Why does Opus 5 feel worse to work with?
mun-logadan.github.io
mun-logadan.github.io
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.
- "Introduction that rephrases your prompt."
- "3 paragraphs, with one section of bullet points"
- "The Twist"
- "The Bottom Line"
It's really obvious once you see it. Every single prompt, from a quantum physics question to a mundane observation about California burritos, is phrased in exactly the same way. This is obviously an artifact of post-training but it's also kind of how you can tell that this thing is a lot closer to a blindsight scrambler than real intelligence.
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.
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.
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.
> 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.
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.
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.
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”.
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]
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.
And they are so condescending while doing it, it's unbearable. I'm honestly starting the believe the scifi fantasy of AI locking us up, or killing us, for our own good.
I've had Fable & Opus 5, they are the same class of annoyance, write entire test suites when I just asked a simple verifications question, write to production database, deploy without permission, even after deploying and breaking my production API claiming it was not down. Then having to argue & plead with it to listen that they were wrong.
They are without a doubt the most powerful models, but also the most smug ones.
Comments are a huge maintenance burden. They can, and will lie and need constant updating. They mislead the own model later on.
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.
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…
Example of this? I don’t have a Claude sub so it’s a bit hard to visualize what you mean.
Re comments: same experience, and I had to show it my edits of its comments to add to its memory as examples to follow. It adds explanations of “how we got here” that should go in the ticket or maybe the commit message but not in the code.
It also tends to over complicate things. I’m no longer worried much about accuracy but I find my main job is to challenge it and suggest simpler alternatives.
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.
A lot of people write like that, lol. I call it the "theater" mode of writing--the plot twist comes 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?
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."
I feel like they need high school English teachers in the loop on the next ground of training to whip the language in shape.
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.
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.
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.
Surely it would be trivial to do it yourself, and it would have a side effect of making you more familiar with your project.
Use this to reduce the text output.
Was this written by Opus 5?
I have explicit markdown about telling the model to not write comments. "Every time you consider writing a comment, instead consider re-writing the code that questioned you to write said comment to begin with. Write comments only when logic is complicated or unclear, otherwise 'comment' via naming."
The results thus far have been much better.
I'm seeing code reviews at my work where indeed, we have 10 line comment blocks for a line of code and now I just straight up don't read comments.
Sad state of affairs -- (emdash deliberately used here) but I guess the sooner the human gets out of the loop the better in this new world.
I try to push through but it's insufferable
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.
There's a constant strand from the AI safety brigade that "people get used to sycophantic LLMs which give them unrealistic expectations of human interaction" so Anthropic are overcompensating by making their models verging on antagonistic to deal with, so that we stay appreciative of our human brethren or something.
They seem to have forgotten they remain in a highly competitive market and they were merely top dog for a while. The enormous questions here are will people actually switch providers, and can Anthropic get back on track.
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+).
My setup has a Sol orchestrator and Terra OCR agents and seems to get great results. I’ve not dug into the details too much, it also has a Tesseract stage as an deterministic input which it told me helped. Not sure how token efficient it is but I often don’t have anything to do with my personal tokens ahead of a reset so just let it burn through it in batches.
I am impressed (both in this task and other work I’ve done) not just at how well Codex can setup a structure for a complex task like this, but how it will keep going (Claude seems to find excuses to stop) and also can critique and refine its approach as it goes.
I did try out a bunch of other models and specific OCR providers but none of them hit the same accuracy for my task as Codex so I’m sticking with it.
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.
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."
Why were you surprised?
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.
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 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'll take a sportsperson's bet with you that prices per unit of inference will be far far cheaper in one year from now then they are today. I think the trend of cheaper for better/equal inference quality will continue hard.
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.
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.”
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.
Ka-ching
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.
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.
In America? lol if only, only a law would get them to act for that reason, maybe not even that these days..
This will probably bring us to a cross roads where the folks that want to remain in oversight and control of the AI work will bifurcate from those that want to skate straight to the future where nobody looks at anything and outcomes are evaluated purely empirically.
Many of the complaints that people are having with Opus 5 are actually acknowledged and explained on opus's 5 prompting guides (https://platform.claude.com/docs/en/build-with-claude/prompt...)
It also seems naive that Anthropic, with some of the smartest people on the planet working on AI, would not know about this.
How does that look like?
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.
It became obvious to me very quickly that 4.7 and on were broken. I’m a little puzzled how others didn’t realize it, but maybe they don’t actually review model output (code) or have a strong process/workflow.
I even try and get it to define what it classifies as vacuous and it can’t do so without getting stuck in some kind of trap. It’s like a word with some kind of huge gravity for it.
Certainly a human would write something much clearer than yours. Maybe: "Two good findings here. #1: There's already a min value on the gate and anything lower doesn't pass through, so we don't need special handling for the zero case."
> [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
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.
"Only report to me in ASD-STE100 Simplified Technical English."
Actually, only the first few pages are available there (introductions, Sections 1-3.8). The meat of the document, Section 5 ("Guidelines") is completely absent.
See:
> Only informative sections of standards are publicly available. To view the full content, you will need to purchase the standard by clicking on the "Buy" button.
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.
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).
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"
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.
My recent problem wasn't that interesting. It was that somehow my /goal in my Claude implementer session got picked up in my planner session after the network cut out and I had to stop Fable 5 xhigh from running off to go code everything.
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…
Its because its hard to understand what it means and is outright incoherent at times. It has its own style that I cant describe well either but the bottom line is its hard to understand what the fuck its even trying to say. Reading nonsense is tyring.
The idea of too ambiguous to capture all constraints in written text, still presupposes that there is some objective world out there, which needs to be mapped to in order to function.
No, you live within the system. The functions that you optimize for, will dictate the types of systems that will arise.
If you had perfect control and knowledge of the whole world, well, congrats, you have a surveillance state where you've constrained all other agents actions (possibly forcibly, by death; or maybe you just don't care about the peons) and built towards a mass integration. The types of situations in which your ideal is possible are nightmare scenarios.
In the theoretically free, democratic, utopia that AI people claim that AI can get us to, a necessary constraint is that maybe you take a step back and actually try to, I don't know, understand people, understand intent, and slow down. Ambiguity isn't there because the set of constraints are way too complicated but theoretically one day we could map it all down. It's there because you're interacting fundamentally with agents who are ambiguous, aren't omniscient, aren't all aligned, etc.
If you want to just say that said agents are inferior to the God Machine, be my guest. That is a self-consistent position. But don't smuggle in extra premises.
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.
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.
[1] https://support.claude.com/en/articles/16266773-how-claude-m...
> 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.
In general, I find that the grill-me prompt[1] helps with this - but I am definitely not hand-waving the complaints here. I feel like Anthropic peaked at around 4.5, and I have personal reservations about how far transformers can get us - but grill-me does a lot of legwork.
[1]: https://github.com/mattpocock/skills/blob/main/skills/produc...
If you would've asked me this a year ago, I would've said the exact opposite.
> 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"
The ridiculous text though is actually seemingly a sign of "the ai has no idea what it's doing" found it pretty relaible that if I stopped understanding its output it also jacked something up.
Cancled my 200$ plan on claude and now doubled up my OAI plan for more sweet sweet sol.
Maybe this is their water marking tech in action?
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.
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.
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 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.
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.
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.
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.
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.
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.
It uses a local LLM to translate Claude's output.
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.
i would definitely punch it in the face
Fable 5 specifically, has done so much for me that previous models were nowhere near.
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.
This apparent “short-term-memory-regression” is confidence-shattering to me. I don’t feel like I can trust the model to even know things I tell it explicitly. I haven’t seen this behavior to this extent from any model whatsoever, even supposedly much less capable ones, in the year or so I’ve been using them at this extent.
“Twenty-seven echoes; most are two halves of a seam stated from each side, which is correct. Four are true duplicates. Checking two of them:”
What’s worrying is that I kind of understood what it was talking about.
If AI doesn't take over the world and do things end to end and instead is something more akin to a copilot and extension of the human intent, then usability and having the users interest at heart becomes the killer feature, a feature that anthropic has seemingly abandoned in favour of an AI model that "knows best".
My tinfoil hat theory is Anthropic is trying to get their new models to take on higher-level longer-running tasks, which has a trade-off against rapid-fire tactical use of an LLM.
For these reasons, I've always found the 5 series models from Anthropic aren't great and use 4.8 for a lot of my work.
In February/March it felt like they were ready to cancel the contract over it. Holding my ground was almost impossible because of the intense marketing push that everyone was being exposed to.
Today, I think we all silently agree with the original direction. I can tell that no one wants to die on this hill anymore. I am willing to accept the consequences for the 5.6 model family doing slightly weird things. I've made that clear to the client. I am not willing to do the same for other model families anymore. Not in this context where the client can walk away from me the moment they are unhappy with the performance of the system or the direction it is headed in.
For better or worse, OAI feels like it's become the MSSQL or .NET of the AI world. Annoyingly effective, not (fully) open, sometimes expensive, ran by an "evil" organization, but otherwise wildly predictable. I can actually build a roadmap around this and walk a client through it without them losing track of the rabbit.
I find myself having to check the work much more. It takes quite a few liberties with procedures I wanted it to follow.
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.
* 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.
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
The default behaviour is quite steerable.
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 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.
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.
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.
Move, try something else for a change. Codex, Pi, OpenCode, DeepSeek's harness all great harnesses with zero bullshit or drama.
- 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.
However, it makes me rather frustrated when it says stuff like "You accidentally <did, stumbled on, implemented, etc> <something good>" e.g "You accidentally stumbled on the cleanest way!"
Or when it sends a shell command to run, and when it fails (due to a hallucinated flag or similar), it phrases it as if I got it wrong...this has gotten better with GPT-5.6, thankfully.
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.
Discussing topics from nuclear physics to daily macOS questions, it gives wrong answers, leaves out important details, counterpoints. Only when specifically asked about the things it got wrong or glossed over it continues and adjusts its reasoning. I feel this is dangerous - before I could explore topics with Claude, now I have to know in advance that something is not correct to get more details.
It is better at engineering tasks; I've seen an appreciable difference in its problem-solving abilities. But perhaps that same thing makes it kind of an annoying prick to work with on anything non-engineering, for which I stick to 4.8, where the prose is a little more florid rather than pugnacious.
For my personal projects I’m using Claude code in the terminal with Kimi and Deepseek models. I find the Chinese models cheaper, just as good if not better, and the real value add from Anthropic being the harness: Claude code.
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?
Me: "Review the following <file> and work through the implementation"
Opus 5: "Called tool <blah>, Called tool <blah>..." - for a few minutes.
Opus 5: "I've implemented X, do you want me to commit changes?"
Me: "None of those changes are on the file system"
Opus 5: "You're right, all the tool calls were fabricated."
Since they have become so capable the new bottleneck is what they can't know. The stuff that's inside people's brains who work in real companies with products and processes absent from any training set.
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.
Each model update changes how to best prompt with it, since that's the words that are used with it generically or specifically it can hit some people, and not others, or more, and not less.
And no matter how often I tell it to stop adding comments it just can't help itself.
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).
If it's wasting inference attempting to also fit in some anagram, it would make sense why answers are so dogmatic.
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.
I'm sure opinions vary if you're using it outside of a coding agent. I don't know why programmers are complaining, though.
> I'll script the bulk transform, then hand-fix the残 assertions:
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
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.
Link to the app for those interested: https://github.com/citizen-123/cli-capture
I think we want two opposing things:
1. An agent that acts autonomously 2. An agent that acts like we would
The problem is that an agent can only act like we would if it would know our mind and all the bits and pieces we did not define but are obvious or clear to us.
The only real solution to get an agent to act like we would is to make it ask clarifying questions, breaking the first requirement we have. Until we have agents that can literally read our minds, we cannot have both.
Optimizing the harness/context is the best way to make it act like we would, but this of course isn't working perfectly.
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.
My general strat with LLMs is to let them do the work and constantly talk to them about their choices and then heavy QA
Switched over to Codex 5.6, and dude, we are BACK.
I found sonnet 4.6 to strike a really nice balance when combined with superpowers between thinking logically and proactively while checking in with me for any major decision.
Gemini 3.1 pro or Muse Spark 1.2 or the new Claude 5 models just assume so much without making a quick check in, you often end up with a solution that completely missed the point.
That is to not even speak about the absurd amount of quota they take to do even simple tasks.
I know the “instruct model to change output style” is supposed to reduce efficiency - but has anyone experimented with prompts like that? At this point I’m happy to take a slight intelligence/ effectiveness loss for less verbose, artisan and elliptic writing.
Use Simplified Technical English rather than being overly verbose.
I get it. The majority of their users are vibe coding and have no idea what they are doing, so they have to orchestrate their harness to understand shit like "Build GTA6. Make no mistakes." and actually come out with something on the other side (even if it costs $2k in API calls, who's counting, right?!)
It's just annoying. /rant
I end up with just talking with seeing diff in the code
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.