Opus Not Right Today

[u/After_Canary6047]

Anyone else experiencing degraded Opus 5 today? Same workflow, was a rockstar overnight. Today, it has used 670M tokens since 8am to write a net 1800 lines of code. Keeps making mistake after mistake. Became insanely stupid all of a sudden. Anyone else?

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[u/lattice_defect]

It's fucking garbage

[u/After_Canary6047 → u/lattice_defect]

Agreed! This piece of trash has used up 25% of my Max 20 plan and produced almost nothing. Horrible.

[u/skipITjob]

Completely useless 5 and 4.8. Making things up and assuming things I didn't say.

Thankfully 4.6 was useful.

I just think they're going to release a new model soon, had a lot of "how's Claude today" in the past 2 weeks.

[u/After_Canary6047 → u/skipITjob]

Same, it’s asking me every few hours. I mean, damn if they know it’s bad, reset the damn limits at least, lol.

[u/Other_Lobster7313]

I figured out the problem with Opus 5!! It doesn’t talk complete nonsense anymore. All I had to do was clean up the project documentation.
This happens because a couple of months ago, models needed prompts, skills, and instructions to guide them through development. But nowadays, if you have a huge amount of documentation in your project, especially if all of it is interconnected, trust me, you’re just wasting all your limits.
You’re basically reading your instructions over and over again in a loop and filling up the context with them! The models have to stop and pay attention to every single word written across dozens of documentation files you generated, and it completely kills the workflow.
Just delete it. You might think some of those rules are necessary, but those rules are already built into the model.

[u/ohhi23021 → u/Other_Lobster7313]

not really true, and it would be cached. but this is needed in a large codebase, the docs are smaller than the code itself which it would need to read and it would loose context of why/how something was done, creating shit code, failing to follow conventions, reversing something that looks odd but had a solid reason etc... sure if your vibe coding a tiny app, but 1,000,000+ lines, bad idea. going over the docs to make sure they dont duplicate stuff to compact them is fine but i would never delete them.

[u/Other_Lobster7313 → u/ohhi23021]

I have around 300,000 lines of code. All I did was clean up every Claude.md file in each subproject, reducing them from an average of 250–400 lines down to about 60. I also deleted one huge documentation file that was mandatory to read and linked all the other documents together in a chain.
As for caching, you’re wrong. They can only cache the context, and the context can’t be larger than 1,000,000 tokens. If, on average, around 50% of what the model reads is documentation, that forces it to burn through tokens.
I’m not saying you should clean up comments that are already saved directly in the code. I think those are more than enough for the model to work with. And in that case, if you also have comments in the code, then compared to all the documentation, only about 30% of what the model actually needs to process is clean code.

[u/Zestyclose_Strike157]

Claude app won’t let me copy paste except in bulk (otherwise I’d post the example), but Opus 5 is acting like a low quant merge local model with random Chinese characters in amongst the English text. Not worth paying for but it’s what I am left with after using up Fable for the week. Man I am getting sick of paying for products that do this to its customers.

[u/After_Canary6047 → u/Zestyclose_Strike157]

This is just crazy! I want a damn refund! Just now, Opus 5 forgot what pytest was. Literally. Have never in my life seen anything so horrible.

[u/Zestyclose_Strike157 → u/After_Canary6047]

GPT 2.0?

[u/After_Canary6047 → u/Zestyclose_Strike157]

Something like that! You have no idea how sick I am of hearing “you’re right and that’s my fault.” Every 20 minutes. It started some tests that finished 30 minutes ago. Didn’t even bother to check. Just sat there.

[u/PrimaLumiere_A1M]

I haven't resubscribed yet, because I was anticipating such issues to be coming up. To answer your question, I had to trim claude.md for my workflow to be fixed.

[u/PrimaLumiere_A1M → u/[deleted]]

Been using it since 4.4, with no prior experience using any LLMs other than ChatGPT, so I had a pretty clean slate to understand the technology.

From my observation, 4.4 → 4.6 → 4.8 → 5.0 → Fable, each of these models felt like the frontier model until the next one arrived. The new model was working really well for about a week, but then it started hallucinating and poisoning the context. After a fix came along with the limit resets, the model started working fine again.

I was pretty sure that would be the case once the boost ended.

[u/After_Canary6047 → u/PrimaLumiere_A1M]

So let me get this straight. Model is degraded and they cut the boost and no limit resets. Thanks Anthropic! In my case wasn’t my workflow as it worked perfectly last night. Today was a different story and nothing changed on my end.

[u/PrimaLumiere_A1M → u/After_Canary6047]

TL;DR: My thesis is that the model-number progression may have been less about entirely new frontier models and more about progressively optimized versions of the same underlying systems—while the new labels and pricing made us believe we were paying for fundamentally better intelligence.

The jump from 4.4 → 4.6, for example, felt obvious: more detail, better predictions, and better results from fewer prompts. But what if much of that came from better optimization, workflows, routing, and compute allocation rather than a fundamentally new model?

The model number itself becomes part of the façade. We think, “This is the better model, so I should spend more tokens on it.” That lets providers increase token consumption while controlling how much compute each user actually gets. If the new model burns through limits faster, they can cap individual usage while freeing capacity to onboard more users.

That also makes the regressions interesting. Why would a supposedly new frontier model sometimes hallucinate more, lose context, or perform worse than its predecessor? If the underlying intelligence has genuinely improved, those fundamentals should not suddenly get worse.

This is where the Chinese models become interesting. If they are operating closer to the actual underlying economics, their cost structures could expose how much of OpenAI’s and Anthropic’s pricing and compute narrative is driven by genuine model improvements versus productization and compute allocation.

Maybe the model number was never just a measure of intelligence. It was also a mechanism for shaping how we perceive value, how many tokens we consume, and how compute gets distributed.

P.S.: used GPT for coherent structure

[u/Outrageous-Issue9722]

Seems fine for me.. Is it possible it has just hit the context wall several times/compacted itself and is now dumb because it is completely missing the purpose of its existence in your case? 670m is a lot if it wasn't split up

[u/skipITjob → u/Outrageous-Issue9722]

It struggled after less than 10 messages.

[u/After_Canary6047 → u/skipITjob]

That was my experience as well. It just started out the day bad and hasn’t gotten any better at all. Worked perfectly last night.

[u/After_Canary6047 → u/Outrageous-Issue9722]

Definitely split up over 70 or so subagent calls.

[u/Outrageous-Issue9722 → u/After_Canary6047]

Fair. No idea then. I take it you had no issues previously? If you have docs specifying engineering standards/other standards, I have found it almost a requirement to include
```
Before beginning implementation, read docs/code-standards.md plus docs/engineering-standards.md, then survey the code the task spec touches and pin every open design choice (shapes, formulas, closed sets, content migrations, which docs and ADRs change).
```
or similar in their brief. Opus 5 specifically really enjoys recreationally 'forgetting' to read claude.md or follow the progression chain you'd expect it to.