Not sure if it’s just me but in Codex, GPT-5.6-Sol and IIRC older 5.5 models can stop dead in the track a couple times a day saying “model is at capacity” (paraphrasing). Then I wait a minute or two and ask it to continue and it’ll more often than not happily use the same model. These frequent mini “outages” are pretty annoying especially if one isn’t supervising. Claude has had long outages but I haven’t run into this kind of mini outages on a daily basis recently.
Hermes seems to pick up after a stoppage. At least I've never had to push it again, it might just take a long time to finish a task and then I'll see the messages in the transcript.
Lots of errors. Opus 5 is also giving me many more hallucinations, including things that aren't even in the right territory. It's also telling me that it's making many mistakes, and the language feels off-kilter as if it's not using typical clear phrases.
It is aggressively proactive in ways that make it very hard to use. I had to turn down the effort level to “low” to stop it from going off in random directions every couple of turns.
I would say "Elected errors _in_ Claude Opus 5" wouldn't be incorrect either.. Opus 5 isn't very reliable for coding and introduces a lot of regressions every single time I use it.
Do you have the same experiences?
I also found it to forget some obvious cases in a quite simple flow (validate the email address of a user who register), that surprised me a lot. Maybe it's because I got used to Fable? But I am quite sure Opus 4.8 wouldn't have make this mistake. If I had time I would try the same prompt with it to see.
Anyway, back on 100% Fable for me.
I don't know what I could be doing differently to you but I found Opus 5 to be more reliable than even myself at times. Maybe your stack is unusual or you have conflicting commands in your prompts vs CLAUDE.md (that really confuses it)? It could be anything but this huge error bar in delivered quality is one of the biggest issues with LLMs.
Getting to grips with each new model does require some tweaking and experimentation. So far I've found Opus 5 to repeatedly pause its work and give me some seemingly randomly invented decisions to make.
Same here, also it lies often to me or implements something else that what was planned. It feels quite strange to see it say casually "I didn't tell you the full truth on X" when I notice the issues. At the same time, maybe it is more honest?
Opus 5 isn't very reliable for coding and introduces a lot of regressions every single time I use it.
And GPT 5.6 Sol over engineers just about everything. No LLM is perfect, its about learning the issues with each LLM and figuring out if you can live with it. Knowledge means that you can anticipate if it tries to pull something funny, and harness it against that behavior.
I remember when 4.7 and 4.8 were released and people were asking what's wrong with them and 4.6 is the best.
But yes, I also think it's not the greatest model for programming. On the other hand, for agentic tasks that are not programming related it's hard to beat Opus 4.8. It can try different things and pivot even when the user is not great with prompting. 5.0 seems to not be worse, but definitely wastes more tokens and costs more.
this would be _Great_ advice if you owned your own LLM and your knowledge was trapped in Amber because you were satisfied.
It's horrible advice given what we've seen consistent: changing alignments, changing guardrails, changing system prompts, changing inference priorities, etc.
Anyone who relies on these for their work product is chaining themselves to a matrix multiple of indetermintism.
I found it eagerly reversing existing product decisions like changing a user given date into created date, since it thought creating something that is in the past is incorrect. This was not even related to the task at hand at all. I noticed this kind of stuff happens more with ultracode for some reason.
A change can introduce regressions in any large project even if 100% of unit tests pass. Unit tests test individual units, regressions can happen at many levels. Especially if we treat performance degradations as regressions.
I detect the regression already in planning with Opus 5, so I do not let Opus 5 implement anything. But it is a waste of time and tokens! Does planning with Opus 5 works out for you?
It sounds like you just need to correct the plan it lays out to avoid the regression? I'm just looking to debug with you, not defending the model. I've mostly used Opus 5 for code reviews & bugfixes.
Yeah fine. I mean my plan wasn't to difficult. For example this morning I started with Opus 5 to tackle a problem. During planning at some point Opus 5 detected _8_ regressions in it's own planning, after I directed it towards those potential regressions. So, in this very moment now, Fable 5 implements code already and the planning before with Fable, done with the same instructions, was flawless and quick. And I am sure, Opus 4.8 would be flawless either. Same harness, same claude.md, etc..
I'd just end up being really annoyed about the downtime if it lands in the middle of a working day.
Elevated errors on Claude Opus 5
https://news.ycombinator.com/item?id=49066591
The post that ends up on front page is usually the one for the previous outage due to the way the algorithm works.
Number of impacted users seems to grow each time per https://downdetector.com/status/claude-ai/ - the first one had peak 19 reports, second 24 and now it's already 39.
Related threads from today/yesterday: https://news.ycombinator.com/item?id=49066591 https://news.ycombinator.com/item?id=49056194
Any examples?
And GPT 5.6 Sol over engineers just about everything. No LLM is perfect, its about learning the issues with each LLM and figuring out if you can live with it. Knowledge means that you can anticipate if it tries to pull something funny, and harness it against that behavior.
But yes, I also think it's not the greatest model for programming. On the other hand, for agentic tasks that are not programming related it's hard to beat Opus 4.8. It can try different things and pivot even when the user is not great with prompting. 5.0 seems to not be worse, but definitely wastes more tokens and costs more.
It's horrible advice given what we've seen consistent: changing alignments, changing guardrails, changing system prompts, changing inference priorities, etc.
Anyone who relies on these for their work product is chaining themselves to a matrix multiple of indetermintism.
Related: https://news.ycombinator.com/item?id=49066591 https://news.ycombinator.com/item?id=49056194 https://news.ycombinator.com/item?id=49067964