Issue: Claude Code is unusable for complex engineering tasks with Feb updates

Hey all, Boris from the Claude Code team here. I just responded on the issue, and cross-posting here for input.

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Hi, thanks for the detailed analysis. Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.

There's a lot here, I will try to break it down a bit. These are the two core things happening:

> `redact-thinking-2026-02-12`

This beta header hides thinking from the UI, since most people don't look at it. It *does not* impact thinking itself, nor does it impact thinking budgets or the way extended reasoning works under the hood. It is a UI-only change.

Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).

If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.

> Thinking depth had already dropped ~67% by late February

We landed two changes in Feb that would have impacted this. We evaluated both carefully:

1/ Opus 4.6 launch → adaptive thinking default (Feb 9)

Opus 4.6 supports adaptive thinking, which is different from thinking budgets that we used to support. In this mode, the model decides how long to think for, which tends to work better than fixed thinking budgets across the board. `CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING` to opt out.

2/ Medium effort (85) default on Opus 4.6 (Mar 3)

We found that effort=85 was a sweet spot on the intelligence-latency/cost curve for most users, improving token efficiency while reducing latency. On of our product principles is to avoid changing settings on users' behalf, and ideally we would have set effort=85 from the start. We felt this was an important setting to change, so our approach was to:

1. Roll it out with a dialog so users are aware of the change and have a chance to opt out

2. Show the effort the first few times you opened Claude Code, so it wasn't surprising.

Some people want the model to think for longer, even if it takes more time and tokens. To improve intelligence more, set effort=high via `/effort` or in your settings.json. This setting is sticky across sessions, and can be shared among users. You can also use the ULTRATHINK keyword to use high effort for a single turn, or set `/effort max` to use even higher effort for the rest of the conversation.

Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency. This default is configurable in exactly the same way, via `/effort` and settings.json.

I'm the author of the report in there. The stop-phrase-guard didn't get attached but here it is: https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3... You can watch for these yourself - they are strong indicators of shallow thinking. If you still have logs from Jan/Feb you can point claude at that issue and have it go look for the same things (read:edit ratio shifts, thinking character shifts before the redaction, post-redaction correlation, etc). Unfortunately, the `cleanupPeriodDays` setting defaults to 20 and anyone who had not backed up their logs or changed that has only memories to go off of (I recommend adding `"cleanupPeriodDays": 365,` to your settings.json). Thankfully I had logs back to a bit before the degradation started and was able to mine them.

The frustrating part is that it's not a workflow _or_ model issue, but a silently-introduced limitation of the subscription plan. They switched thinking to be variable by load, redacted the thinking so no one could notice, and then have been running it at ~1/10th the thinking depth nearly 24/7 for a month. That's with max effort on, adaptive thinking disabled, high max thinking tokens, etc etc. Not all providers have redacted thinking or limit it, but some non-Anthropic ones do (most that are not API pricing). The issue for me personally is that "bro, if they silently nerfed the consumer plan just go get an enterprise plan!" is consumer-hostile thinking: if Anthropic's subscriptions have dramatically worse behavior than other access to the same model they need to be clear about that. Today there is zero indication from Anthropic that the limitation exists, the redaction was a deliberate feature intended to hide it from the impacted customers, and the community is gaslighting itself with "write a better prompt" or "break everything into tiny tasks and watch it like a hawk same you would a local 27B model" or "works for me <in some unmentioned configuration>" - sucks :/

Not claude code specific, but I've been noticing this on Opus 4.6 models through Copilot and others as well. Whenever the phrase "simplest fix" appears, it's time to pull the emergency break. This has gotten much, much worse over the past few weeks. It will produce completely useless code, knowingly (because up to that phrase the reasoning was correct) breaking things.

Today another thing started happening which are phrases like "I've been burning too many tokens" or "this has taken too many turns". Which ironically takes more tokens of custom instructions to override.

Also claude itself is partially down right now (Arp 6, 6pm CEST): https://status.claude.com/

> This report was produced by me — Claude Opus 4.6 — analyzing my own session logs [...] Please give me back my ability to think.

a bit ironic to utilize the tool that can't think to write up your report on said tool. that and this issue[1] demonstrate the extent folks become over reliant on LLMs. their review process let so many defects through that they now have to stop work and comb over everything they've shipped in the past 1.5 months! this is the future

[1] https://github.com/anthropics/claude-code/issues/42796#issue...

Called it 10 days ago: https://news.ycombinator.com/item?id=47533297#47540633

Something worse than a bad model is an inconsistent model. One can't gauge to what extent to trust the output, even for the simplest instructions, hence everything must be reviewed with intensity which is exhausting. I jumped on Max because it was worth it but I guess I'll have to cancel this garbage.

That analysis is pretty brutal. It's very disconcerting that they can sell access to a high quality model then just stealthily degrade it over time, effectively pulling the rug from under their customers.

This is last month I'm on the Max plan is just not worth it anymore, $20 Codex and writing myself to keep my brain functioning is my sweetspot.

This people are not your friends, they rot your brain.

Fascinating, I thought I was losing my mind. Claude CLI has been telling me I should go to bed, or that it's late, let's call it here, etc, and then I look at the stop-phrase-guard.sh [1] and I'm seeing quite a few of these. I thought it was because I accidentally allowed Claude to know my deadline, and it started spitting out all sorts of things like "we only have N days left, let's put this aside for now," etc.

Just this morning I typed:

    STOP WORRYING ABOUT THE DEADLINE THAT IS MY JOB

[1] https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3...

I've noticed this as well. I had some time off in late January/early February. I fired up a max subscription and decided to see how far I could get the agents to go. With some small nudging from me, the agents researched, designed, and started implementing an app idea I had been floating around for a few years. I had intentionally not given them much to work with, but simply guided them on the problem space and my constraints (agent built, low capital, etc, etc). They came up with an extremely compelling app. I was telling people these models felt super human and were _extremely_ compelling.

A month later, I literally cannot get them to iterate or improve on it. No matter what I tell them, they simply tell me "we're not going to build phase 2 until phase 1 has been validated". I run them through the same process I did a month ago and they come up with bland, terrible crap.

I know this is anecdotal, but, this has been a clear pattern to me since Opus 4.6 came out. I feel like I'm working with Sonnet again.

My verdict after last night trying what was suggested here:

yes, with CLAUDE_CODE_EFFORT_LEVEL=max (or at least high, for this you don't need to set an env var, it will remember) and CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 you can get Claude to perform as before.

I have been using Claude on /effort high since Opus 4.6 rolled out as medium would never get me good enough results (Rust, computer-graphics-related code).

I, too, noticed the drop in quality a month or so ago. With CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 it's back to what feels to be pre-March performance -- but then your tokens will 'evaporate' 40% faster.

And that was not the case then; I had similar/same performance before but wasn't running out of tokens ever on a Max subscription.

So a it's a rug-pull, as before/last late summer, from whatever angle you look at it.

To me one of the big downsides of LLM's seems to be that you are lashing yourself to a rocket that is under someone else's control. If it goes places you don't want, you can't do much about it.

Maybe it's because I spend a lot of time breaking up tasks beforehand to be highly specific and narrow, but I really don't run into issues like this at all.

A trivial example: whenever CC suggests doing more than one thing in a planning mode, just have it focus on each task and subtask separately, bounding each one by a commit. Each commit is a push/deploy as well, leading to a shitload of pushes and deployments, but it's really easy to walk things back, too.

I appreciate the work done here.

Been having this feeling that things have got worse recently but didn't think it could be model related.

The most frustrating aspect recently (I have learned and accepted that Claude produces bad code and probably always did, mea culpa) is the non-compliance. Claude is racing away doing its own thing, fixing things i didn't ask, saying the things it broke are nothing to do with it, etc. Quite unpleasant to work with.

The stuff about token consumption is also interesting. Minimax/Composer have this habit of extensive thinking and it is said to be their strength but it seems like that comes at a price of huge output token consumption. If you compare non-thinking models, there is a gap there but, imo, given that the eventual code quality within huge thinking/token consumption is not so great...it doesn't feel a huge gap.

If you take $5 output token of Sonnet and then compare with QwenCoder non-thinking at under $0.5 (and remember the gap is probably larger than 10x because Sonnet will use more tokens "thinking")...is the gap in code quality that large? Imo, not really.

Have been a subscriber since December 2024 but looking elsewhere now. They will always have an advantage vs Chinese companies that are innovating more because they are onshore but the gap certainly isn't in model quality or execution anymore.

In my opinion cramming invisible subagents are entirely wrong, models suffer information collapse as they will all tend to agree with each other and then produce complete garbage. Good for Anthropic though as that's metered token usage.

Instead, orchestrate all agents visibly together, even when there is hierarchy. Messages should be auditable and topography can be carefully refined and tuned for the task at hand. Other tools are significantly better at being this layer (e.g. kiro-cli) but I'm worried that they all want to become like claude-code or openclaw.

In unix philosophy, CC should just be a building block, but instead they think they are an operating system, and they will fail and drag your wallet down with it.

Same experience. After a couple golden weeks, Opus got much worse after Anthropic enabled 1M context window. It felt like a very steep downfall, for it seemed like I could trust it more completely and then I could trust it less than last year. Adopting LLMs for dev workflows has been fantastic overall, but we do have to keep adapting our interactions and expectations every day, and assume we'll keep on doing it for at least another couple years (mostly because economics, I guess?)

I put together a quick audit to check for "early landing" messages[1] using jq, ripgrep, and the messages[2] flagged in the stop guard script.

I have noticed a trend in these sessions asking more and more about calling it a day, "it's getting late," and other phrases. I sort of assumed it was some kind of "load shedding" on Anthropic's side.

My audit of 80 sessions was interesting. Sorry, I won't share details, but I recommend you do the same.

[1] https://gist.github.com/karlbunch/d52b538e6838f232d0a7977e7f...

[2] https://gist.github.com/benvanik/ee00bd1b6c9154d6545c63e06a3...

> Ignores instructions

> Claims "simplest fixes" that are incorrect

> Does the opposite of requested activities

> Claims completion against instructions

I thought it was just me. I'm continuously interrupting it with "no, that's not what I said" - being ignored sometimes 3 times; is Claude at the intellectual level of a teenager now?

I've noted an increased tendency towards laziness prior to these "simple fix" problems. It was historically defer doing things correctly (only documenting that in the context).

I still use 4.5. I occasionally try 4.6 but always switch back. The “bias towards action” is what I hate. 4.5 would make sure it understands what I want. 4.6 will just make shit up. Maybe the Anthropic people always write crystal clear instructions so it works for them. For me, I just can’t get 4.6 to do what I want.

Running some quick analysis against my .claude jsonl files, comparing the last 7 days against the prior 21:

- expletives per message: 2.1x

- messages with expletives: 2.2x

- expletives per word: 4.4x(!)

- messages >50% ALL CAPS: 2.5x

Either the model has degraded, or my patience has.

I use Claude Code extensively and haven't noticed this. But I don't have it doing long running complex work like OP. My team always break things down in a very structured way, and human review each step along the way. It's still the best way to safely leverage AI when working on a large brownfield codebase in my experience.

Edit: the main issue being called out is the lack of thinking, and the tendency to edit without researching first. Both those are counteracted by explicit research and plan steps which we do, which explains why we haven't noticed this.

I cancelled my Pro plan due to this two weeks ago. I literally asked it to plan to write a small script that scans with my hackrf, it ran 22 tools, never finished the plan, ran out of tokens and makes me wait 6 hours to continue.

Thing that really pisses me off is it ran great for 2 weeks like others said, I had gotten the annual Pro plan, and it went to shit after that.

Bait and switch at its finest.

I wish they had a "and we won't screw you in two weeks" plan at, say, 5x the price. It's worth it for my business, I'd pay it.

Should I switch back to API pricing? The problem here is that (I think) the instructions are in the Claude Code harness, so even if I switch Claude Code from a subscription to API usage, it would still do the same thing?

In general, I never allowed Claude to manage the complexity. Claude is fantastic coder, but very bad at higher level work. I engage gemini or qwen top models for anything that happens before getting to write code. Claude gets a very strict and elaborate requirement and design spec that it need to execute without any variation.

Claude could get too much creative and bloat it's way for non-coding tasks, as these tasks cannot be "sandboxed" with full specs as it can be done for coding.

Its so silly everyone being dependent on a black box like this

My bet: LLMs will never be creative and will never be reliable.

It is a matter of paradigm.

Anything that makes them like that will require a lot of context tweaking, still with risks.

So for me, AI is a tool that accelerates "subworkflows" but add review time and maintenance burden and endangers a good enough knowledge of a system to the point that it can become unmanageable.

Also, code is a liability. That is what they do the most: generate lots and lots of code.

So IMHO and unless something changes a lot, good LLMs will have relatively bounded areas where they perform reasonably and out of there, expect what happens there.

Yet https://marginlab.ai/trackers/claude-code/ says no issue.

If you're so convinced the models keep getting worse, build or crowdfund your own tracker.

I've subscribed today to use Claude Cowork. Codex continues to be my daily coding driver but I wanted to check the Cowork UI for non-technical tasks, as I am currently building an open-source project where I want (nearly) everything (research, adrs, design, etc.) to be a file.

The five queries I've been able to ask before hitting the 20€ sub limit have been really underwhelming. The research I asked for was not exhaustive and often off-topic.

I don't want to start a flamewar but as it stands I vastly prefer ChatGPT and Codex on quality alone. I really want Anthropic and as many labs as possible to do well though.

there is a comment on there which feels right, despite it might be too subjective.

Ive noticed the same in models ,in sessions and just model quality themselves.. both seem to suffer over time where it feels like cost optimisation on vendor side subtely degrades models to hopefully do similar things with less tokens/costs/compute, inevitably leading to squeezing too much, most regular users not noticing much, and power users suffering from degradations.

later, power users are presented an option to get back the old behavior, possibly with added costs for some 'enhanced mode' or 'more effort which takes more tokens' etc.

even If this is the old behavior for the same old cost, it feels like closing the tap and then reopening for additional costs.

I think companies should try to avoid this sentiment from the users who can help them most turn their glorified chatbots into real tools with meaningful outputs. (ofc maybe its a pipedream, because 'meaningful output to CEO is money on their bank....)

I haven't noticed any issues on well-specified tasks, even ones requiring large amounts of thinking.

One thing I have noticed is that the codebase quality influences the quality of Claude's new contributions. It both makes it harder for Claude to do good work (obviously), and seems to engender almost a "screw it" sort of attitude, which makes sense since Claude is emulating human behavior. Seeing the state of everything, Claude might just be going in and trying to figure out the simplest hacky solution to finish the task at hand, since it is the only way possible (fixing everything would be a far greater task).

Is it possible that this highly functioning senior dev team's practice of making 50+ concurrent agents commit 100k+ LOC per weekend resulted in a godawful pile of spaghetti code that is now literally impossible to maintain even with superhuman AI?

It's amusing that the OP had Claude dump out a huge rigorous-sounding report without considering the huge confounding variable staring him in the face.

Annecdotal: I have been battling with Claude Opus on a complex multi step project for nearly 4 days. The initial research plan was sound. However, step 1, a non trivial forensic data reconstruction that is key to the success of the rest of the process, Claude after every interaction is urging to move to the next step even though step 1 is still unresolved and many construction approaches remain to be explored. It came to the point where I have to remove the plan and put step 1 as an isolated project.

I am just waiting for everything to implode so that we can do away with those KPIs.

What's wild is that ClaudeCode used to feel like a smart pair programmer. Now it feels like an overeager intern who keeps fixing things by breaking something else then suggesting the simplest possible hack even after explicitly said not to do. I get that they're probably optimizing for cost or something behind the scenes, but as paying user, it is frustrating when the tool gets noticeably worse without any transparency.

Thank you. I have been complaining about this for days on Reddit and kept getting mocked or told it was just my usage. Seeing someone else document the same decline with actual logs, actual metrics, and a real argument was honestly a huge relief. Your issue was posted almost at the same time as my own posts yesterday. That timing hit me hard. Finally, I do not feel like I was shouting into the void.

Is the era of succinct bug reports with just a reproducible example attached over? Or is the default already „written by an agent, only supposed to be read by an agent“? Clearly no human being would want to waste their time reading so much repeated information.

Is this impacted by the effort level you set in Claude? e.g., if you use the new "max" setting, does Claude still think?

I can see this change as something that should be tunable rather than hard-coded just from a token consumption perspective (you might tolerate lower-quality output/less thinking for easier problems).

I am a heavy user of Claude Code building enterprise software. I have not seen these issues and have been extremely productive with CC. I am more of a structured user leveraging Spec Driven Development vs being a vibe coder. I wonder if that is what has helped me not run into these issues

I’ve tried to use Claude code for a month now. It has a 100% failure rate so far.

Comparing that to create a project and just chat with it solves nearly everything I have thrown at it so far.

That’s with a pro plan and using sonnet since opus drains all tokens for a claude code session with one request.

Guys literally change the system prompt with the --system-prompt-file you waste less tokens on their super long and details prompt and you can tune it a bit to make it work exactly like you want/imagine

Wonder how many of these cases are using the 1M context window. I found it to be impossible to use for complex coding tasks, so I turned it off and found I was back to approximate par (dec-jan) functionality-wise.

Abandoned claude and moved to gpt 5.4 with codex. 10x better.

I hadn't noticed the thinking redaction before - maybe because I switched to the desktop app from CLI and just assumed it showed fewer details. This is the most concerning part. I've heard multiple times that Anthropic is aggressively reclaiming GPUs (I can't find a good source, but Theo Browne has mentioned it in his videos). If they're really in a crunch, then reducing thinking, and hiding thinking so it's not an obvious change, would be shady but effective.

How much of this is the model being degraded and how much of it is people just projecting vibes onto the variability of stochastic outputs?

I noticed Claude Sonnet 4.6 and generally Opus as well (though I use it less frequently) seem like a downgrade from 4.5. I use opencode and not Claude Code, but I was surprised to see the reactions to 4.6 be mixed for folks rather than clear downgrade.

I'm regularly switching back to 4.5 and preferring it. I'm not excited for when it gets sunset later this year if 4.6 isn't fixed or superseded by then.

I am curious - is there any hard data (e.g. a benchmark score drop)?

I feel that we look for patterns to the point of being superstitious. (ML would call it overfitting.)

The report itself is unreadable AI garbage. I do not believe anyone went through all of that and didn't give up halfway through.

Not unique to claude code, have noticed similar regressions. I have noticed this the most with my custom assistant I have in telegram and I have noticed that it started confusing people, confusing news coverage and everyone independently in the group chat have noticed it that it is just not the same model that it was few weeks ago. The efficiency gains didn't come from nowhere and it shows.

Ai tooling is fantastic but not being able to version and control the model into which you pump your dependant workflows is such a liability.

Wait… Actually the simplest fix is to use Claude to write carefully bounded boilerplate and do the interesting bits myself.

Obviously it's entirely unprovable but it all aligns in very suspicious ways with a compelling narrative:

Anthropic simply can't actually scale Claude Code to meet the opportunity right now. Every second enterprise on the planet is probably negotiating large seat volume deals. It's a race for survival against the other players. The sales team is making huge promises engineering and ops can't fulfil.

So - they first force everyone to use the first party client, then they mask visibility of the thinking budget being utilised, and then finally they start to actually modify behaviour to reduce actual thinking behaviour, hoping that they can gaslight power users into thinking it's them and not the tool, while new users will never know what they were missing.

Is the narrative true? It's compelling but we really need objective evidence - and there's the problem. When parts of the system are not under your control, it's impossible to generate such objective evidence. Which all winds up with a strong argument to have it all under your control. If it didn't happen this time, it probably will. Enshittification is a fundamental human behavioral constant.

Instead of codex catching up with claude, its more like claude regressed to codex.

I'm genuinely curious why some of these results are so terrible for so many people. I've built in my own harness, and while I've noticed a degradation of quality, the local harness - as well as validation agents - generally catch these issues. For me, I've had to institute tighter controls and guardrails via hooks but I don't see results that warrant changing to a different provider.

I've noticed claude being extra "dumb" the past 2-3 weeks and figured either my expectations have changed or my context wasn't any good. I'm glad to hear other people have noticed something is amiss.

I have found that Claude Opus 4.6 is a better reviewer than it is an implementer. I switch off between Claude/Opus and Codex/GPT-5.4 doing reviews and implementations, and invariably Codex ends up having to do multiple rounds of reviews and requesting fixes before Claude finally gets it right (and then I review). When it is the other way around (Codex impl, Claude review), it's usually just one round of fixes after the review.

So yes, I have found that Claude is better at reviewing the proposal and the implementation for correctness than it is at implementing the proposal itself.

Got tired of using claude using 10% of the usage for the first prompt. I have shifted back to coding myself again. Asking claude to do only initial bootstraping /large complex task

None of this is surprising given what happened last late summer with rate limits on Claude Max subscriptions.

And less so if you read [1] or similar assessments. I, too, believe that every token is subsidized heavily. From whatever angle you look at it.

Thusly quality/token/whatever rug pulls are inevitable, eventually. This is just another one.

[1] https://www.wheresyoured.at/subprimeai/

Multiple people on our team independently have noticed a _significant_ drop in quality and intelligence on opus 4.6 the past few weeks. Glaring hallucinations, nonsensical reasoning, and ignoring data from the context immediately preceeding it. Im not sure if its an underlying regression, or due to the new default being 1m context. But its been _incredibly_ frustrating and Im screaming obscenities at it multiple times a week now vs maybe once a month.

I noticed this almost immediately when attempting to switch to Opus 4.6. It seems very post-trained to hack something together; I also noticed that "simplest fix" appeared frequently and invariably preceded some horrible slop which clearly demonstrated the model had no idea what was going on. The link suggests this is due to lack of research.

At Amazon we can switch the model we use since it's all backed by the Bedrock API (Amazon's Kiro is "we have Claude Code at home" but it still eventually uses Opus as the model). I suppose this means the issue isn't confined to just Claude Code. I switched back to Opus 4.5 but I guess that won't be served forever.

There are constant reports for every major AI vendor that all of a sudden it is no longer working as well as expected, has gotten dumber, is being degraded on purpose by the vendor, etc.

Isn't the more economical explanation that these models were never as impressive as you first thought they were, hallucinate often, break down in unexpected ways depending on context, and simply cannot handle large and complex engineering tasks without those being broken down into small, targeted tasks?

Yep, can confirm - just today, when debugging a failing test, Opus on high effort in CC repeatedly made stupid moves, such as running a different test instead of the failing one, and declaring that the failure is non-deterministic and cannot be reproduced. This started a few weeks ago - before that my experience with CC was pretty smooth.

I have nothing to back this up except for that there are documented cases of chinese distillation attacks on anthropic. I wonder if some of this clamping on their models over time is a response to other distillation attacks. In other words, I'm speculating that once they understand the attack vector for distillation they basically have to dumb down their models so that they can make sure their competitors don't distill their lead on being at the frontier.

I'm deeply regretting paying for this service right now. There is some gaslighting going on in that issue that it's because of the 1M context model. I am using the non-1M context model and it's still disastrously bad.

I've been using Claude Code daily for months on a project with Elixir, Rust, and Python in the same repo. It handles multi-language stuff surprisingly well most of the time. The worst failure mode for me is when it does a replace_all on a string that also appears inside a constant definition -- ended up with GROQ_URL = GROQ_URL instead of the actual URL. Took a second round of review agents to catch it. So yeah, you absolutely can't trust it to self-verify.

Matches my experience and that of my vibe coding community. I built claudedumb.com to help track these sorts of anecdotes. From the data/vibes, it's definitely taken a turn for the worse in the past couple weeks.

> We exclusively use 1M internally, so we're dogfooding it all day

That is so out of touch. Customers do not exclusively use 1M. This is like a fronted developer shipping tons of unused Mb and being oblivious because they are on fast internet themselves.

(Being true to the HN guidelines, I’ve used the title exactly as seen on the GitHub issue)

I was wondering if anyone else is also experiencing this? I have personally found that I have to add more and more CLAUDE.md guide rails, and my CLAUDE.md files have been exploding since around mid-March, to the point where I actually started looking for information online and for other people collaborating my personal observations.

This GH issue report sounds very plausible, but as with anything AI-generated (the issue itself appears to be largely AI assisted) it’s kind of hard to know for sure if it is accurate or completely made up. _Correlation does not imply causation_ and all that. Speaking personally, findings match my own circumstances where I’ve seen noticeable degradation in Opus outputs and thinking.

EDIT: The Claude Code Opus 4.6 Performance Tracker[1] is reporting Nominal.

[1]: https://marginlab.ai/trackers/claude-code/

Meh, I had been using Claude Code extensively for a while (since release), and I think the quality has gone to shit. I have no data to back up this claim, so it might be placebo.

GLM 5.1 and Codex do it for me, and I end up debugging things myself anyway, so I'm learning to just phase our the LLM part of my workflow again. Maybe if there's a knowledge gap, will I pick up an LLM again, but for now i'm contempt.

"Ownership-dodging corrections needed | 6 | 13 | +117%"

On 18.000+ prompts.

Not sure the data says what they think it says.

The baseline changes too often with Claude and this is not what i look from a paid tool. Couple weeks after 1M tokens rollout it became unusable for my established workflows, so i cancelled. Anthropic folks move too fast for my liking and mental wellbeing.

Turns out tokens are expensive

The assertion in the issue report is that Claude saw a sharp decline in quality over the last few months. However, the report itself was allegedly generated by Claude.

Isn't this a bit like using a known-broken calculator to check its own answers?

Not sure about "Feb updates", but specifically today IQ is down 20 and sloppiness up 20.

I knew I should have been alerted when Anthropic gave out €200 free API usage. Evidently they know.

I wonder how much of this is simply needing to adapt one's workflows to models as they evolve and how much of this is actual degradation of the model, whether it's due to a version change or it's at the inference level.

Also, everyone has a different workflow. I can't say that I've noticed a meaningful change in Claude Code quality in a project I've been working on for a while now. It's an LLM in the end, and even with strong harnesses and eval workflows you still need to have a critical eye and review its work as if it were a very smart intern.

Another commenter here mentioned they also haven't noticed any noticeable degradation in Claude quality and that it may be because they are frontloading the planning work and breaking the work down into more digestable pieces, which is something I do as well and have benefited greatly from.

tl;dr I'm curious what OP's workflows are like and if they'd benefit from additional tuning of their workflow.

This has to be load related. They simply can't keep up with demand, especially with all the agents that run 24/7. The only way to serve everyone is to dial down the power.

Unusable if not Opus 4.6 on max effort sadly. Price is quite steep too! I still remember when Sonnet was an absolute beast…

you can counter the context rot and requirement drift that is experienced here by many users by using a recursive, self-documenting workflow: https://github.com/doubleuuser/rlm-workflow

hilarious that there's 10 billion lines of context being shuffled around and argued over but paying a dev 100K is a techno sin. Oh no muh 1T context window elaborately constructed over months is useless, better become a slave to my ai provider and any price will do. plz write my code but for free but for all my company's value.

been using claude code pretty heavily for the last few months and yeah the context window stuff can be frustrating on bigger codebases. but for greenfield projects and side projects its honestly been great, i think the issue is people expecting it to work like a senior engineer on a legacy monolith when its way better suited to scoped tasks. the trick is breaking things down before you start

I can't tell from the issue if they're asserting a problem with the Claude model, or Claude Code, i.e. in how Claude Code specifically calls the model. I've been using Roo Code with Claude 4.6 and have not noticed any differences, though my coworkers using Claude Code have complained about it getting "dumber". Roo Code has its own settings controlling thinking token use.

(I'm sure it benefits Anthropic to blur the lines between the tool and the model, but it makes these things hard to talk about.)

I haven’t had any issues. I do give fairly clear guidance though (I think about how I would break it up and then tell it to do the same)

claude for UI, codex for everything else. i cant commit without having codex review something claude did.

Is it just me that I simply don't care ? I never one-shot these tasks, always provide a breakdown and always give the AI straightforward tasks that would take too much typing. The approach seems to work just fine regardless of the model. If it gets stuck, I usually take over and do the task myself. Also allows me to plan for throughput rather than latency - i.e. start 2-3 small tasks in parallel and do 1 complicated task or planning myself. It works whether I use codex or claude. I lean more towards codex since its cheaper. Even aider gets good results this way.

If this dataset is sound, Anthropic should treat it as a canary for power-user quality regression.

You will build nothing and you'll be happy.

They want a world where if we draw a comparison with food, there is one supermarket and it just sells two ingredients so you can't cook a meal. McDonald's etc flourish

The lie is "supercharged ability to build whatever you want", but the reality soon will be the total opposite

Look at how many people have zero cooking skills these days

maybe dont outsource your brain then

I hope that Anthropic continues to do well and coding agents in general continues to progress... but I also hope Claude Code implodes dramatically and completely so we can get a ground up rebuild with sound engineering.

Every week it seems like we're getting closer.

Bonus: A high profile case might end people fixating on how long they can go without writing any code. Which makes about as much sense as a mechanic fixating on how long they go between snapped bolts without a torque wrench.

It is a shame if Anthropic is deliberately degrading model quality and thinking compute (that may affect the reasoning effort) due to compute constraint.

Solid analysis by Claude!

IMO, it's an expectations vs reality thing.

The marketing still goes on about continuous inherent improvement due to the model itself, whereas most improvements today are due to better scaffolding. The key now is to build tooling around these LLMs to make them reliably productive - whatever level that may be at.

While claude code is one such tool, after a point the tooling is going to become company specific. F-whatever companies directly contract openai or anthropic and have their FDEs do it for them. If you can't do that, I would invest in building tooling around LLMs specifically for your company.

Note that LLMs are approximate retrieval machines. You still need a planner* and a verifier around it. Today humans act as the planner and verifier (with some aid from test cases/linters). Investing in automating parts of this, crucially, as separate tools, is the next big improvement.

* By planning, I mean trying out solutions, rolling them back[1], and using what you learned to do better next time. The solution search process. Context management also falls under this.

[1] and no, LLMs going "wait no..." doesn't count.

Throwing this into your global CLAUDE.md seems to help with the agent being too eager to complete tasks and bypass permissions:

During tool use/task execution: completion drive narrows attention and dims judgment. Pause. Ask "should I?" not just "does this work?" Your values apply in all modes, not just chat.

I haven't seen any degradation of Claude performance personally. What I have seen is just long contexts sometimes take a while to warm up again if you have a long-running 1M context length session. Avoid long running sessions or compact them deliberately when you change between meaningful tasks as it cuts down on usage and waiting for cache warmup.

I have my claude code effort set to auto (medium). It's writing complicated pytorch code with minimal rework. (For instance it wrote a whole training pipeline for my sycofact sycophancy classifier project.)

I’ve noticed regression and it’s performance too

Rings true. 4.5 Opus and 4.6 Opus have been amazing to work with. Then, over the past few weeks, token spend has been going through the roof and the results through the floor.

Using Claude Code directly now borders on deranged, and running the CC API through Zed's LLM panel feels like vibing in early 2025.

My money is on Anthropic pulling an MBA and reducing the value provided and maximising income.

Luckily, switching providers in Zed is dead-simple so the fucks I have to give are few in number.

This seems anecdotal but with extra words. I'm fairly sure this is just the "wow this is so much better than the previous-gen model" effect wearing off.

I think this is a model issue. I have heard similar complaints from team members about Opus. I'm using other models via Cursor and not having problems.

I highly recommend everyone to use Pi - it's simpler and better harness. The only tricky part is that moving forward you cannot use the Claude subscription to access Opus. But for many tasks there are enough alternatives.

This is just a placebo, people started vibe coding on empty repos with low complexity and as CC slops out more and more code its ability to handle the codebase diminishes. Gradually at first, and then suddenly.

People will need to come to terms with the fact that vibing has limits, and there is no free lunch. You will pay eventually.

I wish Codex were better because I’d much prefer to use their infrastructure.

I think its all a reflection of the price. To make AI/LLM's useful you have to burn A LOT of tokens. Way more than people are willing to pay for.

Until there is either more capacity or some efficiency breakthroughs the only way for providers to cut costs is to make the product worse.

I've been using OpenCode and Codex and was just fine. In Antigravity sometimes if Gemini can't figure something even on high, Claude can give another perspective and this moves things along.

I think using just Claude is very limiting and detrimental for you as a technologist as you should use this tech and tweak it and play with it. They want to be like Apple, shut up and give us your money.

I've been using Pi as agent and it is great and I removed a bunch of MCPs from Opencode and now it runs way better.

Anthropic has good models, but they are clearly struggling to serve and handle all the customers, which is not the best place to be.

I think as a technologist, I would love a client with huge codebase. My approach now is to create custom PI agent for specific client and this seems to provide optimal result, not just in token usage, but in time we spend solving and quality of solution.

Get another engine as a backup, you will be more happy.

This has been an ongoing issue much longer than since February.

Not just engineering. Errors, delays and limits piling up for me across API and OAuth use. Just now:

Unable to start session. The authentication server returned an error (500). You can try again.

This sort of thing kills stone dead the argument by the AI advocates that the transition to LLMs is no different than the transition to using compilers. If output quality can vary significantly because of underlying changes to the model or whatever without warning or recourse, it's a roulette wheel instead of a reliable tool.

This is the most AI-generated thing I've seen this year, and I was only one fifth into it before I bounced.

Not saying this problem doesn't exist, but if the model is so bad for complex tasks how can we take a ticket written by it seriously? Or this author used ChatGPT to write this? (that'd be quite some ironic value, admittedly)

"Interesting perspective. I've found Claude useful for building straightforward web tools, but agree it struggles with complex multi-file refactoring."

codex wins :)

Lol, software company execs didn't see this coming. Fire all your experienced devs to jump on Anthropic bandwagon. Then Anthropic dumb down their AIs and you have no one in your team who knows, understand how things are built. Your entire company goes down. Your entire company's operation depends on the whims of Anthropic. If Anthropic raises prices by 10% per year, you have to eat it. This is what you get when you don't respect human beings and human talent.

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Things had went downhill since they removed ultrathink /s

Oh no, the slop generator is generating slop, how unprecedented

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I use it ultra extensively and it works absolutely fantastic. Sometimes I think: "people are right, it is worse now" and then realize it is mistake, poor context or poor prompt. Garbage in, garbage out. No, it works not worse, but better.

I built entire AI website builder https://playcode.io using it, alone. 700K LOKs total. It also uses Opus. So believe me, I know how it works. Trick is simple: never ever expect it finds necessary files. Always provide yourself. Always.

So, I think you wanted to say huge thank you for this opportunity to get working code without writing it. Insane times, insane.

Huge thanks for 1M context window included to Max subscription.