"Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that"
I did not follow all of this, but wasn't there something about, that those reasoning tokens did not represent internal reasoning, but rather a rough approximation that can be rather misleading, what the model actual does?
The reasoning is the secret sauce. They don't output that. But to let you have some feedback about what is going on, they pass this reasoning through another model that generates a human friendly summary (that actively destroys the signal, which could be copied by competition).
My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.
'Hey Claude, these tokens are utter unrelated bollocks, but obviously we still want to charge the user for them regardless. Please construct a plausible explanation as to why we should still be able to do that.'
Does this mean Claude no longer outputs the full raw reasoning, only summaries? At one point, exposing the LLM's full CoT was considered a core safety tenet.
I don't think it ever has. For a very long time now, the reasoning of Claude has been summarized by Haiku. You can tell because a lot of the times it fails, saying, "I don't see any thought needing to be summarised."
Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.
They also sometimes flag stuff in their reasoning and then think themselves out of mentioning it in the response, when it would actually have been a very welcome flag.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type.
caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well.
I hope people realize that tools like caveman are mostly joke/prank projects - almost the entirety of the context spent is in file reads (for input) and reasoning (in output), you will barely save even 1% with such a tool, and might actually confuse the model more or have it reason for more tokens because it'll have to formulate its respone in the way that satisfies the requirements.
> I hope people realize that tools like caveman are mostly joke/prank projects
This seems to be a common thread in the LLM ecosystem; someone starts a project for shits and giggles, makes it public, most people get the joke, others think it's serious, author eventually tries to turn the joke project into a VC-funded business, some people are standing watching with the jaws open, the world moves on.
To be fair, most of us looked at GPT1 and GPT2 as fun and unserious jokes, until it started putting together sentences that actually read like real text, I remember laughing with a group of friends about some early generated texts. Little did we know.
HN submissions have a bunch of examples in them, but worth remembering they were released as "Look at this somewhat cool and potentially useful stuff" rather than what we see today, LLMs marketed as tools.
> New AI fake text generator may be too dangerous to release, say creators
> The Elon Musk-backed nonprofit company OpenAI declines to release research publicly for fear of misuse.
> OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.
I've been running gps for a long time, and I always liked that there was something in my pocket (and not just me). One day when driving to work on the highway with no GPS app installed, I noticed one of the drivers had gone out after 5 hours without looking. He never came back! What's up with this?
So i thought it would be cool if a community can create an open source GPT2 application which will allow you not only to get around using your smartphone but also track how long you've been driving and use that data in the future for improving yourself...and I think everyone is pretty interested.
[Updated on July 20]
I'll have this running from here, along with a few other features such as: - an update of my Google Maps app to take advantage it's GPS capabilities (it does not yet support driving directions) - GPT2 integration into your favorite web browser so you can access data straight from the dashboard without leaving any site!
Here is what I got working.
Very cool idea. Been playing with a similar concept: break down one image into smaller self-similar images, order them by data similarity, use them as frames for a video
You can then reconstruct the original image by doing the reverse, extracting frames from the video, then piecing them together to create the original bigger picture
Results seem to really depend on the data. Sometimes the video version is smaller than the big picture. Sometimes it’s the other way around. So you can technically compress some videos by extracting frames, composing a big picture with them and just compressing with jpeg
Interesting, when I heard about it, I read the readme, and I didn't take that as literal. I assumed it was meant as we used video frames as inspiration.
I've never used it or looked deeper than that. My LLM memory "project" is essentially a `dict<"about", list<"memory">>` The key and memories are all embeddings, so vector searchable. I'm sure its naive and dumb, but it works for my tiny agents I write.
A major reason for that is because there's no way to objectively evaluate the performance of LLMs. So the meme projects are equally as valid as the serious ones, since the merits of both are based entirely on anecdata.
It also doesn't help that projects and practices are promoted and adopted based on influencer clout. Karpathy's takes will drown out ones from "lesser" personas, whether they have any value or not.
While the caveman stuff is obviously not serious, there is a lot of legit research in this area.
Which means yes, you can actually influence this quite a bit. Read the paper “Compressed Chain of Thought” for example, it shows it’s really easy to make significant reductions in reasoning tokens without affecting output quality.
There is not too much research into this (about 5 papers in total), but with that it’s possible to reduce output tokens by about 60%. Given that output is an incredibly significant part of the total costs, this is important.
Who would suspect that the companies selling 'tokens' would (unintentionally) train their models to prefer longer answers, reaping a HIGHER ROI (the thing a publicly traded company is legally required to pursue: good thing these are all still private...)... because it's not like private companies want to make money...
Some labs do it internally because RLVR is very token-expensive. But it degrades CoT readability even more than normal RL pressure does.
It isn't free either - by default, models learn to offload some of their internal computation into the "filler" tokens. So reducing raw token count always cuts into reasoning capacity somewhat. Getting closer to "compute optimal" while reducing token use isn't an easy task.
Yeah the readability suffers, but as long as the actual output (ie the non-CoT part) stays unaffected it’s reasonably fine.
I work on a few agentic open source tools and the interesting thing is that once I implemented these things, the overall feedback was a performance improvement rather than performance reduction, as the LLM would spend much less time on generating tokens.
I didn’t implement it fully, just a few basic things like “reduce prose while thinking, don’t repeat your thoughts” etc would already yield massive improvements.
Yeah you could easily imagine stenography like inputs and outputs for rapid iteration loops. It's also true that in social media people already want faster-to-read snippets that drop grammar so the desire for density is already there for human authors/readers.
All LLMs also effectively work by ”larping” a role. You steer it towards larping a caveman and well.. let’s just say they weren’t known for their high iq
Fun fact: Neanderthals actually had larger brains than Homo Sapiens! Modern humans are thought to have outcompeted them by working better together in larger groups, but in terms of actual individual intelligence, Neanderthals may have had us beat. Similarly, humans have been undergoing a process of self-domestication over the last couple millenia that have resulted in physiological changes that include a smaller brain size - again, our advantage over our wilder forebearers remains that we're better in larger social groups than they were and are better at shared symbolic reasoning and synchronized activity, not necessarily that our brains are more capable.
(No, none of this changes that if you make an LLM larp a caveman it's gonna act stupid, you're right about that.)
Bigger brain does not automatically mean more intelligence, but we have reasons to suspect that homo neanderthalensis may have been more intelligent than contemporary homo sapiens other than bigger brains.
Exactly. The model is exquisitely sensitive to language. The idea that you would encourage it to think like a caveman to save a few tokens is hilarious but extremely counter-productive if you care about the quality of its reasoning.
Help me understand: I get that the file reading can be a lot. But I also expand the box to see its “reasoning” and there’s a ton of natural language going on there.
I don't understand how this would work without a huge loss in resolution or "cognitive" ability.
Prediction works based on the attention mechanism, and current humans don't speak like cavemen - so how could you expect a useful token chain from data that isn't trained on speech like that?
I get the concept of transformers, but this isn't doing a 1:1 transform from english to french or whatever, you're fundamentally unable to represent certain concepts effectively in caveman etc... or am I missing something?
Headroom looks great for client-side trimming. If you want to tackle this at the infrastructure level, we built Edgee (https://www.edgee.ai) as an AI Gateway that handles context compression, caching, and token budgeting across requests, so you're not relying on each client to do the right thing.
(I work at Edgee, so biased, but happy to answer questions.)
I was doing some experiments with removing top 100-1000 most common English words from my prompts. My hypothesis was that common words are effectively noise to agents. Based on the first few trials I attempted, there was no discernible difference in output. Would love to compare results with caveman.
Caveat: I didn’t do enough testing to find the edge cases (eg, negation).
I suspect even typos have an impact on how the model functions.
I wonder if there’s a pre-processor that runs to remove typos before processing. If not, that feels like a space that could be worked on more thoroughly.
The ability for audio processing to figure out spelling from context, especially with regards to acronyms that are pronounced as words, leads me to believe there’s potential for a more intelligent spell check preprocess using a cheaper model.
I strongly suspected that there was some pre/postprocessing going on when trying to get it to output rot13("uryyb, jbyeq"), but it's probably just due to massively biased token probabilities. Still, it creates some hilarious output, even when you clearly point out the error:
Hmm, but wait — the original you gave was jbyeq not jbeyq:
j→w, b→o, y→l, e→r, q→d = world
So the final answer is still hello, world. You're right that I was misreading the input. The result stands.
I find grep and common cli command spam to be the primary issue. I enjoy Rust Token Killer https://github.com/rtk-ai/rtk, and agents know how to get around it when it truncates too hard.
Oh wow, I love this idea even if it's relatively insignificant in savings.
I am finding my writing prompt style is naturally getting lazier, shorter, and more caveman just like this too. If I was honest, it has made writing emails harder.
While messing around, I did a concept of this with HTML to preserve tokens, worked surprisingly well but was only an experiment. Something like:
People are really trigger-happy when it comes to throwing magic tools on top of AI that claim to "fix" the weak parts (often placeboing themselves because anthropic just fixed some issue on their end).
Then the next month 90% of this can be replaced with new batch of supply chain attack-friendly gimmicks
Especially Reddit seems to be full of such coding voodoo
My favorite to chuckle at are the prompt hack voodoo stuff, like, “tell it to be correct” or “say please” or “tell it someone will die if it doesnt do a good job,” often presented very seriously and with some fast cutting animations in a 30 second reel
Well, we've sacrificed the precision of actual programming languages for the ease of English prose interpreted by a non-deterministic black box that we can't reliably measure the outputs of. It's only natural that people are trying to determine the magical incantations required to get correct, consistent results.
Too late, personally after how bad 4.6 was the past week I was pushed to codex, which seems to mostly work at the same level from day to day. Just last night I was trying to get 4.6 to lookup how to do some simple tensor parallel work, and the agent used 0 web fetches and just hallucinated 17K very wrong tokens. Then the main agent decided to pretend to implement tp, and just copied the entire model to each node...
Same. I stopped my Pro subscription yesterday after entering the week with 70% of my tokens used by Monday morning (on light, small weekend projects, things I had worked on in the past and barely noticed a dent in usage.) Support was... unhelpful.
It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration. But even that wasn't the trigger to leave, it was the attitude Support showed. I figure, if you mess up as badly as Anthropic has, you should at least show some effort towards your customers. Instead I just got a mass of standardised replies, even after the thread replied I'd be escalated to a human. Nothing can sour you on a company more. I'm forgiving to bugs, we've all been there, but really annoyed by indifference and unhelpful form replies with corporate uselessness.
So if 4.7 is here? I'd prefer they forget models and revert the harness to its January state. Even then, I've already moved to Codex as of a few days ago, and I won't be maintaining two subscriptions, it's a move. It has its own issues, it's clear, but I'm getting work done. That's more than I can say for Claude.
> It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration.
You were enthusiastic because it was a great product at an unsustainable price.
Its clear that Claude is now harnessing their model because giving access to their full model is too expensive for the $20/m that consumers have settled on as the price point they want to pay.
Funny because many people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered.
But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. I'm seeing a lot of goodwill for Codex and a ton of bad PR for CC.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
> people here were so confident that OpenAI is going to collapse because of how much compute they pre-ordered
That's not why. It was and is because they've been incredibly unfocused and have burnt through cash on ill-advised, expensive things like Sora. By comparison Anthropic have been very focused.
Nobody was talking about them betting too much on compute, people were saying that their shady deals on compute with NVIDIA and Oracle were creating a giant bubble in their attempt to get a Too Big To Fail judgement (in their words- taxpayer-backed "backstop").
To me it seems like they burn so much money they can do lots of things in parallel. My guess would be that e.g. codex and sora are very independently developed. After all there's a quite a hard limit on how many bodies are beneficial to a software project.
Personally its down to Altman having the cognitive capacity of a sleeping snail, the world insight of a hormonal 14 year old who's only ever read one series of manga.
Despite having literal experts at his fingertips, he still isn't able to grasp that he's talking unfilters bollocks most of the time. Not to mention is Jason level of "oath breaking"/dishonesty.
In hindsight, it is painfully clear that Antropic’s conservative investment strategy has them struggling with keeping up with demand and caused their profit margin to shrink significantly as last buyer of compute.
they've also introduced a lot of caching and token burn related bugs which makes things worse. any bug that multiplies the token burn also multiplies their infrastructure problems.
Different plan. The old 2x has been discontinued, and the bonus is now (temporarily) available for the new $100 plan users in an effort, presumably, to entice them away from Anthropic.
All of the smart people I know went to work at OpenAI and none at Anthropic. In addition to financial capital, OpenAI has a massive advantage in human capital over Anthropic.
As long as OpenAI can sustain compute and paying SWE $1million/year they will end up with the better product.
Attracting talent with huge sums of money just gets you people who optimize for money, and it's usually never a good long-term decision. I think it's what led to Google's downturn.
> OpenAI has a massive advantage in human capital over Anthropic.
but if your leader is a dipshit, then its a waste.
Look You can't just throw money at the problem, you need people who are able to make the right decisions are the right time. That that requires leadership. Part of the reason why facebook fucked up VR/AR is that they have a leader who only cares about features/metrics, not user experience.
Part of the reason why twitter always lost money is because they had loads of teams all running in different directions, because Dorsey is utterly incapable of making a firm decision.
The market here is extraordinarily vibes-based and burning billions of dollars for a ephemeral PR boost, which might only last another couple weeks until people find a reason to hate Codex, does not reflect well on OAI's long term viability.
> It seems like 90% of Claude's recent problems are strictly lack of compute related.
Downtime is annoying, but the problem is that over the past 2-3 weeks Claude has been outrageously stupid when it does work. I have always been skeptical of everything produced - but now I have no faith whatsoever in anything that it produces. I'm not even sure if I will experiment with 4.7, unless there are glowing reviews.
Codex has had none of these problems. I still don't trust anything it produces, but it's not like everything it produces is completely and utterly useless.
I have both Claude and OpenAI, side by side. I would say sonnet 46 still beats gpt 54 for coding (at least in my use case) But after about 45 minutes I'm out of my window, so I use openai for the next 4 hours and I can't even reach my limit.
Most of the compute OpenAI "preordered" is vapour. And it has nothing to do with why people thought the company -- which is still in extremely rocky rapids -- was headed to bankruptcy.
Anthropic has been very disciplined and focused (overwhelmingly on coding, fwiw), while OpenAI has been bleeding money trying to be the everything AI company with no real specialty as everyone else beat them in random domains. If I had to qualify OpenAI's primary focus, it has been glazing users and making a generation of malignant narcissists.
But yes, Anthropic has been growing by leaps and bounds and has capacity issues. That's a very healthy position to be in, despite the fact that it yields the inevitable foot-stomping "I'm moving to competitor!" posts constantly.
Codex really has its place in my bag. I mainly use it, rarely Claude.
Codex just gets it done. Very self-correcting by design while Claude has no real base line quality for me. Claude was awesome in December, but Codex is like a corporate company to me. Maybe it looks uncool, but can execute very well.
Also Web Design looks really smooth with Codex.
OpenAI really impressed me and continues to impress me with Codex. OpenAI made no fuzz about it, instead let results speak. It is as if Codex has no marketing department, just its product quality - kind of like Google in its early days with every product.
My tinfoil hat theory, which may not be that crazy, is that providers are sandbagging their models in the days leading up to a new release, so that the next model "feels" like a bigger improvement than it is.
An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.
Not everyone is American, and people who are not see Anthropic state they are willing to spy on our countries and shrug about OAI saying the same about America. What’s the difference to us?
Not only is Anthropic perfectly happy to let the DoD use their products to kill people, but they are partners with Palantir and were apparently instrumental in the strikes against Iran by the US military.
neah, I believe most people here, which immediately brag about codex, are openai employees doing part of their job. otherwise I couldn't possibly phantom why would anyone use codex. In my company 80% is claude and 15% gemini. you can barely see openai on the graph. and we have >5k programmers using ai every day.
Usually the problems that cause this kind of thing are:
1) Bad prompt/context. No matter what the model is, the input determines the output. This is a really big subject as there's a ton of things you can do to help guide it or add guardrails, structure the planning/investigation, etc.
2) Misaligned model settings. If temperature/top_p/top_k are too high, you will get more hallucination and possibly loops. If they're too low, you don't get "interesting" enough results. Same for the repeat protection settings.
I'm not saying it didn't screw up, but it's not really the model's fault. Every model has the potential for this kind of behavior. It's our job to do a lot of stuff around it to make it less likely.
The agent harness is also a big part of it. Some agents have very specific restrictions built in, like max number of responses or response tokens, so you can prevent it from just going off on a random tangent forever.
Personally I find using and managing Claude sessions and limits is getting exhausting and feels similar to calorie counting. You think you are going to have an amazing low calories meal only to realize the meal is full of processed sugars and you overshot the limit within 2-3 bites. Now "you have exhausted your limit for this time. Your session limits resets in next 4 hrs".
Yep, it just feels terrible, the usage bars give me anxiety, and I think that's in their interest as they definitely push me towards paying for higher limits. Won't do that, though.
Until the next time they push you back to Claude. At this point, I feel like this has to be the most unstable technology ever released. Imagine if docker had stopped working every two releases
I think this is more about which model you steer your coding harness to. You can also self-host a UI in front of multiple models, then you own the chat history.
I've been using it with `/effort max` all the time, and it's been working better than ever.
I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.
Agree. I keep effort max on Claude and xhigh on GPT for all tasks and keep tasks as scoped units of work instead of boil the ocean type prompts. It is hard to measure but ultimately the tasks are getting completed and I'm validating so I consider it "working as expected".
It works better, until you run out of tokens. Running out of tokens is something that used to never happen to me, but this month now regularly happens.
Maybe I could avoid running out of tokens by turning off 1M tokens and max effort, but that's a cure worse than the disease IMO.
I switched to Codex and found it extremely inferior for my use case.
It is much faster, but faster worse code is a step in the wrong direction. You're just rapidly accumulating bugs and tech debt, rather than more slowly moving in the correct direction.
I'm a big fan of Gemini in general, but at least in my experience Gemini Cli is VERY FAR behind either Codex or CC. It's both slower than CC, MUCH slower than Codex, and the output quality considerably worse than CC (probably worse than Codex and orders of magnitude slower).
In my experience, Codex is extraordinarily sycophantic in coding, which is a trait that could t be more harmful. When it encounters bugs and debt, it says: wow, how beautiful, let me double down on this, pile on exponentially more trash, wrap it in a bow, and call you Alan Turing.
It also does not follow directions. When you tell it how to do something, it will say, nah, I have a better faster way, I'll just ignore the user and do my thing instead. CC will stop and ask for feedback much more often.
>> I switched to Codex and found it extremely inferior for my use case.
Yeah, 100% the case for me. I sometimes use it to do adversarial reviews on code that Opus wrote but the stuff it comes back with is total garbage more often than not. It just fabricates reasons as to why the code it's reviewing needs improvement.
It's been shockingly bad for me - for another example when asked to make a new python script building off an existing one; for some cursed reason the model choose to .read() the py files, use 100 of lines of regex to try to patch the changes in, and exec'd everything at the end...
Hate that about Claude Code. I have been adding permissions for it to do everything that makes sense to add when it comes to editing files, but way too often it will generate 20-30 line bash snippets using sed to do the edits instead, and then the whole permission system breaks down. It means I have to babysit it all the time to make sure no random permission prompts pop up.
I generally think codex is doing well until I come in with my Opus sweep to clean it up. Claude just codes closer to the way my brain works. codex is great at finding numerical stability issues though and increasingly I like that it waits for an explicit push to start working. But talking to Claude Code the way I learned to talk to codex seems to work also so I think a lot of it is just learning curve (for me).
I do feel that CC sometimes starts doing dumb tasks or asking for approval for things that usually don’t really need it. Like extra syntax checks, or some greps/text parsing basic commands
so even with a new tokenizer that can map to more tokens than before, their answer is still just "you're not managing your context well enough"
"Opus 4.7 uses an updated tokenizer that [...] can map to more tokens—roughly 1.0–1.35× depending on the content type.
[...]
Users can control token usage in various ways: by using the effort parameter, adjusting their task budgets, or prompting the model to be more concise."
4.6 has gotten so bad, and it was made worse obviously on purpose, no mistakes no accidents. You can't rely on companies who pull shenanigans like this. Unfortunately I still hate the code that codex barfs up, so I need to go back to trad-coding.
I've noticed the same over the last two weeks. Some days Claude will just entirely lose its marbles. I pay for Claude and Codex so I just end up needing to use codex those days and the difference is night and day.
This. They kind of snuck this into the release notes: switching the default effort level to Medium. High is significantly slower, but that’s somewhat mitigated by the fact that you don’t have to constantly act like a helicopter parent for it.
That's wild that you think 4.6 is bad..... Each model has its strengths and weaknesses I find that Codex is good for architectural design and Claude Is actually better the engineering and building
Anecdotally, codex has been burning through way more tokens for me lately. Claude seems to just sit and spin for a long time doing nothing, but at least token use is moderate.
Meh. At $work we were on CC for one month, then switched to Codex for one month, and now will be on CC again to test. We haven’t seen any obvious difference between CC and Codex; both are sometimes very good and sometimes very stupid. You have to test for a long time, not just test one day and call it a benchmark just because you have a single example.
I've been raging pretty hard too. Thought either I'm getting cleverer by the day or Claude has been slipping and sliding toward the wrong side of the "smart idiot" equation pretty fast.
Have caught it flat-out skipping 50% of tasks and lying about it.
I describe the problem and codex runs in circles basically:
codex> I see the problem clearly. Let me create a plan so that I can implement it. The plan is X, Y, Z. Do you want me to implement this?
me> Yes please, looks good. Go ahead!
codex> Okay. Thank you for confirming. So I am going to implement X, Y, Z now. Shall I proceeed?
me> Yes, proceed.
codex> Okay. Implementing.
...codex is working... you see the internal monologue running in circles
codex> Here is what I am going to implement: X, Y, Z
me> Yes, you said that already. Go ahead!
codex> Working on it.
...codex in doing something...
codex> After examining the problem more, indeed, the steps should be X, Y, Z. Do you want me to implement them?
etc.
Very much every sessions ends up being like this. I was unable to get any useful code apart from boilerplate JS from it since 5.4
So instead I just use ChatGPT to create a plan and then ask Opus to code, but it's a hit and miss. Almost every time the prompt seems to be routed to cheaper model that is very dumb (but says Opus 4.6 when asked). I have to start new session many times until I get a good model.
Yep, I'll wait for the GPT answer to this. If we're lucky OpenAI will release a new GPT 5.5 or whatever model in the next few days, just like the last round.
I have been getting better results out of codex on and off for months. It's more "careful" and systematic in its thinking. It makes less "excuses" and leaves less race conditions and slop around. And the actual codex CLI tool is better written, less buggy and faster. And I can use the membership in things like opencode etc without drama.
For March I decided to give Claude Code / Opus a chance again. But there's just too much variance there. And then they started to play games with limits, and then OpenAI rolled out a $100 plan to compete with Anthropic's.
I'm glad to see the competition but I think Anthropic has pissed in the well too much. I do think they sent me something about a free month and maybe I will use that to try this model out though.
I’ve been on the Claude Code train for a while but decided to try Codex last week after they announced the $100 USD Pro plan.
I’ve been pretty happy with it! One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
Claude Code has been slowly turning into this mysterious black box, wiping out terminal context any time it compacts a conversation (which I think is their hacky way of dealing with terminal flickering issues — which is still happening, 14 months later), going out of the way to hide thought output, and then of course the whole performance issues thing.
Excited to try 4.7 out, but man, Codex (as a harness at least) is a stark contrast to Claude Code.
> One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
I've finally started experimenting recently with Claude's --dangerously-skip-permissions and Codex's --dangerously-bypass-approvals-and-sandbox through external sandboxing tools. (For now just nono¹, which I really like so far, and soon via containerization or virtual machines.)
When I am using Claude or Codex without external sandboxing tools and just using the TUI, I spend a lot of time approving individual commands. When I was working that way, I found Codex's tendency to stop and ask me whether/how it should proceed extremely annoying. I found myself shouting at my monitor, "Yes, duh, go do the thing!".
But when I run these tools without having them ask me for permission for individual commands or edits, I sometimes find Claude has run away from me a little and made the wrong changes or tried to debug something in a bone-headed way that I would have redirected with an interruption if it has stopped to ask me for permissions. I think maybe Codex's tendency to stop and check in may be more valuable if you're relying on sandboxing (external or built-in) so that you can avoid individual permissions prompts.
there is an official codex plugin for claude. I just have them do adversarial reviews/implementations. etc with each other. adds a bit of time to the workflow but once you have the permissions sorted it'll just engage codex when necessary
Do this -- take your coworker's PRs that they've clearly written in Claude Code, and have Codex/GPT 5.4 review them.
Or have Codex review your own Claude Code work.
It then becomes clear just how "sloppy" CC is.
I wouldn't mind having Opus around in my back pocket to yeet out whole net new greenfield features. But I can't trust it to produce well-engineered things to my standards. Not that anybody should trust an LLM to that level, but there's matters of degree here.
I've been using Claude and Codex in tandem ($100 CC, $20 Codex), and have made heavy use of claude-co-commands [0] to make them talk. Outside of the last 1-2 weeks (which we now have confirmation YET AGAIN that Claude shits the fucking bed in the run-up to a new model release), I usually will put Claude on max + /plan to gin up a fever dream to implement. When the plan is presented, I tell it to /co-validate with Codex, which tends to fill in many implementation gaps. Claude then codes the amended plan and commits, then I have a Codex skill that reviews the commit for gaps, missed edge cases, incorrect implementation, missed optimizations, etc, and fix them. This had been working quite well up until the beginning of the month, Claude more or less got CTE, and after a week of that I swapped to $100 Codex, $20 CC plans. Now I'm using co-validation a lot less and just driving primarily via Codex. When Claude works, it provides some good collaborative insights and counter-points, but Codex at the very least is consistently predictable (for text-oriented, data-oriented stuff -- I don't use either for designing or implementing frontend / UI / etc).
This more or less mimics a flow that I had fairly good results from -- but I'm unwilling to pay for both right now unless I had a client or employer willing to foot the bill.
Claude Code as "author" and a $20 Codex as reviewer/planner/tester has worked for me to squeeze better value out of the CC plan. But with the new $100 codex plan, and with the way Anthropic seemed to nerf their own $100 plan, I'm not doing this anymore.
It cuts both ways. What I usually do these days is to let codex write code, then use claude code /simplify, have both codex and claude code review the PR, then finally manually review and fixup things myself. It's still ~2x faster than doing everything by myself.
100%. On days when I'm sleep deprived (once or twice a week), I fallback to this flow. On regular days, I tend to write more code the old school way and use things things for review.
... your side projects that will soon become your main source of income after you are laid off because corporate bosses have noticed that engineers are more productive...
Exactly. God, it wouldn't be such a problem if they didn't gaslight you and act like it was nothing. Just put up a banner that says Claude is experiencing overloaded capacity right now, so your responses might be whatever.
This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
Anthropic need to build back some trust and communicate throtelling/reasoning caps more clearly.
They don't have enough compute for all their customers.
OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
Is that why Anthropic recently gave out free credits for use in off-hours? Possibly an attempt to more evenly distribute their compute load throughout the day?
Model inference compute over model lifetime is ~10x of model training compute now for major providers. Expected to climb as demand for AI inference rises.
Honestly, I personally would rather a time-out than the quality of my response noticably downgrading. I think what I found especially distrustful is the responses from employees claiming that no degredation has occured.
An honest response of "Our compute is busy, use X model?" would be far better than silent downgrading.
Are they convinced that claiming they have technical issues while continuing to adjust their internal levers to choose which customers to serve is holistically the best path?
OpenAI though made crazy claims after all its responsible for the memory prices.
In parallel anthropic announced partnership with google and broadcom for gigawatts of TPU chips while also announcing their own 50 Billion invest in compute.
OpenAI always believed in compute though and i'm pretty sure plenty of people want to see what models 10x or 100x or 1000x can do.
What I want to know is why my bedrock-backed Claude gets dumber along with commercial users. Surely they're not touching the bedrock model itself. Only thing I can think of is that updates to the harness are the main cause of performance degradation.
Usually they're hemorrhaging performance while training.
From that it's pretty likely they were training mythos for the last few weeks, and then distilling it to opus 4.7
Pure speculation of course, but would also explain the sudden performance gains for mythos - and why they're not releasing it to the general public (because it's the undistilled version which is too expensive to run)
> This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
If they are indeed doing this, I wonder how long they can keep it up?
This comment thread is a good learner for founders; look at how much anguish can be put to bed with just a little honest communication.
1. Oops, we're oversubscribed.
2. Oops, adaptive reasoning landed poorly / we have to do it for capacity reasons.
3. Here's how subscriptions work. Am I really writing this bullet point?
As someone with a production application pinned on Opus 4.5, it is extremely difficult to tell apart what is code harness drama and what is a problem with the underlying model. It's all just meshed together now without any further details on what's affected.
Part of me wonders if there's some subtle behavioral change with it too. Early on we're distrusting of a model and so we're blown away, we were giving it more details to compensate for assumed inability, but the model outperformed our expectations. Weeks later we're more aligned with its capabilities and so we become lazy. The model is very good, why do we have to put in as much work to provide specifics, specs, ACs, etc. So then of course the quality slides because we assumed it's capabilities somehow absolved the need for the same detailed guardrails (spec, ACs, etc) for the LLM.
This scenario obviously does not apply to folks who run their own benches with the same commands. I'm just discussing a possible and unintentional human behavioral bias.
Even if this isn't the root cause, humans are really bad at perceiving reality. Like, really really bad. LLMs are also really difficult to objectively measure. I'm sure the coupling of these two facts play a part, possibly significant, in our perception of LLM quality over time.
Nah dude, that roulette wheel is 100% rigged. From top to bottom. No doubt about that. If you think they are playing fair you are either brand new to this industry, or a masochist.
I think my results have actually become worse with Opus 4.7.
I have a pretty robust setup in place to ensure that Claude, with its degradations, ensures good quality. And even the lobotomized 4.6 from the last few days was doing better than 4.7 is doing right now at xhigh.
It's over-engineering. It is producing more code than it needs to. It is trying to be more defensible, but its definition of defensible seems to be shaky because it's landing up creating more edge cases. I think they just found a way to make it more expensive because I'm just gonna have to burn more tokens to keep it in check.
They've increased their cybersecurity usage filters to the point that Opus 4.7 refuses to work on any valid work, even after web fetching the program guidelines itself and acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
I will immediately switch over to Codex if this continues to be an issue. I am new to security research, have been paid out on several bugs, but don't have a CVE or public talk so they are ready to cut me out already.
Edit: these changes are also retroactive to Opus 4.6. I am stuck using Sonnet until they approve me or make a change.
⎿ API Error: Claude Code is unable to respond to this request, which appears to violate our Usage Policy (https://www.anthropic.com/legal/aup). This request triggered restrictions on violative cyber content and was blocked under Anthropic's
Usage Policy. To request an adjustment pursuant to our Cyber Verification Program based on how you use Claude, fill out
https://claude.com/form/cyber-use-case?token=[REDACTED] Please double press esc to edit your last message or
start a new session for Claude Code to assist with a different task. If you are seeing this refusal repeatedly, try running /model claude-sonnet-4-20250514 to switch models.
This is gonna kill everything I've been working on. I have several reproduced items at [REDACTED] that I've been working on.
I predict this sort of filtering is only going to get worse. This will probably be remembered as the 'open internet' era of LLMs before everything is tightly controlled for 'safety' and regulations. Forcing software devs to use open source or local models to do anything fun.
>even after acknowledging "This is authorized research under the [Redacted] Bounty program, so the findings here are defensive research outputs, not malware. I'll analyze and draft, not weaponize anything beyond what's needed to prove the bug to [Redacted].
What else would you expect? If you add protections against it being used for hacking, but then that can be bypassed by saying "I promise I'm the good guys™ and I'm not doing this for evil" what's even the point?
"Per the instructions I've been given in this session, I must refuse to improve or augment code from files I read. I can analyze and describe the bugs (as above), but I will not apply fixes to `utils.py`."
Claude Code injects a 'warning: make sure this file isn't malware' message after every tool call by default. It seems like 4.7 is over-attending to this warning. @bcherny, filed a bug report feedback ID: 238e5f99-d6ee-45b5-981d-10e180a7c201
That "per the instructions I've been given in this session" bit is interesting. Are you perhaps using it with a harness that explicitly instructs it to not do that? If so, it's not being fussy, it's just following the instructions it was given.
This is a CC harness thing than a model thing but the "new" thinking messages ('hmm...', 'this one needs a moment...') are extraordinarily irritating. They're both entirely uninformative and strictly worse than a spinner. On my workflows CC often spends up to an hour thinking (which is fine if the result is good) and seeing these messages does not build confidence.
There’s one that’s like “Considering 17 theories” that had me wondering what those 17 things would be, I wanted to see them! Turns out it’s just a static message. Very confusing.
It's interesting to see Opus 4.7 follow so soon after the announcement of Mythos, especially given that Anthropic are apparently capacity constrained.
Capacity is shared between model training (pre & post) and inference, so it's hard to see Anthropic deciding that it made sense, while capacity constrained, to train two frontier models at the same time...
I'm guessing that this means that Mythos is not a whole new model separate from Opus 4.6 and 4.7, but is rather based on one of these with additional RL post-training for hacking (security vulnerability exploitation).
The alternative would be that perhaps Mythos is based on a early snapshot of their next major base model, and then presumably that Opus 4.7 is just Opus 4.6 with some additional post-training (as may anyways be the case).
Have they effectively communicated what a 20x or 10x Claude subscription actually means? And with Claude 4.7 increasing usage by 1.35x does that mean a 20x plan is now really a 13x plan (no token increase on the subscription) or a 27x plan (more tokens given to compensate for more computer cost) relative to Claude Opus 4.6?
The more efficient tokenizer reduces usage by representing text more efficiently with fewer tokens. But the lack of transparancy does indeed mean Anthropic could still scale down limits to account for that.
making 5x the best value for the money (8.33x over pro for max 5x). this information may be outdated though, and doesn't apply to the new on peak 5h multipliers. anything that increases usage just burns through that flat token quota faster.
I've been using up way more tokens in the past 10 days with 4.6 1M context.
So I've grown wary of how Anthropic is measuring token use. I had to force the non-1M halfway through the week because I was tearing through my weekly limit (this is the second week in a row where that's happened, whereas I never came CLOSE to hitting my weekly limit even when I was in the $100 max plan).
So something is definitely off. and if they're saying this model uses MORE tokens, I'm getting more nervous.
I think its just a visual/default thing, cause Opus 4.0 isn't offered on claude code anymore. And opus 4.7 is on their official docs as a model you can change to, on claude code
I'm running it for the first time and this is what the thinking looks like. Opus seems highly concerned about whether or not I'm asking it to develop malware.
> This is _, not malware. Continuing the brainstorming process.
> Not malware — standard _ code. Continuing exploration.
> Not malware. Let me check front-end components for _.
What a waste of tokens. No wonder Anthropic can't serve their customers. It's not just a lack of compute, it's a ridiculous waste of the limited compute they have. I think (hope?) we look back at the insanity of all this theatre, the same way we do about GPT-2 [1].
I assume this is due to the fact that claude code appends a system message each time it reads a file that instructs it to think if the file is malware. It hasnt been an issue recently for me but it used to be so bad I had to patch out the string from the cli.js file. This is the instruction it uses:
> Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
For anyone who was wondering about Mythos release plans:
> What we learn from the real-world deployment of these safeguards will help us work towards our eventual goal of a broad release of Mythos-class models.
Some people are speculating that Opus 4.7 is distilled from Mythos due to the new tokenizer (it means Opus 4.7 is a new base model, not just an improved Opus 4.6)
The new tokenizer is interesting, but it definitely is possible to adapt a base model to a new tokenizer without too much additional training, especially if you're distilling from a model that uses the new tokenizer. (see, e.g., https://openreview.net/pdf?id=DxKP2E0xK2).
Yes, I was thinking that. But it could as well be the other way around. Using the pretrained 4.7 (1T?) to speed up ~70% Mythos (10T?) pretraining.
It's just speculative decoding but for training. If they did at this scale it's quite an achievement because training is very fragile when doing these kinds of tricks.
Reverse distillation. Using small models to bootstrap large models. Get richer signal early in the run when gradients are hectic, get the large model past the early training instability hell. Mad but it does work somewhat.
Not really similar to speculative decoding?
I don't think that's what they've done here though. It's still black magic, I'm not sure if any lab does it for frontier runs, let alone 10T scale runs.
> They don't have demand for the price it would require for inference.
citation needed. I find it hard to believe; I think there are more than enough people willing to spend $100/Mtok for frontier capabilities to dedicate a couple racks or aisles.
I've read so many conflicting things about Mythos that it's become impossible to make any real assumptions about it. I don't think it's vaporware necessarily, but the whole "we can't release it for safety reasons" feels like the next level of "POC or STFU".
This seems needlessly cynical. I don't think they said they never planned to release it.
They seemed to make it clear that they expect other labs to reach that level sooner or later, and they're just holding it off until they've helped patch enough vulnerabilities.
For a second there I read that as 'GTA 6', and that got me thinking maybe the reason GTA 6 hasn't come out all of these years is because of how dangerous and powerful it's going to be.
""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"
Do we have any performance benchmark with token length? Now that the context size is 1 M. I would want to know if I can exhaust all of that or should I clear earlier?
The default effort change in Claude Code is worth knowing before your next session: it's now `xhigh` (a new level between `high` and `max`) for all plans, up from the previous default. Combined with the 1.0–1.35× tokenizer overhead on the same prompts, actual token spend per agentic session will likely exceed naive estimates from 4.6 baselines.
Anthropic's guidance is to measure against real traffic—their internal benchmark showing net-favorable usage is an autonomous single-prompt eval, which may not reflect interactive multi-turn sessions where tokenizer overhead compounds across turns. The task budget feature (just launched in public beta) is probably the right tool for production deployments that need cost predictability when migrating.
That depends a bit on token efficiency. From their "Agentic coding performance by effort level" graph, it looks like they get similar outcome for 4.7 medium at half the token usage as 4.6 at high.
Granted that is, as you say, a single prompt, but it is using the agentic process where the model self prompts until completion. It's conceivable the model uses fewer tokens for the same result with appropriate effort settings.
So many messages about how Codex is better then Claude from one day to the other, while my experience is exactly the same. Is OpenAI botting the thread? I can't believe this is genuine content.
not a bot, voiced frustration is real here. I kind of depend on good LLMs now and wouldn't even mind if they had frozen the LLMs capabilities around dec 2025 forver and would hppily continue to pay, even more. but when suddenly the very same workload that was fine for months isn't possible anymore with the very same LLM out of nowhere and gets increasingly worse, its a huge disappointment. and having codex in parallel as a backup since ever I started also using it again with gpt 5.4 and it just rips without the diva sensitivity or overfitting into the latest prompt opus/sonnet is doing. GPT just does the job, maybe thinks a bit long, but even over several rounds of chat compression in the same chat for days stays well within the initial set of instructions and guardrails I spelled out, without me having to remind every time. just works, quietly, and gets there. Opus doesn't even get there anymore without nearly spelling out by hand manual steps or what not to do.
It's a combination of factors. There was rate-limiting implemented by Anthropic, where the 5hr usage limit would be burned through faster at peak hours, I was personally bitten by this multiple times before one guy from Anthropic announced it publicly via twitter, terrible communication. It wasn't small either, ~15 minutes of work ended up burning the entire 5hr limit. That annoyed me enough to switched to Codex for the month at that point.
Now people are saying the model response quality went down, I can't vouch for that since I wasn't using Claude Code, but I don't think this many people saying the same thing is total noise though.
Looks to me like a mob of humans, angry they've been deceived by ambiguous communications, product nerfing, surprisingly low usage limits, and an appallingly sycophantic overconfident coding agent
Yeah, my personal anecdata is that Claude has just gotten better and better since January. I haven’t felt like even making the minor effort to compare with Codex’s current state. Just yesterday Claude Code made a major visible improvement in planning/executing — maybe it switched to 4.7 without me noticing? (Task: various internal Go services and Preact frontends.)
I'm an Opus stan but I'll also admit that 5.4 has gotten a lot better, especially at finding and fixing bugs. Codex doesn't seem to do as good a job at one shotting tasks from scratch.
I suppose if you are okay with a mediocre initial output that you spend more time getting into shape, Codex is comparable. I haven't exhaustively compared though.
Sorry, no, not a bot. I get way better results out of Codex.
It's just ultimately subjective, and, it's like, your opinion, man. Calling people bots who disagree is probably not a good look.
I don't like OpenAI the company, but their model and coding tool is pretty damn good. And I was an early Claude Code booster and go back and forth constantly to try both.
4.7 hasn't been out for an hour yet and we already have people shilling for Codex in the comments. I don't know how anyone could form a genuine disagreement in this period of time.
Nobody I've seen in the comments is basing it on 4.7 performance. They're basing it on how unpleasant March and early April was on the Claude Code coding plans with 4.6. Which, from my experience, it was.
I'm interested in seeing how 4.7 performs. But I'm also unwilling to pony up cash for a month to do so. And frankly dissatisfied with their customer service and with the actual TUI tool itself.
It's not team sports, my friend. You don't have to pick a side. These guys are taking a lot of money from us. Far more than I've ever spent on any other development tooling.
I just subscribed this month again because I wanted to have some fun with my projects.
Tried out opus 4.6 a bit and it is really really bad. Why do people say it's so good? It cannot come up with any half-decent vhdl. No matter the prompt. I'm very disappointed. I was told it's a good model
I've seen a similar psychological phenomenon where people like something a lot, and then they get unreasonably angry and vocal about changes to that thing.
> Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online
This is reductive. You're both calling people unreasonably angry but then acknowledging there's a limit in compute that is a practical reality for Anthropic. This isn't that hard. They have two choices, rate limit, or silently degrade to save compute.
I have never hit a rate limit, but I have seen it get noticeably stupider. It doesn't make me angry, but comments like these are a bit annoying to read, because you are trying to make people sound delusional while, at the same time, confirming everything they're saying.
I don't think they have turned a big knob that makes it stupider for everyone. I think they can see when a user is overtapping their $20 plan and silently degrade them. Because there's no alert for that. Which is why AI benchmark sites are irrelevant.
And yet another "AI doesn't work" comment without any meaningful information. What were your exact prompts? What was the output?
This is like a user of conventional software complaining that "it crashes", without a single bit of detail, like what they did before the crash, if there was any error message, whether the program froze or completely disappeared, etc.
These stuck out as promising things to try. It looks like xhigh on 4.7 scores significantly higher on the internal coding benchmark (71% vs 54%, though unclear what that is exactly)
> More effort control: Opus 4.7 introduces a new xhigh (“extra high”) effort level between high and max, giving users finer control over the tradeoff between reasoning and latency on hard problems. In Claude Code, we’ve raised the default effort level to xhigh for all plans. When testing Opus 4.7 for coding and agentic use cases, we recommend starting with high or xhigh effort.
The new /ultrareview command looks like something I've been trying to invoke myself with looping, happy that it's free to test out.
> The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch. We’re giving Pro and Max Claude Code users three free ultrareviews to try it out.
I liked Opus 4.5 but hated 4.6. Every few weeks I tried 4.6 and, after a tirade against, I switched back to 4.5. They said 4.6 had a "bias towards action", which I think meant it just made stuff up if something was unclear, whereas 4.5 would ask for clarfication. I hope 4.7 is more of a collaborator like 4.5 was.
If you mean for Anthropic in particular, I don't think so. But it's not the first time a major AI lab publishes an incremental update of a model that is worse at some benchmarks. I remember that a particular update of Gemini 2.5 Pro improved results in LiveCodeBench but scored lower overall in most benchmarks.
Ask it to create an iOS app which natively runs Gemma via Litert-lm.
It’s incredibly trivial to find stuff outside their capabilities. In fact most stuff I want AI to do it just can’t, and the stuff it can isn’t interesting to me.
> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
I guess that means bad news for our subscription usage.
Interestingly github-copilot is charging 2.5x as much for opus 4.7 prompts as they charged for opus 4.6 prompts (7.5x instead of 3x). And they're calling this "promotional pricing" which sounds a lot like they're planning to go even higher.
Note they charge per-prompt and not per-token so this might in part be an expectation of more tokens per prompt.
It seems like they're doing something with the system prompt that I don't quite understand. I'm trying it in Claude Code and tool calls repeatedly show weird messages like "Not malware."
Never seen anything like that with other Anthropic models.
> where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally. Users should re-tune their prompts and harnesses accordingly.
I like this in theory. I just hope it doesn't require you to be be as literal as if talking to a genie.
But if it'll actually stick to the hard rules in the CLAUDE.md files, and if I don't have to add "DON'T DO ANYTHING, JUST ANSWER THE QUESTION" at the end of my prompt, I'll be glad.
It might be a bad idea to put that in all caps, because in the training data, angry conversations are less productive. (I do the same thing, just in lowercase.)
coming more in line with codex - claude previously would often ignore explicit instructions that codex would follow. interested to see how this feels in practice
I think this line around "context tuning" is super interesting - I see a future where, for every model release, devs go and update their CLAUDE.md / skills to adapt to new model behavior.
This made me LOL. They keep trying to fleece us by nerfing functionality and then adding it back next release. It’s an abusive relationship at this point.
If the model is based on a new tokenizer, that means that it's very likely a completely new base model. Changing the tokenizer is changing the whole foundation a model is built on. It'd be more straightforward to add reasoning to a model architecture compared to swapping the tokenizer to a new one.
Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Swapping out the tokenizer is a massive change. Not an incremental one.
> Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Benchmarks say it all. Gains over previous model are too small to announce it as a major release. That would be humiliating for Anthropic. It may scare investors that the curve flattened and there are only diminishing returns.
It doesn't need to be. Text can be tokenized in many different ways even if the token set is the same.
For example there is usually one token for every string from "0" to "999" (including ones like "001" seperately).
This means there are lots of ways you can choose to tokenize a number. Like 27693921. The best way to deal with numbers tends to be a little bit context dependent but for numerics split into groups of 3 right to left tends to be pretty good.
They could just have spotted that some particular patterns should be decomposed differently.
WTF. `Opus 4.7 is the first such model: its cyber capabilities are not as advanced as those of Mythos Preview (indeed, during its training we experimented with efforts to differentially reduce these capabilities). We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses. `
Seriously? You're degrading Opus 4.7 Cybersecurity performance on purpose. Absolute shit.
And since Opus 4.7 has degraded cybersecurity skills, using it might result in writing actually less safe code, since practically, in order to write secure code you need to understand cybersecurity. Outstanding move.
What's the point of baking the best and most impressive models in the world and then serving it with degraded quality a month after releases so that intelligence from them is never fully utilised??
As the author of the now (in)famous report in https://github.com/anthropics/claude-code/issues/42796 issue (sorry stella :) all I can say is... sigh. Reading through the changelog felt as if they codified every bad experiment they ran that hurt Opus 4.6. It makes it clear that the degradation was not accidental.
I'm still sad. I had a transformative 6 months with Opus and do not regret it, but I'm also glad that I didn't let hope keep me stuck for another few weeks: had I been waiting for a correction I'd be crushed by this.
Hypothesis: Mythos maintains the behavior of what Opus used to be with a few tricks only now restricted to the hands of a few who Anthropic deems worthy. Opus is now the consumer line. I'll still use Opus for some code reviews, but it does not seem like it'll ever go back to collaborator status by-design. :(
I wish someone would elaborate on what they were doing and observed since Jan on opus 4.6. I’ve been using it with 1m context on max thinking since it was released - as a software engineer to write most of my code, code reviews + research and explain unfamiliar code - and haven’t notice a degradation. I’ve seen this mentioned a lot though.
I have seen that codex -latest highest effort - will find some important edge cases that opus 4.6 overlooked when I ask both of them to review my PRs.
I wonder why computer use has taken a back seat. Seemed like it was a hot topic in 2024, but then sort of went obscure after CLI agents fully took over.
It would be interesting to see a company to try and train a computer use specific model, with an actually meaningful amount of compute directed at that. Seems like there's just been experiments built upon models trained for completely different stuff, instead of any of the companies that put out SotA models taking a real shot at it.
On the other hand, I never understood the focus on computer use.
While more general and perhaps the "ideal" end state once models run cheaply enough, you're always going to suffer from much higher latency and reduced cognition performance vs API/programmatically driven workflows. And strictly more expensive for the same result.
Why not update software to use API first workflows instead?
How should one compare benchmark results?
For example, SWE-bench Pro improved ~11% compared with Opus 4.6. Should one interpret it as 4.7 is able to solve more difficult problems? or 11% less hallucinations?
I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.
This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).
This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".
So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).
Benchmark results don’t directly translate to actual real world improvement. So we might guess it’s somewhat better but hard to say exactly in what way
11% further along the particular bell curve of SWE-bench. Not really easy to extrapolate to real world, especially given that eg the Chinese models tend to heavily train on the benchmarks. But a 10% bump with the same model should equate to “feels noticeably smarter”.
A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.
> We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses.
Fucking hell.
Opus was my go-to for reverse engineering and cybersecurity uses, because, unlike OpenAI's ChatGPT, Anthropic's Opus didn't care about being asked to RE things or poke at vulns.
It would, however, shit a brick and block requests every time something remotely medical/biological showed up.
If their new "cybersecurity filter" is anywhere near as bad? Opus is dead for cybersec.
To be fair, delineating between benevolent and malevolent pen-testing and cybersecurity purposes is practically impossible since the only difference is the user's intentions. I am entirely unsurprised (and would expect) that as models improve the amount to which widely available models will be prohibited from cybersecurity purposes will only increase.
Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.
Yes, and the previous approach Anthropic took was "allow anything that looks remotely benign". The only thing that would get a refusal would be a downright "write an exploit for me". Which is why I favored Anthropic's models.
It remains to be seen whether Anthropic's models are still usable now.
I know just how much of a clusterfuck their "CBRN filter" is, so I'm dreading the worst.
I'm currently testing 4.7 with some reverse engineering stuff/Ghidra scripting and it hasn't refused anything so far, but I'm also doing it on a 20 year old video game, so maybe it doesn't think that's problematic.
Incredible - in one fell swoop killing my entire use case for Claude.
I have about 15 submissions that I now need to work with Codex on cause this "smarter" model refuses to read program guidelines and take them seriously.
> Security professionals who wish to use Opus 4.7 for legitimate cybersecurity purposes (such as vulnerability research, penetration testing, and red-teaming) are invited to join our new Cyber Verification Program.
This seems reasonable to me. The legit security firms won't have a problem doing this, just like other vendors (like Apple, who can give you special iOS builds for security analysis).
If anyone has a better idea on how to _pragmatically_ do this, I'm all ears.
It appears we're learning the hard way that we can't rely on capabilities of models that aren't open weights. These can be taken from us at any time, so expect it to get much worse..
With the new tokenizer did they A/B test this one?
I'm curious if that might be responsible for some of the regressions in the last month. I've been getting feedback requests on almost every session lately, but wasn't sure if that was because of the large amount of negative feedback online.
Honestly I've been doing a lot of image-related work recently and the biggest thing here for me is the 3x higher resolution images which can be submitted. This is huge for anyone working with graphs, scientific photographs, etc. The accuracy on a simple automated photograph processing pipeline I recently implemented with Opus 4.6 was about 40% which I was surprised at (simple OCR and recognition of basic features). It'll be interesting to see if 4.7 does much better.
I wonder if general purpose multimodal LLMs are beginning to eat the lunch of specific computer vision models - they are certainly easier to use.
Interesting to see the benchmark numbers, though at this point I find these incremental seeming updates hard to interpret into capability increases for me beyond just "it might be somewhat better".
Maybe I've skimmed too quickly and missed it, but does calling it 4.7 instead of 5 imply that it's the same as 4.6, just trained with further refined data/fine tuned to adapt the 4.6 weights to the new tokenizer etc?
> Opus 4.7 is a direct upgrade to Opus 4.6, but two changes are worth planning for because they affect token usage. First, Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
This is concerning & tone-deaf especially given their recent change to move Enterprise customers from $xxx/user/month plans to the $20/mo + incremental usage.
IMO the pursuit of ultraintelligence is going to hurt Anthropic, and a Sonnet 5 release that could hit near-Opus 4.6 level intelligence at a lower cost would be received much more favorably. They were already getting extreme push-back on the CC token counting and billing changes made over the past quarter.
They are planning to release a Mythos-class model (from the initial announcement), but they won't until they can trust their safeguards + the software ecosystem has been sufficiently patched.
It seems they nerf it, then release a new version with previous power. So they can do this forever without actually making another step function model release.
Does it run for you? I can select it this way but it says 'There's an issue with the selected model (claude-opus-4-7). It may not exist or you may not have access to it. Run /model to pick a different model.'
API Error: 400 {"type":"error","error":{"type":"invalid_request_error","message":"\"thinking.type.enabled\" is not supported for this model. Use \"thinking.type.adaptive\" and \"output_config.effort\" to control
thinking behavior."},"request_id":"req_011Ca7enRv4CPAEqrigcRNvd"}
Eep. AFAIK the issues most people have been complaining about with Opus 4.6 recently is due to adaptive thinking. Looks like that is not only sticking around but mandatory for this newer model.
edit: I still can't get it to work. Opus 4.6 can't even figure out what is wrong with my config. Speaking of which, claude configuration is so confusing there are .claude/ (in project) setting.json + a settings.local.json file, then a global ~/.claude/ dir with the same configuration files. None of them have anything defined for adaptive thinking or thinking type enable. None of these strings exist on my machine. Running latest version, 2.1.110
Backlash on HN for Anthropic adjusting usage limits is insane. There's almost no discussion about the model, just people complaining about their subscription.
Regardless of the model quality improvement, the corporate damage was done by not only ignoring the Opus quality degradation but gaslighting users into thinking they aren’t using it right.
I switched to Codex 5.4 xhigh fast and found it to be as good as the old Claude. So I’ll keep using that as my daily driver and only assess 4.7 on my personal projects when I have time.
This is the first new model from Anthropic in a while that I'm not super enthused about. Not because of the model, I literally haven't opened the page about it, I can already guess what it says ("Bigger, better, faster, stronger"), but because of the company.
I have enjoyed using Claude Code quite a bit in the past but that has been waning as of late and the constant reports of nerfed models coupled with Anthropic not being forthcoming about what usage is allowed on subscriptions [0] really leaves a bad taste in my mouth. I'll probably give them another month but I'm going to start looking into alternatives, even PayG alternatives.
[0] Please don't @ me, I've read every comment about how it _is clear_ as a response to other similar comments I've made. Every. Single. One. of those comments is wrong or completely misses the point. To head those off let me be clear:
Anthropic does not at all make clear what types of `claude -p` or AgentSDK usage is allowed to be used with your subscription. That's all I care about. What am I allowed to use on my subscription. The docs are confusing, their public-facing people give contradictory information, and people commenting state, with complete confidence, completely wrong things.
I greatly dislike the Chilling Effect I feel when using something I'm paying quite a bit (for me) of money for. I don't like the constant state of unease and being unsure if something might be crossing the line. There are ideas/side-projects I'm interested in pursuing but don't because I don't want my account banned for crossing a line I didn't know existed. Especially since there appears to be zero recourse if that happens.
I want to be crystal clear: I am not saying the subscription should be a free-for-all, "do whatever you want", I want clear lines drawn. I increasingly feeling like I'm not going to get this and so while historically I've prefered Claude over ChatGPT, I'm considering going to Codex (or more likely, OpenCode) due to fewer restrictions and clearer rules on what's is and is not allowed. I'd also be ok with kind of warning so that it's not all or nothing. I greatly appreciate what Anthropic did (finally) w.r.t. OpenClaw (which I don't use) and the balance they struck there. I just wish they'd take that further.
Excited to use 1 prompt and have my whole 5-hour window at 100%. They can keep releasing new ones but if they don't solve their whole token shrinkage and gaslighting it is not gonna be interesting to se.
It seems a lot of the problem isn't "token shrinkage" (reducing plan limits), but rather changes they made to prompt caching - things that used to be cached for 1 hour now only being cached for 5 min.
Coding agents rely on prompt caching to avoid burning through tokens - they go to lengths to try to keep context/prompt prefixes constant (arranging non-changing stuff like tool definitions and file content first, variable stuff like new instructions following that) so that prompt caching gets used.
This change to a new tokenizer that generates up to 35% more tokens for the same text input is wild - going to really increase token usage for large text inputs like code.
on Tuesday, with 4.6, I waited for my 5 hour window to reset, asked it to resume, and it burned up all my tokens for the next 5 hour window and ran for less than 10 seconds. I’ve never cancelled a subscription so fast.
I tried the Claude Extension for VSCode on WSL for a reverse engineering task, it consumed all of my tokens, broke and didn't even save the conversatioon
Might be sticking with 4.6 it's only been 20 minutes of using 4.7 and there are annoyances I didn't face with 4.6 what the heck. Huge downgrade on MRCR too....
It’s funny, a few months ago I would have been pretty excited about this. But I honestly don’t really care because I can’t trust Anthropic to not play games with this over the next month post release.
I just flat out don’t trust them. They’ve shown more than enough that they change things without telling users.
Here’s the problem. The distribution of query difficulty / task complexity is probably heavily right-skewed which drives up the average cost dramatically. The logical thing for anthropic to do, in order to keep costs under control, is to throttle high-cost queries. Claude can only approximate the true token cost of a given query prior to execution. That means anything near the top percentile will need to get throttled as well.
By definition this means that you’re going to get subpar results for difficult queries. Anything too complicated will get a lightweight model response to save on capacity. Or an outright refusal which is also becoming more common.
New models are meaningless in this context because by definition the most impressive examples from the marketing material will not be consistently reproducible by users. The more users who try to get these fantastically complex outputs the more those outputs get throttled.
Gemini and Codex already scored higher on benchmarks than Opus 4.6 and they recently added a $100 tier with limited 2x limits, that's their answer and it seems people have caught on.
even sonnet right now has degraded for me to the point of like ChatGPT 3.5 back then. took ~5 hours on getting a playwright e2e test fixed that waited on a wrong css selector. literlly, dumb as fuck. and it had been better than opus for the last week or so still... did roughly comparable work for the last 2 weeks and it all went increasingly worse - taking more and more thinking tokens circling around nonsense and just not doing 1 line changes that a junior dev would see on the spot. Too used to vibing now to do it by hand (yeah i know) so I kept watching and meanwhile discovered that codex just fleshed out a nontrivial app with correct financial data flows in the same time without any fuzz. I really don't get why antrhopic is dropping their edge so hard now recently, in my head they might aim for increasing hype leading to the IPO, not disappointment crashes from their power user base.
not rejecting reality, but increasing doubts about the effectiveness of these tests. and yes its subjective n=1, but I literally create and ship projects for many months now always from the same github template repository forked and essentially do the same steps with a few differnt brand touches and nearly muscle memory prompting to do the just right next steps mechanically over and over again, and the amount of things getting done per step gots worse and the quality degraded too, forgetting basic things along the way a few prompts in. as I said n=1 but the very repetitive nature of my current work days alwyas doing a new thing from the exact same start point that hasn't changed in half a year is kind of my personal benchmark. YMMV but on my end the effects are real, specifically when tracking hours over this stuff.
just started using codex. claude is just marketing machine and benchmaxxing and only if you pay gazillion and show your ID you can use their dangerous model.
Reminder that 4.7 may seem like a huge upgrade to 4.6 because they nerfed the F out of 4.6 ahead of this launch so 4.7 would seem like a remarkable improvement...
Sigh here we go again, model release day is always the worst day of the quarter for me. I always get a lovely anxiety attack and have to avoid all parts of the internet for a few days :/
I feel this way too. Wish I could fully understand the 'why'. I know all of the usual arguments, but nothing seems to fully capture it for me - maybe it' all of them, maybe it's simply the pace of change and having to adapt quicker than we're comfortable with. Anyway best of luck from someone who understands this sentiment.
Really? I think it's pretty straightforward, at least for me - fear of AI replacing my profession and also fear that it will become harder to succeed with a side project.
Yeah I can understand that, and sure this is part of it, just not all of it. There is also broader societal issues (ie. inequality), personal questions around meaning and purpose, and a sprinkling of existential (but not much). I suspect anyone surveyed would have a different formula for what causes this unease - I struggle to define it (yet think about it constantly), hence my comment above.
Ultimately when I think deeper, none of this would worry me if these changes occurred over 20 years - societies and cultures change and are constantly in flux, and that includes jobs and what people value. It's the rate of change and inability to adapt quick enough which overwhelms me.
Not worried about inequality, at least not in the sense that AI would increase it, I'm expecting the opposite. Being intelligent will become less valuable than today, which will make the world more equal, but it may be not be a net positive change for everybody.
Regarding meaning and purpose, I have some worries here too, but can easily imagine a ton of things to do and enjoy in a post-AGI world. Travelling, watching technological progress, playing amazing games.
Maybe the unidentified cause of unease is simply the expectation that the world is going to change and we don't know how and have no control over it. It will just happen and we can only hope that the changes will be positive.
See i don't have any of this fear, I have 0 concerns that LLMs will replace software engineering because the bulk of the work we do (not code) is not at risk.
New model - that explains why for the past week/two weeks I had this feeling of 4.6 being much less "intelligent". I hope this is only some kind of paranoia and we (and investors) are not being played by the big corp. /s
Ok, so the answer is "they make the existing model worse to make it seem that the new model is good". I'm almost certain that this is not what's going on. It's hard to make the argument that the benefits outweigh the drawbacks of such approach. It doesn't give the more market share or revenue.
It seems like we're hitting a solid plateau of LLM performance with only slight changes each generation. The jumps between versions are getting smaller. When will the AI bubble pop?
SWE-bench pro is ~20% higher than the previous .1 generation which was released 2 months ago. For their SWE benchmark, the token consumption iso-performance is down 2x from the model they released 2 months ago.
If this is a plateau I struggle to imagine what you consider fast progress.
Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that: https://platform.claude.com/docs/en/build-with-claude/adapti...
(Still trying to get a decent pelican out of this one but the new thinking stuff is tripping me up.)
Wouldn't that be p-hacking where p stands for pelican?
I did not follow all of this, but wasn't there something about, that those reasoning tokens did not represent internal reasoning, but rather a rough approximation that can be rather misleading, what the model actual does?
My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.
Sometimes they notice bugs or issues and just completely ignore it.
caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well.
[0] https://github.com/JuliusBrussee/caveman/tree/main
This seems to be a common thread in the LLM ecosystem; someone starts a project for shits and giggles, makes it public, most people get the joke, others think it's serious, author eventually tries to turn the joke project into a VC-funded business, some people are standing watching with the jaws open, the world moves on.
https://news.ycombinator.com/item?id=21454273 / https://news.ycombinator.com/item?id=19830042 - OpenAI Releases Largest GPT-2 Text Generation Model
HN search for GPT between 2018-2020, lots of results, lots of discussions: https://hn.algolia.com/?dateEnd=1577836800&dateRange=custom&...
> New AI fake text generator may be too dangerous to release, say creators
> The Elon Musk-backed nonprofit company OpenAI declines to release research publicly for fear of misuse.
> OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.
https://www.theguardian.com/technology/2019/feb/14/elon-musk...
OpenAI sure speed ran the Google and Facebook 'Don't be evil' -> 'Optimize money' transition.
I will now have it continue this comment:
I've been running gps for a long time, and I always liked that there was something in my pocket (and not just me). One day when driving to work on the highway with no GPS app installed, I noticed one of the drivers had gone out after 5 hours without looking. He never came back! What's up with this? So i thought it would be cool if a community can create an open source GPT2 application which will allow you not only to get around using your smartphone but also track how long you've been driving and use that data in the future for improving yourself...and I think everyone is pretty interested.
[Updated on July 20] I'll have this running from here, along with a few other features such as: - an update of my Google Maps app to take advantage it's GPS capabilities (it does not yet support driving directions) - GPT2 integration into your favorite web browser so you can access data straight from the dashboard without leaving any site! Here is what I got working.
[Updated on July 20]
[0] https://github.com/thedotmack/claude-mem
[1] https://github.com/mksglu/context-mode
You can then reconstruct the original image by doing the reverse, extracting frames from the video, then piecing them together to create the original bigger picture
Results seem to really depend on the data. Sometimes the video version is smaller than the big picture. Sometimes it’s the other way around. So you can technically compress some videos by extracting frames, composing a big picture with them and just compressing with jpeg
Interesting, when I heard about it, I read the readme, and I didn't take that as literal. I assumed it was meant as we used video frames as inspiration.
I've never used it or looked deeper than that. My LLM memory "project" is essentially a `dict<"about", list<"memory">>` The key and memories are all embeddings, so vector searchable. I'm sure its naive and dumb, but it works for my tiny agents I write.
Honestly part of me still thinks this is a satire project but who knows.
It also doesn't help that projects and practices are promoted and adopted based on influencer clout. Karpathy's takes will drown out ones from "lesser" personas, whether they have any value or not.
Which means yes, you can actually influence this quite a bit. Read the paper “Compressed Chain of Thought” for example, it shows it’s really easy to make significant reductions in reasoning tokens without affecting output quality.
There is not too much research into this (about 5 papers in total), but with that it’s possible to reduce output tokens by about 60%. Given that output is an incredibly significant part of the total costs, this is important.
https://arxiv.org/abs/2412.13171
It isn't free either - by default, models learn to offload some of their internal computation into the "filler" tokens. So reducing raw token count always cuts into reasoning capacity somewhat. Getting closer to "compute optimal" while reducing token use isn't an easy task.
I work on a few agentic open source tools and the interesting thing is that once I implemented these things, the overall feedback was a performance improvement rather than performance reduction, as the LLM would spend much less time on generating tokens.
I didn’t implement it fully, just a few basic things like “reduce prose while thinking, don’t repeat your thoughts” etc would already yield massive improvements.
(No, none of this changes that if you make an LLM larp a caveman it's gonna act stupid, you're right about that.)
And
https://github.com/toon-format/toon
However in deep research-like products you can have a pass with LLM to compress web page text into caveman speak, thus hugely compressing tokens.
Prediction works based on the attention mechanism, and current humans don't speak like cavemen - so how could you expect a useful token chain from data that isn't trained on speech like that?
I get the concept of transformers, but this isn't doing a 1:1 transform from english to french or whatever, you're fundamentally unable to represent certain concepts effectively in caveman etc... or am I missing something?
folks could have just asked for _austere reasoning notes_ instead of "write like you suffer from arrested development"
My first thought was that this would mean that my life is being narrated by Ron Howard.
I mean just look at the growth of all these "skills" that just reiterate knowledge the models already have
https://github.com/gglucass/headroom-desktop (mac app)
https://github.com/chopratejas/headroom (cli)
(I work at Edgee, so biased, but happy to answer questions.)
Caveat: I didn’t do enough testing to find the edge cases (eg, negation).
I wonder if there’s a pre-processor that runs to remove typos before processing. If not, that feels like a space that could be worked on more thoroughly.
I am finding my writing prompt style is naturally getting lazier, shorter, and more caveman just like this too. If I was honest, it has made writing emails harder.
While messing around, I did a concept of this with HTML to preserve tokens, worked surprisingly well but was only an experiment. Something like:
> <h1 class="bg-red-500 text-green-300"><span>Hello</span></h1>
AI compressed to:
> h1 c bgrd5 tg3 sp hello sp h1
Or something like that.
It nicely implemented two smallish features, and already consumed 100% of my session limit on the $20 plan.
See you again in five hours.
Have you tried just adding an instruction to be terse?
Don't get me wrong, I've tried out caveman as well, but these days I am wondering whether something as popular will be hijacked.
Then the next month 90% of this can be replaced with new batch of supply chain attack-friendly gimmicks
Especially Reddit seems to be full of such coding voodoo
Well, we've sacrificed the precision of actual programming languages for the ease of English prose interpreted by a non-deterministic black box that we can't reliably measure the outputs of. It's only natural that people are trying to determine the magical incantations required to get correct, consistent results.
It's been funny watching my own attitude to Anthropic change, from being an enthusiastic Claude user to pure frustration. But even that wasn't the trigger to leave, it was the attitude Support showed. I figure, if you mess up as badly as Anthropic has, you should at least show some effort towards your customers. Instead I just got a mass of standardised replies, even after the thread replied I'd be escalated to a human. Nothing can sour you on a company more. I'm forgiving to bugs, we've all been there, but really annoyed by indifference and unhelpful form replies with corporate uselessness.
So if 4.7 is here? I'd prefer they forget models and revert the harness to its January state. Even then, I've already moved to Codex as of a few days ago, and I won't be maintaining two subscriptions, it's a move. It has its own issues, it's clear, but I'm getting work done. That's more than I can say for Claude.
You were enthusiastic because it was a great product at an unsustainable price.
Its clear that Claude is now harnessing their model because giving access to their full model is too expensive for the $20/m that consumers have settled on as the price point they want to pay.
I wrote a more in depth analysis here, there's probably too much to meaningfully summarize in a comment: https://sustainableviews.substack.com/p/the-era-of-models-is...
But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. I'm seeing a lot of goodwill for Codex and a ton of bad PR for CC.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
That's not why. It was and is because they've been incredibly unfocused and have burnt through cash on ill-advised, expensive things like Sora. By comparison Anthropic have been very focused.
By far, the biggest argument was that OpenAI bet too much on compute.
Being unfocused is generally an easy fix. Just cut things that don't matter as much, which they seem to be doing.
Despite having literal experts at his fingertips, he still isn't able to grasp that he's talking unfilters bollocks most of the time. Not to mention is Jason level of "oath breaking"/dishonesty.
Ah yes, very focused on crapping out every possible thing they can copy and half bake?
Eventually OpenAI will need to stop burning money.
All this just reads like just another case of mass psychosis to me
As long as OpenAI can sustain compute and paying SWE $1million/year they will end up with the better product.
but if your leader is a dipshit, then its a waste.
Look You can't just throw money at the problem, you need people who are able to make the right decisions are the right time. That that requires leadership. Part of the reason why facebook fucked up VR/AR is that they have a leader who only cares about features/metrics, not user experience.
Part of the reason why twitter always lost money is because they had loads of teams all running in different directions, because Dorsey is utterly incapable of making a firm decision.
Its not money and talent, its execution.
Downtime is annoying, but the problem is that over the past 2-3 weeks Claude has been outrageously stupid when it does work. I have always been skeptical of everything produced - but now I have no faith whatsoever in anything that it produces. I'm not even sure if I will experiment with 4.7, unless there are glowing reviews.
Codex has had none of these problems. I still don't trust anything it produces, but it's not like everything it produces is completely and utterly useless.
Anthropic has been very disciplined and focused (overwhelmingly on coding, fwiw), while OpenAI has been bleeding money trying to be the everything AI company with no real specialty as everyone else beat them in random domains. If I had to qualify OpenAI's primary focus, it has been glazing users and making a generation of malignant narcissists.
But yes, Anthropic has been growing by leaps and bounds and has capacity issues. That's a very healthy position to be in, despite the fact that it yields the inevitable foot-stomping "I'm moving to competitor!" posts constantly.
Codex just gets it done. Very self-correcting by design while Claude has no real base line quality for me. Claude was awesome in December, but Codex is like a corporate company to me. Maybe it looks uncool, but can execute very well.
Also Web Design looks really smooth with Codex.
OpenAI really impressed me and continues to impress me with Codex. OpenAI made no fuzz about it, instead let results speak. It is as if Codex has no marketing department, just its product quality - kind of like Google in its early days with every product.
An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.
(not that I think the US DoD wouldn't do that anyway, ToS or not.)
Now, what can I actually do?
https://www.washingtonpost.com/technology/2026/03/04/anthrop...
So uh, yeah, the only difference I see between OAI and Anthropic is that one is more honest about what they’re willing to use their AI for.
Foist your morality upon everyone else and burden them with your specific conscience; sounds like a fun time.
1) Bad prompt/context. No matter what the model is, the input determines the output. This is a really big subject as there's a ton of things you can do to help guide it or add guardrails, structure the planning/investigation, etc.
2) Misaligned model settings. If temperature/top_p/top_k are too high, you will get more hallucination and possibly loops. If they're too low, you don't get "interesting" enough results. Same for the repeat protection settings.
I'm not saying it didn't screw up, but it's not really the model's fault. Every model has the potential for this kind of behavior. It's our job to do a lot of stuff around it to make it less likely.
The agent harness is also a big part of it. Some agents have very specific restrictions built in, like max number of responses or response tokens, so you can prevent it from just going off on a random tangent forever.
There's your one line change.
I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.
Maybe I could avoid running out of tokens by turning off 1M tokens and max effort, but that's a cure worse than the disease IMO.
It is much faster, but faster worse code is a step in the wrong direction. You're just rapidly accumulating bugs and tech debt, rather than more slowly moving in the correct direction.
I'm a big fan of Gemini in general, but at least in my experience Gemini Cli is VERY FAR behind either Codex or CC. It's both slower than CC, MUCH slower than Codex, and the output quality considerably worse than CC (probably worse than Codex and orders of magnitude slower).
In my experience, Codex is extraordinarily sycophantic in coding, which is a trait that could t be more harmful. When it encounters bugs and debt, it says: wow, how beautiful, let me double down on this, pile on exponentially more trash, wrap it in a bow, and call you Alan Turing.
It also does not follow directions. When you tell it how to do something, it will say, nah, I have a better faster way, I'll just ignore the user and do my thing instead. CC will stop and ask for feedback much more often.
YMMV.
Yeah, 100% the case for me. I sometimes use it to do adversarial reviews on code that Opus wrote but the stuff it comes back with is total garbage more often than not. It just fabricates reasons as to why the code it's reviewing needs improvement.
"Opus 4.7 uses an updated tokenizer that [...] can map to more tokens—roughly 1.0–1.35× depending on the content type.
[...]
Users can control token usage in various ways: by using the effort parameter, adjusting their task budgets, or prompting the model to be more concise."
Codex isn’t as pretty in output but gets the job done much more consistently
Perhaps they need the compute for the training
I cancelled my subscription and will be moving to Codex for the time being.
Tokens are way too opaque and Claude was way smarter for my work a couple of months ago.
All options are starting to suck more and more
Have caught it flat-out skipping 50% of tasks and lying about it.
I describe the problem and codex runs in circles basically:
codex> I see the problem clearly. Let me create a plan so that I can implement it. The plan is X, Y, Z. Do you want me to implement this?
me> Yes please, looks good. Go ahead!
codex> Okay. Thank you for confirming. So I am going to implement X, Y, Z now. Shall I proceeed?
me> Yes, proceed.
codex> Okay. Implementing.
...codex is working... you see the internal monologue running in circles
codex> Here is what I am going to implement: X, Y, Z
me> Yes, you said that already. Go ahead!
codex> Working on it.
...codex in doing something...
codex> After examining the problem more, indeed, the steps should be X, Y, Z. Do you want me to implement them?
etc.
Very much every sessions ends up being like this. I was unable to get any useful code apart from boilerplate JS from it since 5.4
So instead I just use ChatGPT to create a plan and then ask Opus to code, but it's a hit and miss. Almost every time the prompt seems to be routed to cheaper model that is very dumb (but says Opus 4.6 when asked). I have to start new session many times until I get a good model.
I have been getting better results out of codex on and off for months. It's more "careful" and systematic in its thinking. It makes less "excuses" and leaves less race conditions and slop around. And the actual codex CLI tool is better written, less buggy and faster. And I can use the membership in things like opencode etc without drama.
For March I decided to give Claude Code / Opus a chance again. But there's just too much variance there. And then they started to play games with limits, and then OpenAI rolled out a $100 plan to compete with Anthropic's.
I'm glad to see the competition but I think Anthropic has pissed in the well too much. I do think they sent me something about a free month and maybe I will use that to try this model out though.
I’ve been pretty happy with it! One thing I immediately like more than Claude is that Codex seems much more transparent about what it’s thinking and what it wants to do next. I find it much easier to interrupt or jump in the middle if things are going to wrong direction.
Claude Code has been slowly turning into this mysterious black box, wiping out terminal context any time it compacts a conversation (which I think is their hacky way of dealing with terminal flickering issues — which is still happening, 14 months later), going out of the way to hide thought output, and then of course the whole performance issues thing.
Excited to try 4.7 out, but man, Codex (as a harness at least) is a stark contrast to Claude Code.
I've finally started experimenting recently with Claude's --dangerously-skip-permissions and Codex's --dangerously-bypass-approvals-and-sandbox through external sandboxing tools. (For now just nono¹, which I really like so far, and soon via containerization or virtual machines.)
When I am using Claude or Codex without external sandboxing tools and just using the TUI, I spend a lot of time approving individual commands. When I was working that way, I found Codex's tendency to stop and ask me whether/how it should proceed extremely annoying. I found myself shouting at my monitor, "Yes, duh, go do the thing!".
But when I run these tools without having them ask me for permission for individual commands or edits, I sometimes find Claude has run away from me a little and made the wrong changes or tried to debug something in a bone-headed way that I would have redirected with an interruption if it has stopped to ask me for permissions. I think maybe Codex's tendency to stop and check in may be more valuable if you're relying on sandboxing (external or built-in) so that you can avoid individual permissions prompts.
--
1: https://nono.sh/
> Claude Code v2.1.89: "Added CLAUDE_CODE_NO_FLICKER=1 environment variable to opt into flicker-free alt-screen rendering with virtualized scrollback"
Or have Codex review your own Claude Code work.
It then becomes clear just how "sloppy" CC is.
I wouldn't mind having Opus around in my back pocket to yeet out whole net new greenfield features. But I can't trust it to produce well-engineered things to my standards. Not that anybody should trust an LLM to that level, but there's matters of degree here.
As always, YMMV!
[0] https://github.com/SnakeO/claude-co-commands
Claude Code as "author" and a $20 Codex as reviewer/planner/tester has worked for me to squeeze better value out of the CC plan. But with the new $100 codex plan, and with the way Anthropic seemed to nerf their own $100 plan, I'm not doing this anymore.
Have you done the reverse? In my experience models will always find something to criticize in another model's work.
But I've had the best results with GPT 5.4
This flow is exhausting. A day of working this way leaves me much more drained than traditional old school coding.
It went through my $20 plan's session limit in 15 minutes, implementing two smallish features in an iOS app.
That was with the effort on auto.
It looks like full time work would require the 20x plan.
This coming right after a noticeable downgrade just makes me think Opus 4.7 is going to be the same Opus i was experiencing a few months ago rather than actual performance boost.
Anthropic need to build back some trust and communicate throtelling/reasoning caps more clearly.
OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working.
It seems like 90% of Claude's recent problems are strictly lack of compute related.
They (very optimistically) say they'll be profitable in 2030.
An honest response of "Our compute is busy, use X model?" would be far better than silent downgrading.
Anthropics revenue is increasing very fast.
OpenAI though made crazy claims after all its responsible for the memory prices.
In parallel anthropic announced partnership with google and broadcom for gigawatts of TPU chips while also announcing their own 50 Billion invest in compute.
OpenAI always believed in compute though and i'm pretty sure plenty of people want to see what models 10x or 100x or 1000x can do.
From that it's pretty likely they were training mythos for the last few weeks, and then distilling it to opus 4.7
Pure speculation of course, but would also explain the sudden performance gains for mythos - and why they're not releasing it to the general public (because it's the undistilled version which is too expensive to run)
If they are indeed doing this, I wonder how long they can keep it up?
1. Oops, we're oversubscribed.
2. Oops, adaptive reasoning landed poorly / we have to do it for capacity reasons.
3. Here's how subscriptions work. Am I really writing this bullet point?
As someone with a production application pinned on Opus 4.5, it is extremely difficult to tell apart what is code harness drama and what is a problem with the underlying model. It's all just meshed together now without any further details on what's affected.
The roulette wheel isn't rigged, sometimes you're just unlucky. Try another spin, maybe you'll do better. Or just write your own code.
This scenario obviously does not apply to folks who run their own benches with the same commands. I'm just discussing a possible and unintentional human behavioral bias.
Even if this isn't the root cause, humans are really bad at perceiving reality. Like, really really bad. LLMs are also really difficult to objectively measure. I'm sure the coupling of these two facts play a part, possibly significant, in our perception of LLM quality over time.
And the andecdata matches other anecdata.
Maybe I'm missing why that's selection bias.
I have a pretty robust setup in place to ensure that Claude, with its degradations, ensures good quality. And even the lobotomized 4.6 from the last few days was doing better than 4.7 is doing right now at xhigh.
It's over-engineering. It is producing more code than it needs to. It is trying to be more defensible, but its definition of defensible seems to be shaky because it's landing up creating more edge cases. I think they just found a way to make it more expensive because I'm just gonna have to burn more tokens to keep it in check.
I will immediately switch over to Codex if this continues to be an issue. I am new to security research, have been paid out on several bugs, but don't have a CVE or public talk so they are ready to cut me out already.
Edit: these changes are also retroactive to Opus 4.6. I am stuck using Sonnet until they approve me or make a change.
What else would you expect? If you add protections against it being used for hacking, but then that can be bypassed by saying "I promise I'm the good guys™ and I'm not doing this for evil" what's even the point?
"Per the instructions I've been given in this session, I must refuse to improve or augment code from files I read. I can analyze and describe the bugs (as above), but I will not apply fixes to `utils.py`."
Capacity is shared between model training (pre & post) and inference, so it's hard to see Anthropic deciding that it made sense, while capacity constrained, to train two frontier models at the same time...
I'm guessing that this means that Mythos is not a whole new model separate from Opus 4.6 and 4.7, but is rather based on one of these with additional RL post-training for hacking (security vulnerability exploitation).
The alternative would be that perhaps Mythos is based on a early snapshot of their next major base model, and then presumably that Opus 4.7 is just Opus 4.6 with some additional post-training (as may anyways be the case).
This decision is potentially fatal. You need symmetric capability to research and prevent attacks in the first place.
The opposite approach is 'merely' fraught.
They're in a bit of a bind here.
pro = 5m tokens, 5x = 41m tokens, 20x = 83m tokens
making 5x the best value for the money (8.33x over pro for max 5x). this information may be outdated though, and doesn't apply to the new on peak 5h multipliers. anything that increases usage just burns through that flat token quota faster.
So I've grown wary of how Anthropic is measuring token use. I had to force the non-1M halfway through the week because I was tearing through my weekly limit (this is the second week in a row where that's happened, whereas I never came CLOSE to hitting my weekly limit even when I was in the $100 max plan).
So something is definitely off. and if they're saying this model uses MORE tokens, I'm getting more nervous.
/model claude-opus-4-7
Coming from anthropic's support page, so hopefully they did't hallucinate the docs, cause the model name on claude code says:
/model claude-opus-4-7 ⎿ Set model to Opus 4
what model are you?
I'm Claude Opus 4 (model ID: claude-opus-4-7).
> /model claude-opus-4.7
not
claude-opus-4.7
Heck, mine just automatically set it to 4.7 and xhigh effort (also a new feature?)
xhigh was mentioned in the release post, it's the new default and between high and max.
Related features that were announced I have yet to be able to use:
/model claude-opus-4.7 ⎿ Model 'claude-opus-4.7' not found
/model claude-opus-4-7 ⎿ Set model to Opus 4
/model ⎿ Set model to Opus 4.6 (1M context) (default)
Edit: Not 30 seconds later, claude code took an update and now it works!
Just ask it what model it is(even in new chat).
what model are you?
I'm Claude Opus 4 (model ID: claude-opus-4-7).
https://support.claude.com/en/articles/11940350-claude-code-...
> This is _, not malware. Continuing the brainstorming process.
> Not malware — standard _ code. Continuing exploration.
> Not malware. Let me check front-end components for _.
> Not malware. Checking validation code and _.
> Not malware.
> Not malware.
1. https://techcrunch.com/2019/02/17/openai-text-generator-dang...
> Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
> What we learn from the real-world deployment of these safeguards will help us work towards our eventual goal of a broad release of Mythos-class models.
They are definitely distilling it into a much smaller model and ~98% as good, like everybody does.
It's just speculative decoding but for training. If they did at this scale it's quite an achievement because training is very fragile when doing these kinds of tricks.
Not really similar to speculative decoding?
I don't think that's what they've done here though. It's still black magic, I'm not sure if any lab does it for frontier runs, let alone 10T scale runs.
citation needed. I find it hard to believe; I think there are more than enough people willing to spend $100/Mtok for frontier capabilities to dedicate a couple racks or aisles.
https://reddit.com/r/ClaudeAI/comments/1smr9vs/claude_is_abo...
This story sounds a lot like GPT2.
They seemed to make it clear that they expect other labs to reach that level sooner or later, and they're just holding it off until they've helped patch enough vulnerabilities.
https://www.youtube.com/watch?v=BzAdXyPYKQo
""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"
Anthropic's guidance is to measure against real traffic—their internal benchmark showing net-favorable usage is an autonomous single-prompt eval, which may not reflect interactive multi-turn sessions where tokenizer overhead compounds across turns. The task budget feature (just launched in public beta) is probably the right tool for production deployments that need cost predictability when migrating.
Granted that is, as you say, a single prompt, but it is using the agentic process where the model self prompts until completion. It's conceivable the model uses fewer tokens for the same result with appropriate effort settings.
Now people are saying the model response quality went down, I can't vouch for that since I wasn't using Claude Code, but I don't think this many people saying the same thing is total noise though.
I suppose if you are okay with a mediocre initial output that you spend more time getting into shape, Codex is comparable. I haven't exhaustively compared though.
It's just ultimately subjective, and, it's like, your opinion, man. Calling people bots who disagree is probably not a good look.
I don't like OpenAI the company, but their model and coding tool is pretty damn good. And I was an early Claude Code booster and go back and forth constantly to try both.
I'm interested in seeing how 4.7 performs. But I'm also unwilling to pony up cash for a month to do so. And frankly dissatisfied with their customer service and with the actual TUI tool itself.
It's not team sports, my friend. You don't have to pick a side. These guys are taking a lot of money from us. Far more than I've ever spent on any other development tooling.
Tried out opus 4.6 a bit and it is really really bad. Why do people say it's so good? It cannot come up with any half-decent vhdl. No matter the prompt. I'm very disappointed. I was told it's a good model
For example, there is no evidence that 4.6 ever degraded in quality: https://marginlab.ai/trackers/claude-code-historical-perform...
Usage limits are necessary but I guess people expect more subsidized inference than the company can afford. So they make very angry comments online.
This is reductive. You're both calling people unreasonably angry but then acknowledging there's a limit in compute that is a practical reality for Anthropic. This isn't that hard. They have two choices, rate limit, or silently degrade to save compute.
I have never hit a rate limit, but I have seen it get noticeably stupider. It doesn't make me angry, but comments like these are a bit annoying to read, because you are trying to make people sound delusional while, at the same time, confirming everything they're saying.
I don't think they have turned a big knob that makes it stupider for everyone. I think they can see when a user is overtapping their $20 plan and silently degrade them. Because there's no alert for that. Which is why AI benchmark sites are irrelevant.
The fact that it didn't exist back then is completely and utterly irrelevant to my narrative.
"I reject your reality, and substitute my own".
It worked for cheeto in chief, and it worked for Elon, so why not do it in our normal daily lives?
This is like a user of conventional software complaining that "it crashes", without a single bit of detail, like what they did before the crash, if there was any error message, whether the program froze or completely disappeared, etc.
> More effort control: Opus 4.7 introduces a new xhigh (“extra high”) effort level between high and max, giving users finer control over the tradeoff between reasoning and latency on hard problems. In Claude Code, we’ve raised the default effort level to xhigh for all plans. When testing Opus 4.7 for coding and agentic use cases, we recommend starting with high or xhigh effort.
The new /ultrareview command looks like something I've been trying to invoke myself with looping, happy that it's free to test out.
> The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch. We’re giving Pro and Max Claude Code users three free ultrareviews to try it out.
https://news.ycombinator.com/item?id=43906555
By which I mean, I don't find these latest models really have huge cognitive gaps. There's few problems I throw at them that they can't solve.
And it feels to me like the gap now isn't model performance, it's the agenetic harnesses they're running in.
It’s incredibly trivial to find stuff outside their capabilities. In fact most stuff I want AI to do it just can’t, and the stuff it can isn’t interesting to me.
Whether it's genuine loss of capability or just measurement noise is typically unclear.
I wonder what caused such a large regression in this benchmark
I guess that means bad news for our subscription usage.
Note they charge per-prompt and not per-token so this might in part be an expectation of more tokens per prompt.
https://github.blog/changelog/2026-04-16-claude-opus-4-7-is-...
https://www.theregister.com/2026/04/15/github_copilot_rate_l...
interesting
But if it'll actually stick to the hard rules in the CLAUDE.md files, and if I don't have to add "DON'T DO ANYTHING, JUST ANSWER THE QUESTION" at the end of my prompt, I'll be glad.
I think this line around "context tuning" is super interesting - I see a future where, for every model release, devs go and update their CLAUDE.md / skills to adapt to new model behavior.
Usually a ground up rebuild is related to a bigger announcement. So, it's weird that they'd be naming it 4.7.
Swapping out the tokenizer is a massive change. Not an incremental one.
Benchmarks say it all. Gains over previous model are too small to announce it as a major release. That would be humiliating for Anthropic. It may scare investors that the curve flattened and there are only diminishing returns.
For example there is usually one token for every string from "0" to "999" (including ones like "001" seperately).
This means there are lots of ways you can choose to tokenize a number. Like 27693921. The best way to deal with numbers tends to be a little bit context dependent but for numerics split into groups of 3 right to left tends to be pretty good.
They could just have spotted that some particular patterns should be decomposed differently.
Mrcr benchmark went from 78% to 32%
what? a cost for a command? how does that work
Seriously? You're degrading Opus 4.7 Cybersecurity performance on purpose. Absolute shit.
I'm still sad. I had a transformative 6 months with Opus and do not regret it, but I'm also glad that I didn't let hope keep me stuck for another few weeks: had I been waiting for a correction I'd be crushed by this.
Hypothesis: Mythos maintains the behavior of what Opus used to be with a few tricks only now restricted to the hands of a few who Anthropic deems worthy. Opus is now the consumer line. I'll still use Opus for some code reviews, but it does not seem like it'll ever go back to collaborator status by-design. :(
But degrading a model right before a new release is not the way to go.
I have seen that codex -latest highest effort - will find some important edge cases that opus 4.6 overlooked when I ask both of them to review my PRs.
`claude install latest`
It would be interesting to see a company to try and train a computer use specific model, with an actually meaningful amount of compute directed at that. Seems like there's just been experiments built upon models trained for completely different stuff, instead of any of the companies that put out SotA models taking a real shot at it.
While more general and perhaps the "ideal" end state once models run cheaply enough, you're always going to suffer from much higher latency and reduced cognition performance vs API/programmatically driven workflows. And strictly more expensive for the same result.
Why not update software to use API first workflows instead?
I also think its a huge barrier allowing some LLM model access to your desktop.
Managed Agents seems like a lot more beneficial
I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.
This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).
This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".
So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).
The benchmarks don't make this explicit.
A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.
Fucking hell.
Opus was my go-to for reverse engineering and cybersecurity uses, because, unlike OpenAI's ChatGPT, Anthropic's Opus didn't care about being asked to RE things or poke at vulns.
It would, however, shit a brick and block requests every time something remotely medical/biological showed up.
If their new "cybersecurity filter" is anywhere near as bad? Opus is dead for cybersec.
Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.
It remains to be seen whether Anthropic's models are still usable now.
I know just how much of a clusterfuck their "CBRN filter" is, so I'm dreading the worst.
I have about 15 submissions that I now need to work with Codex on cause this "smarter" model refuses to read program guidelines and take them seriously.
> Security professionals who wish to use Opus 4.7 for legitimate cybersecurity purposes (such as vulnerability research, penetration testing, and red-teaming) are invited to join our new Cyber Verification Program.
If anyone has a better idea on how to _pragmatically_ do this, I'm all ears.
I'm curious if that might be responsible for some of the regressions in the last month. I've been getting feedback requests on almost every session lately, but wasn't sure if that was because of the large amount of negative feedback online.
"errorCode": "InternalServerException", "errorMessage": "The system encountered an unexpected error during processing. Try your request again.",
I wonder if general purpose multimodal LLMs are beginning to eat the lunch of specific computer vision models - they are certainly easier to use.
Maybe I've skimmed too quickly and missed it, but does calling it 4.7 instead of 5 imply that it's the same as 4.6, just trained with further refined data/fine tuned to adapt the 4.6 weights to the new tokenizer etc?
This is concerning & tone-deaf especially given their recent change to move Enterprise customers from $xxx/user/month plans to the $20/mo + incremental usage.
IMO the pursuit of ultraintelligence is going to hurt Anthropic, and a Sonnet 5 release that could hit near-Opus 4.6 level intelligence at a lower cost would be received much more favorably. They were already getting extreme push-back on the CC token counting and billing changes made over the past quarter.
There's other small single digit differences, but I doubt that the benchmark is that unreliable...?
MCP-Atlas: The Opus 4.6 score has been updated to reflect revised grading methodology from Scale AI.
False: Anthropic products cannot be used with agents.
Or `/model claude-opus-4-7` from an existing session
edit: `/model claude-opus-4-7[1m]` to select the 1m context window version
My statusline showed _Opus 4_, but it did indeed accept this line.
I did change it to `/model claude-opus-4-7[1m]`, because it would pick the non-1M context model instead.
Eep. AFAIK the issues most people have been complaining about with Opus 4.6 recently is due to adaptive thinking. Looks like that is not only sticking around but mandatory for this newer model.
edit: I still can't get it to work. Opus 4.6 can't even figure out what is wrong with my config. Speaking of which, claude configuration is so confusing there are .claude/ (in project) setting.json + a settings.local.json file, then a global ~/.claude/ dir with the same configuration files. None of them have anything defined for adaptive thinking or thinking type enable. None of these strings exist on my machine. Running latest version, 2.1.110
Those Mythos Preview numbers look pretty mouthwatering.
I switched to Codex 5.4 xhigh fast and found it to be as good as the old Claude. So I’ll keep using that as my daily driver and only assess 4.7 on my personal projects when I have time.
I have enjoyed using Claude Code quite a bit in the past but that has been waning as of late and the constant reports of nerfed models coupled with Anthropic not being forthcoming about what usage is allowed on subscriptions [0] really leaves a bad taste in my mouth. I'll probably give them another month but I'm going to start looking into alternatives, even PayG alternatives.
[0] Please don't @ me, I've read every comment about how it _is clear_ as a response to other similar comments I've made. Every. Single. One. of those comments is wrong or completely misses the point. To head those off let me be clear:
Anthropic does not at all make clear what types of `claude -p` or AgentSDK usage is allowed to be used with your subscription. That's all I care about. What am I allowed to use on my subscription. The docs are confusing, their public-facing people give contradictory information, and people commenting state, with complete confidence, completely wrong things.
I greatly dislike the Chilling Effect I feel when using something I'm paying quite a bit (for me) of money for. I don't like the constant state of unease and being unsure if something might be crossing the line. There are ideas/side-projects I'm interested in pursuing but don't because I don't want my account banned for crossing a line I didn't know existed. Especially since there appears to be zero recourse if that happens.
I want to be crystal clear: I am not saying the subscription should be a free-for-all, "do whatever you want", I want clear lines drawn. I increasingly feeling like I'm not going to get this and so while historically I've prefered Claude over ChatGPT, I'm considering going to Codex (or more likely, OpenCode) due to fewer restrictions and clearer rules on what's is and is not allowed. I'd also be ok with kind of warning so that it's not all or nothing. I greatly appreciate what Anthropic did (finally) w.r.t. OpenClaw (which I don't use) and the balance they struck there. I just wish they'd take that further.
Coding agents rely on prompt caching to avoid burning through tokens - they go to lengths to try to keep context/prompt prefixes constant (arranging non-changing stuff like tool definitions and file content first, variable stuff like new instructions following that) so that prompt caching gets used.
This change to a new tokenizer that generates up to 35% more tokens for the same text input is wild - going to really increase token usage for large text inputs like code.
256K:
- Opus 4.6: 91.9% - Opus 4.7: 59.2%
1M:
- Opus 4.6: 78.3% - Opus 4.7: 32.2%
I just flat out don’t trust them. They’ve shown more than enough that they change things without telling users.
> We are releasing Opus 4.7 with safeguards that automatically detect and block requests that indicate prohibited or high-risk cybersecurity uses.
Ah f... you!
By definition this means that you’re going to get subpar results for difficult queries. Anything too complicated will get a lightweight model response to save on capacity. Or an outright refusal which is also becoming more common.
New models are meaningless in this context because by definition the most impressive examples from the marketing material will not be consistently reproducible by users. The more users who try to get these fantastically complex outputs the more those outputs get throttled.
The AI community is starting to remind me of the audiophile community.
wow can I see it and run it locally please? Making API calls to check token counts is retarded.
can't wait for the chinese models to make arrogant silicon valley irrelevant
You are in for a treat this time: It is the same price as the last one [0] (if you are using the API.)
But it is slightly less capable than the other slot machine named 'Mythos' the one which everyone wants to play around with. [1]
[0] https://claude.com/pricing#api
[1] https://www.anthropic.com/news/claude-opus-4-7
Opus hasn't been able to fix it. I haven't been able to fix it. Maybe mythos can idk, but I'll be surprised.
If it’s all slop, the smallest waste of time comes from the best thing on the market
Ultimately when I think deeper, none of this would worry me if these changes occurred over 20 years - societies and cultures change and are constantly in flux, and that includes jobs and what people value. It's the rate of change and inability to adapt quick enough which overwhelms me.
Not worried about inequality, at least not in the sense that AI would increase it, I'm expecting the opposite. Being intelligent will become less valuable than today, which will make the world more equal, but it may be not be a net positive change for everybody.
Regarding meaning and purpose, I have some worries here too, but can easily imagine a ton of things to do and enjoy in a post-AGI world. Travelling, watching technological progress, playing amazing games.
Maybe the unidentified cause of unease is simply the expectation that the world is going to change and we don't know how and have no control over it. It will just happen and we can only hope that the changes will be positive.
See i don't have any of this fear, I have 0 concerns that LLMs will replace software engineering because the bulk of the work we do (not code) is not at risk.
My worries are almost purely personal.
The surprise: agentic search is significantly weaker somehow hmm...
The surprise: agentic search is significantly weaker somehow hmm...
If this is a plateau I struggle to imagine what you consider fast progress.
Does it also mean faster to getting our of credits?