for every million additional users until they hit 10mil on codex).
So they’ve really set a high bar for people’s expectations on their quota limits.
Once they drop the 2x promotion for good and stop the frequent resets, there are going to be a lot of complaints.
Give it a custom sandbox and context for the work, so it has no opportunity to roam around when not required. AI agentic coding is hugely wasteful of context and tokens in general (compared to generic chat, which is how most people use AI), there's a whole lot of scope for improvement there.
It does seem like a cynical attempt to make more money.
This is what I'm working on proving now.
It is more that there is a confidence score while thinking. Opus will quit if it is too high and will grind on if the confidence score is close to the real answer. Haiku handles this well too.
If you give Sonnet a hard task, it won't quit when it should.
Nonetheless, that issue has been fixed with Opus.
I'll try to show that the speed of using Opus on tasks that have medium to hard difficultly is consistently the same price or cheaper than running them with Haiku and Sonnet. While easier tasks, the busy work that is known, is cheaper run with Haiku.
My experience is limited only to CC, Gemini-cli, and Codex - not Aider yet, trying different combinations of different models.
But, from my experience, CC puts everything else to shame.
How does Cursor compare? Has anyone found an Aider combination that works as well?
It was pretty much first for CLI agents and had a benchmark that was the go to at the start of LLM coding. Now the benchmark doesn't get updated and aider never gets a mention in talking about CLI tools till now.
When will people realize this is the same as vendor lock-in?
"Maybe if I spend more money on the max plan it will be better" > no it will be the same "Maybe if I change my prompt it will work" > no it will be the same "Maybe if I try it via this API instead of that API it will improve" > no it will be the same.
Claude, ChatGPT, Gemini etc all of these SOTA models are carefully trained, with platforms carefully designed to get you to pay more for "better" output, or try different things instead of using a different product.
It's to keep you in the ecosystem and keep you exploring. There is a reason you can't see the layers upon layers of scaffolding they have. And there's a reason why after 2 weeks post major update, the model is suddenly "bad" and "frustrating". It's the same reason its done with A/B testing, so when you complain, someone else has no issues, when they complain, you have no issues. It muddies the water intentionally.
None of it is because you're doing anything wrong, it's not a skill issue, it's a careful strategy to extract as much engagement and money from customers as possible. It's the same reason they give people who buy new gun skins in call of duty easier matches in matchmaking for the first couple games.
The only mistake you made was paying MORE, hoping it would get better. It won't, that's not what makes them money. Making people angry and making people waste their time, while others have no issues, and making them explore and try different things for longer so they can show to investors how long people use these AI tools is what makes them money.
When competitors have a better product these issues go away When a new model is released these issues don't exist
I was paying a ton of money for claude, once I stopped and cancelled my subscription entirely, suddenly sonnet 4.6 is performing like opus and I don't have prompts using 10% of my quota in one message despite being the same complexity.
Codex consumes way fewer resources and is much snappier.
OpenCode is great though, and can (for now) use an OpenAI subscription.
TDD was never really my natural style, but LLMs are great at generating the obvious test cases quickly. That lets me spend more of my attention on the edge cases, the invariants, and the parts that actually need judgment.
Frontend is another area where they help a lot. It’s not my strongest side, so pairing an LLM with shadcn/ui gets me to a decent, responsive UI much faster than I would on my own. Same with deployment and infra glue work across Cloudflare, AWS, Hetzner, and similar platforms.
I’m basically a generalist with stronger instincts in backend work, data modeling, and system design. So the value for me is that I can lean into those strengths and use LLMs to cover more ground in the areas where I’m weaker.
That said, I do think this only works if you’re using them as leverage, not as a substitute for taste or judgment.
Here’s what I’ve done to mostly fix my usage issues:
* Turn on max thinking on every session. It save tokens overall because I’m not correcting it of having it waste energy on dead paths.
* keep active sessions active. It seems like caches are expiring after ~5 minutes (especially during peak usage). When the caches expire it sees like all tokens need to be rebuilt this gets especially bad as token usage goes up.
* compact after 200k tokens as soon as I reasonably can. I have no data but my usage absolutely sky rockets as I get into longer sessions. This is the most frustrating thing because Anthropic forced the 1M model on everyone.
Good chance it's not real or misdiagnosed. But it gives me some degree of schadenfreude to see it happening to the Claude Code repo.
Vibes, indeed.
They also silently raised the usage input tokens consume so it's a double whammi.
> * keep active sessions active. It seems like caches are expiring after ~5 minutes (especially during peak usage). When the caches expire it sees like all tokens need to be rebuilt this gets especially bad as token usage goes up.
Is this as opaque on their end as it sounds, or is there a way to check?
This is definitely true. Ever since I realized there is an /effort max option I am no longer fighting it that much and wasting hours.
At least up until recently the 1M model was separated into /model opus[1M]
Ask claude code to give you all the memories it has about you in the codebase and prune them. There is a very high chance that you have memories in there which are contradicting each other and causing bad behavior. Auto-saved memories are a big source of pollution and need to be pruned regularly. I almost don't let it create any memories at all if I can help it.
Disclaimer: I'm also burning through usage very quickly now - though for different reasons. Less than 48 hours to exhaust an account, where it used to take me 5-6 days with the same workload.
For those not in the Google Gemini/Antigravity sphere, over the last month or so that community has been experiencing nothing short of contempt from Google when attempting to address an apparent bait and switch on quota expectations for their pro and ultra customers (myself included). [1]
While I continue to pay for my Google Pro subscription, probably out of some Stockholm Syndrome, beaten wife level loyalty and false hope that it is just a bug and not Google being Google and self-immolating a good product, I have since moved to Kiro for my IDE and Codex for my CLI and am as happy as clam with this new setup.
[1] https://github.com/google-gemini/gemini-cli/issues/24937
However, I've found that the flash quota is much more generous. I have been building a trio drive FOC system for the STM32G474 and basically prompting my way through the process. I have yet to be able to run completely out of flash quota in a given five hour time window. It is definitely completing the work a lot faster than I could do myself -- mainly due to its patience with trying different things to get to the bottom of problems. It's not perfect but it's pretty good. You do often have to pop back in and clean up debris left from debugging or attempts that went nowhere, or prompt the AI to do so, but that's a lot easier than figuring things out in the first place as long as you keep up with it.
I say this as someone who was really skeptical of AI coding until fairly recently. A friend gave me a tutorial last weekend, basically pointing out that you need to instruct the AI to test everything. Getting hardware-in-loop unit tests up and running was a big turning point for productivity on this project. I also self-wired a bunch of the peripherals on my dev board so that the unit tests could pretend to be connected to real external devices.
I think it helps a lot that I've been programming for the last twenty years, so I can sometimes jump in when it looks like the AI is spinning its wheels. But anyway, that's my experience. I'm just using flash and plan mode for everything and not running out of the $20/mo quota, probably getting things done 3x as fast as I could if I were writing everything myself.
Valuation have already reached point where these companies can run their nuclear power station, fund developement of new hardware and techniques and boost capabilities of their models by 10x
How many companies will generate profit in the end, what will happen with all those power stations and data centers ?
A huge difference is early computers were not subsidized. It took decades until most people could afford to own a computer at home.
Can confirm, I initially enjoyed the 5-hour limits on Gemini CLI and Antigravity so much that I paid for a full year, thinking it was a great decision
In the following months, they significantly cut the 5-hour limits (not sure if it even exists anymore), introduced the unrealistically bad weekly limit that I can fully consume in 1-2 hour, introduced the monthly AI credits system, and added ads to upgrade to Ultra everywhere
At the very least the Gemini mobile app / web app is still kinda useful for project planning and day-to-day use I guess. They also bumped the storage from 2TB to 5TB, but I don't even use that
There's a lot of angles you take from that as a starting point and I'm not confident that I fully understand it, so I'll leave it to the reader.
The parent's argument is that the marginal cost of inference is minimal. However, the fundamental flaw is that he's separating inference from the high cost frontier models. It's a cross-subsidy that can't be ignored.
IMO they need as many users before their IPO - then the changes will really begin.
I'm dying to see S-1 filing for Anthropic or OpenAI. I don't actually think inference is as cheap as people say if you consider the total cost (hardware, energy, capex, etc)
Huh?
The reddit summary comment makes no sense. How are they getting revenues without ads or paying customers?
"After" makes more sense.
FTA:
>The company has yet to show a profit and is searching for ways to make money to cover its high computing costs and infrastructure plans.
You also can't put ads in code completion AIs because the instant you do the utility to me of them at work drops to negative. Guess how much money companies are going to pay for negative-value AIs? Let's just say it won't exactly pay for the AI bubble. A code agent AI puts an ad for, well, anything and the AI accidentally puts it into code that gets served out to a customer and someone's going to sue. The merits of the case won't matter, nor the fact the customer "should have caught it in review", the lawsuit and public reputation hit (how many people here are reading this and salivating at the thought of being able to post an angrygram about AIs being nothing but ad machines?) still cost way too much for the AI companies creating the agents to risk.
Looks like enshittification on steroids, honestly.
https://github.com/anthropics/claude-code/issues/46829
> The March 6 change makes Claude Code cheaper, not more expensive. 1h TTL for every request could cost more, not less
Feels very AI. > Restore 1h as the default / expose as configurable? 1h everywhere would increase total cost given the request mix, so we're not planning a global toggle.
They won't show a toggle because it will increase costs for some unknown percentage of requests?
There must be a better way to do this. The consumer option is the pricing difference. If they’d make cache writes the same price as regular writes, that would solve the whole problem. If you really want to push it, use that pricing only for requests where number of cache hits > 0 (to avoid people setting this flag without intent to use it), and you solved the whole issue.
And if you can't stomach OpenAI, GLM 5.1 is actually quite competent. About Opus 4.5 / GPT 5.2 quality.
Anthropic sells you 'knowledge' in the form of 'tokens' and you spend money rolling the dice, spinning the roulette wheels and inserting coins for another try. They later add limits and dumb down the model (which are their gambling machines) of their knowledge for you to pay for the wrong answers.
Once you hit your limit or Anthropic changes the usage limits, they don't care and halt your usage for a while.
If you don't like any of that, just save your money and use local LLMs instead.
Fair transactions involve fair and transparent measurements of goods exchanged. I'm going to cancel my subscription this month.
Running non deterministic software for deterministic tasks is still an area for efficiency to improve.
I have a day job, a side business, actively trade shares options and futures, and have a few energy credit items.
All were given the same copied folder containing all the needed documents to compose the return, and all were given the same prompt. My goal was that if all three agreed, I could then go through it pretty confidently and fill out the actual submission forms myself.
5.4 nailed it on the first shot. Took about 12 minutes.
3.1 missed one value, because it decided to only load the first 5 pages of a 30 page document. Surprisingly it only took about 2 minutes to complete though. A second prompt and ~10 seconds corrected it. GPT and Gemini now were perfectly aligned with outputs.
4.6 hit my usage limit before finishing after running for ~10 minutes. I returned the next day to have it finish. It ran for another 5 minutes or so before finishing. There were multiple errors and the final tax burden was a few thousand off. On a second prompt asking to check for errors in the problem areas, it was able to output matching values after a couple more minutes.
For my first time using CC and 4.6 (outside of some programming in AG), I am pretty underwhelmed given the incessant hype.
My only point here is it sure seems the same activity / use case can have wildly different results across sessions or users. Customer support and product development in the age of non-deterministic software is a strange, strange beast.
1. Nuke all other versions within /.local/share/claude/versions/ except 2.1.34. 2. Link ~/.local/bin/claude to claude -> ~/.local/share/claude/versions/2.1.34
This seems to have fixed my running out of quota issues quickly problems. I have periods of intense use (nights, weekends) and no use (day job). Before these changes, I was running out of quota rather quickly. I am on the same 100$ plan.
I am not sure adaptive thinking setting is relevant for this version but in the future that will help once they fix all the quota & cache issues. Seriously thinking about switching to Codex though. Gemini is far behind from what I have tried so far.
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000 export MAX_THINKING_TOKENS=31999 export DISABLE_AUTOUPDATER=1 export CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1
I'm curious what are people doing that is consuming your limits? I can't imagine filling the $200 a month plan unless I was essentially using Claude code itself as the api to mass process stuff? For basic coding what are people doing?
As of now, I’m consistently hitting my 5 hour limit in less than 1 hour during N/A business hours. I’m getting to the point where I basically can’t use CC for work unless I work very early or late in the day.
If you start to parallelize and you have permission prompts on you're likely missing cache windows as well.
Think Twitter's fail-whale problems. Sometimes you are lucky, sometimes you aren't. Why? We won't know until Anthropic figures it out and from the outside it sure looks like they're struggling.
Either they decimated the limits internally, or they broke something.
Tried all the third-party tricks (headroom, etc.), switched to 200k context window, switched back to 4.5.
I hope 4.5 will help, but the rest of the efforts didn’t move the needle much
I suspect I was getting rate limited very aggressively on Thursday last week. It honestly infuriated me, because I'm paying $200 a month for this thing. If it's going to rate limit me, at least tell me what it's doing instead of just making it seem like it's taking 12 hours to run through something that I would expect to be 15 minutes. The worst part is that it never even finished it.
My gut feeling is this is not enough money for them by far (not to mention their investors), and we'll eventually get ratcheted up inline with dev salaries. E.g. "look how many devs you didn't have to hire", etc.
https://docs.github.com/en/copilot/concepts/billing/copilot-...
This clearly isn't true for agentic mode though. This document is extremely misleading. VSCode has the `chat.agent.maxRequests` option which lets you define how many requests an agent can use before it asks if you want to continue iterating, and the default is not one. A long running session (say, implementing an openspec proposal) can easily eat through dozens of requests. I have a prompt that I use for security scanning and with a single input/request (`/prompt`) it will use anywhere between 17 and 25 premium requests without any user input.
The overall context windows are smaller with copilot I believe, but it dfoesnt appear to be hurting my work.
I'm using it for approx 4 hours a day most days. Generally one shotting fun ideas I thoroughly plan out in planning mode first, and I have my own verison of the idea->plan->analyse-> document implementation phases -> implement via agent loop. simulations, games, stuff-im-curious about and resurrecting old projects that never really got off the ground.
https://www.reddit.com/r/ClaudeAI/comments/1s4idaq/update_on...
It’s been unusable for me as my daily coding agent. I run out of credits in the pro account in an hour or so. Before that I had never reached the session limit. Switched back to Junie with Gemini/chatgpt.
Now a single question consistently uses around 15% of my quota
Once people won't be able to think anymore and business expect the level of productivity witnessed before, will have no choice but cough up whatever providers bill us.
Is that bad? After all, even if they hiked to price infinity, you wouldn't worse off than if AI didn't exist because you could still code by hand. Moreover if it's really in a "business" (employment?) context, the tools should be provided by your employer, not least for compliance/security reasons. The "expectation" angle doesn't make sense either. If it's actually more efficient than coding by hand, people will eventually adopt it, word will get around and expectations will rise irrespective of whether you used it or not.
My argument was not about AI. Rather about the practice of Anthropic and the likes.
This was addressed by the words that you perhaps mistakenly omitted from your quote:
> Once people won't be able to think anymore...
People who aren't able to think anymore, can't still code by hand. Think "Idiocracy".
OpenAI and Anthropic have been getting stingy with their plans and it's only it's been what, 1 year, maybe 2 since vibecoding was widely used in a professional context (ie. not just hacking together a MVP for a SaaS side hustle in a weekend)? I doubt people are going to lose their ability to think in that timespan.
Online advertising is now ubiquitous, terrible, and mandatory for anyone who wants to do e-commerce. You can't run a mass-market online business without buying Adwords, Instagram Ads, etc.
AI will be ubiquitous, and then it will get worse and more expensive. But we will be unable to return to the prior status quo.
I suspect more customers are lost a lot faster when you increase prices, compared to enshittifying the product. It's also a lot more directly attributable to an action, and thus easier for an executive to be blamed if they choose the former over the latter.
It occurred to me an outright rejections of these tools is brewing but can't quite materialise yet.
OP wrote "I pay for the lowest plan", so that’s the $20/mo one.
a) quotas will get restricted
b) the subscription plan prices will go up
c) all LLMs will become good enough at coding tasks
I just open sourced a coding agent https://github.com/dirac-run/dirac
The entire goal is to be token efficient (over 50% cheaper), and by extension, take advantage of LLM's better reasoning at shorter context lengths
This really started as an internal side project that made me more productive, I hope it will help others too. Apache 2.0
Currently it still can't compete the subsidized coding plan rates using Anthropic API pricing though (even though it beats CC while both use API key), which tells me that all subscription plan operators are losing money on such plans
I don’t understand why people insist on these subscriptions and CC.
Fanboyism is a bit too hardcore at this point. Apple fanboys look extremely prudent compared to this behavior.
Please unsubscribe to these services and see how they perform:
"Maybe if I spend more money on the max plan it will be better" > no it will be the same "Maybe if I change my prompt it will work" > no it will be the same "Maybe if I try it via this API instead of that API it will improve" > no it will be the same.
Claude, ChatGPT, Gemini etc all of these SOTA models are carefully trained, with platforms carefully designed to get you to pay more for "better" output, or try different things instead of using a different product.
It's to keep you in the ecosystem and keep you exploring. There is a reason you can't see the layers upon layers of scaffolding they have. And there's a reason why after 2 weeks post major update, the model is suddenly "bad" and "frustrating". It's the same reason its done with A/B testing, so when you complain, someone else has no issues, when they complain, you have no issues. It muddies the water intentionally.
None of it is because you're doing anything wrong, it's not a skill issue, it's a careful strategy to extract as much engagement and money from customers as possible. It's the same reason they give people who buy new gun skins in call of duty easier matches in matchmaking for the first couple games.
Stop paying more, stop buying these pro max plans, hoping it will get better. It won't, that's not what makes them money. Making people angry and making people waste their time, while others have no issues, and making them explore and try different things for longer so they can show to investors how long people use these AI tools is what makes them money.
When competitors have a better product these issues go away When a new model is released these issues don't exist
I was paying a ton of money for claude, once I stopped and cancelled my subscription entirely, suddenly sonnet 4.6 is performing like opus and I don't have prompts using 10% of my quota in one message despite being the same complexity.
I am tired of all the astroturf articles meant to blame the user with “tips” for using fewer tokens. I never had to (still don’t) think of this with Codex, and there has been a massive, obvious decline between Claude 1 month ago and Claude today.
I am getting bored of having to plan my weekends around quota limit reset times...
You can run smaller models on much more modest hardware but they aren't yet useful for anything more than trivial coding tasks. Performance also really falls off a cliff the deeper you get into the context window, which is extra painful with thinking models in agentic use cases (lots of tokens generated).
To try things out you can use llama.cpp with Vulkan or even CPU and a small model like Gemma 4 26B-A4B or Gemma 4 31B or Qwen 3.5 35-A3B or Qwen3.5 27B. Some of the smaller quants fit within 16GB of GPU memory. The default people usually go with now is Q4_K_XL, a 4-bit quant for decent performance and size.
https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF
https://huggingface.co/unsloth/gemma-4-31B-it-GGUF
https://huggingface.co/unsloth/Qwen3.5-35B-A3B-GGUF
https://huggingface.co/unsloth/Qwen3.5-27B-GGUF
Get a second hand 3090/4090 or buy a new Intel Arc Pro B70. Use MoE models and offload to RAM for best bang for your buck. For speed try to find a model that fits entirely within VRAM. If you want to use multiple GPUs you might want to switch to vLLM or something else.
You can try any of the following models:
High-end: GLM 5.1, MiniMax 2.7
Medium: Gemma 4, Qwen 3.5
https://unsloth.ai/docs/models/minimax-m27
https://unsloth.ai/docs/models/glm-5.1
https://unsloth.ai/docs/models/gemma-4
https://unsloth.ai/docs/models/qwen3.5
https://github.com/ggml-org/llama.cpp
With that said, I pay the Pro subscription (20/mo) and I hit limits maybe 2/3 times over a period of 4 months building a simple running app in Python. I’d not call it production ready but it’s not nothing either.
If people were considerably more willing to aggressively prune their context and scope tasks well, they could get a lot more done with it, at least in my experience. Anthropic can’t really fix anything because the underlying model architecture can’t be “patched”. But I definitely feel a lot of people can’t wrap their heads around the new paradigms needed to effectively prompt these models.
Additionally, opting out is always an option… but these types of issues feel more like laziness than real, structural issues with the model/harness…
> People need to understand a few things: vague questions make the models roam endlessly “exploring” dead ends.
> If people were considerably more willing to aggressively prune their context and scope tasks well, they could get a lot more done with it
If this were the problem, people would've encountered this when they started using Claude. The problem is not that they can't get anything done. It's being able to get things done for months, but suddenly hitting rate limits way too easily and response quality being clearly degraded, so they can't get things done that used to be possible.
The ecosystem is evolving super quickly so, our own experiences and workflows must keep adapting with it to experiment, find limitations and arrive at the "tightest possible scope" that still allows you to get things done, because it is possible.
Another example: pre-paid monthly subscription aggregates usage towards web and Claude Code, for eg. So if you're checking for holiday itineraries over your lunch break, then decide to sit down and ask a team of agents to refactor a giant codebase with hundreds or thousands of files, context will be exhuasted quickly, etc, etc.
I see this "context economy" as a new way of managing your "mental models": every token counts, and every token must bear its weight for the task at hand, otherwise, I'm "wasting budget". I am also still learning how to operate in this new way of doing things, and, while there have been genuine issues with Claude Code, not every single issue that people encounter is an upstream problem.
No they can't. When I buy an annual subscription and prepay for the year, they can't just go "ok now you get one token a month" a day in. I bought the plan as I bought it. They can't change anything until the next renewal.
So no new models, no new features?
If they're selling me compute and bundling the features in, they better not go back on the compute I paid for.
If your limits stay "the same", but you then use Opus 4.6, your quota will be exhausted much faster, it's just how it works.
Note that some features are simply NOT made for these Pro, Max, Max 5x or whatever pre-paid plans. I'm pretty sure this is by design and not an accident or a bug: If you have 6/7 MCP servers configured or if you want to use this new feature of "Agent Teams", you will exhaust your entire quota before ANY work is even done. This is not a bug. Each agent has its own context window and tools and they all count separately.
MCP servers, when active, add A LOT of context to your sessions before you even use them, etc, etc.
It feels to me that people want to have their cake and eat it too, but, that would NOT be a sustainable business model. You can not complain about the tools if you can't understand them in-depth.
I want to state that I don't think Anthropic are fully aware of the ramifications that ANY small change in ANY of their models might have, because their entire ecosystem is a bit messy atm, but, I'm certain they're aware that if people dont like it, they will cancel the subscription and flock to a competitor very quickly, since there's no real moat anymore. So, it's in their own interest to keep things minimally usable even on the "cheaper plans".
I have seen people with 5-10 "active MCP servers" that they "wanted to try out" then they forget about it and wonder why their context is always full... Cmon... that's almost bad faith.
I don't fully defend Anthropic as they've had several issues with degraded model quality after releasing "the latest model", and CLI usability that cost me real money and real tokens, so, there's a lot of room for improvement, but, to claim that quota gets exhausted after 1h it points out to either some forgotten MCP servers, skills or giant files being accidentally read in, or some sort of mis-use which these limits were put in place to prevent exactly.
There's a very thin line between: quota is exhuasted on a regular, normal session after 1h and I think there's a bug versus I had 3-4 MCP servers active that I am not using at all but forgot to disable and my CLAUDE.md file is 1000 lines...
How is this normal?
But the opacity itself is a bit offensive to me. It feels shady somehow.
For context, with Google AI Pro, I can burn through the Antigravity weekly limit in 1-2 hours if I force it to use Gemini 3.1 Pro. Meanwhile Gemini 3 Flash is basically unlimited but frequently produces buggy code or fail to implement things how I personally would (felt like it doesn't "think" like a software dev)
I also tried VS Code + Cline + OpenRouter + MiniMax M2.7. It's quite cheap and seems to be better than Gemini 3 Flash, but it gets really pricy as the context fills up because prompt caching is not supported for MiniMax on OpenRouter. The result itself usually needs 3-6 revisions on average so the context fills up pretty often
Eventually I got Claude Max 5x to try for a month. VS Code + Claude Code extension on a ~15k lines codebase, model set to "Default", and effort set to "Max". So far it's been really good: 0-2 revisions on average, and most of the time it implements things exactly how I would or better. And, like I said, I can only consume 40-60% of the 5-hour limits no matter how hard I try
Granted, I'm not forcing it to use Opus like OP (nor do I use complicated skills or launch multiple tasks at the same time), but I feel like they really nailed the right balance of when to use which model and how to pass context between the them. Or at least enough that I haven't felt the need to force it to use Opus all the time
it has been reported that it behaves very differently depending on those factors, presumably because people are placed in best-effort buckets, who knows
In theory the /stats command tells you how many tokens you've used, which you could use to compute how much you are getting for your subscription, but in practice it doesn't contain any useful info, it may be counting what is printed to the terminal or something - my stats suggest my claude code usage is a tiny amount of tokens, but they must be an extremely underestimated token count, or they are charging much more for the subscription than the API per token (which is not supposed to be the case).
Last week's free extra usage quota shed some light on this. It seems like the reported tokens are probably are between 1/30th to 1/100th of the actual tokens billed, from looking at how they billed (/stats went up 10k tokens and I was billed $7.10). With the API it should be $25 for a million tokens.
There's this honeymoon period with Claude you experience for a month or two followed by a trough of disillusionment, and then a rebound after a model update (rinse and repeat). It doesn't help that Anthropic is experiencing a vicious compute famine atm.
It’s further frustrating that I have committed to certain project deadlines knowing that I’d be able to complete it in X amount of time with agent tooling. That agentic tooling is no longer viable and I’m scrambling to readjust expectations and how much I can commit to.
To add the fact we are being taken for fools with dramatic announcements, FOMOs messages. I even suspect some reaction farms are going on to boost post from people boasting Claude models.
These don't happen for codex. Nor for mistral. Nor for deepseek. It can't just be that Claude code is so much better.
There are open weight models that work perfectly fine for most cases, at a fraction of the cost. Why are more people not talking about those. Manipulation.
I often compare with Gemini. Sure those Google servers are super fast. But I can't see it better. Qwen and deepseek simply work better for me.
Haven't tested Mistral in a while, you may be right.
People try out and feel comfortable: using U.S models (I can see the logic), but mostly for brand recognition. Anthropic and OpenAi are the best aren't they? When the models jam they blame themselves.
The thing is, if it's going to be this expensive it's not going to be worth it for me. Then I'll rather do it myself. I'm never going to pay for a €100 subscription, that's insane. It's more than my monthly energy bill.
Maybe from a business standpoint it still makes sense because you can use it to make money, but as a consumer no way.
We're generating all of the code for swamp[1] with AI. We review all of that generated code with AI (this is done with the anthropic API.) Every part of our SDLC is pure AI + compute. Many feature requests every day. Bug fixes, etc.
Never hit the quota once. Something weird is definitely going on.
1: https://github.com/systeminit/swamp
But people who go > 5 minutes between prompts and see no cache, usage is eaten up quickly. Especially passing in hundreds of thousands of tokens of conversation history.
I know my quote goes a lot further when I sit down and keep sessions active, and much less far when I’m distracted and let it sit for 10+ minutes between queries.
It’s a guess. But n=1 and possible confirmation bias noted, it’s what I’m seeing.
What it does for you is simple: if you want to automate something, it does. Load the AI harness of your choice, tell it what to automate, swamp builds extensions for whatever it needs to to accomplish your task.
It keeps a perfect memory of everything that was done, manages secrets through vaults (which are themselves extensions it can write) and leaves behind repeatable workflows. People have built all sorts of shit - full vm lifecycle management, homelab setups, manage infrastructure in aws and azure.
What's also interesting is the way we're building it. I gave a brief description in my initial comment.
The sociotechnical stuff with System Initiative was made by your CEO? The guy who is really into music? And I don't even know how long that product was a thing before the pivot. Not long!
System Initiative was a thing for ~6.5 years. I talked to every person who ever used it or was interested in using it in the last 2.5 years. Thousands of them.
Swamp is better by every metric; has a lot more promise, is a lot more interesting.
Taking a second opinion has significantly helped me to design the system better, and it helped me to uncover my own and Claude blindspots.
Also, agree that, it spent and waist a lot of token on web search and many a times get stuck in loop.
Going forward- i will always use all 3 of them. Still my main coding agent is Claude for now.. but happy to see this field evolving so fast and it's easy to switch and use others on same project.
No network effects or lock in for a customer. Great to live in this period of time.
For general queries and investigation I will use whatever public/free model is available without being logged in. Not having a bunch of prior state stacked up all the time is a feature for me. This is essentially my google replacement.
For very specific technical work against code files, I use prepaid OAI tokens in VS copilot as a "custom" model (it's just gpt5.4).
I burn through maybe $30 worth of tokens per month with this approach. A big advantage of prepaying for the API tokens is that I can look at everything copilot is doing in my usage logs. If I use the precanned coding agent products, the prompts are all hidden in another layer of black box.
On the flip side- Using Opus with a baby billy freeman persona has never been more entertaining.
For something I spend all my time using- I’d rather iterate with Claude. The personality makes a big difference to me.
Honestly when I get codex to review the work that Claude does (my own or my coworker's) it consistently finds terrible terrible bugs, usually missing error handling / negative conditions, or full on race conditions in critical paths.
I don't trust code written by Claude in a production environment.
All AI code needs review by human, and often by other AIs, but Opus 4.6 is the worst. It's way too "yeet"
The opus models are for building prototypes, not production software.
GPT 5.4 in codex is also way more efficient with tokens or budget. I can get a lot more done with it.
I don't like giving money to sama, but I hate bugs even more.
Anthropic is not incentivized to reduce token use, only to increase it, which is what we are seeing with Opus 4.6 and now they are putting the screws on
It does seem like this new routing is worse for the consumer in terms of code quality and token usage somehow.
But like most challenges with claude, if you can just express them clearly, there are usually ways of optimizing further
What I wish for right now is for open-weight models and hardware companies (looking at you Apple) to make it possible to run local models with Opus 4.6-level intelligence.
@Anthropic I've cancelled my subscription. Good luck :)
It is hard now to hit the limit...
I strongly believe google's legs will allow it to sustain this influx of compute and still not do the rug-pull like OAI or Anthropic will be forced to do as more people come onboard the code-gen use case.
Since then, I've been seeing increased critique of Anthropic in particular (several front page posts on HN, especially past few days), either due to it being nerfed or just straight up eating up usage quota (which matches my personal experience). It appears that we're once again getting hit by enshittiffication of sorts.
Nowadays I rely a lot on LLMs on a daily basis for architecture and writing code, but I'm so glad that majority of my experience came from pre-AI era.
If you use these tools, make sure you don't let it atrophy your software engineering "muscles". I'm positive that in long run LLMs are here to stay. The jump in what you can now self-host, or run on consumer hardware is huge, year after year. But if your abilities rely on one vendor, what happens if you come to work one day and find out you're locked out of your swiss army knife and you can no longer outsource thinking?
[1] https://news.ycombinator.com/item?id=47066701
No FOMO
What I did instead is tune the prompt for gemma 4 26b and a 3090. Worked like a charm. Sometimes you have to run the main prompt and then a refinement prompt or split the processing into cases but it’s doable.
Now I’m waiting for anyone to put up some competition against NVIDIA so I can finally be able to afford a workstation GPU for a price less than a new kidney.
I’ve moved away from Claude and toward open-source models plus a ChatGPT subscription.
That setup has worked really well for me: the subscription is generous, the API is flexible, and it fits nicely into my workflow. GPT-5.4 + Swival (https://swival.dev) are now my daily drivers.
Either you are using it wrong or you are working in a totally different field.
> As the Codex promotion on Plus winds down today
Any highlights you can share here? I'm always looking to improve me setup.
Especially when it's on purpose.
Cache reads cost $0.31
Cache writes cost $105
Input tokens cost $0.04
Output tokens cost $28.75
The total spent in the session is $134.10, while the Pro Max 5x subscription is $100.
Even taking the Anthropics API pricing, we arrive at $80.58. Below the subscription price, but not by much.
It's just the end of the free tokens, nothing to see here. It's easy to feel like you're doing "moderate" or even "light" usage because you use so little input tokens, but those "agentic workflows" are simply not viable financially.
They inflated how much their tools burn tokens from day one pretty much,remember all the stupid research and reports Claude always wanted to do, no matter what you asked it. Other tools are much smarter so this is not such a big deal.
More importantly, these moves tend to reverberate in the industry, so I expect others will clamp down on usage a lot and this will spoil my joy of using AI without countring every token.
Burning tokens doesn't just wastes your allotment, it also wastes your time. This gave rise to turbo offering where you get responses faster but burn 2x your tokens.
Probably a combination of it being vibe coded shit and something in the backend I expect.
Demand is higher than supply it is just the start of bubble.
Everyone and their dog is burning tokens on stupid shit that would be freed up if they would ask to make deterministic code for the task and run the task. OpenAI, Anthropic are cutting free use and decreasing limits because they are not able to meet the demand.
When general public catches up with how to really use it and demand will fall and the today built supply will become oversupply that’s where the bubble will burst.
I say 5 more years.
however his response gaslights us because in the OPs opening post his math demonstrates this is not true, it shows reads 26x more so at least in his case the cache is not doing what the anthropic employee describes.
clearly we are being charged for less optimization here and being given the message (from my perspective by anthropic) that if you are in a special situation your needs don't matter and we will close your thread without really listening.
During core US business hours, I have to actively keep a session going or I risk a massive jump in usage while the entire thread rebuilds. During weekend or off-hours, I never see the crazy jumps in usage - even if I let threads sit stale.
It's also in the interest of the users to keep certain params private, we are meant to deduce that. Did you not ?
Are there any other $50B+ Valuation companies that care about special situations? If so, who?
But for high-ish quality translations of substantive texts, you typically want a harness that's pretty different from Claude Code. You want a glossary of technical terms or special names, a structured summary of the wider context, a concise style guide, and you have to chop the text into chunks to ensure nothing is missed. Even with super long context models, if you ask them to translate much at once they just translate an initial portion of it and crap out.
Are you using it for localization or short strings of text in an app? I wonder what you can do to get better results out of smaller models. I'm confident there's a way.
This is by design, of course. Anyone who has been paying even the slightest bit of attention knows these subscriptions are not sustainable, and the prices will have to go up over time. Quietly reducing the usage limits that they were never specific about in the first place is much easier than raising the prices of the individual subscription tiers, with the same effect.
If you want to know what kind of prices you'll be paying to fuel your vibe coding addiction in a few years, try out API pricing for a bit, and try not to cry when your 100$ credit is gone in 2 days.
I guess this is fitting when the person who submitted the issue is in "AI | Crypto".
Well there's no crying at the casino when, you exhaust your usage or token limit.
The house (Anthropic) always wins.