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.
> 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.
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!"
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...
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.
The surprise: agentic search is significantly weaker somehow hmm...
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.
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.
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.
As always, YMMV!
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.
> Claude Code v2.1.89: "Added CLAUDE_CODE_NO_FLICKER=1 environment variable to opt into flicker-free alt-screen rendering with virtualized scrollback"
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.
--
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.
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.
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.
Ah yes, very focused on crapping out every possible thing they can copy and half bake?
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.
Eventually OpenAI will need to stop burning money.
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.
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.
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.
Perhaps they need the compute for the training
"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."
There's your one line change.
Codex isn’t as pretty in output but gets the job done much more consistently
Foist your morality upon everyone else and burden them with your specific conscience; sounds like a fun time.
My guess is OAI has more money to put towards people supporting it. speculative
(not that I think the US DoD wouldn't do that anyway, ToS or not.)
Only that Dario didn't have the foresight to realize he didn't control the relationship and that, regardless of the politics of the current administration [D or R], its a non-starter for a government entity to have a private company dictate terms of use for critical functionality of the government.
If Anthropic felt that way they shouldn't have taken the deal in the first place.
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.
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.
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.
Have caught it flat-out skipping 50% of tasks and lying about it.
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.
All options are starting to suck more and more
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]
If it’s all slop, the smallest waste of time comes from the best thing on the market
The surprise: agentic search is significantly weaker somehow hmm...
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.
An honest response of "Our compute is busy, use X model?" would be far better than silent downgrading.
They (very optimistically) say they'll be profitable in 2030.
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.
If they are indeed doing this, I wonder how long they can keep it up?
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)
caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well.
I mean just look at the growth of all these "skills" that just reiterate knowledge the models already have
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.
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.
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.
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?
(No, none of this changes that if you make an LLM larp a caveman it's gonna act stupid, you're right about that.)
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.
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.
And
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 reliable measure the outputs of. It's only natural that people are trying to determine the magical incantations required to get correct, consistent results.
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.
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 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.
(I work at Edgee, so biased, but happy to answer questions.)
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
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?
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 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.
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.
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
> 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 wonder if general purpose multimodal LLMs are beginning to eat the lunch of specific computer vision models - they are certainly easier to use.
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.
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.
/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).
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-...
> /model claude-opus-4.7
⎿ Model 'claude-opus-4.7' not foundnot
claude-opus-4.7
/model claude-opus-4.7
⎿ Model 'claude-opus-4.7' not found
Related features that were announced I have yet to be able to use:
$ claude --enable-auto-mode
auto mode is unavailable for your plan
$ claude
/memory
Auto-dream: on · /dream to run
Unknown skill: dream/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!
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.
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.
Does it also mean faster to getting our of credits?
wow can I see it and run it locally please? Making API calls to check token counts is retarded.
> 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!
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.
> 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.
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.
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.
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 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.
If this is a plateau I struggle to imagine what you consider fast progress.
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.
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?
/model claude-opus-4.7
⎿ Model 'claude-opus-4.7' not foundIt 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.
I also think its a huge barrier allowing some LLM model access to your desktop.
Managed Agents seems like a lot more beneficial
Mrcr benchmark went from 78% to 32%
I guess that means bad news for our subscription usage.
> 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. I have enough context to start the clarifying discussion.
1. https://techcrunch.com/2019/02/17/openai-text-generator-dang...
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.
And the andecdata matches other anecdata.
Maybe I'm missing why that's selection bias.
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?
`claude install latest`
"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`."
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.
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.
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 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.
I just flat out don’t trust them. They’ve shown more than enough that they change things without telling users.
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.
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.
Those Mythos Preview numbers look pretty mouthwatering.
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.
⎿ 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.
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?
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.
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.
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.
"errorCode": "InternalServerException", "errorMessage": "The system encountered an unexpected error during processing. Try your request again.",
The AI community is starting to remind me of the audiophile community. Rejecting hard data to substitute their own reality.
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.
256K:
- Opus 4.6: 91.9% - Opus 4.7: 59.2%
1M:
- Opus 4.6: 78.3% - Opus 4.7: 32.2%
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.
Seriously? You're degrading Opus 4.7 Cybersecurity performance on purpose. Absolute shit.
Kim_Bruning•1h ago
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.
erdaniels•58m ago
ls612•56m ago
nope1000•54m ago
velcrovan•49m ago
jp0001•9m ago
johnmlussier•15m ago
dgb23•5m ago