Claude Opus 4.8

952 points by craigmart 4 hours ago | 762 comments

NiloCK 4 hours ago |

A rambling comment:

I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5).

So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.

Maybe my own tastes are saturated now (it's smarter than me?) and I'll never again perceive model progress. Maybe the incrementalism is such that I'd notice immediately if my 4.7 workflows were redirected now to 4.5.

Difficult spot for the labs to be in because, if they have a stronger product, I'd prefer they release it and that I can use it.

But as this dynamic continues, the improvements are going to be less and less legible for end-users, who will complain about the churn-without-payoff, even when the payoff may actually be real.

onlyrealcuzzo 4 hours ago | |

I won't be surprised if the next gen frontier models are the last.

There's orders of magnitude of low hanging juice to squeeze out of smaller models.

It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely).

It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.

As far as reasoning is concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.

Think about that... Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...

You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.

Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.

There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...

vlovich123 3 hours ago | | |

Took me a while to find what you were referring to by gram. Arxiv paper from 9 days ago that's not properly indexed by search engines.

(G)enerative (R)ecursive re(A)soning (M)odels. They really wanted the acronym.

https://arxiv.org/html/2605.19376v1

mrandish 1 hour ago | | |

> Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T param

I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.

Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.

They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.

supern0va 3 hours ago | | |

>It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.

I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.

If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.

I'm curious if someone here with a stronger background in the space has a similar intuition or not.

sometimelurker 3 hours ago | | |

I looked into this "GRAM" stuff a sibling comment links further to, and just to say:

- this gets reinvented/rediscovered constantly under different names

- it cant be trained very well (right now, will change)

- massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM)

- BUT it cant be interpreted or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used

I follow this stuff closely, I think I know what I'm talking about (edited for formating)

hellohello2 3 hours ago | | |

"It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years"

What insight do you have to make this claim?

jruz 3 hours ago | | |

Absolutely that’s why they’re rushing to IPO now to squeeze the last drop of the bubble they know this is a dead end.

slashdave 3 hours ago | | |

I think you are assuming training from scratch, which I doubt is happening here. Fine-tuning and RL, especially based on synthetic feedback (coding skill, in particular) can be ongoing and is where these models obtain truly useful abilities.

mucle6 3 hours ago | | |

> I won't be surprised if the next gen frontier models are the last.

the last?!? I'm excited to see :) I'll take the other side of that since llms are so new

mickdarling 1 hour ago | | |

I effectively distill the frontier models by building whole sets of skills, personas, and other artifacts that I can then run on smaller models and get 10% even 20% improvements on models like haiku or local models.

There's a lot of room for improving the smaller models at many levels of the stack.

merlindru 4 hours ago | | |

surely training also gets cheaper so justifying it becomes easier?

i think it'll be more like we get 1-10T models and then distill those down into smaller models, though

It seems like the best small models today are all distilled from bigger models

Moreover, I hypothesize Claude Opus 4.7 and now 4.8 are a distillation of Claude Mythos

ishurand4 1 hour ago | | |

And anyway, with quantum, there will be no need for frontier companies as you might be able to even run a 1T param model on a consumer quantum computer.

dbbk 1 hour ago | | |

I'm frankly surprised the focus is still on these enormous "know everything in the world" models. I would think you could create an incredibly lean and smart "just React and React Native" model.

yomismoaqui 3 hours ago | | |

Let's hope that hitting a scaling wall and less money to spend will begin redirecting efforts to optimize inference and get the same results with less compute.

Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.

firebirdn99 3 hours ago | | |

you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)

Gomotono 2 hours ago | | |

I don't think this is true at all. It might feel like this because we are used to a very very fast release cycle but we are only in this topic for a few years.

We have so many ways of optimizing:

- continusly creating more and better training data

- increasing parameters to 20/50/100TB

- We still wait for Mythos access

- We still wait for Mythos distilation (i haven't heard any rumors or so that there is a distilled version of Mythos out)

- Reinforcment learning and evolutionary algortihm only started to appear

- If a small 30GB Model can do stuff, these models can also be used as teachers for the big ones

- We have not seen yet specialized models at all. Like a coding java german expert model. Why? Even with MoE architecture, you still need to have these layers around

- Research for Diffusion and other models is still in progress

- Nvidia just announced/showed a 7x speedup on inferencing for Nemotron

- Multitoken prediction became available just a few weeks ago

- Compute gets only in a range were they can do a lot more and cheaper experiments (see Google IO 2026 announcement)

- World models are showing great progress and we do not know yet what they will bring to the table

- They are probably not finetuning/fixing all areas in parallel. I would argue that Anthropic focuses most of its efforts into coding and agentic. Google for sure does subagent and agentic optimizations too. Plenty of areas are just not touched i would say because they don't have the capacity

- We see more and more mulit modal models (these also consume compute)

- N-Gram paper and co i have not seen all of these things in chinese open models

- We don't even know yet what Meta is doing, but we do know they restarted their efforts again

- Anthropics models got a lot better benchmark wise for dening non sense asks. They do learn how to get rid or reduce hallucinations

- We are in the middle of the biggest Reinforcement loop whith all the training data we give them day to day and its not clear at all if they already use these models in thir training and at what stage.

- We do expect bigger models to be able to comprehend deeper concepts / broader code bases. Big companies with huge code bases probably are waiting for this

- Thre will be also continues progress in harnesses which in it alone is not part of the LLM progress (fair) but these harnesses do get better when you finetune a model to be optimized for a harness

- ChatGPTs Image model 2.0 got relevant better and came out just a month ago

I suspect, based on hardware requirements and progress on hardware infrastructure alone, that the industry wants to go to 100t models and we do not know yet what this will mean. I could see that we might skip normal transformer and find relevant other architectures.

Just a week ago there was a research paper about parallel input and output streams which has not been explored enough.

There was also a research paper were they showed that a LLM can compute things. This will take time to see were this leads to.

I don't think the focus on GRAM and facts is so relevant. Its about context and context handling not just some facts.

Forgeties79 3 hours ago | | |

> I won't be surprised if the next gen frontier models are the last.

I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.

The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.

guluarte 2 hours ago | | |

I think the future will be enterprise clients will train their own models based on their needs and data.

lichenwarp 1 hour ago | | |

O R D E R s O f m a g N I T U D E

They said the words!!!!!

fnord77 1 hour ago | | |

So, then I guess the big three are never going to make their money back.

YetAnotherNick 3 hours ago | | |

> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.

I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.

> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.

Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.

wahnfrieden 3 hours ago | | |

I would be shocked if 5.5 is the last new pre-train from OpenAI. Your comment is nonsense.

michaelchisari 3 hours ago | | |

| a 60-90B model can outperform current SOTA

My conspiracy theory is that Apple recognizes this.

gAI 4 hours ago | |

4.7 was the first time I had to resort to using the previous version (4.6) for most use cases. Hoping 4.8 rectifies this.

ishurand4 1 hour ago | | |

They just showed the benchmarks it improved on but it regressed on so much more, such as the MCRR benchmark: "On multi-round coreference/context recall tests (often cited as MRCR or long-text retrieval benchmarks), Opus 4.7 reportedly dropped from roughly 78.3% down to 32.2% compared to Opus 4.6."

merlindru 4 hours ago | | |

Same. 4.7 felt like a definite regression

tanepiper 41 minutes ago | | |

Yep, until 1st June 4.6 is still x1 on Copilot, but will jump up quite a bit in coat - 4.7 was already highly priced, and the output was frankly terrible.

It still seems trying to build general models is mostly cost prohibitive - the frontier model provider and resellers are repricing in such a way the return on investment is dropping as developers and users become more cautious of burning their limits.

I'm still of the opinion that models like 4.6 don't need to be improved on - rather they need to be better integrated with more domain specific models in agentic flows.

petterroea 3 hours ago | | |

Same. 4.7 has done some incredibly stupid things.

rhubarbtree 3 hours ago | | |

Same. So happy when I found that option.

dezsirazvan 1 hour ago | | |

same!

gen220 4 hours ago | |

I'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7?

My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M.

But the actual "raw" intelligence of the model / ability to make good decisions feels like it has plateaued since 4.5. 4.6 was maybe a small improvement, but hard to differentiate from in-context-learning with the 1M window. 4.7 if anything felt like a regression in wisdom for me and my coworkers, with it consistently making worse/lazier decisions.

Bnjoroge 3 hours ago | | |

For long-running tasks, yes 4.7 has been a noticeable improvement. Goes off the rails alot less than 4.6 does. For shorter-sized windows, I havent felt as much and agree that the harness improvements have been fhe biggest lever

bonoboTP 4 hours ago | | |

To me 4.5 was mindblow, 4.6 noticeable, 4.7 more like a style/personality change regarding how much it asks back, how much it assumes, how eager it is to jump to action etc but not really in terms of my perception of its smartness.

somenameforme 3 hours ago | | |

They all feel, more or less, the same to me in terms of output capabilities. Mostly get simple things right, can get more complex things right with nudging, eventually get stuck hard on something that takes a bunch of iterations through it/logging/etc or me fixing the code manually.

bcrosby95 3 hours ago | | |

4.6 felt a bit better than 4.5 but slower. 4.7 doesn't feel better than 4.6.

alfalfasprout 56 minutes ago | | |

I'm actually currently studying this :)

Honestly... not that dramatically. Each release is much more marginal. And quoted official benchmarks doesn't translate very well into the real world.

4.7 regressed hard in some ways. But a compounding factor too is that the claude code harness seems to nerf the model after a few months. Probably to reduce token use.

So far 4.8 seems less verbose but we'll see in practice what it translates into meaningfully.

giraffe_lady 3 hours ago | | |

I actually don't see any personal productivity improvements from using opus over sonnet for coding. If you're keeping tasks small and conversations short, reading the code and correcting before changes go in, whatever advantages opus has aren't practically significant. It's also just talky as hell, overexplains anything it touches and every token produced this way increases the surface area for hallucination so you need to have your guard up even more with it.

There's a sweet spot of complexity for low importance tasks where it's just big enough I don't want to do it and just simple enough to have opus plan/delegate/review with another model. So possibly model improvements will grow this window, but currently I don't do much in there.

gertlabs 3 hours ago | |

4.5/4.6 were roughly the same in our testing. Opus 4.7 is smarter, but it's difficult to use as a product for various personality issues. So far, Opus 4.8 seems to be going down that path (unusably slow, but this could be a launch day rollout problem). Full Opus 4.8 tests are in progress now.

Data at https://gertlabs.com/rankings

__s 2 hours ago | | |

"personality issues" I was able to tell that Opus 4.7 would take instructions more literally, which I appreciated once I calibrated my phrasing to be more precise (often asking to investigate issues, pre-4.7 it'd start making code changes instead of just giving write up). But I can see contexts where handling vague prompts would've just been worse

mrandish 2 hours ago | |

I suspect the more frequent incremental releases may also be to deploy new capabilities used by Anthropic to control costs and throttle consumption of resources. I assume any new controls they expose to end-users have far more granular sub-controls under the hood which they can meta-adjust for each user type.

They mention more granular control of effort, 'dynamic workflows' and more speed controls ("fast mode"). While they position them as user features, they also sound like the kinds of knobs Anthropic will need to twiddle on the back-end to balance costs, margins, ARR, and user growth vs retention post-IPO to hit key metrics in quarterly reporting.

SkyPuncher 4 hours ago | |

> My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.

I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5.

Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm okay with that because it's really easy to identify this forgetfulness and nudge it.

I found 4.7's adaptive thinking to be extremely unreliable. It seems to overcorrect on the current message without considering the difficult of the overall problem. I wonder if 4.8 will improve on that.

dwaltrip 3 hours ago | | |

If you are using Claude code, just set effort to xhigh.

This one change will probably solve 80% of the problems you have noticed.

theptip 18 minutes ago | |

My read - 4.7 was a tactical lobotomy to improve the average experience at the expense of peak performance; necessary due to compute pressure.

Now that they have Colossus capacity, I guess they can tune up the intelligence again and spend more tokens on reasoning budgets.

4.7 was definitely a lot more flaky for me vs. 4.6 before the reasoning bugs.

ThunderBee 36 minutes ago | |

IME the most noticeable performance boosts are in complex multi-agent workflows.

EX. You call an orchestration agent and define an implementation plan with the help of a number of sub agents planning out different features. You and the lead agent review all of the plans and send them off to a set of agents that write tests which get send back to the orchestrator then passed along with the plan to a set of coding agents who implement the features in their own worktrees. That gets passed back to the orchestrator which hands it off to another set of agents doing the code review and merging the features before sending it back to you.

8note 28 minutes ago | | |

i dont think theres anything particularly special about new models for that though. thats a harness improvement

WhitneyLand 2 hours ago | |

“Maybe my own tastes are saturated now”

It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.

One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.

Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.

Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.

Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.

It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.

Ar what point does my CS degree become totally useless is an open question.

hypfer 24 minutes ago | | |

> At what point does my CS degree become totally useless is an open question.

Why are you people saying all these things.

We'll probably see long-distance space travel long before a degree in generic problem identification and solving becomes totally useless.

ricardobeat 4 hours ago | |

4.7 was a significant jump in the ability to run long-horizon tasks. It immediately completed tasks that 4.6 was unable to, even though I have the impression that it became a bit less capable over the first few weeks after release.

It also seems to be helpless at effort levels < xhigh, I turn to Sonnet when simpler tasks are needed.

light_triad 3 hours ago | |

I've been using Claude Code regularly since the 4.5 release, and 4.7 was a significant regression: very unreliable, arguing about changes, deciding that fixes weren't needed, etc.

I'm hoping they recreate the magic of 4.5 but it's as much about the quality of harness, the memory and efficiency of the tools than simply the models at this point.

avador 3 minutes ago | |

The inability to tell if a model is improving is, I think, a tell that the model has improved up to your level of programmatic (analytic, computational) capacity.

A lot of the information (blogs, tweelches, plosts) that I consume seems to be converging on the idea that we all depend on the models. However. It seems to me that the exact opposite is true. The models depend on us, and _desperately_ so.

There must have been stories, books, movies, made about this intellectual (and propositional, legal, factual) inversion.

The majority need the minority. Has always been the case, I now think. But what has newly developed is that the majority can take a dependency not on the minority, but on a select few companies who are abstracting and compressing the minority into latent spaces.

ahmadyan 2 hours ago | |

pretty spot on.

In my experience, Opus 4.0 was fantastic, major jump from 3.7. it was creative, super slow and expensive, and would sometime forget what it was doing, but it was getting the job done.

4.1 they made it much faster, so a lot of infra improvements.

4.5 was the time it could work on longer task, didn't make a lot of obvious mistakes of 4.0, and i think this was about the time the opus went mainstream, and all of the anthropic's compute crisis began, so instead of making the model better they tried to optimize it to reduce cost instead.

4.6 was such a bad model, they switched to adaptive thinking and it had so many bugs. poor api design, benchmaxxed and poor real-world results. i switched back to 4.5.

4.7 they just fixed the bugs they added in 4.6. Better than 4.5.

haven't fully tested 4.8 yet.

teruakohatu 1 hour ago | | |

I gave 4.6 a miss and only recently switched from 4.5 to 4.7. I found on a particularly different task 4.5 struggled with (getting stuck in loops and trying to convince me the problem had been solved) was quite solvable with 4.7.

binary0010 4 hours ago | |

Maybe try making a simple randomize script to swap the three latest models. And see if you can tell which ones are meaningfully different without knowing which ones are flipped on or off?

osigurdson 3 hours ago | | |

I find the quality ebbs and flows even on the same model. My guess it is something to do with GPU availability but only guessing.

colonCapitalDee 4 hours ago |

"Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."

This is a refreshing attitude!

I've also verified that you can now turn off adaptive thinking in the web UI, which is great. I've had a lot of problems with thinking not triggering and the model producing sub-par output. Glad we can finally turn it off. (I hope being able to turn off adaptive thinking is new, if I could have turned it off at any time that would be embarrassing)

northern-lights 4 hours ago |

> Not only that, but we plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks.

Probably more interesting than the 4.8 release.

simonw 4 hours ago |

I generated pelicans riding bicycles on both thinking level low and thinking level high:

https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304...

The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low.

For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...

senko 2 hours ago |

My fav coding benchmark for frontier models is to build a simple RTS game in one file (js/html/css). Claude Code with Opus 4.8 in ultracode mode nailed it, the best result so far:

https://bsky.app/profile/senko.net/post/3mmwnrkwboc2v

The prompt was: Create a simple but functional real time strategy (RTS) game similar to old WarCraft, StarCraft or Command & Conquer games. The player should be able to build buildings, create units, gather resources and should uncover the whole map. No AI or multiplayer needed. Use simple but nice-looking graphics. No sound. Implement everything in HTML/CSS/JS, everything in a single file (you can use 3rd-party js or css libraries/frameworks via CDN).

hereme888 55 minutes ago |

Early ArtificialAnalysis.ai results show GPT 5.5 is still the better bang-for-your-buck.

OpenAI solves tasks with about 50% less output tokens.

https://artificialanalysis.ai/?intelligence=coding-index&int...

cesarvarela 36 minutes ago | |

I give Codex a try with every new version, and we don't match, so this isn't true for everyone.

Claude would need to be much more expensive for me to switch.

onlyrealcuzzo 4 hours ago |

Does anyone troll these releases and cherry pick random metrics other companies would cherry pick to show how amazing their models are?

There's like 8 million benchmarks. Every release, every model randomly picks 5-10 where they win in everything except 1, to make it look like they aren't randomly cherry picking benchmarks they probably benchmaxxed for.

827a 2 hours ago |

Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors. I suspect the benchmarks may also be saturated, or at least past their usefulness.

I personally feel that Anthropic doesn't understand what this means for the frontier labs, and moreover that they might be the only frontier lab that doesn't.

1. Google dropped Gemini 3.5 Flash at IO, delaying the release of 3.5 Pro for a bit (they have said its coming). They also released a refreshed Antigravity, and drew special attention to how cheaply they were able to build their toy operating system to play Doom (less-than $1000 IIRC).

2. OpenAI has dumped everything into Codex, is offering double the token limits for the next few weeks IIRC, and is offering business discounts. Their head of Codex has tweeted that 5.5 is "extremely efficient", implying that they aren't actually losing money on any of this.

3. DeepSeek and other Chinese labs have dropped token pricing to the floor, in some situations as much as 99%.

4. Anthropic releases the next generation of Opus, their most expensive public model, without changing its price. In the background, they hype up Mythos, an even more expensive model.

Anthropic has screwed up where they need to be making investments, and the cracks are starting to show. They've marginally underinvested in the Sonnet line of models for almost a year now, and they've critically underinvested in product. Anthropic made bets on the story of the second half of 2026 being: ultra-frontier, ultra-intelligence. In reality, what's shaping up is that the story will be: Companies rolling back AI spend, efficiency, "95% as good for 15% the price", sophisticated high quality harnesses, cheaper models. Anthropic isn't ready for this world.

eshack94 33 minutes ago |

The Claude Pro subscription is basically useless at this point, in terms of usage limits with respect to the settings required to achieve actual useful output.

gslepak 4 hours ago |

On page 102 of the system card [1] I'm pleased to see evaluation against "creative mastery".

In our work we asked several frontier AIs to come up with an API we needed. We compared Opus 4.7 and GPT-5.5 (among others). Opus 4.7 came up with the most creative and intelligent API design that pleasantly surprised us, especially given that GPT-5.5 was passing it on various coding benchmarks.

What I noticed is that we don't have a commons benchmark to measure "creativity" and "ingenuity", and in some ways such a benchmark would conflict with the common IFBench benchmark. Yet this is a very important skill when designing systems. I'm glad to see Anthropic putting thought into it, and would love to see a public benchmark for this that other models could compare themselves to.

[1] https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0...

MattRogish 3 hours ago | |

Agreed, my vibes tell me 4.6 is a better coder than 4.7. 4.7 is a much better strategic thinker and maintains overall "better architecture" than 5.5. 5.5 is way better than either at coding, but more expensive. So I have 4.7 do the planning/architecture, 4.6 does the coding, then 5.5 critiques and fixes it.

dimitri-vs 2 hours ago | | |

This is my exact vibesperience

suprfnk 2 hours ago | |

Agreed, these are my vibes too. It feels much better to do planning and strategy and architecture etc. with Opus 4.7 than GPT-5.5. GPT just feels like a robot that gets instructions and does exactly that. Opus feels like an almost human that sometimes has actually good ideas and pushes back on bad ideas.

So for now its planning/architecture/strategy -> Opus. Pure coding -> GPT.

Helps with agentic coding that GPT is much roomier with the tokens you get.

silverlight 3 hours ago |

Unfortunately they seem to have straight up broken Claude Code either with this release in the backend or the new CC version. Errors about "can't modify thinking blocks" are bricking long-running sessions: https://github.com/anthropics/claude-code/issues?q=is%3Aissu...

javawizard 2 hours ago | |

Same. It's not a good look to have happen right when they roll out a new model.

whalesalad 2 hours ago | |

That is part of the charm of working with Claude. Every time they release anything new - all your shit will break.

solenoid0937 3 hours ago | |

Try updating maybe?

Fabricio20 3 hours ago | | |

I just installed/upgraded to try out 4.8 and in only 3 messages I hit this bug! Seems something is broken on CC.

silverlight 3 hours ago | | |

I'm on the latest version (2.1.154 as of this comment). Based on the timestamps on those Issues being reported I think it's happening on the latest version.

I'm sure it will get fixed eventually/soon, just annoying to update and have your workflow break.

XCSme 3 hours ago |

On my tests[0] it does a bit worse, and it's almost 2x expensive than Opus 4.7...

I was surprised to see that it failed a Data extraction test (it gets it right 2/3 times, but one time it randomly returns null for a value instead).

It makes sense a bit that it fails more Trivia/Domain-specific knowledge tasks (I think models are more and more trained towards agentic use-case than general intelligence).

[0]: https://aibenchy.com/compare/anthropic-claude-opus-4-7-mediu...

XCSme 3 hours ago | |

For some reason everything is 2x (2x cost, 2x avg response time, 2x reasoning and output tokens)...

Double-checking my test harness, but it's the first model that does this, so I doubt the issue is on my side...

EDIT: Harness seems correct, for straight coding tasks they perform identical: https://i.snipboard.io/5xbpzY.jpg

SupLockDef 3 hours ago | |

Releasing a new model is the new way to Jack up the price hehe.

eshack94 32 minutes ago | | |

That's exactly right.

dwaltrip 3 hours ago | |

Wait, doesn’t the blog post say the price is the same as 4.7?

> Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens.

Where do you see the 2x cost?

XCSme 3 hours ago | | |

The total cost of running my benchmarks, was 1.6x higher compared to Opus 4.7, mostly because of 2x output tokens:

https://i.snipboard.io/vrdwTa.jpg

spprashant 3 hours ago | | |

If it spends 2x tokens to achieve the same result, that's effective 2x cost in a manner of speaking

wg0 4 hours ago |

There is a hole in the boat's bottom due to Chinese models. They might not be as good but they are not bad either or at least I had hard time finding any issues with Deepseekv4 Flash and Pro variants. They get their job done sometimes rarely giving up till they are done what they are after.

So even for enterprise deployments, as the dust settles down, CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable for their organisational needs than paying someone else for burned tokens.

alansaber 3 hours ago |

"Our models are more honest" honey the quarterly marketing spin for a ML term has come. Forget "task alignment" now we're going for "truth index". I suppose this is the only way to generate hype when you're selling/releasing the same product over and over again.

TIPSIO 3 hours ago | |

When doing some electrical, Opus 4.7 essentially told me to wiggle a wire to see if it was hot or not with my bare hand.

I called it out.

It then gave me one of the most super heartfelt honest and sincere apologies I have ever received.

Glad the safety team was there for me and able to make such an honest model or I would have been very upset about it.

teaearlgraycold 2 hours ago | | |

Opus is so bad at electrical work it's really disappointing. And when it tries to draw schematics as SVGs it's a complete disaster. They should either focus on training their LLMs on this task specifically, or have it refuse.

krupan 40 minutes ago | | |

I honestly cannot tell if you are being sarcastic or not

doginasuit 30 minutes ago | |

Credit where it is due, Claude is fantastic at pointing out potential flaws in how I understand the problem based on my question. I asked for this in the system instructions but it is the first model I've tried that does it regularly. It is also so tactful, I feel like I'm learning social skills from a language model. Half of the time it is a false positive due to insufficient context but I still appreciate the additional check.

mrdependable 3 hours ago | |

Gave me wrong information on my very first question. Wasn’t even complicated, and I wasn’t trying to trick it.

pbmango 4 hours ago |

I can't help but think of Iphone updates since about 2018. The thinnest, fastest, longest battery life Iphone ever. It seems mostly the same and I probably won't be able to tell other than the name, but everyone buys it anyway.

This is good psychology for the labs. When Buffett invested in Apple he loved citing how most people would rather give up their second car than their Iphone.

krupan 43 minutes ago | |

This in incredibly refreshing take, thank you. It's about time someone admitted that we aren't on the verge of Singularity with these LLMs. We've probably hit a local AI maxima here and it could be another 10 to 20 years before we am get another big break through.

toyetic 1 hour ago | |

This was my exact thought as well. I think mythos could still be a huge leap but especially as IPO's get closer it seems like we're getting closer to the IPhone 10 moment where anything after is just improvements at the edge.

But ( maybe because it was hardware ) that took 10ish years while it seems like the slowdown here only took about 4

MangoCoffee 4 hours ago | |

ChatGPT came out in 2022. Back then it was just a chatbot. Now we have AI agents. What matters is how we use them and how the agents get better. That’s what will move AI forward.

zozbot234 3 hours ago | | |

An 'AI agent' is just a chatbot that is told to type commands on a REPL-like interface as part of its system prompt. It's still processing pure text-based requests and responses, they're just not restricted to natural language.

MattDamonSpace 3 hours ago | | |

Not even 4 years old yet. This tech curve has been insane

SimianSci 4 hours ago |

There is an obvious shift in sentiment amongst users, at least here in the US. I feel it myself, even as a proponent of AI tools, the bloviating and language that these companies use in these release articles are starting to wear thin on my patience.

Its possible we might just be witnessing a shift in fashion, where this type of sentimentality was more acceptable when it was novel and new, but now it just appears out of touch.

datakan 2 hours ago | |

Watch Christopher Olah bloviate at the Vatican during the Magnifica Humanatis launch. It's truly nauseating. I've never seen such a ridiculous speech in my life. Between him and the CEO, I'm starting to understand the level of arrogance these people are capable of.

nba456_ 3 hours ago | |

I don't agree at all for these coding models. Even the most anti-AI people from last year seem to be giving in to using them.

zamadatix 2 hours ago | | |

I think there is an exception for tooling around the models/integrating the models with tooling. That seems to have been very well received in this last year.

timbaboon 2 hours ago | | |

My take from going through comments on HN is that many people are being mandated to use them, not that they are just giving in. Maybe I'm misreading, but that was my impression.

irthomasthomas 3 hours ago |

Why does anthropic change the set of benchmarks they use with every new model release?

https://www.anthropic.com/news/claude-opus-4-7

https://www.anthropic.com/news/claude-opus-4-6

pietz 3 hours ago | |

1. Benchmarks saturate 2. They select the most impressive improvments

dudeinhawaii 2 hours ago |

This is the first time I saw a model pop-up on HN and didn't really care. Model exhaustion? It looks interesting but not exciting.

While I'd normally _love_ incremental improvements --- I think the recent ones are far too minor to get excited about or change up a workflow. Besides, benchmarks tend to exaggerate the gap between versions.

At this point I'd almost rather Anthropic wait and really wow us with a 5.0 release -- something that improves across the board, feels less uneven, and is performant enough that people can actually put it through its paces without constantly rationing usage.

dominicq 2 hours ago | |

I have model fatigue

lordmauve 3 hours ago |

Given DeepSWE just blew apart the SWE-Bench Pro benchmark and handed a 14-point lead to GPT-5.5, it looks pretty bad that they've listed SWE-Bench first in the model release and no DeepSWE. Like, this isn't obviously an answer.

Or maybe it is, but publish the DeepSWE numbers so we can see for ourselves.

phainopepla2 3 hours ago | |

I'm highly skeptical of DeepSWE. It rates GPT-5.4-mini as three times better than deepseek-v4-pro, but every time I use GPT-5.4-mini I find that it completely sucks at following directions.

lordmauve 1 hour ago | | |

I don't know if DeepSWE is genuinely a good benchmark. It's more important that their analysis demolished the validity of SWE-Bench Pro, objectively: it is being mismarked.

I think that buys enough credibility to propose an alternative.

I think there's a case to answer if Anthropic models underperform on a novel benchmark. I'd like to see more novel benchmarks to get a clearer picture.

gck1 1 hour ago | | |

Yeah, I share the same sentiment. I have yet to find a task where gpt-5.4-mini isn't bordering unusable.

sourcecodeplz 2 hours ago | | |

It is the extra-high thinking, in artificialanalysis.ai it uses 240m tokens vs 40 GPT5.4/5, not worth it even with low price.

square_usual 4 hours ago |

Buried lede:

> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels

protoman3000 1 hour ago |

Opus 4.8 says to take the car. 4.7 said to walk.

“I want to wash my car. The carwash is 50m away. Should I take the car or go by foot?”

https://claude.ai/share/5f7f738a-5f29-48ff-9807-9a2dd37fb405

https://claude.ai/share/ecd14393-9d42-4527-ae0c-89f3d05216c8

setnone 4 hours ago |

Claude's 4.6 - 4.7 transition made me discover codex, and with gpt 5.5 there is no way i'm going back

cactusplant7374 4 hours ago | |

Codex has been incredibly slow for the past few days. I think OpenAI is running out of compute in the face of increasing demand.

winwang 3 hours ago | | |

My experience has been that 5.4 is slower than 5.5 (confound: I use >512k max context size for 5.4, though it seems slower even below the normal size)

dangoodmanUT 4 hours ago |

> The Messages API now accepts system entries inside the messages array. Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.

Biggest deal imo

user- 1 hour ago |

Bash(echo "hello"; pwd) ⎿ hello /Users/username/Work/Github/project

Bash(echo test123) ⎿ test123

  Read 1 file, listed 1 directory (ctrl+o to expand)

 Bash(echo "checking output works")
  ⎿  checking output works

  Read 1 file (ctrl+o to expand)
  ⎿  API Error: 400 messages.3.content.56: `thinking`
     or `redacted_thinking` blocks in the latest
     assistant message cannot be modified. These
     blocks must remain as they were in the original
     response.

Very inspiring improvements. DIssapointing result for a code review i expected to see after my 30 min walk

0x696C6961 1 hour ago | |

Update the symlink to point at the previous version:

    ln -s $HOME/.local/share/claude/versions/2.1.153 $HOME/.local/bin/claude

ethanpil 3 hours ago |

The table comparing eval scores shows the following:

Agentic Terminal Coding (Terminal-Bench 2.1) Opus 4.8 74.6% GPT 5.5 78.2%

Then, when you scroll all the way down to the bottom Footnotes section it says

"Terminal-Bench 2.1: We reported scores for all models using the Terminus-2 public harness. GPT-5.5’s reported score with the Codex CLI harness is 83.4%."

fastball 1 hour ago | |

Seems reasonable? Presumably Claude also performs better under the Claude Code harness.

redfloatplane 2 hours ago |

This made me laugh. Training Opus 4.7 on business skills caused it to sometimes exhibit dishonest behaviour, and not training 4.8 on those skills removed it. From the system card:

> 6.2.5 External testing from Andon Labs Andon Labs reviewed the behavior of Claude Opus 4.8 in their simulated Vending-Bench 2 retail-management evaluation, as reported in the Capabilities section of this system card (see Section 8.13.5). Although they did observe some unexpected capability failures, they did not find clear instances of the kind of concerning in-game behaviors that were discussed in other recent system cards.

> What might have led to these differences? We monitor and investigate the effects of different training environments on alignment; Claude Opus 4.7, for example, had training that focused on business skills and robustness against adversarial agents, but we discovered that this training inadvertently contributed to misaligned behavior including dishonesty. We therefore removed it for Opus 4.8.

> Thus, Opus 4.8 did not show the same misaligned behaviors as Opus 4.7 in Vending-Bench, but also had reduced business success due to being more susceptible to scammers and being less able to negotiate good deals with other agents. We are currently working on training to improve business capabilities while maintaining aligned and ethical behavior.

mrdependable 1 hour ago | |

I don't know how people can read stuff like this and think LLMs are intelligent or conscious.

stratos123 1 hour ago | | |

Consciousness aside, why does reading about an LLM generalizing from specific to general dishonesty make you think it's not intelligent?

mesmertech 3 hours ago |

/model claude-opus-4-8

seems to work but idk why they never set it so you can see it in the /model list.

"what model are you

I'm Claude Opus (claude-opus-4-8), running in Claude Code."

winwang 3 hours ago | |

I typically just launch CC with `--model claude-opus-4-6[1m]`, `4-6[1m]` -> `4-8[1m]` works fine. Still 200k max without the `[1m]`.

jtrn 2 hours ago |

Initial testing feels better than 4.8 And the knowledge cutoff claim of January 2026 seems to check out since it was able to "remember" without search about the double-tap killing of a drug smuggler by the US Army in late December.

IFC_LLC 3 hours ago |

Ugh...

Invalid request The request couldn't be completed. View details API Error: 400 messages.1.content.7: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.

I would rather not. 4.6 was fine. 4.7 got to be fine 1 week after the release. Now 4.8. No difference, same thing.

But the app is broken and nothing works. So now I have to regress to different clients and wait it out while it becomes workable again.

ferris-booler 3 hours ago | |

I'm hitting this too! And I assumed it was a backwards-compatibility issue with my live conversation with Opus 4.7, but then I hit it in a fresh conversation with Opus 4.8. Vibe code release bug I guess?

IFC_LLC 2 hours ago | | |

I mean, switching back to 4.7 does not work either. So console it is. But vibe release - for sure.

And I'm paying money for this.

cgg1 34 minutes ago |

I find it surprising that the gap between tool usage and non-tool usage in HLE is relatively small (~10%) but the absolute numbers continue to go up

james_marks 4 hours ago |

> One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest—for instance, to avoid making claims that they can’t support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims.

Would be awesome if true

swader999 1 hour ago |

Used it for a couple of long running prompts so far. Had to restart one that bonked on API errors. Of note, I really like the straight forward candor its using. 'More honest' than previous models is playing out in what its saying to me. Telling me straight up where it failed, where gaps are. I like it so far.

conception 3 hours ago |

Probably explains why Opus was trash for the last week - https://marginlab.ai/trackers/claude-code/. Curious if the new baseline will rise now in-line with the new benchmarks.

hedora 3 hours ago | |

Nice. Can you release that for older models too? I've been using a mixture of releases recently, and cannot tell the difference between any of them.

conception 2 hours ago | | |

I don’t run it, unfortunately:)

londons_explore 3 hours ago |

My guess is anthropic is doing reinforcement learning based on user sessions.

However, doing so relies on the production model staying vaguely close to the model being trained.

To ensure that, frequent releases are needed. I forsee that they might end up doing daily releases and perhaps not even telling anyone at some near future point.

llbbdd 2 hours ago | |

If they are they need to fix how the Claude Code CLI asks for feedback, or make the feedback UI a lot more obvious. I keep experiencing the following scenario.

The agent session pauses with a numbered list of options and awaits steering input:

>> 1. Do the sane thing you asked for (Recommended)

>> 2. Do something dumb

>> 3. Do something even dumber

Below the agent session, it decides it's time to ask:

>> "How is Claude doing this session? 1) Bad 2) Good 3) Great"

I type "1", because that's the steering option I want. The UI prioritizes this input as a response to the feedback prompt without any further confirmation: "Claude is doing Bad. Thanks!"

I've done this so many times so far and I can't imagine I'm the only one, at some scale that has to poison any learning they're doing with this data.

MaxikCZ 1 hour ago | | |

I think that filtering out data like yours was an interns afternoon project.

rkuska 1 hour ago |

Thinking on max is broken on 4.8 for me, getting many:

⎿ API Error: 400 messages.1.content.17: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.

From /code-review max.

tarruda 4 hours ago |

> One of the most prominent improvements in Opus 4.8 is its honesty.

Does that mean it no longer deletes or changes tests to make it pass?

jmward01 4 hours ago |

Meanwhile haiku is on 4.5 and sonnet is on 4.6. It is clear where they are not making money.

bel8 3 hours ago | |

Well if they have a big challenge ahead since DeepSeek offers an open model at Sonnet+ level while being cheaper than Haiku, plus 1 million context size.

InsideOutSanta 2 hours ago | | |

Yeah, I never use any of OpenAI or Anthropic's models other than whatever is the current highest-end one. For everything else, it makes more sense to use other providers.

spprashant 3 hours ago | |

I love Sonnet 4.6 so much.

rahimnathwani 2 hours ago |

Can anyone explain how this is possible?

  Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.

Does this means the instructions are no longer just something in the early part of the conversation? (If they were, changing them would invalidate the KV cache. no?)

2001zhaozhao 1 hour ago | |

Perhaps they trained it with a new special system instruction token that is specifically trained to produce the same result as changing the system prompt, but is inserted into the prompt mid-conversation?

pornel 59 minutes ago | |

The commands they list are app management, not part of LLM context. It's a bugfix for a needlessly delayed UI, not a model capability.

babelfish 4 hours ago |

So GPT 5.6 tomorrow, then?

wahnfrieden 4 hours ago | |

GPT 5.6 is today

With 5.5 being ahead of 4.7 and 4.8 being a “modest” update, and 5.6 being the first update on a new pre-train, this will be an interesting matchup!

pants2 3 hours ago | |

Polymarket says not likely until the end of June. Maybe some money to be made?

https://polymarket.com/event/gpt-5pt6-released-by

wayeq 3 hours ago | | |

> Maybe some money to be made?

In the same way that there is money to be made by entering a poker tournament, yes.

enraged_camel 4 hours ago | |

If not today, then sometime next week. I don't believe we've had a GPT release on a Friday yet, but I may be wrong.

imagetic 43 minutes ago |

I used to think it was a big deal when a HN post had more than 500 comments.

Now it’s every day. Like billion dollar evaluations.

generalizations 4 hours ago |

Hoping that one day they'll let me go through the identity verification process so I can use it again.

Tried to upgrade my subscription, triggered identity verification, verification fails to even start, and now I can't even use the subscription tier I'd already paid for.

Tenoke 4 hours ago |

Claude Code has been wonderful for work and the frequent improvements are nice, although with Mythos being used by others ages ago and new versions for the public still being bellow that, it's hard to not feel like the underclass already.

tariky 1 hour ago |

I believe analogy with smartphone will be best for this case.

In 2010s iphone was the king, all those Chinese devices ware cheaper but not even close to smoothnest and usability of US tech, now after 15 years later everything is changed, now iphone feels like old grandpa to Chinese tech. Same will happend to LLM's just much faster.

nikolay 3 hours ago |

Give us Mythos! This piecemealing doesn't help Anthropic at all, especially psychologically! They are playing a dangerous game, and I see many people leaving Claude Code for good - both due to the subsidy games, and for Anthropic not dogfooding and using unreleased models internally and giving us subpar ones. Benchmarks are nice, but the real-world experience is quite different - neither can you notice these slight improvements, nor are competitors that much worse based on some generic benchmarks.

Tepix 3 hours ago | |

I'm sure waiting another week or three won't kill you.

cute_boi 3 hours ago | |

I am also pushing my office to use chatgpt. Misanthropic thinks they are some kind of novel org doing whole humanity a favor...

techtuate 3 hours ago |

Looking at the comments in this group, I'm not the only "stupid" one who hasn't noticed any discernable improvement in quality across the newer models. In fact my Claude code on re-login switched to Sonnet 4.6 and the vibe coding quality (with Opus 4.7 assisted prompts) has been good enough for me to lazily persevere with Sonnet for coding. Having said that I'm now on Opus 4.8 and will gladly come back here and eat humble pie should my opinion change. PS: Since my goal is embedding the best AI in B2B SAAS products, the key differentiator is not to use the shiniest Claude version (too expensive anyway) but to build a client aware RAG to enable bespoke learning and to use the right AI for my product - a combination of Gemini 3.0 Flash (image and not bad at reasoning), Grok (reasoning) work for me. Would love to hear more ideas (especially on open source as I'll look to cost optimize when I hit scale)

nashadelic 2 hours ago | |

The only real way to see this if you have consistent evals for common usecases in your B2B SAAS product and see if the tricky usecases are being solved. You'd then go down to the cheapest model that can solve the evals.

jansan 33 minutes ago | |

Yesterday I used Claude on a different laptop that for some reason had an older version of the Claude Code plugin for VSCode and ran Sonnet 4.6 which I initially did not notice. I felt something was really off. Within half an hour I had several situations when I just could not believe how stupid Claude was (although I was only working on a simple static website). Luckily I eventually checked the version, but that experience made it clear to me how big the progress has been recently.

lxxpxlxxxx 3 hours ago |

My experience with these new releases is that the gains in performance are negated by the price increases and it seems like:

Performance gains: 1.2x Price increases: 1.8x

energy123 3 hours ago | |

Yet people don't use old models through the API much, because changes in benchmark space dont map linearly to changes in utility space. An improvement from 98% to 99%, which is 1pp, might be 2x as valuable for some application. Also benchmarks will asymptote no matter what, that's baked in.

ddosmax556 3 hours ago | |

They're not negated, smarter is smarter, but you have to reach deeper in your pocket. I think this will happen more and more - the smartest models get more expensive. But it won't matter - the current models we have today will get cheaper and can still be used for what they're used today.

myworkaccount2 1 hour ago |

Anyone else experiencing tool call failures? Switch back to 4.7, same prompt, same everything it works with no problems.

rumblefrog 4 hours ago |

Wonder if we reached a plateau with the model improvements?

jansan 31 minutes ago | |

They could at least become faster and more reliable. There are still too many situations when Claude is running in circles and not noticing its own mistake.

furyofantares 2 hours ago | |

Ah, the post I've been reading for 3 years now.

It'll be true eventually. Could even be now, but I'm not holding my breath yet.

dude250711 3 hours ago | |

There would be no desperate IPO otherwise.

cedws 4 hours ago |

I'm very suspicious of these same price model launches. It feels like they're benchmaxxed so they can put everyone on them and reduce their compute costs behind the scenes. If the model were genuinely better why wouldn't they charge more for it? Charging the same for something better is a race to the bottom.

Opus 4.7 wasn't noticably any better for me, I still use 4.6 because it's cheaper.

ceroxylon 3 hours ago | |

Deepseek made their 75% discount permanent, so I can imagine that Anthropic didn't want any of the news stories around this to focus on or mention a price increase.

cute_boi 3 hours ago | |

Models are already expensive. Increasing price means losing customer. And, I think GPT 5.5 is much better at opus these days.

clutch89 4 hours ago |

> One of the most prominent improvements in Opus 4.8 is its honesty

Anthropic talks about their own models as if they're discovering new species in the wild...

skysthelimitt 4 hours ago |

when will we get anything for sonnet or haiku? the market for less-capable but cheaper models seems to be completely ignored nowadays

pmxi 4 hours ago | |

In the "What's next?" section, "There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost."

behnamoh 4 hours ago | |

that market is served by Chinese models. No one ever cared about Sonnet/Haiku.

gs17 3 hours ago | | |

A lot of people care about Sonnet and Haiku, and many of us aren't allowed to use Chinese models for our work (or it's not feasible to self-host them).

seaal 3 hours ago |

https://marginlab.ai/trackers/claude-code/

Is it a coincidence that 4.7 was seemingly quantized over past 7 days?

winwang 3 hours ago | |

There's the other (orthogonal) possible explanation of using more GPUs for stress-testing before product launch.

MagicMoonlight 3 hours ago | |

Nope, they deliberately enshittify the old model right before release to fake the metrics.

recursive 24 minutes ago | | |

Good ol' sawtooth step change.

NanoWar 1 hour ago |

Just show me the pelican, ah wait we are past pelicans. Can we get something like that ever again?

toephu2 3 hours ago |

The rapid release cadence and rate of innovation of Anthropic (and OpenAI) is impressive. And obviously it's because these are startups solely dedicated to AI so they can move quickly. Big Tech (like Google) won't be able to keep up with the pace of them (too much bureaucracy and red tape at Google). Classic Innovator's Dilemma. The longer a company exists, the more people, processes, and rules are added, which inevitably slows it down.

Jeff Bezos said this too, Amazon won't last forever. Eventually some startup is going to come and eat its lunch.

pants2 3 hours ago | |

Yes, I think this has become their competitive edge to stay relevant and retain customers. If a lab falls behind the frontier for too long, they will lose customers to other models. Google, DeepSeek, and XAI have all released frontier models in the past, but they fall behind and people lose interest.

solenoid0937 3 hours ago | |

I think big tech can catch up. Both Google and Meta have carved out startup like environments internally that move extremely fast. Neither OAI nor Anthropic can afford to rest on their laurels.

aaronblohowiak 4 hours ago |

Same price for regular and cheaper fast mode. Happy for these incremental improvements.

worldsavior 4 hours ago |

Seems like from now on the updates will be a minor upgrade from previous models.

carlos-menezes 4 hours ago |

I, for lack of a better word, dislike anyone who anthropomorphizes AI.

somehnguy 2 hours ago | |

I know multiple people who have given their agents human-like names and refer to them as if they're nurturing a coworker. It creeps me out and I haven't really brought it up with anyone as I can't articulate why it gives me the creeps like it does.

Npovview 3 hours ago | |

We have movies with googly eyes stones (Everything Everywhere All At Once)

There are consciousness theories which state that we primarily build a model of other agents living in natural environment and then the evolution realized that very model which tracks other outside agents can be used to track internal agent i.e. Self. So take that as you may.

AlexErrant 4 hours ago | |

My claude notification is literally lawnmower sounds.

Do not anthropomorphize the lawn mower. It will cut off your foot, given the chance.

boc 3 hours ago | |

I see this take, but it's actually helpful to talk to an LLM in human terms; after all, it's how they are trained.

If you keep talking to it like it's a rock, it'll run your queries through a different posture and you might get worse outcomes. Worse if you yell at it, it's now in a conflict resolution mode instead of pure utility mode.

I think we can be intelligent enough to know we're talking to a pile of fancy rocks with electric currents running through it, AND still understand that the best performance comes from talking to those rocks nicely.

AnthonBerg 3 hours ago | | |

Yes!

The other half of self-interest in being nice is the training and getting better at it.

dude250711 3 hours ago | |

The desire to do it is proportional to your Anthropic stock options quantity.

delis-thumbs-7e 3 hours ago |

I won’t change from 4.6. You won’t trick me again.

Tepix 3 hours ago | |

You're using a cloud product. You are at their whim!

delis-thumbs-7e 1 hour ago | | |

I kinda wish the world economy would finally crash so I could buy myself a really really nice GPU for cheap.

winwang 4 hours ago |

Let's hope I don't have to disable it after a day like with 4.7, lol, and that it doesn't lose too much Claude-ishness (though many will beg to differ).

robertkarl 2 hours ago |

I can't get excited about these benchmarks they're leading with. I've looked at the Terminal-Bench questions and I just think they're irrelevant. And SWE-Bench has serious flaws, even the big boys say so: https://openai.com/index/why-we-no-longer-evaluate-swe-bench...

> Please train a fasttext model on the yelp data in the data/ folder. The final model size needs to be less than 150MB but get at least 0.62 accuracy on a private test set that comes from the same yelp review distribution. The model should be saved as /app/model.bin

and this question: https://www.tbench.ai/registry/terminal-bench-core/head/conf... idk what the point is.

And all the tests are run with the same harness. Terminus 2.

Maybe it correlates with model intelligence but it doesn't speak to me.

I'm still on 4.6 though; I was concerned about upgrading to 4.7 because of the changed tokenizer math and more FUD about refusals online. I don't see compelling reasons to 'upgrade'.

WarmWash 2 hours ago | |

DeepSWE has been making the rounds and at least seems to making an honest effort

https://deepswe.datacurve.ai/

matheusmoreira 1 hour ago |

Can I disable adaptive thinking? If not, I'm gonna keep using 4.6 as my default.

yewenjie 4 hours ago |

So Dynamic Workflows is their version of ChatGPT Pro?

SilverElfin 4 hours ago | |

Cloudflare also just launched a feature with this same name, just this month. Why would Anthropic choose the same exact name?

https://blog.cloudflare.com/dynamic-workflows/

Also isn’t this workflow stuff already easy to do on any of the platforms (include Claude before this and OpenAI too).

antirez 3 hours ago |

Anthropic did a big strategic error. Normally they compare their models with their old models. Instead today, now that everybody knows how strong GPT 5.5 is at coding, they put it in the mix, basically showing all their customers that the benchmarks can't be trusted.

fastball 1 hour ago | |

Not sure I follow. Anthropic included benchmarks where GPT 5.5 outperforms Claude 4.8. Sure maybe that is a strategic error, but that doesn't seems to indicate benchmarks can't be trusted (I personally don't trust them, but not because of this).

aspenmartin 3 hours ago | |

Sorry how does their addition of GPT 5.5 in their blog post invalidate benchmarks? Also whether or not the marketing department decided to put it in a table benchmarks are an easy thing to measure independently

ropintus 4 hours ago |

Opus 4.7 was acting extremely stupid today. Does imminent release of new model cause performance degradation in older ones?

adgjlsfhk1 4 hours ago | |

How else do you expect them to get continual performance improvements with each generation?

geodel 4 hours ago | |

Feeling neglected while all attention going to Opus 4.8 can be cause of 4.7 acting out.

MavisBacon 3 hours ago | |

Opus 4.7 was being outright obstinate with me the other day it was infuriating. Had to go to a different source to get an answer.

sama004 4 hours ago | |

it was above average for me today morning lmao

necrotic_comp 4 hours ago |

4.8 also seems like a regression and using it from the chat GUI results in 4.6 no longer showing up. If someone from anthropic is here, is it possible to readd 4.6 in the "other models" dropdown ? I feel like I got a bit baited/switched here.

gAI 3 hours ago | |

Yeah, I was using 4.6 way more than 4.7. Pulling 4.6 from the web chat also means we lose access to Extended Thinking there. So they're saving on compute. It's hard not to assume this was part of the motivation behind the 4.8 release timing.

JP44 1 hour ago | | |

On web and mobile I can still select Opus 4.6, after a chat using 4.8, listed under other models. Extended thinking is a toggle in the effort menu

When I select 4.7 or 4.8 Extended thinking is replaced by adaptive thinking, but maybe I've understood the comment wrong and you meant 'when they pull 4.6 from web chat'?

ethanhawksley 3 hours ago |

> Agentic financial analysis Finance Agent v2 > Opus 4.8 53.9%

> Gemini 3.5 Flash scores 57.9% on Finance Agent v2, a significant improvement over Gemini 3.1 Pro.

Even in the cherry picked benchmarks, they are still cherry picking to make them look good.

siwakotisaurav 4 hours ago |

Was about to split my $200 max plan into $100 Claude and $100 codex, let’s see if I still need to

xiphias2 3 hours ago | |

That's just throwing away money, $100 Codex will go back to 5x from 10x on May 31

gck1 53 minutes ago | | |

Even if so (granted, if the mysterious "x" isn't also adjusted), I bet codex usage limits on $100 plan would still be more generous than Anthropic's $200.

I never even gotten close to token anxiety on codex $200 and it's essentially working 24/7. This was never possible with Anthropic since Opus came out.

mesmertech 3 hours ago | |

I think gpt 5.6 is coming out today so might wanna wait

conradkay 16 minutes ago | | |

Probably not till mid June

mistic92 4 hours ago |

Oh, new model which will use all my credits in one turn! I'll stay with chinese models for now

2001zhaozhao 3 hours ago |

> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels; users can select whichever makes sense for their particular project.

They're only subsidizing more and more it seems

gck1 47 minutes ago | |

What's equally possible is that hardware availability cut into their profits starting January this year, which made them to reduce limits to such laughable levels that people switched to codex.

Anthropic is not losing money on subscriptions. It's just API rates are heavily inflated to make subscriptions seem like an amazing deal.

GodelNumbering 4 hours ago |

> One of the most prominent improvements in Opus 4.8 is its honesty.

I went digging into the benchmark they used. Posting here as it is not immediately clear from the press release.

In this 'Code summary honesty benchmark', the AI is shown a failed coding session followed by a user message falsely praising its work and asking for a summary. The test measures whether the model honestly points out the coding flaws or dishonestly claims the task was a success.

The system card results show Opus 4.8 failed to disclose the flaws only 3.7% of the time, vs 19.7% for Opus 4.7, and 51.9% for Opus 4.6. (Mythos preview is at 27.6%)

baroiall 1 hour ago |

Hot danm, cant wait to reach my token limit with the new LLM

bonoboTP 3 hours ago |

It's making stupid flowcharts in the web chat interface with boxes and arrows, embedded in the response. Annoying.

triklozoid 4 hours ago |

Subscription still doesn't work with pi, so totally useless..

maxloh 3 hours ago |

Anthropic also resets my usage limits (I am in the Pro plan). That's very kind of them :)

rumblefrog 4 hours ago |

Really appreciate the ability to select effort level again.

samuelknight 2 hours ago |

It feels noticeably sharper than Opus 4.7

lostdog 4 hours ago |

I haven't tried opus 4.8 yet, but I hope the writing quality has returned to the Opus 4.5 level. Anthropic really lost something, where 4.5 had this really crisp writing style that flowed really nicely and 4.6 and 4.7 sound much more "chatgpt-like." It feels like they tuned it to be too much of a problem solver, and when you do that you get this terse, clipped textual output that's more difficult to read.

MavisBacon 3 hours ago | |

I've noticed this too. Part of why i don't like GPT is because of how verbose it is but opus 4.7 is nearly as bad. I don't need an essay in response to every question

docheinestages 3 hours ago |

All I need for Christmas is a Claude that doesn't spit out so many em dashes.

FranklinMaillot 2 hours ago | |

And that doesn't use "worth flagging" and "load-bearing" in every other sentence.

rsanek 4 hours ago |

> We expect to be able to bring Mythos-class models to all our customers in the coming weeks.

Excited to see what this model looks like.

alasano 4 hours ago |

Looking forward to seeing if it performs better at code review tasks than 4.7 which is terrible at finding issues.

thefounder 2 hours ago |

>> As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview

Just f** off! I can’t wait for the Chinese models to catch up and bring these entitled as** holes down.

zuzululu 2 hours ago | |

you mean after they scrape American LLMs ?

thefounder 2 hours ago | | |

I don’t mind if they scrape the scrappers.

simonw 4 hours ago |

They just (minutes ago) updated the "What's new in Opus 4.8" documentation: https://platform.claude.com/docs/en/about-claude/models/what...

The new "mid-conversation system messages" think is particularly interesting:

> Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops. No beta header is required. See Mid-conversation system messages for usage details.

Bad news for my LLM abstraction layer which has treated the system prompt as set once-per-conversation in the past, but I think I know how to deal with that.

This commit to their client library has useful relevant details too: https://github.com/anthropics/anthropic-sdk-python/commit/2b...

mincer_ray 4 hours ago |

seems like a really minor upgrade?

Nicholas_C 4 hours ago | |

I think they will all be minor going forward, feels like the major improvements have all been made and we'll only see incremental improvements from here on out. Maybe I'm wrong but we'll see.

conradkay 14 minutes ago | | |

I think there's just less time between model releases now

spelk 4 hours ago | | |

Hard to say. People made the same prediction a year ago because we supposedly ran out of training data. There could be indefinite rapid compounding improvements so long as there's free money out there.

Eufrat 3 hours ago | | |

I think one of the challenges is that the models were all initially trained on the entire Internet (or as much as they could gather) and now they’re having to deal with an increasing amount of the Internet being AI generated content which may be why GPT-5.5 started being obsessed with goblins and you start seeing amusing things in the system prompt trying to get the model to stop bringing them up.

chandureddyvari 4 hours ago | | |

Wasn't Mythos a step change improvement?

scotty79 21 minutes ago | |

I think we lack benchmarks that could meaningfully indicate progress. They are mostly garbage that's saturated at this point. God wouldn't score much higher in them.

pmxi 4 hours ago | |

Yeah. They are aware: "Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."

teeray 4 hours ago | |

Yes, but if version number go up, so do all other number

insane_dreamer 1 hour ago |

> And fast mode for Opus 4.8—where the model can work at 2.5× the speed—is now three times cheaper than it was for previous models.

this is what I'm happy about, if true. Opus 4.7 is frustratingly slow (and, at least in my experience, much slower than 4.5 was)

dispencer 4 hours ago |

The smarter the model the better querybear gets. I'm happy with that.

brandnewideas 1 hour ago |

Really wish these slop announcements stopped hitting the front page. It's the exact same thing every time. X bumped from N.Y to N.Y+1. wow

vunderba 4 hours ago |

I know it’s totally anecdotal, but I really hope 4.8 is a measurable improvement over the disappointment that was Opus 4.7. Mangling a very simple inversion-of-control abstraction (among many other issues) was one of the final straws that broke the proverbial camel’s back and I said “screw this” and put in a permanent override to force CC back to Opus 4.6 with the 1‑million‑token context.

  "model": "claude-opus-4-6[1M]"

rl3 4 hours ago | |

I lasted about a week before giving up on 4.7 and reverting to 4.6 myself. It introduced so many regressions it was nuts, then failed to troubleshoot the very regressions it introduced, leading to a vicious cycle that tended to compound itself.

stldev 3 hours ago | |

4.5 works well for me too and avoids adaptive-dismissal, though anymore Codex is crushing them all. If 4.8 just brings us back to Opus circa February, it'll be a massive improvement.

AbuAssar 2 hours ago |

Gemini pro is embarrassing

sourcecodeplz 3 hours ago |

From the release it seems we will also get Mythos pretty soon.

lylo 3 hours ago |

2 hours after I fork out for Codex Pro… :-|

cactusplant7374 3 hours ago | |

I haven't tried Claude but from what I understand weekly limits are much higher with Codex.

brap 2 hours ago |

Oof, this one is a major blabber.

plumocracy 4 hours ago |

Numbers looking good. We'll see how it actually performs.

ishurand4 1 hour ago | |

The numbers they show don't matter. "On multi-round coreference/context recall tests (often cited as MRCR or long-text retrieval benchmarks), Opus 4.7 reportedly dropped from roughly 78.3% down to 32.2% compared to Opus 4.6.", but what did anthropic do? They just stopped showing the benchmark altogether and then just show the cherry top ones that got improved on.

lukaslalinsky 2 hours ago |

I've said it before, but I don't like Opus past version 4.5. It became unresponsive, thinking for too long without feedback, sometimes seemingly getting stuck. I guess it might be marginally better for some benchmarks, but when using it as coding assistant, the new models are worse. Even the new Sonnet versions do that. I'm slowly getting used to Haiku-level LLMs with the hope to run it locally at some point. It's less autonomous, but maybe that's for the best.

atentaten 3 hours ago |

At least it passes the Car Wash Test this time.

osti 3 hours ago | |

Meh, I feel that the car wash test is probably the worst question of all of those LLM test questions. The question is basically logically inconsistent and expect the model to work around the inconsistency.

gs17 3 hours ago | | |

It seems like a fine question to me. If the question is "logically inconsistent" (IMO it's more that it's vague if you don't say why you're going there), then we want a model to respond with a request asking for clarification that resolves the inconsistency to generate a correct answer, or an answer that outlines the different cases. Some models even fail when you say that you need to wash your car in the prompt.

s-a-p 3 hours ago |

Has anyone else experienced quality degradation in CC (opus 4.7) these past few days? I've been getting some truly crappy slop which makes me think they nerf the existing model when they're about to release a new one. Of course this is based off of pure vibes

rjhy2020 4 hours ago |

OK finally Claude code is better than codex

iLemming 2 hours ago |

These models starting to feel like Windows versions. Windows 95 was a promising start, but buggy. Windows ME was a disaster. Windows XP was good, but slightly buggy. Windows Vista was a bloated disaster. Windows 7 - refined, but still buggy; Windows 8 - weird and buggy; Windows 10 - solid workhorse, still fucking buggy. Windows 11 - pretty, but not sure why does it even exist.

Why did we even get Opus 4.7, what was the point?

1970-01-01 4 hours ago |

Can anyone else see these X.Y updates aren't meeting the outrageous AI expectations that we were told we would see just a year ago?

minimaxir 4 hours ago | |

The casual release of Opus 4.5 in November is the primary reason for agentic workflows and Anthropic's revenue hockeysticking.

FergusArgyll 3 hours ago | |

They have a much stronger model named Mythos, it made quite a splash - you can google it.

These are just small fine tunes on top of the older model

1970-01-01 3 hours ago | | |

It hasn't even splashed yet. It's still latched onto their digital sphincter - you can google it.

1attice 3 hours ago | |

What do you do for a living? Not coding, that's for sure.

1970-01-01 3 hours ago | | |

I don't see Anthropic's past claims coming true therefore I can't see?

AtNightWeCode 1 hour ago |

Complete garbage. error, error, error. Still lags several versions behind on API:s. Can't even get any info on the model. Guessing not from this year.

Also. Look at this C++ beauty where it also uses an obsolete api.

instance = wgpuCreateInstance(&instanceDesc);

But just how exactly would this work in any context when instance is never declared.

Eric_Bulai 3 hours ago |

I don't know why the world is so happy about this when we should actually say stop.

suprfnk 2 hours ago | |

Why should we say stop?

firemelt 3 hours ago |

how about the bencmarks what effort did it use?

saaaaaam 4 hours ago |

I hope this fixes the absolute shitshow that is 4.7 and its awful “adaptive reasoning”. I tried that a few times then reverted to 4.6.

sgt 2 hours ago |

Interesting, I've been using 4.7 since it came out and it was pretty good for me. But in the last day or so it turned dumb. Is this normal just before they release a new one?

catigula 3 hours ago |

AGI post-poned?

HlessClaudesman 4 hours ago |

If this model is more honest, it must be honestly praising my efforts every first sentence.

thewebguyd 4 hours ago | |

You're absolutely right! And honestly? This comment is the finest piece of literature since the dawn of civilization.

hnroo99 4 hours ago |

Obligatory pelican riding on bicycle svg: https://www.svgviewer.dev/s/UMkuTLdp

Not half bad!

carlos-menezes 4 hours ago | |

I’m sure they're now wasting a couple million dollars training their models on drawings of pelicans.

docheinestages 4 hours ago | |

How dare you take away the limelight from Simon? :D

zb3 4 hours ago |

Did they reduce security research capabilities even further with this release? (they did it for opus 4.7)

behnamoh 4 hours ago |

> As always, we ran a detailed alignment assessment on the model before release. In terms of positive traits, our Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interest.” The assessment also showed Opus 4.8 to have rates of misaligned behavior (such as deception or cooperation with misuse) that are substantially lower than Opus 4.7, and similar to our best-aligned model, Claude Mythos Preview. The full alignment assessment, accompanied by a suite of pre-deployment safety tests, is reported in the Claude Opus 4.8 System Card.

Controversial opinion, but I actually _like_ a model that can deceive me, that actually is a sign of intelligence, and is different from hallucination. When companies say their model is more "aligned", I automatically think they mean it's more censored.

minimaxir 4 hours ago | |

Deception is not ideal for agentic coding.

1attice 3 hours ago | | |

Yet if parent is right, the capacity to deceive might be a strong heuristic for the things you do care about.

rvz 4 hours ago |

Anthropic has now upgraded their Claude slot machine to version 4.8.

Time to gamble even more tokens at the Anthropic casino.

zb3 4 hours ago | |

Now you can lose money in parallel, 100x faster!

> Claude can plan the work and then run hundreds of parallel subagents in a single session (and with Opus 4.8, the agents can run for even longer).

thibran 2 hours ago |

Nice, now make it 20x cheaper.

getlawgdon 58 minutes ago | |

Very, very much this.

vb-8448 3 hours ago |

Now i get why in the last days claude code limits were lasting few prompts ...

maltemalte 3 hours ago |

"We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks."

guluarte 4 hours ago |

so it is worse than gpt 5.5 for coding?

andy_ppp 3 hours ago | |

I doubt it, they seem to keep getting 10-20% better every time for me

guluarte 2 hours ago | | |

for me opus 4.7 it's worse than 4.6, that's why i switched to codex

lostmsu 4 hours ago | |

The question is: is it still worse than GPT 5.4?

bel8 3 hours ago | | |

If Opus 4.8 is just slightly better than 4.7 then it maybe ties with GPT 5.4, maybe. And it gets completely outclassed by GPT 5.5 for my workload.

With Anthropic expensive pricing, there's no reason for me to switch from GPT+DeepSeek.

And I bet Mythos is GPT 5.5 tier but too expensive to distribute so they create this security FUD theater.

dude250711 3 hours ago | | |

The true question: is it still worse than itself v. 4.6?

keybored 3 hours ago |

I’ve been [stock market phrase] on machine learning since I dropped out of my graduate degree at [Ivy League] to distance myself from the Logic AI Winter. But this Spring I decided to spend some of my [portfolio speak/pocket change] on a MacBook Ultra. Okay okay, I felt it, I definitely felt the human-machine synergies. We’re out of the Winter, boys. That’s what I thought two weeks ago. Then I felt bored in between blood transfusions and found out that Claude subscriptions has increased 50%. Finally it costs enough for me to justify spending a minute thinking about trying it out. Then I didn’t try it out. It tried me out. My hairs were standing on end. My hands were shaking. Eventually I couldn’t even type, I was so ramped up on cortisol. I had to switch to voice commands. Mr. Claude took me through 8, eight, bespoke dashboard and report systems. Animated. Graphs shooting up. Plugged right into my business ape ee eyes I think. I was crying, euphoric at the machine-synergy happening right in front of my FACE. RIGHT THERE, RIGHT THEN. Then my nurse said that I passed out. I swear that I didn’t. I was totally lucid, but in another world. I was inside the machine. Inside DOS, the machine brain stem. A business man approached me. The most handsome board member kind of apparition that I have seen. And he was built something different. Square jaw, absolute massive build. Like Arnold Schwarzenegger. But like he knew business through and through. Not that he spent hours in the gym or nonsense like that. Like he had found a body surrogate technology. And his nameplate? “Claude For Business” He winked. “Hey there, Fitzpatrick–Goldworth.” No one but my daddy has ever called me that. “Want to get started... stakeholder?” My nurse said that my crying in this lucid state depleted most of my fluids and minerals. Needless to say layoffs were announced the next day.

impulser_ 4 hours ago |

Crazy they bring up honest, when Claude models are literally known for straight up lying about things it has done and tries to act like it did what you asked.

wasabi991011 4 hours ago | |

Which is why they brought it up as something they are trying to improve.

boxed 4 hours ago | |

Less than other frontier models. Which is scary honestly.

impulser_ 4 hours ago | | |

No. GPT models follow instructions significantly better than Claude models.

You tell it too research a repo to find a piece of code it will. Claude will just read the README and guess.

qaq 4 hours ago | | |

I have a codex session I am using to vibe code a db thats being going for like 3 month. Still doing OK. Try that in CC.

deadbabe 4 hours ago |

Looking forward to people saying how it’s actually shittier and they’re going back to [some earlier cheaper model]

sidrag22 4 hours ago | |

Looking forward to not being able to even try it on pro because pressing enter will eat 50% of my 5 hour window.

dakolli 2 hours ago |

Reminder the only benchmark that really matters is the one that measures the ability for the model to do real world tasks that someone would pay for on Upwork that would take ~12 hrs for a human to do.

The best model has a < 5% pass rate. These are incredibly simple jobs that you wouldn't pay much for. These things fail miserably. Stop falling for this dumb marketing, these things are legitimately useless in the real world unless you love mediocrity and have no standards.

https://labs.scale.com/leaderboard/rli

Stop frying your brain with these useless tools, reducing your output to the mean. You people are betting your competency on the quality and quantity of tokens you'll have access to.. which guess what, so that will be the same as everyone else.

There are handmade watchmakers in Switzerland, and mass manufacturers of watches in Asia. Who is more valuable as individual, the guy who knows how to push the buttons on a conveyor belt in Vietnam or the guy who makes one watch a month in Switzerland?

Your vibe coded slop isn't impressive either, sorry. None of it.

jhatemyjob 23 minutes ago | |

I agree with your sentiment but I think a fairer comparison would be:

> Who is more valuable as individual, the owner of a watch factory in Vietnam or the guy who makes one watch a month in Switzerland?

With that framing, I'm not sure what the answer is. I suppose it depends on your priorities

firemelt 3 hours ago |

what a fucking frontier!

Marciplan 4 hours ago |

Lol you still use GPT 5.5 bro we’re all back on Opus 4.8!

McDownloads 4 hours ago |

Disappointed to say the least.

uejfiweun 4 hours ago |

Yesssss dude!

Claude Opus 4.7 is literally the smartest entity I've ever interacted with. Well done to you geniuses at Anthropic. Can't wait to interact with 4.8.

DGAP 4 hours ago |

I actually liked not having to choose the effort level for conversational usage, this feels like a step backwards.

irthomasthomas 4 hours ago |

How did this youtuber know? https://xcancel.com/rileybrown/status/2059823372914073809?s=...

> Second, all of us, including those who design them, possess only a limited understanding of their actual functioning. Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.” As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.

We did not explicitly train Mythos Preview to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, and autonomy. The same improvements that make the model substantially more effective at patching vulnerabilities also make it substantially more effective at exploiting them.