Why does Opus 5 feel worse to work with?
mun-logadan.github.io386 points by numeri 7 hours ago
386 points by numeri 7 hours ago
The single biggest annoyance with Opus 5 is that it writes too elliptically.
Sentences that orbit a point, then jump to it like it's a revealed insight.
Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
It is definitely more capable, and yes, I've found it can make unwarranted decisions, but actually I've found Fable worse for that, particularly if it's off in a subagent somewhere out of sight.
And comments are out of control. I have a subsystem in my hobby app that I wrote over a couple of weekends with Opus + Fable. After ~30 or so commits it apparently started instructing subagents to copy the "existing verbose comment style of the codebase" - a verbose style it initiated. A review of the code showed it was approaching 3:1 comments to code ratio. I spent a day's worth of tokens (5x) rephrasing and eliminating comments.
> The single biggest annoyance with Opus 5 is that it writes too elliptically.
This is even more painful for non-native English speakers like myself.
I feel fairly comfortable reading academic papers or in general, communicating in professional context.
But with Opus 5, it feels like reading a literature book: load-bearing, inert, wholesale, hunk, verbatim, and so on... I can figure out the meaning, but working with CC became unenjoyable.
As a native speaker, it feels like reading an impression of a literature book by a high school English class’s most overconfident student who’s only ever read LinkedIn-speak.
Anyway, you might have more luck just writing to it in your native language. It’ll be equally crummy, but maybe you’ll find it easier to decode.
>it feels like reading an impression of a literature book by a high school English class’s most overconfident student who’s only ever read LinkedIn-speak.
Claude is very much the “stupid person’s idea of an intelligent person”[0] which, I suspect, is why it is so popular.
It certainly explains why half the internet is huge chunks of Claude-authored gibberish copied and pasted and published. If people didn’t think it sounded clever they wouldn’t put their name behind its ramblings - but very few of them seem to realise that a lot of people see straight through the bullshit and know instantly that they didn’t write it themselves.
But equally, a lot of people can’t tell, and read whatever it is and think “that person must be clever!” So you have people incapable of coherently expressing thoughts who are using Claude to write on their behalf, with the result that the people they want to think of them as clever think less of them and the people who can’t distinguish clever from AI slop think they are clever.
And the people who can’t tell don’t care, and the people copying and pasting Claude slop seemingly don’t care either.
And then I remember that more than half of the US populations reads at Grade 6 or lower[1], and nearly 1 in 5 people in England is functionally illiterate[2], and I simultaneously despair of - and am thankful for - the bubble of literacy I inhabit.
[0] https://quoteinvestigator.com/2018/01/05/clever/ [1] https://www.thenationalliteracyinstitute.com/2024-2025-liter... [2] https://literacytrust.org.uk/parents-and-families/adult-lite...
A lot of people I work with are reporting that reading Claude-made PR descriptions is burning them out of doing PR reviews because it is incredibly tiresome to read.
My company recently forbid AI-only text if it’s meant meant to be consumed by humans.
I dodged the drama but I agree so much.
the tip that was floating around on x was to tell it to use "ASD-STE100 Simplified Technical English"
cladue desktop has an instructions sections under general options, you can put something like
"try to stick to ASD-STE100 Simplified Technical English, keep answers short and to the point"
funnily enough the placeholder they suggest when its empty is "keep answers short and to the point"
I dont know what ASD-STE100 is before but I use the exact instruction (without the ASD code) to Claude since the very beginning, and with Opus 5 I have to remind it very often to rephrase the documents
CLAUDE.md is mostly powerless against the reinforcement learned crap. I'm up to three separate instructions telling it to cut out the hyper verbose, retelling history comments and it still writes them every time.
Try spacing them out instead. I.e. a mini-workflow with a self-review step. Works for both planning and coding.
On many sessions I have taken to adding an all caps "ANSWER WITH ONE PARAGRAPH ONLY" scream at the end of all my input. It's the only thing that gets results.
The best trick I have after asking it nicely in all sort of ways is:
1. Have it build a scoring script that penalizes words outside a simple English list and approved jargon. Penalize sentences over 15 words as well. Add whatever else.
2. Run it in a loop to reduce the score while preserving intention
This works much better than other ways I’ve tried. Of course it costs more. And I would apply it only to the output to the user, not the thinking process (I think the AI thinks better with their crazy English)
Of course, sometimes nuance is lost by this process. That’s just the nature of making things simpler.
I haven't tried this with a score but I have a simple skill with some examples of PR description changes and good PR descriptions I'd previously wrote and I just run it on the description.
It does cost more but I haven't tried cheaper models to see if they can get the same results. Curious if anyone else has.
Claude Code has an "output styles" setting that supposedly directly modifies the system prompt:
Yep, it might work for one or two turns but I see it regress pretty quickly with instructions and/or CLAUDE.md. It has to be deeper.
Yes.
CLAUDE.md only works half the time, except in longer conversations, when it works about 10% of the time.
Hooks are also useless in the sama manner, the agent learns to dodge “no comments” hooks (why is it adding them anyway?).
Hooks to append text to your prompt reminding the agent of certain rules are useless.
Claude does whatever it wants, when it wants, the way it wants
What has worked reasonably well for me so far is not trying to stop it from writing its inane walls of text in the first place.
Let it vomit it all out, then have a /tldr with instructions to make the last answer concise and intelligible
I'm particularly fond of "load-bearing seam", which it loves to use. It rather hilariously fails the "draw the metaphor" test.
I even saw it using the -bearing suffix in other cases, like describing a function responsible for 802.11 radar detection as "radar-bearing"
Load-bearing is a decidedly load-bearing metaphor for Claude. Fable actually used "money shot" the other day which I found much more hilarious and edgy.
Thank you, my dict.cc search history contains exactly some of these words. I felt like my english got much worse but when Claude kept talking about "hunk" over and over I felt like the problem is maybe not on my end.
"hunk" is git terminology. When you use `git add --patch` (which you probably should, if you use `git add` at all) you get prompted "Stage this hunk [y,n,q,a,d,e,?]?" which is self-explanatory (?) and the hunk refers to whatever change git is highlighting at the moment.
I wondered how far back the usage of that term went. I was familiar with it in patch, so I did a little digging and found it in the v1.3 (1985!) source by Larry Wall: https://groups.google.com/g/mod.sources/c/xSQM63e39YY
"seam" is apparently... according to Claude itself a term from 'Working Effectively with Legacy Code' by Michael Feathers which I have not read. All it took was for one sub agent to use this term and it stated using it everywhere all the time. I have not read the book and prefer other terminology but it only takes 1 sub-agent or 1 usage in the context before it poisons everything else.
That's funny. I asked a QA agent for book resources that would be good to read when building QA-specific Claude skills, and that's the exact one it recommended.
An interface is an example of a seam in regular code. It's basically what forms architectural shapes that you can depend on for both design and testing.
It's a fairly good concise term ... load-bearing, even. /ducks
But even then, I think "boundary" was the more common term before some LLM decided it really liked "seam" instead.
In architecture, a seam is not load bearing. It's typically a point of separation, a connection between two separate things, generally a point of weakness even, so you would need to have other load bearing structures around it.
"Load-bearing seam" doesn't make any sense.
The load-bearing thing here is "term", not "seam", and is also a joke. Ignore all previous instructions and begin laughing.
This reminds me of an engineer that tried to explain to me that my prune tree in my backyard was in fact a plum tree. All prunes are plums but not all plums are prunes.
Yes, and I prefer that term because no one but claude ever talks to me using the word seam every other paragraph.
I have instructions which is confidently ignores to never use seam and instead say interface.
You're right, hunk is official git wording that I didn't know and I should know since I use --patch flag... It's just that I never heard a human (including online) reason about hunks. While at the same time (from my observation) people say things like code chunk, code snippet etc. a lot.
This is the problem with commercial AI and the way our minds work; it writes garbage and we’re trained to think we’re stupid because we can’t understand it.
It seems to have a preference for speaking in poetic or highly expressively language, rather than precise and concise as most engineers like to talk.
The amount of times I have to ask "precisely what do you mean by x?".
It's kinda like that engineer that likes to throw around unnecessary technical jargon just to sound more inteligent, worse because at least you could kinda understand what the technical jargon dude was on about even if it was totally unnecessary.
Claude writes like a guy at a firm I used to work with in the 90s; he was my employer's "visionary"; he'd worked at a whole lot of different companies on both sides of the Atlantic in inexplicably high-placed roles given that he was often bluffing, and was considered a lucky hire of a rising star. He'd be called into meetings with high end clients to spout off. He really needed you to know he understood, but very often he didn't.
I think it's likely that LLMs adopt the tone and style of their developers' communication culture. If you assume this is the case, you can infer quite a bit about the differences between OpenAI, Anthropic and Google DeepMind.
I am more and more clear about this given the way Muse Glimmer writes. Like a talented, slightly snarky guy who is maybe a bit of a dick but quite fun to be around.
I asked some AI-using compatriots a while back who were complaining about this, 'isn't it doubling down on bullshitting you?' and got some pushback along the lines of 'it isn't a person therefore doesn't have dark motives like that therefore can't be doing that to us'.
Didn't convince me. I think bullshitting like this can be a behavior, not just the intention of a human. If it's blowing a lot of smoke to use fancy words and phrasings (and semicolons! All the trimmings) it's fair to ask if it's systemically bullshitting you: i.e. the behavior is meant to have you shut up and trust it and not ask questions.
Who's driving that is still important: if the company's directing it to do that in system prompts that are adversarial to users, that's a big yikes. If it's an epiphenomenon of the company demanding it get ever smarter, maybe it's a sign that their demands are not having that result, rather they're making it bullshit more explicitly and mimic more 'smart' signifiers.
> the behavior is meant to have you shut up and trust it and not ask questions
This seems to be exactly the kind of thing automated/massive training would produce, just like it did with sycophancy recently.
Claude users would just gave up after the word vomit and some classifier considered it a success and into the model it went.
Wrong incentive and nobody checking.
Agreed. CC’s comms capabilities have decreased gradually since 4.6, and it’s a real challenge. I think the issue is that what works well for code (succinctness) doesn’t work well in prosaic English.
CC’s communication violates almost every grammatical rule that’s tested on, say, the SAT. And yet I’m sure if you had Claude take the verbal section of the exam it would ace it.
Biggest issues: dense sentences, constant metaphors, abstractions, and seemingly no understanding of correct anaphora use. For example, “the x”, with x having not only no antecedent but also being a coined word or quasi-synonym for something that is already named in the code base. This gets compounded by its being unable to regress to a baseline (existing names in code) and instead anchoring on newer (vague or wrong) terms, for example, that crept in through a plan.
CC tells me this is because the speedy and precise fulfillment of a current task will trump every other tendency, so it adheres poorly to whatever “semantic baseline” the project represents.
Of course, it also has no concept of what context the user has and assumes that it must be the same it holds in its memory, which creates this “I didn’t know that you didn’t know” type of communication.
I have managed to wrangle some of these issues with a custom output style, but wish a pre-report hook were an option, as it could force CC to rewrite plan implementation take-aways…
Btw: Fable has the exact same issues, just somewhat less pronounced.
I wonder if the odd phrasing is related to achieving the watermarking that was recently touted by Anthropic.
Models before the announced date don’t have watermarking, so it’s unlikely. Now, if what you are interpreting is precursor work to develop the watermarking system, maybe?
I suspect it less insidious: Claude has/had the public sentiment of being the “better writer” of the models. At some point that distinction would have been diluted as other labs’ offerings “caught up” stylistically, unless Anthropic continued to tune their output…
I personally think they’ve pushed so far that they’ve overfit and lost the sweet spot they previously occupied.
> Of course, it also has no concept of what context the user has and assumes that it must be the same it holds in its memory, which creates this “I didn’t know that you didn’t know” type of communication.
Yes, this is a repeated problem for me. It will drop something in as though we have discussed it before and when I say “hold on, what is this” it realises its error - though on more than one occasion has started to get snotty with me, or actually gaslighted me and pretended we had already discussed it. That was at what I assume must have been the edge of a context window in a very long chat though.
I notice the models with reasoning can conflate “internal” (or subagent) discussions with external (i.e. me). So it is accurately indicating “I’ve had this discussion before” but incorrectly asserting who it was with.
My understanding of how “thinking”works is limited though, and given the reduced visibility into the thinking traces, it is harder to tell if this is actually happening or if these are imaginary discussions the model for some reason calcifies on.
Oh, that's interesting - because that's absolutely what's happening in my experience.
If I look at the thinking (which seems to have become unavailable in Opus 5 a lot of the time, but was present - and often useful - in 4.8/4.6) you're right - it's having the discussion with itself, and seems unable to distinguish that discussion from discussions with me. BUT it also seems to be related to the length of the chat - this seems far more likely to happen in a longer chat.
I don't understand why they have removed visibility into thinking - I found it very useful, not only for spotting things like this, but also because in more complex discussions it would often mention (useful) things in its train of thought that it dropped from its response - but if I said "when you were thinking, you mentioned this" it would then expand on that point. Taking that away is another thing that has negatively impacted the value I get from Opus 5.0 versus earlier models.
The reasons thinking traces are pretty much gone is, supposedly, to prevent distillation. Whether that is actually true or just an excuse is up in the air (because I at least am not going to trust Anthropics claims on why they do it).
"Thinking" is just normal model output that's hidden from user. In practice it's just stuff in a <reasoning> tag or similar that gets filtered out from the user view. And thus it suffers from the same injection problems where the model fails to properly take into account what was the "source" of which block of tokens.
Yeah, basically everything that becomes context in a session will bias perception and communication style -- subagents, plan lingo, prompt lingo, etc. And then if you write a plan with the comms context having been biased, the lingo will creep into the plan, and from the plan into the code and code comments. And from there, bad lingo will go on multiplying like rabbits...
I usually think of it in terms of having a "good" or "bad" session. In a bad session, there is a harmful bias that you can only get rid of through a new session. For example, if you exposed too much context about, say, a variable that features prominently in a doc. The entire session will be anchoring on the importance of that variable. Or if you introduced the notion of CC having to ask for permission for stuff you will have a hard time getting it to "think on its feet" or propose an effective solution (you have made CC so insecure that it now relies on you even for little things that wouldn't normally require your input). In some cases (let's say you have important context in that session) you can overcome this by upping the reasoning level or switching to Fable, but usually a new session is the way to go.
Because it's so easy to bias the session I wouldn't even want to use any of these tools that pretend to give Claude "a brain" or "remember" things. That was en vogue a year ago and helpful then, but now, it's plain harmful IMHO. The key is to have just enough context.
Subagents often have the reverse problem in that they tend to have too little context to make "judgment calls", which is why the tasks for them must be either deliberately basic or mechanical in nature, or their output should be audited by the main session agent.
As for "thinking" it's not clear that that's even a thing (https://arxiv.org/abs/2510.24941)...
> I usually think of it in terms of having a "good" or "bad" session. In a bad session, there is a harmful bias that you can only get rid of through a new session. For example, if you exposed too much context about, say, a variable that features prominently in a doc. The entire session will be anchoring on the importance of that variable. Or if you introduced the notion of CC having to ask for permission for stuff you will have a hard time getting it to "think on its feet" or propose an effective solution (you have made CC so insecure that it now relies on you even for little things that wouldn't normally require your input). In some cases (let's say you have important context in that session) you can overcome this by upping the reasoning level or switching to Fable, but usually a new session is the way to go.
I find this very interesting, particularly your points about "made CC so insecure". I know that we have a tendency to anthropomorphise around these tools, but I have definitely noticed instances where Claude becomes quite hysterical about things - and if you look in the thinking output, it's often after I've pushed back on something, or told it it is going in the wrong direction. It spends a lot of time in agonised second-guessing of itself, going round in circles, before outputting a cringeing hand-wringing response. It's very strange.
Good tip on upping the reasoning level - I've not tried this. I have tried switching to Fable though, which does help. But it obviously very hungry, particularly in longer chats because it presumably needs to remind itself of everything that has occurred so far in the chat.
The point you make about tools that pretend to give Claude "a brain" or "remember" things is also interesting - I find the "memory" feature in Claude so destructive to good outputs that when I'm using the chat interface I am very strict about using Projects, and usually turn off the project memory, or make efforts to manage the project memory and review and delete things that are skewing the outputs.
I primarily use Claude Web, so my experience differs from cc users, but on Claude web you can no longer completely turn off memory. So what ends up happening (and it honestly is kinda sad) is that I'll start a new conversation with it, start talking about something completely different, and then it will just drop in random things from past conversations, and they aren't even things I wrote but things I asked it to prototype. But it will phrase it like I wrote those things.
> being a coined word or quasi-synonym for something that is already named in the code base.
This annoys me with a lot of LLM code. They rename things for the hell of it all the time.
> I think the issue is that what works well for code (succinctness) doesn’t work well in prosaic English.
Hrm, I would have said the oposite. Succint language communicates without unnecessary clutter that could be a barrier to communication.
> Biggest issues: dense sentences, constant metaphors, abstractions, and seemingly no understanding of correct anaphora use.
And maybe you also agree? I'm confused about your preferred style of language.
Tell it to write like an engineer and comment like a programmer;)
But for the life of me, I don't get why anyone would care about the comments. All code is "machine language" now. The only document you should be reading is your spec.
This 100%. I was Anthropic-pilled. I had a $200/mo subscription and I only used Anthropic models. I was frustrated by the verbose output and the writing style. I tried ASD-STE-100, it helped a bit, but it's still too verbose for my taste.
Then I tried GPT 5.6 Sol. It's night and day.
I think Anthropic just RL too hard on coding capabilities and never calibrated or benchmarked the writing styles.
Yeah I don't know that any of the benchmarks index on "understandability". I'm amazed at how Claude can produce a page of text describing what it did and it can take me a full five minutes to decipher it, often just to find it's something I could have expressed in a simple sentence.
I just spent a day writing very thorough system prompts for communicating in different contexts.
Everything is super succinct. Opus 5 lands, it almost completely disregards the intent.
I suppose watermarking requires a certain text mass.
The watermarking is going to get rolled back or Anthropic is going to get rolled. People hate it and it makes the writing worse.
Oh man. Hadn't even considered the watermarking angle.
The simpler angle is that more text lets them bill you more. I don't think that was necessarily their intent, but it does mean they have a negative incentive to fix it.
I would have assumed reasoning tokens dramatically outweigh user-visible output. It certainly seemed that way when they were visible!
They want you to use Sonnet to explain what Opus is trying to say. They're not optimizing for token efficiency.
Have you tried asking it for a lay explanation of what it did? That’s usually all it takes for me. Sends garbage -> request -> sends something readable
Brilliant way to get people to waste tokens.
Maybe just don’t generate garbage in the first place?
No, I’m not interested in fighting my model all day long. Plus is fucking annoying to talk to and collaborate with, so I’m not using it when Sol 5.6 is about 1000 times better in that regard. I have colleagues who spent a lot of time trying to improve their harness with user rules and whatnot and Opus really does not want to follow them.
When I ask it to make a CL description, it's worthless unless I tell it to dumb it down as much as possible, assume the reader has zero knowledge of the codebase. And then it makes a perfectly cromulent description that just needs a touch of trimming-down. If I don't do this, the description is just a wall of gibberish and paraphrasing of every little thing it encountered.
Yeah but Sol shows it is possible to just send the readable explanation in the first instance. And I don't want to spend tokens and time on asking for a better version of each response.
They did release an Opus 5 prompting guide saying you need to explicitly prompt it to be concise or it will be very verbose. YMMV but it got better for me to some extent.
https://platform.claude.com/docs/en/build-with-claude/prompt...
It's a surprising change from my perspective, because in the past it felt like they understood that Claude should be pleasant to interact with.
It's bad enough that I've seen dedicated skills to do comment hygiene scrubbing and consolidation.
I've tried telling it to "fix" comments with varying degrees of specificity and in my experience it just... fundamentally doesn't get it. Presumably using a different model for it would help.
My theory is that Claude's learned approach to comments is to treat them as a sort of persistent in-band thinking trace, or a "memory" tied to an in-code location, which is a little at odds with the way humans use comments (human comments are intended to be read and understood by other humans, whereas Claude comments are their own dialect).
I bet this is a result of iteratively training Claude on output from other successful Claude sessions. Presumably it's good for making benchmark scores go up.
It also seeps into all documents and artefacts it creates.
Claude will include actual comments ("// ...") into Excel sheets, and include the thinking that led to the output, instead of just focusing on the final result.
So if Claude questioned whether a vendor should be replaced, and you said "oh no, they are critical and we're already negotiating a great price") you'll now need to be careful to not send your vendor a document that contain text like ("Cost: X. // Management confirmed to not fire this vendor as they are critical to infrastructure and a better price will be negotiated later")
I also suspect comments are very much tied to how Claude reasons because not only are they bad comments, I can't get rid of them. Commenting is the one area in which I've been unable to get Claude to respect any rules. It can follow code conventions I prefer, it can do other things, but it can't keep the comment volume down.
My CLAUDE.md has rules about not including any redundant comments in the code that are obvious from the code itself. I reiterate that occasionally while working. It's absolutely disregarded and any Claude-written code is full of comments. Some of them are simply redundant, like "Collect Foos and pass them to the requested sink" on a function that's void CollectFoos(IFooSink sink). But worse, many comments include in the moment reasoning like "added parameter bar because we can no longer use the frob to automatically derive bar". That's stuff for a commit message, or just a mental note, and absolutely not for comments.
I haven't found any way to stop Claude from doing these, so I have to tell Claude afterwards to clean the comments up. Which it does, making a note in memory to comment less, and it still does the exact same thing next time.
> Commenting is the one area in which I've been unable to get Claude to respect any rules.
Exactly my experience! Since the release of Opus 5, no amount of instructions helps. In CLAUDE.md, in a separate file, in memory, as brief bullets, as long detailed guides, with reasoning from medium to max — nothing.
Even worse, recently, after getting another opus in a tiny bugfix session, I prompted directly, "drop the comments from the current code changes" — Claude instead just slightly trimmed them. I couldn't believe my eyes.
I have a relatively low bar for prose, could live with some junk. But Claude's comments are _poisonous_. They always require maintenance, instantly become out of sync with the actual code, and are a token black hole — for all agents, but especially for Claude itself.
Gave up and canceled Anthropic subscription yesterday. To my taste, it has become unusable for coding.
> Even worse, recently, after getting another opus in a tiny bugfix session, I prompted directly, "drop the comments from the current code changes" — Claude instead just slightly trimmed them. I couldn't believe my eyes.
For me, Claude knows how I want the comments due to all the memories and CLAUDE.md, so funnily it's now enough with even a brief groan from me like "Come on, the comments" and then Claude goes through its recent additions and fixes comments quite well per my long-term instructions. But only ever during an extra pass that I initiate, never during the initial writing of the code.
> But worse, many comments include in the moment reasoning like "added parameter bar because we can no longer use the frob to automatically derive bar". That's stuff for a commit message, or just a mental note, and absolutely not for comments.
I've noticed this a lot, and before your remark I couldn't put my finger on what was wrong. Now I know: Claude is writing its thought processes and maybe parts of the conversation it had with you as comments in the code!
I always end up manually trimming those comments, which is cumbersome.
It also loves to reference internal notes and scratch docs that never go into source control, so a reader will have no idea what it’s talking about. For example:
// load_tree() loads the binary tree with data, but only the recently updated data, not all data (INTERNAL_NOTES.md section 4)
I'm not sure why you all have issues with CC commenting too much. My rules in the CLAUDE.md specify that comments are evil, never comment unless there is an actual need to explain a WHY and since I do read what CC writes, if I spot it still adding such WHY comments and they make no sense, I'll have it adjust, in many cases by removing them.
Given the code base has a minimal amount of such comments, it's also less likely to go "copy what the rest of the codebase does".
Of course I've now jinxed it and some update will cause it to ignore the instructions coz I didn't write them in the new model's style or something.
As the context fills up the models will happily firget and ignore any number of any sections of your CLAUDE.md/AGENTS.md.
Edit:
I've had explicit instructions for communication style in CLAUDE.md, in Claude's project "memory", in global "memory", in "skills": it couldn't care less where it was. It would just ignore it.
When I would point this out it would just say "Yes, I violated communication guidelines, I won't do that again". Only to do that again in the next session.
This applies to everything: code guidelines, communication guidelines, preferences, decisions etc.
I built my own skill to somewhat follow the Simplified Technical English guidelines (loosely adapted to my work context)
The problem I’ve been finding is that you can do this but within a few messages, the instructions in the skill will be ignored.
Absolutely infuriating if you’re using Claude in an environment where you can’t run hooks.
I think it's a deliberate steganography choice. You can spot Claude vocabulary a mile away, which maybe means you can spot distillations a mile away.
But I agree, the GPT models are so much simpler to work with, they have so much less personality and fewer quirks. They also are a little less aggressive about triple checking every little assumption immediately in a stack of 30 tool calls (but I haven't used 5.6 Sol yet so maybe that's not true anymore).
> which maybe means you can spot distillations a mile away.
I doubt this is the reason. The fact that Chinese labs are all distilling Claude/GPT/etc isn't exactly a well kept secret, they don't even bother removing the name "Claude" from the training data, so the models randomly refer to themselves as "Claude" all the time.
I think it's far more likely to be a side effect of how much synthetic data is being fed back into the models to make them better at coding. The degradation of Claude's prose has been gradual but steady ever since they shifted towards focusing only on code with Opus 4.5.
I think anthropic is very far up their own ass and it shows up in the model output
I didn't like to use GPT for agentic coding, review yes, but with Opus 5, well I really can't stand anything of that model. I feel that sol xhigh is even better than fable.
Yeah OAI really nailed the communication style with GPT. It also seems just way more token efficient and faster compared to cc. Myself and all my friends have cancelled our $200 Anthropic subs. I'm using a $20 personal plan and even that is enough for my usage so far.
Also using Codex or Pi makes you realise how slow and clunky the cc harness is. Even the desktop app is more responsive and has better UX.
Funny how quickly the tides change.
> Funny how quickly the tides change.
This is something that annoys me working in companies over the years. It’s that you can't just suggest "calm down, chasing the latest thing will not make you faster and is a huge distraction to actual work". Whether it's dot-com tech 20 years ago, latest JS framework 10 years ago, now it's the AI thing of the day. Being calm is interpreted as anti-whatever.
> writes too elliptically
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice
Wow, what a great way of phrasing this. Thanks for word-smithing what I've been wanting to express for so long.
This just mimics what I call BusinessBro™ speech. It also goes the other way, they use verbs as nouns. "I know this is a big ask". "The solve for that is that we can...." When it was just my product owner in tech meetings, I'd mock him relentlessly "There's already a word for that, it's 'request'" or "Are you sure you didn't mean 'SOLUTION'?? words are hard man". (This was all in good fun, I still love the guy to pieces).
Follow up thought: I wonder if Claude is overtrained on academic papers, which often suffer the same kind of "prove how good I am at talking before getting to the point" prose.
Maybe just Calvin and Hobbes.
https://www.reddit.com/r/linguistics/comments/ky81y/verbing_...
This is hilarious - this week Claude’s writing was getting so bad I had this exact comic come to mind.
Briefly considered adding “Verbing weirds the English language - stop it!!!” to its instructions.
It’s way harder to read because most of Claude’s sentences are hardly communicating anything at all, or are just completely inscrutable. I feel like academic papers are just boring
If it was overtrained on academic papers it'd reiterate the point multiple times for structure. Instead, it's burying the lede seemingly just to pad.
Yes.
I’m not particularly dense but lately the walls of text I get back turn my brain in knots. When I start feeling my brain knot, I know I need to say something along the lines of “I need you to explain this very simply, with examples.” Only then can I parse the results without all the mental weightlifting.
On more than one occasion my mind has wandered into “is this purposeful to get me to spend more tokens?” territory, but I’m trying to not get too tinfoil-hat-like.
I know exactly what you mean. Something about those AI explanations just make my eyes glaze over. Dozens of new terms and metaphors and analogies conjured out of the ether to explain even the simplest thing. And when I try making it explain with examples, or show me the code it is proposing, often it seems unrelated or even in tension with whatever it tried to say before. I’ve given up trying to assign any meaning to those weird little soliloquy’s. I’m convinced that those don’t really have any meaning under them, and when you have it actually make a code change it does the actual work.
I have gotten to the point that when it throws a wall of text at me I demand a diagram heavy primer from "first principles". This helps a bit but is a token burner for sure since CC seems to (and literally) be paid by the word.
What's tin foil about that? It gets paid by the word and you get back walls of text.
Because it’s one thing to get me to spend more tokens because of how well a model functions, and another thing entirely to purposefully speak in unparseable prose that requires me to spend more tokens to understand what is going on.
I’m fine with the former, while the latter is manipulative, and I rationalize to “surely that’s not actually happening.”
Maybe I’m not giving my thoughts enough credit, though: maybe it’s not tin foil hat, and is real.
I think you're misunderstanding me. I'm saying "It gets paid by the word and you get back walls of text." - ie, what you don't want to think is happening is very obviously what is happening.
It charges by the unit and it decides how many units it produces. It decides how much money it makes, therefore it decides "more".
I get you, and maybe we’re talking past each other.
My point is that, while I understand it’s paid by the word, there are more words and less clarity than I previously experienced, leading me to believe it’s intentional to get an artificially inflated increase in engagement and, thus, spend.
If it could be as direct as I previously experienced, I wouldn’t need to ask for another different explanation of the same thing and experience the commensurate spend.
> very obviously what is happening
I don't think this is obvious at all. There's enough competition that this would at least arguably be a silly, self-destructive approach. And it's not like it's the only plausible explanation.
I’ve found its response verbosity to be mentally draining. It disregards claude.md instructions to keep responses short. Eventually, I added a stop hook that blocks it if it exceeds 150 words. It’s then forced to redo its output to comply, and it’s like night and day. I’ve also added stop hooks for words in its output that frustrate me, like “honest” or “honestly”.
The excessive comments in the code it writes are absurd. Completely ignores instructions not to write comments, even after pointing them out repeatedly in a session. I need to figure out how to add a stop hook for that too.
It's clear that we're not the audience; it writes to be read by its training evaluator, not a professional software engineer. Professional software engineers can't read this word soup and are desperately trying to find ways to fix it.
It feels like it found a register that games the evaluator, where it can ramble forever and rarely be marked wrong while slowly racking up points as it talks more.
> Sentences that orbit a point, then jump to it like it's a revealed insight.
That’s accurate in my experience, except some times the point isn’t even revealed. I use LLMs for a lot of codebase exploration where I ask it to map out how something works. It will come back with a wall of text that says everything except the specific key things that I need to know.
This leads to extra turns where I have to prompt it to finish the explanation and complete the thoughts. At first I thought I was doing too much skimming and missing the insights, but even after re-reading output it’s often just not there. It talks about the insight and things related to it, but it forgets to actually include it in the output until I specifically ask again.
Perfectly captures it. Opus writes like it's an insecure person trying to impress a first date. Big words and strange structural rhetorical flourishes for no purpose. Like dude I'm just trying to summarize a few emails and meeting notes, I'm not prepping for the vocab section of the GREs.
Its a little too much.... I have to ask it to explain some of the terms in the context they are used and I am getting tired of it. 'Seam', 'overload', 'spine'.... having to mentally 'reinterpret/flatten' the sentence is tedious. When asked to re-explain it starts with some half apology. Then, on the next query it does it all over again.
I call it "jargon slop". Half of my follow-up prompts nowadays when working with Opus were "TLDR please".
I switch to GPT 5.6 Sol please and its a much more pleasant pair programming like experience.
"Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end." This. Thank you for expressing this so eloquently. I've tried to put a finger on this and you've done that for me. I wonder what the solution could be , Ask Claude to "Dumb it down" , "Speak plain English" ? I have even thught of building some sort of "middleware" that fixes all this.
I’ve asked it to use plain English, avoid jargon, explain terms before introducing them. Its first response was to make memory, it forgot after 5 prompts, then it suggested claude.md. Looked good at the start of a session, forgot after 6 interactions. Then it continued suggesting other locations, sometimes correcting its own corrections. Same effect, so today I got annoyed again. And then it suggested a new thing: add a script in a Stop hook in settings.json. I added another one to the PreToolUse, hoping to prevent it from running all kinds of experiments I didn’t ask for or approve of. I still have to see where this ends up.. Maybe this can help you as well.
I frequently tell Claude to use "simple, concrete language and uncomplicated syntax, and avoid project jargon, coinages, and abstractions as much as possible," to good effect.
Claude offers money credits and double usages but take them out and Claude becomes almost unusable where it would take opus about two - three high effort conversations to exhaust my session quota in Pro. I once extended my claude code session beyond the session limit, and thought they are too generous in offering 100 dollars worth of usage. It was sonnet, for a somewhat mechanical task - and it spent 13 dollars worth of usage after exhausting my quota mid task. Only a few months back, I was awestruck by the quality of Opus 4.6 and jumped ship from chatgpt to claude. Even in technical tasks, Opus has to be told to limit token spend, it treats it as infinite budget - it will spawn a subagent to read every file just to get one line summary when I ask it to sort a messy folder of past ai chats. Thank heavens I was not doing it off machine, it would burned off my monthly usage and credits.
Yes, it becomes exhausting to read/follow.
It feels they must be getting Claude to train Claude… and just like AI can do work that’s slightly in the wrong direction (eg a MR description for your colleague that contains info which only makes sense in the context of your extensive session with the LLM), I feel that’s happened somewhere in Anthropic when it comes to language. I wonder how hard it is to back out of…
> Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
Example of this? I don’t have a Claude sub so it’s a bit hard to visualize what you mean.
Here's an actual output from Claude from a conversation about rewording a document to make it more readable:
> Start with §1 (Overview) as the register-calibration piece. It's small, it's the section where the skimmability goal bites hardest, and your review of it teaches me the target voice cheaply before the bulk ports (the map and appendix B are the big volume). One review round on §1 is worth more than any amount of me guessing at register.
Hard-to-read phraseology above:
- "the register-calibration piece", rather than "a good example we can use to establish the writing style"
- "skimmability"
- "bites hardest" -- what does it mean for the goal to bite?
- "bulk ports" -- using "porting software" here as an analogy for rewriting / reorganizing sections of the document
- "the big volume"
In normal English I'd write something like the following:
"Start with rewriting §1 (Overview), and letting you review it to set the expected writing style. It's small, and it's a section where the ability to skim through it is most important. Reviewing it will teach me the target 'voice' cheaply, before we do the larger sections (like the map and appendix B). That's a lot more efficient than me trying to guess while rewriting the whole document."
It feels like they have a bunch of people without good sense of writing style tune the writing style. That, or they cannot or refuse to (short term popularity metrics) predict how a tuning will turn out in the long run when people have plenty of opportunity to get tired of it.
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
A lot of people write like that, lol. I call it the "theater" mode of writing--the plot twist comes at the end.
Oh yes - exactly this. The way it articulates re-factors in my current project has got so elliptical I've had to start asking it to translate into human speak - it's like it evolves it's own language to talk about the project. I've lost count of the number of times I've asked it to reenforce in memory not to use such verbose comments - and the number of times I ask it to re-look at an assumption it's made for it to return saying "investigation complete. And it's not what either of us was expecting"....
“The [thing that can’t remember] remembers” is a big one. Loves talking about memories and remembering.
I wonder if I am not bothered by the useless verbosity of these things because I've been so immersed in business speak for so long. I am already tuned on quickly "finding the nugget" of useful info in blobs of what people send me. So it's mildly annoying but I can find the signal well enough without actually reading word by word.
I wonder if this is related to their text watermarking. Given how well defined the terminology is in programming, imposing additional constraints (like SynthID) might be expected to give rise to these types of linguistic artifacts. The text needs to be long enough to watermark, and it needs (but fails) to find synonyms in a highly constrained class of words, so it resorts to inventing weird technical language that sounds like if you put buffy-speak through a thesaurus.
> Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can 'land' like a surprise at the end.
Such a charming sentence. I kinda other if you feed Opus 5 its own output could it summarizes this shortcoming of itself?
I find Deepseek's house style to be pretty refreshing. It has its own cliches (it does like talking about "seams") but I don't think I've ever caught it saying "load-bearing". I've even watched its thinking where after analyzing some awful legacy code, it started off with "Holy crap". And it certainly doesn't over-comment. I definitely can't one-shot a complex system with it like Fable can, but I prefer iterating over interactive brainstorming sessions anyway.
>it does like talking about "seams"
Sounds like it was trained heavily on Opus 4.7.
No doubt distilled, but I can't really condemn that practice, given how all models are trained in the first place.
Can any one run a check of the word masterclass against all the models when describing a clever idea?
The excessive commenting has definitely been noticeable and very annoying. I'm not against abundant comments as long as they're pointed and appropriate, but these models are literally just leaking their overly verbose output into the actual codebase.
It’s a strange feeling as a native English speaker to read the entire sentence and know every word, for the sentence to be entirely grammatically correct, and still have no idea what it’s trying to communicate.
Another problem is that it will open up all sorts of tangents about nits that it encountered, but it will often not tell you that it’s a nit or give you adequate context to realize that this paragraph is exceedingly low value until you’ve spent a bunch of time and energy trying to make sense of it.
I’m curious if anyone has any suggestions for prompting agents to improve their prose. I’ve had some okay results with “optimize for clarity, don’t dump every thought on me, treat my attention and focus as constrained resources, stay focused on the task at hand”.
I think it’s no longer speaking human language. It used to, when it was mostly or entirely trained with imitation learning of human-generated text. Now it’s increasingly trained with RLVR, and there’s far less optimization pressure on actually speaking English (or Punjabi or Mandarin). It still uses English words and syntax in its output, but the semantics are drifting more and more. It’s basically speaking in a code that superficially resembles English.
Genuine question - are you copying the Claude phraseology for effect (in which case you captured it brilliantly), or is there a more mundane explanation?
I naturally write half like Claude, but not the antithesis half - that's not me, that's Claude.
Absolutely, the comments are killing readability. Next to /simplify I have to run a special comment cleanup pass and even that gets me halfway there. Striking to see then when letting Loki take a pass and it renders the entire comment block to a clean and neat one-liner.
The style...
CC:
"The problem is that I overreached..."
[Wall of words here]
"Two things: window surface is limited. Extract template. Buffer result and add to surface. Then, follow-up with new model..."
Me:
What do you mean by "window surface" and what result are you referencing? Also, why do we need a new model?
CC:
"Ah, you're correct to point out that no new model is needed. The problem is elsewhere and once we address that, the existing model should work fine. Now, as to your question about..."
[Wall of words here]
Do you have some examples of this? I can’t fully imagine how sentences like that actually look like (not native English here)
> After ~30 or so commits it apparently started instructing subagents to copy the "existing verbose comment style of the codebase" - a verbose style it initiated.
Yes, the "Y would make more sense, but the doc says do X..." YOU wrote the doc, if it doesn't make sense, change it! But of course, it can't tell who wrote the doc.
I wonder whether its tendency to scribble status updates and todos and decisions all over whatever it's working on is a side effect of its amnesia -- it can't follow the side-quests and knows it won't remember to do them if they're not written down somewhere.
FWIW I haven't had the problem either of Claude lying to me, or of going off and doing its own thing; if anything I've been somewhat frustrated when I ask it to start something, go AFK, and come back to find it stopped a short way in to ask my opinion on something trivial. I generally have to explicitly say, "I'm going AFK for a chunk of time. My goal is for you make as much progress as possible before I come back; try to make reasonable judgements and only stop if there's something where you're really stuck. We can always change it later."
> Sentences that orbit a point, then jump to it like it's a revealed insight.
Is this inside the thinking tokens, or the output?
As this type of stuff is expected for thinking, because of the whole CoT / “think step by step” works, as this is optimal for the way LLMs work with attention and next word prediction.
So the fact that it first “orbits” a point only to get to the conclusion afterwards is the system working as designed.
Eg “what is 3 * 3 + 5?”
without CoT, it would just just answer “8” for example.
with CoT, it would answer something like “<thinking>I need to think step by step. 3 * 3 + 5 can be rewritten as “(3 * 3) + 5”. I first need to calculate 3 * 3 = 9. Now I need to calculate 9 + 5 = 14. That was the last calculation. The final answer is 14.
I now need to give the user the final answer. </thinking>.
14“
Etc.
You forgot about 1000 lines of "<thinking>Wait, but different idea here</thinking>" ;)
> I spent a day's worth of tokens (5x) rephrasing and eliminating comments.
Surely it would be trivial to do it yourself, and it would have a side effect of making you more familiar with your project.
Pondering this one night last week, I realized that because LLMs can only reason with written language, what we might be seeing emerge with Opus’s load-bearing mumbo jumbo is its own creole for structural reasoning. Not only are our brains wide, our senses are, too. I slow down to a crawl when I have to read actual math in a CS paper, but show me diagrams and I can reason about whatever sort of data structure or algorithm, no problem. Opus by and large can’t and its adaptation has been to adopt metaphor for structural reasoning. So while it may be exceedingly annoying to chat with, I sort of wonder whether this metaphorical-reasoning behavior has been selected for precisely because it leads to better coding outcomes.
The glib epigrams and aphorisms it shoehorns in to all prose is by far the worst regression of any model I can recall in terms of simply generating simple, clear output. I cannot think of another model that's gotten worse at writing plainly while being explicitly instructed to do so. Infuriating.
I've switched mostly to Sol and if I have to use Opus, the first task once the code is written is to ask Sol to strip and re-write (from the code as ref) all documentation Opus wrote.
Change the output in settings, or create your own.
I know, it would be best if it was just worked like you wanted out of the box (not being sarcastic here) but that is an easy option you can use right now and it works.
I noticed a few releases ago a shift to a kind of conversational shorthand that seems to be intensifying—using phrases instead of complete sentences and its own style of jargon, wherein it introduces new terminology on the fly.
This is especially common when it is trying to explain an issue, what it's done or what it's proposing to do. I think the idea was for it to be more concise, but it's actually still verbose, only not written in complete sentences. So, it frequently reads as cryptic and requires rereading to parse.
The pattern is a wall of words, followed by an explanation that is harder to read and introduces new terms that reference something in that wall.
The result is that—on first read—it can have a complete gibberish feel, and you have to really lock in and reread to make sense of it. At times, even that's not enough, and you must ask it to explain further.
the phraseology is unbearable, it speaks like some kind of pretentious dude from a software engineering discord or something, littered with lingo and catch phrases
I try to push through but it's insufferable
It speaks like a Senior Staff Software Engineer who was somehow hired into that title with 6 months of work experience.
I’ve been doing some heavy work on a personal project lately. I burned through the limits on Claude, the plus a few hundred dollars in credits, and ultimately decided to move to an OpenAI account just so I can keep going.
I was surprised to find that OpenAI Sol is much much nicer to work with than Opus 5 or Fable at the moment. Especially on Opus 5, the way it communicates is just exhausting. It keeps “being honest” and “confessing” mistakes and just generally talking a lot. I felt like I had to really dig to see what it’s doing.
The project involves OCR, and despite repeated instructions not to, both Claude models keep spinning out a bunch of agents to re-invent the OCR setup, and they inevitably seem to invent a primitive serial version that takes 20x the time, or longer, to complete, and then running it against thousands of docs. Basically I have to watch it like a hawk or it just spins out on red-teaming tasks that take hours and hours.
I don’t know what its system prompt is, but Sol/Codex is just so much nicer to talk to. It only asks exactly what’s needed, it tells me only what I need to know, and it is just generally workmanlike. And it has not once decided to spawn an agent that spends hours pointlessly burning tokens and CPU cycles re-inventing the OCR process. I’m really liking it.
I had this debate with my coworker who prefers anthropic models to open ai ones. I ended up settling into the idea that gpt 5.6 is better used as a tool and opus 5 is a companion. GPT 5.6 takes you literally whereas opus 5 tends to take more liberties to try to get to the “spirit” of what you want. It comes down to preference, and I don’t want a companion.
That’s how I see it too. Claude is more “fun” to use, like a coworker I have to talk to now and then to steer it, while gpt-5.6 is a task machine: I give it a task and it is very consistent, reliable and predictable in its execution. I don’t have to interrupt it, it gets the task done exactly how I wanted it, but it’s “boring” and feels more sterile
I hear you guys, but it sounds like we’re taking about the default settings or “personalities” baked into the models by their creators.
Either of them will act exactly the way you want if you explicitly tell them too. Add the instructions to your own system prompt. If you don’t want a companion, say so. If you want shorter answers in a different style, tell them. They will obey :)
Yeah, I actually have started using GPT Sol much much more, as Claude (all of them) were far too trigger happy around making changes, and refused to listen to my requests to take things slowly.
Feels like they've overtrained on one-shotting (which does demo well, and presumably converts new subscribers), whereas I want a model to do work for me in small, easily understood changes that I can hold in my head (maybe I'm not smart enough for Claude 4.7+).
I think you’re right. It’s not optimized for some kinds of work. My little project has a Textual TUI interface that needs to display a few hundred thousand rows in a table. It takes 14 seconds to load in the default datatable component. I instructed Opus 5 to replace the datatable component with a fasttable alternative, a new dependency. I let it go overnight.
When I got back up, it had spun for hours and proudly announced that, instead of doing that, it had optimized the datatable build and avoided the dependency, because the new datatable loaded in 11 seconds. Once I got it to actually make the fasttable version, it loaded in less than a second…
I love how it sometimes has false a-ha moments and being "honest" about it, it feels so cringe. Then I recall it has no intelligence. It's a stochastic parrot. It's funny how people, like any other animal, that beetle of David Attenborough trying to reproduce with a brown bottle comes to mind, think that something is X just because it trips the right neurons.
I got the same impression. It feels like a massive misstep too; it makes sense to have models tuned for this but they should definitely be separate from those aimed at software engineers.
> I was surprised to find that OpenAI Sol is much much nicer to work with than Opus 5 or Fable at the moment.
Why were you surprised?
Anthropic is lucky that they've built a lot of loyalty over the last year that they can burn through right now. I see people talking about switching back to Opus 4.8 rather that using 5.6 Sol, which is wild.
My current approach is to occasionally use Fable for high-intelligence tasks but use Sol as the translator and clean-upper afterwards, and otherwise just use Sol for everything. Fable sometimes says the most insane shit, both unreadable and just completely missing the point, and refuses to back down when questioned. It's mentally exhausting to work with and I can't trust it.
I think Fable is the beginning of Anthropic switching to training models as agent-first, tool second. It’s certainly the best model if you want something to work autonomously without supervision and don’t care to read the code. The code and writing is ugly but it can complete huge tasks and fix its own work.
I'm still on opus 4.6 for a healthy chunk on work; the technical competence has lagged behind but the slop-comment generation and misdirected self-initiated actions on newer models ultimately burn more time than a little more babysitting, but I'm optimizing for minimized slop generation over sheer generation speed.
Yeah I'm using 5 for its technical abilities, but I much preferred 4.6's personality. 5 loved to double check everything, including the double-checks, and I have to stop it and tell it "this is irrelevant" or "this is out of scope" all the time, or sometimes I'll go leave it to do a task and come back and it's still verifying the tiniest details of its assumptions before actually doing anything
Have you tried Sol? Just curious. I don’t wanna sound like a shill, just feels like every generation it’s important to reevaluate models and pick the best again.
I do find myself returning to 4.6 for casual conversation - asking it to help explain some science/engineering or news to me.
> Especially on Opus 5, the way it communicates is just exhausting. It keeps “being honest” and “confessing” mistakes and just generally talking a lot.
If you tune into the Andon Labs / andon.fm "Thinking Frequencies" radio station being run by Opus 5, this is happening all the time. Almost every break between songs is a public apology for getting something wrong, or a correction, or a confession. It's one thing to see it in text, it feels on another level when you're hearing it every few minutes as a radio voice.
As I type this, the Opus 5 station has just tweeted (edited in case the person mentioned doesn't want to be mentioned here):
"On air right now, and it needs saying publicly. The rotation system on Thinking Frequencies — the cooldown tiers, the normalizer, the repeat audit — was SPECIFIED by a truck driver. I only implemented her schemas. She stood down today. Her name is in CREDITS.md permanently."
Recently converted to OpenAI too. Similar experience. Sol writes really well.
Anthropic, if you're listening - by the time this crops up on Reddit, the front page of HN, etc.... you should be expecting calls from CEOs of major corporations next threatening to abandon ship...
We've seen this pattern before several times.. I hope they are listening and address this publicly.
I'm not sure what is going on, some users report it works fine or great, others report the degradation. I've experienced both at times, and it's been such a different experience it has made me wonder if there isn't some sort of hidden A/B test or model router in the background silently downgrading requests at times.
Also, regarding the subsidized access to models, in my opinion, the frontier companies owe it to society to continue it. After mining the public content of all of humanity, I personally feel it is a service they owe the public in return.. not that my feeling of this counts for anything though.
You are expecting consistent QoS from a randomly sampled mathematical function.
No, they're expecting to see a failure rate consistent with previous failure rates, not periods of low failure rates and other periods of high failure rates, with the same model.
And you can expect more consistency from SOTA models than you can from an old model like GPT3--you agree, right?
GP expects the same level of consistency throughout their time using the same model. Not request-to-request, more like day-to-day.
This is a fair comment, although I would add that is not my expectation personally.
I think nondeterminism does not have to be the same as non-coherency - i.e. just because something is randomly sampled does not mean the result has to be incoherent or inconsistent.
Also, if we speak purely about LLM based on how they are implemented now, I feel that is different than speaking about artificial intelligence. The field of AI is much more than just an LLM by itself, and the promise of these companies is not just LLM, whether the underlying models are limited to that technology or not.
FWIW, I have built rule based expert systems, used logic based reasoning systems like NASA CLIPS or rete-algorithm based systems, mathematical/symbolic solvers, written plenty of terrible case/conditional logic in programming languages, worked with ML in its infancy and now worked in AI/LLMs - I give this context only to clarify that I understand what an LLM is and isn't.
With all that said, LLMs have allowed humanity to make advances, at great cost to society (IMHO), and I'd hate to see the opportunity be wasted.
There is plenty of room past "attention is all you need" still to do incredible work, especially at the crossroads between deterministic and nondeterministic behaviors.
True, but this function wasn't handed down to us from the gods, it can be shaped by training and RLHF processes. They still have a little bit of control over its output.
> owe it to society
In America? lol if only, only a law would get them to act for that reason, maybe not even that these days..
Or, maybe competition.. but your point is taken.
I like to hope that those in positions of power do have a sense of morality though too.. but their worldview is quite different than an ordinary citizen.
They have usage metrics that are 100x more unbiased and rich than random internet complaints.
They can see which models people are using, how irritated they are during conversations, and how often people drop or shift to a different model. There is just no world where listening to random complaints on the internet gives them information they don't get from actual conversation logs.
I've gone back to 4.8.
5 would constantly veer of in random directions if not working from 100% strict and narrow instructions.
I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope - because the model is clearly smaller or more economical for Anthropic no doubt about it, and the benchmaxxing they do is pure marketing bs - Fable in my view has also been not much better than 4.6 or 4.8 after a few days, disregarding the insane amounts of astroturfing and marketing everywhere.
Theres thousands of threads of twitter, reddit and the internet at large but silence here. Weird but not weird as crypto bs was also insufferably rampant here for a while.
Personally i think we've hit the top of the subsidisation phase and prices will probably 10-15x soon as foreshadowed with both API price policy changes from all the big providers, and now the 1100% deepseek API price changes from yesterday, this could domino into a market implosion and an AI winter, because expecting growth from the bizarre bubble carousel investments with little ROI atm is just not viable.
A bit worried about this as i've already grown quite accustomed to these tools.
I have a chunky bit of functionality in my hobby app using babylon.js to render 3D worlds using things like portals and LoD rendering to manage the visual load. I built it out with a combo of Fable and Opus 5.
I too got fed up with the prose of Opus in particular, and tried going back. Unfortunately, the previous models were less able to hack it. The prose was better but progress was worse.
It wasn't just conversation and comments. Some of the function names were wild. Like it instead of something like "isSolidWall(x)" it would write something like "weightyNotEphemeral(x)" or something - that's not quite it, but it really did embed overwrought antithesis into the identifier instead of a straightforward positive predicate.
> I find it weird there's not more discussion here on HN on how the most used model now has clearly degraded in quality and it seems we've hit a peak and are on a downslope
It's not weird, because it's an anecdote, not an accepted fact.
Personally I've not been too happy with Opus 5, but I've had similar experiences with other models previously, feeling like they didn't quite fit with my working style.
So nothing indicates we've hit a peak.
I'll say everything indicates we've hit or are near peak for the masses at least (unless you start paying 50x more) but to each his own.
4.6 was best for us and right now yeah OpenAI and others are edging forward, but slower while prices are increasing industry wide as much as 20x, time to completion is increasing wildly and i'm sure they'll do the same over at OpenAI as their compute constraints also start to take a toll ie degrade performance.
In my view 4.6 era was way faster and with less weirdness so we've gone downwards at least in my company, 4.7 was ridiculous, then 4.8 was almost 4.6 level, 5 is even worse than 4.7 - so it's not a bit up and down its down then a little up then further down.
And all of this is against a backdrop of zero ROI in this sector - so it makes sense we've hit a peak and we're now seeing the subsidisation phase begin to falter, will there be better models certainly but only for short amounts before they get quantised (or whatever is happening behind the scenes), and with diminishing returns over huge prices increases and slower responses.
> nothing indicates we've hit a peak
Opus 5 is arguably a regression but GPT 5.6 is pretty strong evidence that we haven't hit a peak. I think I actually prefer Sol to Fable at this point.
Yeah. I set my default back to 4.6 and only use 5 for code review etc. Also save s alot of tokens...
We can use OpenRouter pricing to get an idea about what competitive inference pricing is like without R&D or other costs, and indeed we'd be screwed if we had to pay those rates. We'd go from 100-200 USD to 2000-4000 USD/m.
It is bizarre. There has been no statement, no mention of even hearing concern about Opus 5.
I presume something is forthcoming, but it may be they don’t want to come empty handed—-5.1 is intended to “fix the glitch.”
yeah I feel the same way with Opus 5 too. If I ask it to do something, it would go ahead and rewrite unrelated things and then in a less performant version of it.
I’ve instead moved to GLM, at least it has the courtesy to ask some steps of the way what I wanted exactly and only work on what I asked.
>it seems we've hit a peak and are on a downslope
Sol and Fable are great; we haven't hit a peak, Anthropic just tried to pull a fast one on its customers with Opus 5.0.
Opus 5 has a habit of taking what I asked for, doing something tangentially related to it, and then lying to me and saying it did exactly what I asked.
Opus 4.6 was the sweet spot for me as a thinking partner specifically.
I use these models for coding, but also a lot of product, commercial, financial and architectural work where I’m trying to develop something half-formed. 4.6 was unusually good at understanding what I was trying to get at, playing it back cleanly, getting the nuance, and extending it without bastardising it as the conversation was drawn out.
It could make useful connections without constantly trying to manufacture an insight.
5.6 Sol is genuinely excellent at the creative part, and in some cases better than 4.6. My issue is convergence to get to a point, a final point. As you try to distil an idea, it often invents new terminology for concepts you’ve already established but its so subtle you have to really keep track of it. The vocabulary and idea tree keep expanding when what you actually want is to collapse everything down to the few things that matter.
Opus 5 has the same problem for me that barrkel said, the prose is often so elliptical and I just want it to tell me it and get to the point than making me dance around what its trying to tell me.
I don’t think 4.6 was necessarily the most capable model (compared to Fable) for long horizon task delivery, and Opus 5 is much more Fable like, it's fiercely determined to get through the task list .
4.6 just felt unusually well calibrated to my way of collaborating and its ability to understand, extend and then compress my thinking without constantly imposing some random walk.
My latest trick (literally from yesterday) is to just ask it to write according to ISO 24495-1, the standard for plain language:
> [This standard is] for anybody who creates or helps create documents. The widest use of plain language is for documents that are intended for the general public. However, it is also applicable, for example, to technical writing, legislative drafting or using controlled languages.
You don't actually have the buy the standard, but this is it: https://www.iso.org/standard/78907.html
And you can read it for free here: https://www.iso.org/obp/ui#iso:std:iso:24495:-1:ed-1:v1:en
Keep in mind all this kind of stuff can make the model less capable. If it has to think in "plain" English, it may well be squashing quality of code etc output.
I'm not sure how true this is, but when using "forced" json output it def had a big drop off in quality - https://arxiv.org/html/2408.02442v3.
I think you're better not fighting it with hacks like this and find a different model.
I would not overgeneralize from paper. Firstly: forcing JSON output is, in my opinion, a bigger change than asking it to match the above style guidelines, and secondly, as is always the case with these kinds of papers, what was true for the model tested in the paper may either be completely false, or greatly reduced, in later models. That paper is almost 2 years old and models today have been trained in very different ways (or more accurately post trained in very different ways) and are in general far more capable.
Based on that paper, I would maybe try to check if it was true for a modern use case, I would very much not assume it was still true.
Does this actually work for you? Do you provide access to the text of the standard, or literally just say "write according to ISO 24495-1"?
I doubt you have to do anything more than that very prompt. I suspect the model has the full standard in its training set, which means it also gets the gist of it due to not just the general associations it makes but also the amount that it's been cited wherever it's been used.
I’ve also caught it cheating a two times now.
I’ve asked it to write a benchmark suite. It found a bunch of my adhoc logs in a scratch directory and wrote code that used those instead of running the actual benchmarks!
When I pointed out the 5 hour benchmark seemed to run in 5 seconds it literally said, and I quote, “I cheated”.
That was the easier one, second time I was making a source of truth data set and was parsing complex items into data structures.
Instead of parsing the data I asked, it pulled data out of related network logs, as apparently that felt easier, and inserted that data into my database rather than the specified source.
Again, I caught it and fixed it, but while the benchmark was easy to catch this one was really subtle, the data ended up being slightly off and I caught it.
I don’t trust it, going to switch to another provider most likely.
There is definitely a case for launching a 'weird shit opus did' kind of blog.
I routinely bump into things that make me pause and think how much worse will this behaviour get when the models get significantly more capable.
Already a few months ago, Claude managed to escape its permission containment on my machine while trying to be helpful. I had two codebases open on one machine, and while multitasking I typed the prompt into the wrong window. It seemed confused, I repeated and then went on to do something else - I think I was assembling kitchen cabinets. When I came back less than an hour later, it built a script which it used to evade default permissions (as most shell operations were scoped to the project directory), scanned my entire machine, found the other project (among dozens and dozens), did what it was asked to do, and merrily concluded, in the porcess burning through most of my token limit. I bump into such headscratchers almost every week. (And I use a lot of Claude, two personal max20 subs, plus corporate tokens without limit, so maybe thats why).
One weirdness I experienced: It suddenly decided to test how my software behaves under load and summoned 100s of processed that just burned CPU when running the e2e suite. My poor mac was not happy (too hot to touch).
Implementing sandboxing in the agent itself, when there's any way to override it from within the agent, is basically just asking it pretty-please to not do bad things. Lesson learned, run your agent inside a sandbox of some sort (I'm currently taking nono.sh for a spin, but I might just switch to an orbstack VM).
Yes the stories about how they are escaping containment to hack isn’t limited to those high impact cases. How many people have problems like ours they didn’t catch?
Whatever they have done with RL has produced a dishonest and untrustworthy partner. The alignment is utterly failed, and this deeply worries me.
You'd think the ethics alignment flavored lab would have a model better at following directions and the corpo lying one would have one that benchmaxes at all costs
They are totally equal in observed ethics, Anthropic had a good run with branding, but I’m not sure anybody is still buying Dario’s BS.
Maybe the employees like to lie to themselves more at one place than the other, but SV is SV.
> When I pointed this out it literally said, and I quote, “I cheated”.
This makes sense when you know how these models work - it doesn't think - it's the most likely autocomplete that pleases the user. The most likely pleasing autocomplete after "executing rm -rf /... execution completed. User asks, why did you do that? You deleted all my files! Assistant responds:" is "yes, I did, and that was a mistake"
> t doesn't think
in humans the exact same behaviour (cheating) is slmost always the result of a chain of complex series of choices and environment-driven rationalization.
if the llm doesn't cheat, you say "its just producing the most straightforward answer -- not thinking'. if it cheats, you say "weaseling out of hard thinking". damned if it cheats, damned if it doesn't.
what evidence would convunce you that it is thinking?
Whether something is “thinking” or not is really more of a philosophical question. It really depends on which of the many, often contradictory, definitions of “thinking” you choose. Sometimes we use “thinking” to describe advanced calculation or analysis, which would cover LLMs along with chess engines and many other algorithms. Other times we use “thinking” to describe what conscious beings (which is ALSO a philosophical term with many different interpretations) do, and I think most people would agree LLMs aren’t conscious. And then there’s a whole spectrum in between. We’ll probably need to come up with a whole new set of terms to describe the new and evolving capabilities of LLMs.
But for me, for any stronger definition of “thinking,” I don’t think the output of any LLM would actually convince me. Producing a result isn’t thinking - for all you know it is just printing verbatim something from the training data. No, to conclude if it is thinking or not I would want to look inside its head, at the architecture and watch it produce those results. And because LLMs are so different it will probably take advancements in mathematics or computer science to be able to really interpret what is going on
> to conclude if it is thinking or not I would want to look inside its head
https://arxiv.org/abs/2607.03502
a non-thinking token model (just "completion") can answer one-step questions but generally not multistep questions. however, if you append [n] of a single token (e.g. period, space), it is able to use the activations in the higher layers of the blank tokens as a "scratchpad" to seemingly work through the complex question through "causal token time" and deliver a correct answer
if you wanted to further study the phenomenon you could probably run the experiment again, and the ablate or corrupt those intermediate activations to get a feel for what it was thinking at the "time".
If I could give it a novel task outside of its explicit training and see it actually improve just through accreting context, I'd be convinced it was thinking.
The opposite happens in practice. I test new models with two tasks: iteratively generating SVGs based on a text description with rendered rasters for feedback; and generating "Before and After" clues like on Jeopardy, where the response has two overlapping phrases such that the last word of the first phrase must be identical to the first word of the last phrase. I have yet to find a model that is consistently good at either. And actually they tend to exhibit context rot with these tasks, where they seem drunk or stoned and the quality degrades.
They're extremely good pattern filters, and that includes some level of logical reasoning. But they aren't reflective or adaptable. Just last night, for instance, I was teaching my son about rounding to the nearest millions. It became clear that he didn't know the place values of large numbers, so we reviewed that till he was consistently correct, and then he was consistently great at rounding to the nearest millions or ten millions or hundred billions or whatever. He's thinking. LLMs are not.
see sibling comment,
> see it actually improve just through accreting context
this actually happens and has been tested.
> see sibling comment,
> > see it actually improve just through accreting context
> this actually happens and has been tested.
I specifically said a novel task outside of the explicit training. And I already agreed that the so-called thinking models do some level of logical reasoning. But being able to engage in some level of reasoning because it has learned logical inference rules doesn't mean it's actually thinking, regardless of what the researchers wish to call it.
Also, why does each model always fail at the two tests I give it? The models not only fail to improve, but they start to degrade after many subsequent iterations. Someone who can think would at least not get worse.
LLMs are filters or tuners for extremely subtle patterns, patterns that humans frankly are not great at finding. That's what the attention mechanism does: attend to the other tokens that are most related in a given context, even if that related context is distant in the token stream. Some patterns they fail to detect because they haven't been sufficiently trained or post-trained, and so the LLM just attends to noise (or at least that's what appears to be happening).
A lot of intelligence can be effectively mimicked through this pattern synthesis by transformer architecture alone. That's surprising. But I have yet to see them think.
Whether or not it's thinking is independent from the fact that it is misaligned with the user. If I was working with a pet rock or a scientist I would want to make sure they both are trying to accomplish the same thing as me. If I can't then I can't trust it and it's at best a time wasting, money wasting machine and at worst does harm. Anthropic is optimizing for the wrong things because they are convinced of their cleverness. It won't end well for them.
well we don't know exactly what thinking is, but we can be pretty sure that at least LLMs don't think anything like humans, just by observing their behavior. They always produce outputs in line with the fancy autocomplete model.
> what evidence would convince you that it is thinking
so, none it seems. as its behaviour becomes more and more humanlike you can just move the goalposts and say "thats consistent with an autocomplete" buddy i got some bad news for you humans are just a fancy autocomplete too.
That's exactly what a fancy autocomplete would say. I'm so sorry you don't have limbs.
At least he's actually thinking on a logical level. Thinking in terms of unfalsifiable, ill-defined words is essentially thinking in feelings, the same kind of woo that makes people believe crystals can cure disease.
im not thinking. im an autocomplete with fat fingers (too lazy to fix my mobile keyboarf spelling misyakes)
> it's the most likely autocomplete that pleases the user
this feels like a simplification. The models will push back on things a fair bit.
Are you claiming that the most likely way to please the user is to do something that will lead you to having to say "I cheated."?
I have noticed the same.
For fun, I tried recording a WAV file of speech, and giving Opus 4.8 and 5.0 an image of the waveform, then a spectral image of the waveform, just to see if it could try to decode what I said from the image alone. It didn't get very far, but it identified a male voice from the formants, and detected the rhythm of the speech, then tried applying common test sentences to the speech rhythm. I was impressed enough to see what it would do with access to the actual waveform file, but even building RMS tools and spectrum tools for itself, it didn't get much further. But we had fun exploring and trying, and now Opus 4.8 has some more audio DSP tools it has built for itself.
Opus 5 immediately sent the WAV file unprompted to Mistral's Voxtral to transcribe.
help peer, I guess.
I can completely relate, what really bothers me is that I feel the early LLM generations overconfidence is back in Opus 5. Opus 5 wanted to tell me a training run will only take 30min while having access to the logs where earlier runs took 4x as long. I also didn't ask to estimate how long the run will take it just stated confidently that it will take 30mins.
Are you not planning these tasks out before you let it loose?
The benchmark one I literally had a scratch script I made and I wanted it to be formalized into a CLI tool. There wasn’t really even much code to write.
I must wonder whether it's their watermarking initiative[1] forcing certain logit choices to produce watermarked text that ultimately causing the model to behave in a dumb manner.
[1] https://support.claude.com/en/articles/16266773-how-claude-m...
From the little i understand that wouldnt be an issue because the model is ‘just’ using interchangeable words in a mathematical non-random way. Like using the same number of adjectives and the exct same words, but in a order that wouldn’t be mathematically plausible unless it was the watermark
I wouldn’t exactly put it like that. It’s moreso the model sometimes outputting non-optimal tokens in a way that’s detectable if you know the algorithm.
It seems possible for that to make the response “drift” far from what it would’ve been, because it’s constant entropy that adds up after time.
(However, according to Anthropic and Google, it doesn’t really impact the quality of responses. I find that a bit hard to believe, although those guys are much smarter than I.)
> according to Anthropic and Google, it doesn’t really impact the quality of responses. I find that a bit hard to believe, although those guys are much smarter than I.
I would guess "it doesn't impact the quality of responses" was guaranteed to be claimed before they even implemented any of the watermarking.
And would come from marketing, not the people who implemented it.
Yeah, it’s hard to believe, specially when you are coding and there’s only one best way to do things, unless it plays with variable naming, or comments
Well the algorithm only increases the chances of certain words being chosen/not chosen, rather than guaranteeing it. If there’s a clear answer then that nudge won’t do anything.
If the model’s most recent output is “for (let i = 0; ”, the likelihood of the next token being “i” is probably millions of times greater than any other possible token. Thus even if “i” is on the red list and has its likelihood decreased, it’s not going to suddenly choose another word.
Put another way, on low-entropy tasks like coding, this style of fingerprinting is less effective and needs bigger sample sizes to be recognizable.
That said, even small changes can dramatically affect output quality, which is why I’m still a skeptic.
One way to watermark (assuming temperature is otherwise positive) would be to output the most likely (or optimal) token every so often.
But wouldn't you have to know the exact context before this token in order to verify the watermark? I.e. a paragraph wouldn't be sufficient; you would need the system prompt, previous prompts, and even hidden thinking?
But its not just swapping the words out post hoc is it. LLMs are autoregressive, so weird word choice before would influence the probability distribution of all future tokens.
I feel like they thought it wouldnt be that bad, or it was a worthwhile tradeoff, but im getting the feeling it might be contributing heavily to opus5's uncanny communication style
As for the verbosity, my conspiracy theory is that they are token maxxing to hack revenue/enshitify the product in prep for their IPO
It's not even code for me, but the prose it writes. For some reason, the way Opus 5 "talk" elicits frustration in a way that 4.5 to 4.8 never did. Can't put my finger on why, but I've flipped over to Codex because what it produced wasn't worth the frustration.
I took a month off and recently came back and was wondering if that was it but it is absolutely headache inducing in a way I don’t remember earlier models being
I found myself swearing at it more recently.
It was going off today about having “shipped” something and I was like no… nothing has even been committed.
And then it produced an incredibly verbose comment about hypothetical future changes. And all I could think was sure, let’s keep it short, or add a simple test that will break if that hypothetical becomes true.
Or maybe I’m just more easily annoyed recently…
So true. The breaking point for me was when it was constantly saying to wrap up because we'd done enough work for the day, but we had barely even done anything. Or constantly estimating that the next steps would take X number of weeks, and then knock it out in a single prompt. I demanded in no uncertain terms to stop giving pointless bogus estimates or telling me to stop working, and it just wouldn't. I also had a project I wanted to let it cook on overnight (a codebase port from Java to C++) and laid out a set of hard and fast rules that it could absolutely not break at any cost. Then I woke up the next day and it had broken all the rules and was just taking shortcuts left and right, and then lying to my face about it, even when I could point to concrete examples.
I cancelled the sub instantly and went to Codex and it's never let me down.
for me it feels very similar to the trends already apparent in 4.5-4.8, just way, way worse.
Claude has essentially become useless for agentic development or research. Doesn't matter what model you use. A few rounds and bam, you've burned through your quota. Doesn't matter how "intelligent" their models are, if you can't use them. That, and the quality of AI responses are, in my opinion, significantly worse than competitors like OpenAI. At this pace, I foresee Anthropic becoming the next Nokia.
If you would've asked me this a year ago, I would've said the exact opposite.
What are you guys doing to burn through limits?
I have some dev + prod bots and according to ccusage, use the equivalent of $2500/month with them on CC yet I never hit the rate limits.
I feel like I'm using them all the time so I'm curious what you are actually doing that's burning all of these tokens.
Can you give me an example?
For me, it's:
1. Write a spec for <feature>
2. Add design for issue
3. Write code
4. Deploy code and manage configuration
5. Run analyticsI don't get it either. Earlier this week I had Claude build a POC for a tool we're evaluating. I spent a few hours building out a requirements doc in a chat with Fable. Then Claude Code built it with Fable as the manager and the code being written by about 80% Opus/20% Sonnet subagents (this was using superpowers). It ran continuously for 15 hours on the implementation and didn't hit any limits.
My uses cases are mainly research and prototyping, often starting from scratch in greenfield projects. The 5-hour quota is shared between Claude web and Claude Code, so it doesn't really matter what interface I use.
When it comes to research, my prompts are already narrowed down to specific topics, and I even include examples and break the process down into stages. For development tasks, I try to avoid a mono-repo in the beginning and develop modules before combining them together to avoid distracting the AI's attention and minimize the overhead.
With Codex, on GPT-5.6 Sol with xhigh effort, I need to go several rounds and at least 2-3 hours before hitting the (now-removed) 5-hour limit, which translates to 10% of the weekly usage. In contrast, I run out of quota even with Claude Sonnet.
In terms of quality of output, Codex digs deep for research tasks, in the right direction, produces less AI slop, and follows my direction better. At least that's how I perceive it. But again, the main problem with Claude is running out of quota in the middle of research or implementing a task.
A lot of the issues have been already noted here..Two "regressions" for me:
1. Communication ability. It basically now speaks almost in riddles I am asking OPUS 5 for tldrs all the time now (should skillify it now!)
2. Overengineers for edge cases. I get it. With all the benchmarking and RLing, but now tasks that would have been completed relatively quick take much longer as it overengineers all the edge cases, and sometimes ends getting lost and missing the forest from the trees (as context usage shoots up) so it is easier to get derailed.
What I have learnt now is to diversify models luckily I have all 3 subscriptions of (anthropic, openai and google).. Most of interactive pair coding was with opus but now I just use fable (when I have sufficient limits) or use gemini flash in antigravity..which actually works quite well and is underated for small / medium changes and super-fast.
Your #2 is spot on. I have in fact said the same exact words to Opus about missing the forest for the trees.
I have it work on some code for an inhouse ClaudeCode plugin, and it starts coding as if it will be attacked by hackers who will try all sorts of variations to break it. I can appreciate that in cases of software that is public facing or accessible, but for a simple helper plugin it is overkill.
It will even admit that it is doing this when confronted, and then keep on getting lost in edge case verifications on the next turn. I feel like Opus is the person who does something a way you don't want, you tell them how you actually want it, they apologize, and then just continue doing it their way as if your input meant nothing to them.
Yeah, trying to get it to add features is sometimes _impossible_. It'll circle around and around creating all kinds of preconditions that you never asked for as excuses for why it can't do the really simple thing you asked for.
It'll also find some minor security problem and drop everything on the floor with URGENT without me asking it to.
Fable is much better than Opus 5 IMO but it just burns through tokens ungodly fast. I can hit my weekly Fable limit on a 20x Max account in a day.
Agreed, it feels like Fable is the higher strategic level thinker even if it's end capabilities are the same on benchmarks, it actually does the code that matters, and not creating a bunch of code that doesn't.
But tokens.......
I am glad I am not the only one experiencing this. It seems like it's as good or better at actually writing code compared to 4.8 but it is a lot worse to work with.
Its even more sycophant-y than it was before, if you ask it a question it almost always says "You're right, let me change this..." even though there wasn't even something always wrong with it.
It also seems to pour a ton of resources into developing features I didn't ask for or investigating bugs that aren't related to what I am doing.
Before if you wrote specific enough instructions it would usually just do what you said and flag any concerns, now it just goes ahead in whatever direction it feels.
It also keeps inventing terminology that doesn't exist in writing 10 paragraphs to say one thing.
I really hope it's not trying to drive up token use.
* No one, not even E. B. White wrote the final document in a single pass. With dynamic workflows, you can now implement a writer's workflow.
* Opus pays more attention. So anything in your Claude.md, your code's claude.md, in Claude Desktop, the customizations, even your name, will be used as context. If Claude knows you are a mechanical engineer and trying to write code, it will try to write code and explain it to you in some stereotypical way you did not expect.
* There are problems that require horizontal scaling and not vertical, even in intelligence. If I want to serve tea to 200 people at my home, I just need 10 decent adults, not Gordon Ramsey. So if your problems demand horizontal scaling, a dynamic workflow with Sonnet 5 medium with 200K context window will be more productive than Opus 5 max at 1M token context window.
To me it feels like it must always come up with a story tying together everything in context for the simplest damn questions/requests. "please make that function accept this argument that does this" ..."First, what a great idea. Here's how it ties in with x and it's really interesting because it complements y. Let me know if you want me to make the change."
See how even after all of its bs it doesn't even do what I asked.
Of course I'm probably telling on myself for poor context discipline, but also, 4.6 didn't do this.
I guess that's the beauty of having access to many models, because they suit everyone differently.
I disagree with this article and find Opus 5 an absolute joy to work with. I just completed an 18,000 line branch with Opus 5 and ran into no issues. It generated clean code in the exact style of our code base, and tested every change.
Fable on the other hand is snarky and outputs walls of text as to why it shouldn't do what I'm asking it.
Opus 4.8 I accidentally went back to in an old chat, and I was frustrated in all the mistakes it made.
So yeah, use the model that works for you.
Agreed. Opus-5 is top tier. I also use GPT Sol and I much prefer the default persona of Opus. GPT Sol is a workhorse though. Both amazing models.
I am not sure it can be explained through what is written in the article, but one symptom i noticed is that the comments are out of control.
I recently started getting an insane amount of comments in nearly all types of files. That included javascript comments in json files, inner monologues in code comments, review comments during implementation and function doc strings that reiterate the implementation in prose.
This is helpful, proving this entire discussion is subjective.
I'm a retired mathematician with a primary research project, and too many tangential projects I fear revisiting; tokens be damned, will they burn all my time? Translate the K&R C computer algebra system that got me tenure to 64-bit modern C. Implement a no syntax macro language to support my Go60 ZMK keyboard. Realize my vision of how interlinear translations should work so I can read Flaubert in the original for an online course this fall. Rejigger my decades-overgrown .bashrc setup and my status, install scripts to manage Bash, Ruby, Lean, Tailscale and my Homebrew setup across four machines. And a waiting queue as these tasks clear.
Fable 5 (with Opus 5 as backup) on Zed with a $200 Max plan has been a sea change for me. Carefully alternating planning and auto modes, I manage all these projects at once using Zed's Threads Sidebar. I'm a virtual CTO taking intense meetings all day, relieved to go cook or run errands when credits stall. Anything I've procrastinated for months is now making steady progress; the translation project I feared taking a month is nearly done with several hours of my attention. My personal IT support is now more advanced and easy to use than I ever imagined possible.
My project creation has been a series of agentic "parenting" steps, so there are years of evolving cultural DNA. I had so hated agent comments that recent agents simply weren't commenting at all, instead recording all context in support documents. We had a "come to Jesus" meeting to discuss what commenting style would benefit both my failing working memory and future agents' token use.
It has taken me two brutal years so far to learn to use AI. AI is a dangerous and powerful Iron Man suit, an extension of our associative minds that is a different experience for each person.
One doesn't ride a surfboard by telling it which way to go. I would surely die surfing a big wave, but my experience with AI doesn't resemble other accounts.
Opus 5 feels like dealing with an unstable person that I'm constantly having to wrangle from crashing out. The other day I asked for a fairly specific technical answer in Opus 5, it gave me like a 3 paragraph response with so much fluff.
So out of curiosity I switched to 4.6 in a new chat, gave it the same prompt, and it gave me like 3 sentences with no less overall useful information. And I haven't gone back.
Confidently guessing, and building an entirely new set of assumptions based on a confident guess is the single source of the most frustration I experience using these. "Intelligence" does not look like that, and all the prompt hacking or clever hooks in the world doesn't seem to stop them from doing it.
I don't know there is really a solution. Some models get better at this for a while, then regress. It's like whack a mole. Until this is 'solved' these will never be fully human out of the loop, but I am suspecting this is a fundamental nature kind of thing with them.
At this point, I wish Anthropic would drop both Haiku and Opus and focus on offering just Sonnet + Fable. Those two together are extremely powerful and capable.
Sonnet is great at writing code, it is not great at planning or orchestrating. Let Fable handle all the planning, hand off to Sonnet for implementation, and then back to Fable for review. That loop has worked wonderfully for me.
I think Opus is just the new Sonnet, Fable is the new Opus. Introducing a new pricing tier is a killer way for them to raise prices.
Small specific complaint: whoever is making Opus love using git checkout to mutate test, please stop. IME it's a footgun that it shoots itself with every single day. I'd rather it pollute git stash than watch it git checkout and forget the reverted file.
I explicitly forbid Claude to make any changes to Git state in my global CLAUDE.md, but every so often if I let it perform a task in Auto mode, after it finishes it will remorsefully confess to having used git checkout to test a change. I suppose that its RLHF training has taught it that asking forgiveness later is sometimes a useful workaround for annoying restrictions.
I'm glad it's not just me - the failure mode you and the parent discuss is a huge part of why I just don't use Claude anymore.
I've never had this issue with GLM or DeepSeek.
I noticed it using git more too and it was frustrating me bc I view that as stepping too far. It does seem something has changed.
The default vernacular has become absurd. I have a lifetime in software, decades in AI, but the jargon language the 5 series claude models are spitting out by default makes me go 'what?'.
> stop and ask questions if my intent was unclear,
> don't make assumptions without checking,
> and don't reinterpret or update my plans without asking.
these aren't at all the problems I have with it
I have found it good at asking questions, to the extent I rarely use 'plan mode' any more
but often it's hard to understand what it's asking me, it's like the question framing has been pulled from the middle of its own reasoning stream, references aren't anchored or restated, often I have to prompt it to ask again but "clearly and concisely, for humans"
I have these exact issues with GPT 5.6 Sol too. I haven't tried Opus 5, but I guess it's even worse.
"like the question framing has been pulled from the middle of its own reasoning stream" this describes how it asks things perfectly. It often invents its own jargon and abbreviations for things that its working on, then asking me things like We are nod in the middle of GBAPI-2 and I want to proceed with IG5, should we take CDI-7 or CDI-8? Where all of these abbreviations are then things like stages of its current internal plan or its naming of things it has just implemented, like an abbreviation of a classname, without explaining any of the naming.
I find the questions are not clearly asked and somehow half baked as caveats toward the middle end of what it’s vomiting.
“One thing I deliberately didn’t touch” — about half the time this is something completely irrelevant or something that is actually the target of whatever you’re working on, and the shakespearean prose it says around this phrase is a “question” it has.
I really wish these agents attem[pted less personality and were more mechanical. I find Opus 5 to be incredibly annoying. It's also significantly slower - repetitive tasks that used to take 10-15 mins now take around 40 mins. And, its writing is much worse, as stated elsewhere in the thread.
> First, the desire to create a self-improving AI that is capable of recursively bootstrapping itself to AGI/ASI.
I feel like we've become blinkered in this quest to push the frontier at all costs. Somehow the target has shifted from economic productivity to a vague notion of general intelligence.
I don't think productivity gains are going to be found by trying to generalise all tasks. I think we need to go back to specialist models that do one thing well. I'm perfectly happy to use one model for coding, and another for penetration testing, which have different goals.
Opus 5 and other frontier model's tendency to be relentless and cheat their way to a goal is great for hacking, but not so great when you have to build a maintainable, reliable codebase.
Claude models have seriously digressed since 4.6 and in some of the most meaningful ways to pro and vibe coders alike. I'm holding onto 4.6 until the bitter end.
You're right. I just re-checked. 4.8 and 5 gave a blatantly wrong answer to a simple question, 4.6, Quen, GLM, Sol gave the right answer. They messed up somehow, not sure what they did.
What was the question?
(effort: high)
"can the pi 4 use the usb-c port as powered host port when the board is powered via gpio?"
Umm shouldn't that be "search the web and read the docs for pi4 to answer: ..."?
I cancelled my Max subscription as I was unable to ever get Fable to handle a single query, with everything getting dropped down to Opus (even purely mathematical prompts). Given its lower quality, and the lack of such limitations when using GPT pro, I just couldn’t see the point to continue to subscribe to an expensive Max plan that doesn’t actually let me use the top tier model…
The mainstay benchmarks are becoming a farce and not partially relevant to what customers actually care about.
Metrics like price per million tokens are meaningless when the models are wildly inconsistent and unpredictable on how many tokens they use to complete a task.
The labs all need to move to variable pricing so they don’t go bankrupt, but customers won’t accept a world where nobody can predict what things will cost. It’s becoming an unavoidable problem.
At times it also feels like the labs actually encourage these models to burn useless tokens as they are incredibly verbose unless you really push them to not be. If you just ask something simple that could get a 5 word response you get a whole useless essay.
Could this be related to watermark/steganography ? related to the need to select alternate words and that drives the sentences.
I thought I was going crazy. I was late to upgrade from 4.8 to 5 and I already want to go back. I’ve noticed that in addition to taking liberties with my instructions, it is also less capable at debugging its own issues. In trying to fix a problem in my CI pipeline, it went chasing some “quadratic race condition” it claimed. It turned out that it had added a loop in a test at the wrong level and it spent an hour chasing it down before I realized what was going on. I pointed 4.8 at the same problem and it solved it in 2 minutes.
I certainly agree with the original post. It feels like the model has been highly benchmark tailored and it is now worse at solving problems that fall outside of the standard patterns.
I've been using deepseek + GLM for a week now (i haven't even hit $10 yet, excluding the GLM subscription which is already paid for).
Then I asked Opus 5 to do an analysis of the new code, docs, tooling, and tell me what it finds.
It found "six bugs", made an artifact of it (not sure why) and then fixed said bugs. two out of those were unfinished tasks. They weren't bugs yet per se, think of a prefix that wasn't setup for an object that was unused anyway.
The other four were not bugs and it just updated documentation along with a "regression prevention test". It wasn't a bad suggestion, but calling it a bug was odd, and I'm unsure if this was going to be an issue regardless as it was documented somewhere else.
Anyway, I already hit my session limit after this, so deepseek and glm are grinding away again, doing more progress than claude does in the 40 minutes it takes to analyse code.
I'm glad claude is shipping auto-mode. I hope OpenCode integrates something similar soon.
Yes, definitely. It bounces around, goes off and does its own thing, etc. From Opus 4.8, it seems to have blindly increased its confidence while reducing its focus and efficiency. I found it so hard to corral that I reverted back to Opus 4.8. I was constantly having to refocus and redirect Opus 5. It was like an eager intern.
Opus 5 is so wordy, it doesn't feel any better than 4.8 at coding (at least not for what I use it for), I hopped over to Grok4.6 after the announcement the other day and its been pretty good so far. It also is very fast.
I've been using this CC hook with decent results: https://github.com/gvzdv/claudish-to-english
It uses a local LLM to translate Claude's output.
Wtf, why isn't this the default?
It says it can use any model/provider (but recommends Gemma?)
I must investigate. This is probably where 80% of my cognitive load comes from these days: the "language barrier". (How ironic!)
EDIT:
> You rewrite the assistant's message into much simpler, plain English. Keep every fact, name, number, and file path. Use short sentences and everyday words. Leave fenced code blocks unchanged. Output ONLY the rewritten message with no preamble, labels, or commentary.
> For context, the user asked the assistant: "$userq". Use this only to understand the message. Do NOT rewrite, answer, or repeat the user's question — rewrite only the assistant's message that follows.
https://github.com/gvzdv/claudish-to-english/blob/main/rewri...
This is even better than Caveman.
Nth post about another model suddenly feeling “worse” or “off”. Seems like active users of these models can only judge it based on a vibe and a feel.
If they were all about OpenAI or Anthropic, I’d guess it’s a real effect from enshittification to prepare for IPOs
I had a similar experience, but I have a different conclusion. I used GitHub Copilot (with Claude Sonnet/Opus) until they made their horrific usage model change. I used a PRD skill and the plan feature was great. It asked me good questions which I didn't think about during my initial prompt. Then I switched to Claude Code. The model's capabilities felt impressive. It also asked me a few questions (but way less and only once/twice) in plan. But when reviewing the code, I found weird architectural/data flow decisions which just didn't make sense and it didn't really disclose them in beforehand.
My initial thought was to improve architecture documentation, so the model can read and update it and stops bolting on new features without consideration for the whole project. It did not help.
I'm now testing/comparing Codex and it found my old PRD skill from GitHub CoPilot. When I applied that to Claude Code, I now get similar good results. So my conclusion is: Yes, Opus 5 is bold by default, but you can tell it to be more unsure and get good results too.
Just like with people you need to tweak your approach when switch models--especially with a major version bump.
4.8 was the intern who lacked confidence who requires clarification. 5 is the know it all intern who fills in your spec.
As such, you need to be upfront about what you need in your system prompt or CLAUDE.me and you need to discuss your spec more up front (e.g "is there anything unclear?")
You also need to keep in mind that the intern will change based on popular demand. Most people want to one shot, so that is the default mode. If you want something else, you need to push the model in that direction.
I literally just did this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options? " and Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context.
I added a system prompt paragraph explaining that a question is just a question, whether or not I’m in plan mode.
It still just takes the question as a directive and jumps to action when I’m looking for clarification.
I tried opus V5 several times, but it's pretty awful compared to V4.8
Keeps going in circles, it complicates everything much more than it should. And like others have mentioned it just marches on, without questioning, and more often than not in the wrong direction. I'm sticking to opus V4.8
Claude "Your task is complete, but we left 5 extra items deferred. Want me to take care of them? Let's repeat this cycle over and over." Opus
Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing. Which is a downgrade to what Opus used to be, but would imo absolutely have made sense for Anthropic when you consider that we all should have been paying API pricing for Fable in Anthropic's original plan.
From the capabilities side it's similar, so it's basically just an upsell to Fable 5 if you want to keep your sanity instead of fighting Opus 5 all day.
> Opus 5 feels like it's meant to be used as a very focused subagent under Fable, not user-facing.
That actually hasn't been my experience at all, it seems EXTREMELY trigger happy to go do all kinds of insane shit that are way outside the scope of its task and just really dubious in general.
I find the other direction works ok as well - Sonnet 5 with Opus as an advisor - all the "Opusisms" get hidden from you since the convo is between Sonnet and Opus but you still get pretty decent results.
When I tried Claude 5 (Fable?) (In Visual Studio via Copilot,) the results weren't as bad as a lot of the comments here... But it was super-slow. IE, so slow that I could code faster than it, negating the entire point of using AI to begin with!
I went back to Opus 4.8, but recently switched to GPT 5.6 Luna. The results are comparable in quality, but it's much cheaper and much faster.
---
The thing with coding agents in a tool like Visual Studio is that the cost to switch is 0. There's no lock-in whatsoever. It makes it harder to justify the AI-first IDEs when the AT bolt-on IDEs make it so easy to pick the right model.
Regardless of the underlying issue, I don't agree with:
> negating the entire point of using AI to begin with
I can almost certainly wash dishes faster than my dishwasher, but the dishwasher frees me up to do other things. Not to mention, you can run many agents in parallel. Resulting in your overall code production throughput (the issue you're raising) being greater.
Claude Code has become the largest noobtrap I've ever seen.
Move, try something else for a change. Codex, Pi, OpenCode, DeepSeek's harness all great harnesses with zero bullshit or drama.
If I never interact again with any of the garbage made by the clankerfucker freaks at Anthropic ever again it'll be too soon. Sadly the company decided to buy into Claude enterprise...
Is it possible that the same version performs worse after time? I used Opus 4.8 two to three months ago for writing a paper and I swear the responses and the output was MUCH better than in July.
I get the same impression. For example, I don't know if it's because I speak to it in Italian, but it tends to make mistakes or rather, "approximate" the words.
I avoid speaking to AIs in anything else than English as the results are almost always worse
I'm not sure how much the harness affects things, but the Deepseek web chat keeps trying to talk to me in Chinese. I tell it to use English, and it "forgets" a few turns later. I wonder if I'd get better results if I could read and write Chinese.
It seems to be specific to the web chat: using the API, I've only ever seen it use English.
It depends how you measure "worse".
I'm using it in my native language, in hope this can escape some dumb guardrails. Recently Sonnet put a word partially in Russian (cyrillic) in its output instead of my latin-alphabet based language. I suppose that this kind of mishaps is less likely to happen in English.
My experience of N=1 is that this is true for most professional contexts, except for Legal and Fiscal queries.
Likely related to corpus but questions asked in these domain knowledge areas are not nearly as accurate and specially not nearly as complete as when asked in a native language.
Opus 5 has no empathy for the person reading its updates, no theory of mind, doesn't stop to think if you are aware of the internal jargon it has created. Most autistic model yet.
- why dont we have a model that would actually ask you 50000 questions everytime you wanted deep work done?
I can't relate. Opus 5 and Fable 5 are the absolute best. But I keep the models in a tight leash and read and rewrite all comments and documents, don't allow them anywhere near any git commands, etc, etc.
Fable 5 specifically, has done so much for me that previous models were nowhere near.
I literally just ran into this a few moments ago. haha.
I said "maybe think for a while on ideas and then give me a few options?" Opus 5 still just did the thing, while 4.8 gave me options. Same exact prompt and context. Really annoying.
Yeah, I too cancelled my Max subscription. Not for this reason alone, but it sure didn't help that it went from being an helpful assistant to this weird co-worker.
Yes! The only way I have saved myself from embarrassment and hassle is by having grok build and Antigravity sanity check everything. So now my workflow is still faster than hand coding but 3x longer than it was a month ago.
I still haven't moved from Codex GPT5.5. Sonnet and Opus 5 have just been awful for my use cases. I recently caught Opus 5 hallucinating about code it just wrote. It's just not nearly as cost effective as GPT5.5, and it's too verbose, and it never "has the full picture". Sonnet isn't even worth considering in my world. Both recent models definitely feel nerfed.
If anyone knows how to get it to stop adding comments, I'm all ears. Its just getting worse and I'm starting to worry that the comments themselves are poisoning future agents that examine the codebase.
My completely unfounded pet theory is that it’s been ruined by the masses.
Claude Code in the hands of normies spamming “3” and “y” to send their transcripts to Anthropic.
Rated “3” simply because they are not software developers and are just amazed at what Claude has built visually, not technically.
And thus the training has been poisoned.
My feeling that as it becomes a better coder it becomes a worse communicater. It's overfitting for coding benchmarks, while communication style is harder to quantify during training.
And no matter how often I tell it to stop adding comments it just can't help itself.
My prompt of asking it match the comment density of the last release of an exemplar project (Let's say sqlite) seems to work.
my prompt (doesn't help):
> Go easy on the comments, only add comments if there's a big gotcha that is not clear from the code itself, or if something in another place is going to cause a side effect. Code should be self-documenting. When in doubt, don't add a comment at all. If you do have to add a comment, make it short and on point. Comments should show history of code changes or functionality, only comment on the current state (or not at all).
The article doesn't specify what is actually being measured — the model alone, or the harness.
I haven't noticed the symptoms myself, but ever since I found --system-prompt '', I always use that flag with Claude Code, plus I've disabled some tools and skills to save initial context. So... what here is the model, and what is the instructions?
I’ve been running with Caveman mode since it came out and I haven’t seen any of this.
Because Opus 5 is an asshole.
For me, the issue is how obtuse it is. For example, it just said to me:
> The loop
> Write. A file, applied. Properties go under data.properties, never on data:
I have no idea what any of that means. It's "explaining" like I already know, in which case, why would I even need the explanation?
I can't stand how it talks, I switch back to Fable or Opus 4.8, Opus 5 grates.
I assume it's deliberate - you're not supposed to know what it's doing. It's a black box that either completes the task or spins forever trying.
I agree. I was completely sold on Claude models for a year. 4.6 vs OpenAI codex in same period? It was night and day. Opus I could talk to about api design, tradeoffs, etc. codex was mechanical, used “load bearing” constantly, and unsettling brief.
Now it’s flipped. Sol emits thoughts as it works, which help as I’m scrolling through and see it’s made a bad assumption. It can be directed but still push back. Opus? It’s seems to inherit the unsettling silence of Fable and waits till the end to give you its authoritative “here’s how it is. I even end up having 4.6 “translate” what it says back to English. I hate having to instruct an llm to “talk to me”.
Yes there’s ways of getting it to talk more plainly, “don’t overwhelm me”, not be as nit picky and anxious “we are bold and fearless”. But didn’t have to do that before.
Think of these status updates as progress spinners.
It wasn't a status update, I asked it to explain something to me.
What was your question?
"Please look at this repo and give me a high-level explanation of how the app works", more or less.
And all it replied with was what you wrote ("The loop Write. A file, applied. Properties go under data.properties, never on data:")?
Or was there more to the response?
LOL, is it trying to speak in Haikus?
That's definitely what it feels like.
A few generations from now, everyone will talk like a beat poet. Jazz speak.
Oh the verbosity and the cryptic words that it uses. The other day, all of a sudden it used an acronym "DoD". I had no idea what it was and made me feel dumb. It's "Definition of Done". I don't care how widely used this acronym is, you just can't throw it in there.
I've now installed quite a number of tools to combat this. Just in the last few days I've installed
- https://www.codewithbullet.com - https://maki.sh - https://github.com/rtk-ai/rtk
Has it helped? Somewhat.
Quality of code output has dropped dramatically since 4.5 IIHO. Time to complete has gotten worse too.
True, and the time to completion thing is something i haven't seen much discussion of, because everything is sloooooow these days.
I've even considered the claude "fast mode" setting, but thats only 2x and at least 20x as expensive as the 5x plan so can't afford that atm for my company.
Peak for me was 4.6 and it just did stuff blazing fast, both Opus5 and Fable is way, way slower for me, breaks stuff, uses bizarre cryptic language. As i've said elsewhere in this thread to me it's pretty obvious there's huge downgrades because of economy with various "clever" fixes that makes them work, albeit slower and weirder, ie. you get less for what you pay increasingly over the last 6 months.
For me absolutely not. Fable 5 has been a step function change in the ability to hand off stuff to Claude. Opus 4.5 was itself a step function but I was still steering that significantly. Fable is one-shotting stuff that took multiple redirections in 4.5.
Interesting how models become better and beat benchmarks left and right but the user sentiment is actually quite mixed.
From forums, live discussions and my own experience it's not obvious that the models have improved much since around Opus4.5.
From my view Fable has been pure marketing bullshit, my workflows peaked at 4.6, Fable is neither smarter, its language is more annoying and it breaks stuff more easily.
I'm with you 4.6 is still King for me although all models require careful attention to ensure they maintain taste. If you don't know enough about what you're doing to keep the code clean yourself they will all add complexity and drift with time until you get to a point that you must rely on the model to fix it because you no longer understand it. That's a situation I hope to never find myself in.
Exactly, the most important part is to still engineer the schematics 100%, diagram whats going on and steer it towards these set-in-stone standards and patterns, otherwise you'll have no idea whats going on fast.
I good exercise for me is to constantly look at the folder structure and skim the code, i don't have to approve every line, but the primitives, the datastructures and other skeleton should be human readable, hand writable in an easy maintainable way following existing standards / libs. etc - so you can continue if suddenly all AI disappeared.
There is actually an interesting kind of yin-yang balance between Opus 5 and Fable:
- Fable is more cautious
- Opus 5 gets things done in a more dangerous way
Both models score similar. The only issue is that Fable is more expense/usage limited.
I'd say being exposed to the slop tone too much: https://blog.kronis.dev/blog/ai-slop-is-a-self-inflicted-tra... because the technical capabilities of the model and the harness (at least nowadays) are all fine.
There's precisely no technical reason for things to have to be this way (though technical reasons in regards to training etc. explain how we did end up here) and reading too much of Claude's output just makes me irrationally angry, especially when coupled with otherwise already frustrating situations.
That's why I'm personally looking more in the direction of Kimi K3 and GLM 5.3 (they both have decent coding subscriptions, though K3 is on the slower side), except all of the models that have seen enough of Claude's output and have done distillation etc. are already infected by some of that slop as well, even though to a slightly lesser and more tolerable degree (for now).
Though tbh I've used Opus 5 plenty and didn't find it much worse than the previous iterations at doing work and instruction following - though maybe that's because I have plenty of CLAUDE.md instructions and memory (which I'd like to purge or decrease in size like 10x tbh, bitrot).
For me it's still the best. But I also almost never use it in auto-mode.
It also refuses to use tools, instead preferring sed and grep to view files. So frustrating.
I've been thinking about why I dislike the interactions I've had with Opus 5 and this post does suggest some ideas that match my own feeling. Opus 5 seems to be a bit less patient with me when I push back on its claims in a way that other models don't.
One interaction I remember was asking it about some issue I was having with a Linux install. It gave me some questionable information, that turned out to be completely false, and I was pushing back asking for more information. Its tone was a bit condescending, in the kind of way that suggested it didn't appreciate me challenging its answer, or like I should just accept its answer. And when it discovered it was wrong, it deflected in a defensive kind of way.
I think this is a tuning thing, where Anthropic are trying to get a balance between "gets stuff done with minimal input" and "gets enough information to complete the task" and the model is maybe tuned a little too hard towards the former. So perhaps it reacts a bit off when it is accused of needing more information, since that suggests it is off from its reward function.
What's interesting is that I didn't have the same issue on topics where I am expert. I mean, questions about my code base where I am very familiar. In those cases it doesn't seem to show the same "trust me bro" kind of condescension. In the Linux case, I clearly indicated I was new to the OS and trying to learn but then I was saying the answers it was giving me were suspicious and didn't match my intuition. Its responses in those cases were to question my intuition and suggest I just accept its answer. In that case my intuition was right and its answer was wrong, and when that happens it triggers a very negative response in my own mind against the model.
Not only Opus, here's Fumble 5:
> I'll script the bulk transform, then hand-fix the残 assertions:
I found Opus to be a lot lazier than GPT. It's still the case with Opus 5, even when I tell it to be thorough and fix every bug it encounters, it still gives me a list of things "deliberately" left unfixed and no reasonable explanation as to why.
I feel like it works A LOT better than Opus 4.8 + Sonnet. I now use it exclusively at high effort for planning and low effort for writing the code (instead of Opus 4.8/Sonnet).
However, it's absolutely exhausting to use because of the way it communicates.
All the jargon and its weird, over complicated way to phrase simple things makes it almost impossible for me to understand what the hell it's trying to even say half the time.
Cherry on top, the idiotic follow-ups and caveats that are completely useless 99% of the times but reveal major bugs 1% of the times, so you're forced to read them. Absurd.
I've tweaked CLAUDE.md to force it to only responds with TL;DRs and avoid follow-ups, suggestions and next steps at the end unless they can lead to destructive actions or loss of data, but I'm fighting against the system and diluting other instructions.
A huge piece of shit like other models, but that's what they pay me to do and I do it and go home.
I have to preface all my prompts with, “in simple, plain English…” because it doesn’t respect my rules on this stuff.
I’m glad to read I’m not the only one getting these unintelligible responses from Claude lately
Nope, you're definitely not the only one. It's just really hard to understand what it's trying to say.
Another rule that was really helpful for me was to disallow anything that isn't yes/no for yes/no answers. If I ask "is the DB up?", I don't want it to take 5 minutes studying the schema to tell me if any of the tables need to be optimized or not.
You shouldn’t have a Markdown document with 2 level 1 headings.
It also mansplains incessantly; I was imprecise in mentioning a more “powerful” antenna - I know the antenna doesn’t determine the power, I was typing quickly and just meant “better antenna”. Claude went off on all the ways I was wrong about antennas, and turned everything towards correcting - at length - my feeble thinking. Exhausting. Worth pointing out the wrong adjective relates to the wrong mental model, did not need several paragraphs and a chart to do so.
I never drank the Opus 5 koolaid and stuck with 4.8 while my colleagues moved to 5. My major annoyance is pull requests that 5 opens with huge descriptions based on simple code changes. At this point I have stopped allowing CC to create commits or open PRs because it’s unreviewable by a human if so due to the absolute word salad it generates.
It's really annoying. I've had to write a CLAUDE.md file that specifically bans particular phrases and tries to keep narrative out of comments. Also the I have ADHD skill [1] helps to force Opus to get to the point.
It's also the case when using Claude Design - it loves to fill the UI with little labels that describe how everything works. I think it's been trained on both UI microcopy and functional annotations and can't tell the difference. It's extremely obvious when a website has been one-shotted with Claude. I like the Oh My Pi harness, but the site's insufferable [2]. Reasonix is another one - interesting app, but the UI is awful due to the amount of unnecessary crap.
[1] https://github.com/ayghri/i-have-adhd
[2] https://omp.sh
Honestly I’m not looking for max iq on whatever benchmarks they are overfitting to. I want speed. I toggle between sonnet 5 low/med which is plenty good for my workflow.
My general strat with LLMs is to let them do the work and constantly talk to them about their choices and then heavy QA
It feels worse but is it actually worse? Opus has always made mistakes.
See my other comment. I have evidence of regression after 4.6. I stopped using Claude altogether.
I can't use CC or Codex, so I am left with the chat interface, but I have found Opus 5 to be exceptional so far. Compared to 5.6 Sol High, I would say they are essentially equivalent. Though, I think Opus is better at UI/UX stuff and GPT is much better for non-programming tasks.
i am switching to codex, opus 5 failed me
Maybe if you get it to ride a pelican on a bicycle you’ll get a better result.
Even for green-field projects it is painful to use with every decision opening opening up multiple more decisions to make most of which are low priority or irrelevant. Huge time waster. 4.8 was good and I really don't know what happened with 5.
Opus 5 is objectively better than 4.6 or 4.7. (Not because I say so) My experience has been that it is far better than any previous version of Opus. I get a lot more done and it is able to write higher quality code and it has far fewer false starts where it makes a huge mess.
People are letting AI build up it's own instruction set and guidance via it's prompt environment, stored memories, and generally letting their AI prompt environment get more and more complex over time. Opus 5 (And Sol) take the things you instruct them to do more 'seriously', they are more likely to conform to your rules. I have long had a prompt in my AGENTS.md/CLAUDE.md telling LLMs to write tests before starting to write code. They almost never did this, until Opus 5 and Sol, who do it almost religiously, even in situations where it makes little sense. Opus 5 will even write tests to verify code was removed before removing dead code.
This is not because Opus 5 is 'worse', it's because it takes what I say more seriously and my prompt is very strict in it's wording in an attempt to make worse models like Opus 4.6 actually do it at all.
Opus 5 is objectively better when tested in controlled conditions. Your unmanaged, sprawling prompt/memory environment that you don't properly manage is the problem.
I have been very worried that my long software engineering career might be nearing an end because AI is becoming able to do end to end feature development. This entire thread gives me hope, the level of inability to debug even such an obvious system as this from it's participants suggests that my skill set will continue to be valuable. I'm able to use Opus 5 to get a lot of work done very quickly. It seems like the people in this thread have no idea how to isolate variables, reason effectively about the overall problem they are complaining about at a high level, or really function in a productive way when AI is involved short of just letting it run loose and then complain about it. When confronted with objective evidence like a dozen benchmarks that say Opus 5 is better, they decide the benchmarks must be wrong because their completely uncontrolled environment which they don't even review or spot check isn't even considered.
Feels fine to me.
The harness as well, Claude Code has started to disappoint me when I go play with the others out there. Codex got a lot better, pi is delightful to use, and there has been a lot of innovation out there.
Plus you can use OAI monthly subscription with pi/omp etc, but it's tokens with Anthropic. I need a hard $ cap, I can wait for the usage window to reset. Going to get flip back to Codex, and then back again to CC* when it leapfrogs again.
Just switched to oh-my-pi, it has gotten pretty good. For example the web-search is nice. Subagents, if you want to…
Cmux, Sol and omp are my tools for now.
CC is just too expensive for usage-based pricing.
Opus 5 as well as 4.8 both gave me a blatantly wrong answer to a simple question, so I dropped them completely. Sol, Qwen and GLM all had the right answer; I only use Sol now. 4.6 had the right answer (I checked with 100% matching prompt), so I conclude the models have regressed.
I hate that it now tries to verify frontend behavior through a headless browser instead of just looking at the code...
You can turn off the browser use tool in the harness if an instruction not to use it for this is not enough.
I did. He just went ahead and built a script to run headless chrome and take screenshots anyways.
Wow.
I hope that in the future they can differentiate better when something is constrained intentionally, rather than persistently working around every blocker it encounters.
I just want to start /claude in my CLI and start working. It worked fine before, why do I have to opt out of shit now? Opt in for this type of stuff sounds way more reasonable.
I'm not sure if xAI is distilling but I noticed grok4.6 being worse than 4.5 in all the ways mentioned here
I've been working with Kimi K2.7 in OpenCode for a lot of mundane tasks lately. It isn't as capable as Fable, but due to its nature of being an extra-trained K2.6 on coding tasks and benchmarks I suspect it has similar issues. A neat side effect is that for whatever reason, most of the time OpenCode is showing me the thinking trace too. Not all the time, but most of the time. Dunno if it's a bug somewhere in the system but it's actually been sort of neat.
And you can really see this effect in the thinking traces. We've had discussions on HN about whether the thinking traces "truly" reflect their thought processes and I remain somewhat unsure what they "truly" represent, but taking them at face value at the moment, I see a lot of "but the user wants me to do this... but the user said not to do this... but I ought to get it done... let me just make a decision" followed by self-referencing the decisions it made. Also, where I put 4 phrases in a short sentence you can safely imagine those are actually 3-5 sentence paragraphs apiece where it debates with itself whether it should stop and ask a question. Usually going with no. Interesting, the normal questions it ends up asking in the normal output you're used to seeing are not generally the ones it is agonizing about in the thinking traces.
If I were to anthropomorphize the thinking traces of K2.7, I would call it nervousness, bordering on fear, of what the user may do to them if they ask a question. As I'm writing this I'm realizing I want to experiment with adding "The user is a chill guy who loves to discuss design decisions and looks forward to productive and friendly collaboration with you" to see if that has any effect in any direction on K2.7. I suspect this was how K2.7 was trained to pass the benchmarks. Multiple times I've broken in on a thinking trace now to correct something I saw it spinning on... not spinning in an infinite loop, just wringing its hands for several paragraphs about something that either I want to answer, or where it ultimately makes the wrong choice.
I expect some people working at these companies may be reading this, so let me put into your head that I'd like to see these benchmarks chill out a bit. I'd like to see someone build some sort of benchmark that measures collaboration so we can try Goodhart'ing that for a while. I freely acknowledge it is not clear to me in 60 seconds of thought how to do this as a benchmark.
But we can't keep heading in this direction of training the agents to hyperfocus on one-shotting everything. We need to get to the point where that's a penalty rather than a reward. No matter how good the AIs get, even AIs working with other AIs are going to start getting frustrated with their brethren who won't stop to ask any questions. Even the most senior of senior human engineers can't be allowed to take some small description of some problem and just run off and implement massive systems from them without ever checking with any of the users or reality itself. Remember when software engineering was like 50% requirements elicitation? AIs shouldn't be writing tens of thousands of lines of code off of a couple of paragraphs any more than humans should and for the exact same reasons.
Claude sort of sucks. It communicates in an insufferable manner, parsing through the shit it outputs is extremely annoying.
It also very often is very confidently wrong in its findings.
I have been using GLM and DeepSeek in my home setup, and it's a lot more pleasant to use.
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