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"I think the models are being optimized for wealth extraction from users and companies, instead of solving problems."

YES! They introduced the new tokenizer to increase token generation by upto 33%.

On top of this, Anthropic are generating almost twice as much revenue per paid user than openai - whilst their subscriptions have lower usage limits than openai's:

https://youtu.be/gK-7TKC7kvY?si=kx0qPE1rw-UCI-Jn&t=650

> I think the models are being optimized for wealth extraction from users and companies, instead of solving problems.

I don't think so. Expect that in a market with high vendor lock-in but that's not the case here. The market is extremely competitive and switching cost are near zero. Anthropic can't afford to pull shit like this and sacrifice quality.

> More and more I find myself trying to stop Opus from doing something stupid, and at every turn I need to tell it to stop overcomplicating things

Yeah, that’s my thoughts as well. I feel it’s great for benchmarks and some tasks while in other it tries to spend as much tokens as possible, tries to overcomplicate task and needs seconds or third round of steering that costs. With the scale Anthropic operates I bet it’s huge amount of extra money just to make sure their model works.

Yeah. Mine really likes to read excess code. I'll ask it questions like "If I move all these three ETL jobs into a subfolder will it break anything?" It'll start with giving me the simple answer but then continue on to consider another question and realize it requires reading my entire other repo that handles all of my cloud's infrastructure. And it'll proceed to read through tens of thousands of lines of terraform.

> I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.

Because it reasons in one direction. First it encounters some kind of issue with 2-3 lines of Python that might make it not work, and then it goes onto plan B, which is making a library, but it doesn't circle back and compare the effort of making the library to working around whatever might make the 2-3 lines not work. Except sometimes it does, because it's inscrutable.

My experience with Opus in the last weeks is the opposite. I have the feeling Opus got smarter since they released and blocked Fable. Maybe they got more compute available since a) they finished Training Mythos/Fable and b) couldn't provide inference for it?

It's really bad when you let opus do investigations on broken java or infrastructure stuff. It starts decompiling .jar, sometimes multiple versions of the same dependency, reading every single kubernetes/terraform file and loading all the logs and info kubectl offers.

There were many of us who predicted and saw this months ago.

Should I refer to those who are only realising this now as stupid? I believe so.

Its not wealth extraction btw - the correct economic term is capturing/extracting surplus. They have a wide range of schemes - quality discrimination being one very obvious one.

Swear most of you on here pretend to be soooo smart when you def are not.

Yeah... Sonnet becomes the new cheap model, and some Fable class model becomes the more expensive/better one.

Wat. Price/perf has been going down massively over the last few years.

That is certainly an improvement then. Sonnet 4.6 is a great everyday agent for the limited Pro plan, but it’s not much better than M3 or Kimi 2.7, both significantly cheaper models.

Not sure of any out-of-the-box tool. But Anthropic has a token count API which gives a near estimate of the input tokens for messages [1].

So this API can be used in a UserPromptSubmit hook [2] in the harness, get the token count for any model, calculate the cost and compare.

[1] https://platform.claude.com/docs/en/build-with-claude/token-...

[2] https://code.claude.com/docs/en/hooks

They have updated it

Opus with low reasoning effort would be faster than Sonnet with high reasoning. So that won't exactly help. I think it would just be what those models are optimized to perform

I’m just dipping my feet in the water of local models and I really feel this. I had a simple alignment task (align known quality transcript without timestamps with timestamped but lower quality whisper transcriptions) and I went through 12 rounds of testing across 4 generations of 3 models. The results were all over the map even across versions of the same model. Spin out the task to something as big as coding and wow.

If you have any advice/blogs on doing project specific benchmarks I’d love to hear it. I’m trying, but it’s haphazard at the moment

We have found similar when plugging GLM 5.2 into actual benchmarks in our product. The open-source models are really dialled into the public benchmarks, until you try them in context you won't have a solid idea of how they perform (Sonnet is a higher quality model than 5.2, both in prose, reasoning, and alignment).

> Completely different results as LLM is non-deterministic.

You'd need to produce this like 20 times by each model and then do 2x20x20 cross comparisons by both models and ultimately distill the 2x20x20 comparison results into two reports of how they differ.

In this non deterministic computing future, everything else is voodoo, feelings and "vibes".

I think instead they should sell super hackers and get their product banned instantly and go bankrupt

Judging by the events of recent weeks, I'm guessing the low cyber results are why they were allowed to release it

I'm not sure what else can be said? I've found benchmarks to be a very weak signal for how good/bad the model is, but it's the #1 thing the companies highlight.

20 minutes after the announcement there's no real useful statement that can be made about it.

I feel the same way sometimes.

I read a comment earlier that said "I think it's likely that they've scraped all the code regardless of license and trained on it, given how much they scrape the web."

That's what every other comment said like 3 years ago. Where has this guy been?

The trends in discussion about LLMs gets very, very tired--there's little added but personal opinions.

Yeah you definitely have to be skeptical regarding sentiment for open/local model capabilities, since there's bias from what people want to be true.

I generally agree with this in spirit https://www.seangoedecke.com/are-new-models-good/ , but I think you can read Anthropic's results showing Sonnet 5 as almost strictly worse than Opus 4.8 as very credible/meaningful, and then draw comparisons from that

"It's totally obvious they quantitized Claude Z"

There’s no way to justify their valuations if they get downgraded to a pair programming tool. They need fully agentic stuff to work and replace human engineers to even come close.

Offhand, I’m not even certain whether a model like that could justify the constant retraining we’re doing on the agentic models.

It doesn’t make a lot of sense to spend millions or billions on training to reduce hallucinations by 0.3% if your model assumes a human is in the loop to course-correct them.

And every benchmark is "build GTA-6 from nothing, as a single-page web app".

They have to, but also everyone working at 3D printing companies thought "industry 4.0" is going to completely override everything, we are going to print housing and going to print a mug at home and drink coffee out of it.

Today's news that Amazon is hiring 11k interns. I think part of the AI story was used as a convenient excuse to get rid of some "fat" and some covid overhiring and gave companies an out to change course.

I wonder how portable the existing models are for different use cases. As good as they are for greenfield development or working in a single or across a few tightly coupled repos, they're absolutely terrible at debugging distributed systems and make incredibly wrong yet extremely confident assertions all the time.

I don't know if it's a matter of just requiring a tiny amount of optimization or wholesale redesign.

Whether they believe it or not is immaterial. It is the end-goal they want to achieve, because then they own the means of production entirely.

As I said, working ourselves out of our jobs within the span of a few years.

What is your motivation? Privacy and/or data protection?

I currently don't see a world where it makes sense to run a local model that will eats up 60% of my RAM, 20-30% of my disk space while providing worse quality output than a $20/month subscription.

Which quant do you use? I have a similar setup and the speed is atrocious at 4-bit.

This is the way

I prefer GPT 5.5 to Opus but both are absurdly expensive token hogs, I can't afford to use either as my main model at $work with the monthly spend cap we have.

I use Composer (since we use Cursor) or GPT 5.3-codex as my workhorse models and only break out the big guns when I have a genuinely difficult problem to solve.

IMO somewhat weirdly 5.3-codex might be the best overall coding model OpenAI have ever released. It's 90% as good as 5.5 and costs about 20% as much, since it's both cheaper per token and uses fewer tokens for the same task.

I'll miss it when they inevitably deprecate it, but hopefully I can use Kimi K2.7 by then

I use GLM 5.2 Fast from Fireworks and its very fast. Where are you using it from?

Which inference provider do you use? (Admittedly, I currently use K2.7 a lot more currently.)

Tokens and speed are a factor but does it require less back and forth to get things right? Being "fast and cheap but wrong" still has a cost that an otherwise "expensive and slow" exchange does not

Composer 2.5 fast (via Grok) is honestly amazing. Its been implementing everything I've asked and getting it right first time. Been impressed with it's front end ability.

If this was the last model I could ever use I think I would be happy.

Same for me, downgraded Cursor Subscription because when i use Cursor i use 90% Composer 2.5 fast

Not for a business model that scales revenue by token usage. But other business models are available.

I think this seems purposeful, as there's 2 opposing forces at play: - Have a model that follows the users instructions - Have a model that follows the system prompt instructions more

For the 'safety' argument (Re: Fable), they need these models to have basically a 2-tier instruction system, but given LLMs aren't great with actual Logic unless they program it out to test, this runs afoul and we get one or the other.

Feels like optimizing for either precision or recall, but can't have both

Totally agreed. I sometimes wonder if they are making the model "lazy" with each iteration, it keeps getting better at avoiding work.

I keep adding selected cases of CLAUDE.md instructions non-compliance reported on claude-code github to that issue https://github.com/anthropics/claude-code/issues/13689. Subjectively the amount of such cases seems lower during the past month. It may be that claude-opus-4-8 (default thinking) is a bit better at instructions following than past models.

> or as if there was a third person in the chatroom whose messages I can't see.

If you set off a classifier, that's how it looks to Claude.

Try to run your prompts through Claude to pinpoint any ambiguous parts that can be interpreted in multiple ways, or self-contradictory sections. I typically resolve any prompt-ignoring issues with that.

By design, unfortunately. If they are just assistants, they can't sell the dream of "we're going to replace human labor completely" to the C-suite.

Yep, this is why experiences and ratings of models vary so wildly.

I recently migrated a very large web app to Tailwind and Opus kept screwing up over and over, refactoring and changing the design, the more complex the component became.

I ended up asking Haiku to do it and it managed to do everything correctly, pretty much without intervention.

> I don't like it but don't know what to do, Anthropic models especially increasingly ignore instructions whether in memory or agents files.

I've taken to instructing the agent to manage the subagent, and the principal agent's sole job is to ensuring the subagent follows instructions to the letter.

Just to follow up on what I mean, this was my first interaction with Sonnet 5:

"I just cloned this repo, investigate how to set it up, don't install anything, just collect information"

_spews information_

I proceed with the setup, but get a Linux specific dependency in a bash script, so I want to evaluate whether it can be rewritten...

"There's this error on MacOS, I think it's because we need linux-utils from brew, verify whether the script can be written in bare posix"

_proceeds installing linux-utils and all the rest_

"Didn't I tell you to not install anything?"

_you're absolutely right_

F*k me..

Have you tried '/model opusplan' I've had strong results mixing opus for planning with sonnet implementing.

I am in the same position. Do you think they are going to remove it and deprecate it as some point?

It is on top for many benchmarks, only not the coding/agentic ones.

Still one of the most intelligent models overall, most likely to get any question you ask correctly (without tools).

This guy had a terrible broken benchmark that gets hawked every release, and I wish HN would ban accounts that essentially exist to hawk a personally owned site, especially such a bad one.

Opencode Go/Zen claim to use infrastructure based in the EU, USA and Singapore that have a 0 retention policy.

What’s everyone favorite GLM provider?

z.ai doesnt always have the most reliable AI

but I don’t mind the party seeing my trade secrets and thoughts compared to an American corporation + the party seeing my trade secrets and thoughts. So thats not a functional difference to me, and the Chinese one won’t reply to subpoenas so thats a value add tbh

So I’ll consider all, fastest tokens/sec wins

Yeah, GLM have been beating Anthropic on the pelicans for a while now.

(I suspect that's more of an indication that Anthropic have chosen not to waste resources training on animals riding vehicles, personally.)

Just need the legs to interact with the pedals now :D

Censorship on image generation models works on another level. The models can generate NSFW, but there are extra computer vision models checking if the images can be shown to the users. It's especially obvious for Grok and ChatGPT.

Interesting, you find that in medieval painting, due to the authority of the catholic church.

Correct. Albeit the nuance here is that a more capable model might solve problems more efficiently and faster, possibly saving you tokens.

As with any new model, you won't know the real impact until you start using it for your workload.

I think the incentives are less bad since a good chunk of usage comes from subscription plans.

There was a fairly major regression in Claude Code performance for some time when they changed the system prompt to try and make it less verbose (saving tokens). And if I'm not misremembering, there were a lot of complaints when they changed the default effort from high to medium.

Wouldn't it be more malicious for them not to mention this at all?

I was surprised to learn that Sonnet generally has the same tokens per second as Opus

> I don't think they're a net gain if you're a skilled senior

I'm a skilled senior (I'm 54 and been coding since I was about 8; I've been 100% AI-generated code for at least 6 months now and have produced a combination of speed and quality that has astonished me; my velocity is apparent at https://github.com/pmarreck/) and this has been a massive net gain, so your claim is now officially in sheer defiance of reality.

In a skilled senior's hands, this is like an expert power tool. In the hands of someone less-skilled, it is likely also... less-skilled. It's a magnifier.

> and the hidden cost in terms of technical debt and skill atrophy is just being swept under the rug.

Nope, no it's not. It's being reviewed, measured, and controlled against. Because... you WILL need more controls to take full advantage. Look, I even invented a whole new control methodology around it called MFIC: https://gist.github.com/pmarreck/b30aa3ca69cb70a5526f8a63ab8...

> I don't think they're a net gain if you're a skilled senior

I've had Claude Code running a /loop for the last week driving down complex crashing bugs in a prototype compiler entirely unilaterally. I occasionally glance over.

A few of those crashing test cases were ones I've spent more than a week trying to track down myself. I have 30 years of experience of doing this.

It's worked 24/7.

So far it has fixed over 500 of them.

Will there be technical debt? Yes. But nothing that remotely compares to the cost I'd have incurred of fixing all of those myself.

It is hard to reconcile those gains without thinking that if people are saying these are not a net gain, they haven't really tried learning how to get the full benefit. If you sit and watch a model work and keep intervening all the time, then sure, they're not going to be a net gain.

Why even bother posting, especially as a reply to a completely unrelated comment? This is just not substantive or useful to the conversation.

(And I say this as someone who agrees with you that it's garbage that these companies are trying to legislate their way into an oligopoly.)

The irony is that an authoritarian country is leading the world in open models

Making software illegal is not an easy task.

Sure about Dario (and all billionaire) weirdness, but no gains if you are a skilled senior is well, very far out in our experience (our company is 30 years old with mostly the original employees and founders): what we deliver now at the speed and quality we deliver it would have been impossible 10 years ago with our team size of skilled seniors. We replaced all the commercial products our clients and ourselves used with our own, giving us millions more revenue and profit with the upselling and efficiency benefits. We work for regulated clients: our code is reviewed, pentested and audited regularly by us and 3rd parties so its not slop either. You are definitely leaving money on the table. We do mostly use chinese models on our own hardware (we colocate cages of racks) so this is not about Anthropic but about AI in general.

Skill athrophy is a real thing though; we try to prevent this by have hackethons (for lack of a better word) without AI where I pick something extremely non trivial and we implement it for fun and profit without AI (with would not matter much as they are currently bad at these things); last one was flex paxos for our in house db with obvious metrics for the endresult: data integrity (duh) under failure and performance better or at least the same as our raft production version.

good luck actually enforcing that.

> They're actively trying to use lobbying power to make open weight models illegal.

What is your evidence?

I always use Opus 4.8 at max effort for everything. The $20 subscription didn't have enough tokens, but the $100 one had too many of them. So now I just max out Opus in order to maintain 100% weekly utilization.

I've been plugging this perhaps too many times now, but I am trying to bootstrap a user-sourced corpus of exactly "what model is good at task X". So, not benchmarks, but high-level tasks. There's a bit of a ordering problem in that nobody wants to bother commenting on a site that has few comments - so PTAL and contribute if you can. https://model.reviews

Same boat as you, and my answer is "... Except when I ask and overall or checkup task that is specifically heavy or overseeing in which case I use the maximum level" which lately meant ultracode.

I'm not going to play around with thinking level every request because the goal is to make me save time not spend it in a different setting menu.

What I want is a harness that knows how to optimize this kind of thing for me.

I tend to run it on High and then step it up for problems where I'm noticing it struggles, bump it back down after. Sometimes I accidentally leave a session in Ultracode for a day and wonder why things are taking so long, but generally happy with the results.

Exactly this is my problem with all AI tools. I want someone else to create working tools for me so I can focus on my product. It is the same with other tools. I do not want to spent huge amounts of energy and time to setup my IDE, operating system or desk layout. I guess it is too early to have that now.

It's really not that much. It's a bit hard to make sense of it not because it's hard to keep track of, but because they are being deceptive and opaque about what you're actually buying, and the thing you're paying for is different from one day to the next, as they fuck around with the parameters to boost subjective performance during a launch, then quietly degrade the service to cut costs.

I also ended up using max effort/reasoning for both coding and general chat. They don't spend too much extra time on simple tasks these days.

Same advice as ever? We call it context engineering now, but prompt engineering still matters a lot. Most of the failures I run into are unspecified assumptions made by the model that derails the conversation, but usually updating the first prompt fixes it. Opus in my experience is a bit better about checking assumptions, while Sonnet will plow on ahead. An example is mentioning a file that doesn't exist: Sonnet will go ahead and try to grep your entire hard drive for it. Opus will say it's not local and request the path.

I trust neither for general knowledge and I still find Opus giving me answers that are completely BS. But the token spend for Q&A is nothing compared to coding, so I always use Opus + a lot of thinking. For coding, I find Opus to be better value/token but I haven't done any sort of rigorous test.

There are token optimization consultants that can help organizations find the right balance of models for their employees to minimize costs.

Just because it’s hard to keep track of doesn’t mean it’s not relevant.

Playing around with learning the differences is incredibly helpful to schedule on ones calendar weekly for an hour or two, while saving links throughout the week to try out.

It's almost like you want an automatically intelligent choice of your artificial intelligence.

Understandable frankly.

Just use deepswe as a reference point.

> This is why there was a "Sonnet only" usage bar for Max tier for the longest time.

it's still there. I still don't totally grok why I can't use all my tokens on Sonnet if I want to... maybe that signals something?

From my benchmarks, sadly, it doesn't seem to be the case much. Surprisingly. I found Sonnet comparable in speed to Opus (sic), but perhaps I was testing it wrong?

What's interesting, is that Sonnet 5 is actually worse[0] than 4.6 without reasoning.

It makes some sense, as models are trained more and more with reasoning, than without.

[0]: https://aibenchy.com/compare/anthropic-claude-sonnet-4-6-non...

You're the second person that has said this but I cannot understand why you are interpreting the "Agentic computer use" graph in this manner.

The graph shows that Opus is cheaper than Sonnet for the same performance. Unless I am suffering a cognitive blindness thing right now.

> Too expensive to perform daily tasks - open souce models are much cheaper

There is a real advantage, especially for businesses, in using an off the shelf solution from a corporate provider.

Personally, the advantage of not having to set up multiple solutions from multiple sources outweighs the cost of a $20 a month subscription. Think about why a lot of consumers prefer Apple devices over Linux. There are a lot of advantages to Linux, but "never having to think about my tools" is its own advantage.

The specific market positioning is... for me to use at my big tech company job, where we aren't allowed to use GLM and similar, but have fixed caps on how much token usage we're allowed to rack up a month.

No it doesn't? It's worse than Opus across the whole shared frontier on both plots.

They've absolutely both changed. The initial version I saw didn't include max effort data points on the first chart, and the plot itself was much less favorable to Sonnet at high/xhigh relative to Opus, but the new chart shows them as closer competitors. Weird.

I’ve found input tokens is around 5x more than output, so a task could be a couple million thinking tokens and then a few couple 100k output tokens?

3.5 Pro not yet launched, you mean 3.1 pro?

"but you can't trust 4.8 to go off on its own."

And what (avaiable) model do you trust to go off on its own?

I tried 3 more times. Two were nearly identical and 1 recognized Latitude 41 as a restaurant but had a similar useless reply

100% this. I read these caveats in new models and all I hear is "we made sure this model has no idea about computer security." Such a weird thing to brag about.

This is code for "this model can't be used to hack other systems as effectively as Opus or Mythos."

That's the literal mission of the NSA. Security and strong cryptography for the US while everyone else gets "export grade" nonsense.

I think they don’t understand that cybersecurity skills are what prevent bad code from making it into production.

It’s like telling a chef to cook without a knife because knives can kill people.

Dario and his lackeys at Anthropic aren’t visionaries.

I think that increasingly, the US will have to be passed by for these things. Clearly we’ll have to start looking to China for world leadership, to be the land of the free.

> any American granny with access to Fable 5,

Fable is effectively not available to the general public in the US either

That’s not even close to true. Unless you’re vibe coding trash that a better model might catch.

They will release it eventually. Once they see the Chinese models are close to Mythos level they will release it before, so it will be "revolutionary".

I'm starting to think it discovered a 0-day held hidden by our government.

There's also Chinese models, which aren't trying to self-limit capabilities.

this seems rather counter-productive, wouldn't a model with less cybersecurity capabilities be more likely to produce insecure code? Not to mention, Chinese models don't have these restrictions and can be used to exploit said unsecure code.

I supposed I shouldn't be surprised at how the trump admin is approaching AI regulation, counter-productive is really all they do

I'm absolutely certain that their marketing team has input on (if not owning) these announcements.

The preceding sentence is

>Our safety assessments found that Sonnet 5 shows an overall lower rate of undesirable behaviors than Sonnet 4.6, and is generally safer to use in agentic contexts.

which is obviously painting that as a good thing. So reading the next sentence as "in other good news" is reasonable.

Anthropomorphic, most in-depth? That's laughable given how closed down they've been over the years. If you want in-depth, DeepSeek actually still publishes papers of their methods for anyone to implement leading to being by far the most cost efficient model provider for the performance.

He has the opportunity to do the funniest thing ever

Yes, this has been my experience. They all struggle with long-horizon tasks and eventually start going in circles.

If you look at the account history, it's pretty clearly an account-level thing, not a comment-level thing.

What you are doing, is producing an unnecessary summary of the result, not reasoning that models do to come up with the result.

I don't get what value you get out of this.

Won't any input be charged uncached, and the output of the small model charged again as uncached input to the bigger model?

I don't know whether that comes out ahead compared to just staying with the better model in the first place.

needs to be on bedrock for me to use it at work

According to CursorBench [0], Fable is the first runner-up, scoring 72.9% ($18.02, Max), while Opus 4.7 Max hits 64.8% ($11.02) and GPT-5.5 Extra High sits at 64.3% ($4.37).

I bet most American companies would choose Fable over GPT-5.5. Employee salaries cost far more than token costs. Getting the job done right is much more important.

[0]: https://cursor.com/cursorbench

Okay I don’t care about “eventually”, I want Fable now.

I haven't because I'm not made of money but maybe I will

I agree with this assessment, IMO my takeaway from this is "Generally run Sonnet on low, otherwise use Opus". It's kind of like an "extra low" setting of Opus. (depends on the application for sure).

I agree that the rapid improvement from like 2023-24 era is over (from a perspective of going from a 3/10 to a 7/10, you can’t then go to a 11/10). There was just so much more space to grow back then.

But isn’t Fable supposed to be another step change? I never used it, myself.

Tbh, at this point I think top tier models are smart “enough” (I’m sure this will look antiquated in a year), and the way to give me MORE noticeable improvement is to make them much faster rather than much smarter. Or even a way to automatically and accurately pick faster models when it makes sense. I know that IDE’s have Auto modes, but it’s not something that I trust right now to pick smart+fast instead of picking “maybe smart enough”+”cheaper for harness owner”

A great many people were predicting this would be the case a year ago and being told they were wrong and to get on the boat.

Why would a company explain how limited their own major release is?

Codex was running a 2x usage promotion from around the time when Claude introduced rate limiting during peak hours, until May 31st. The various relevant subreddits were (more) insufferable: just 1000 posts per day to the tune of "Just switched the codex! So much more usage!" only to have that tone flip immediately after the promo ran out.

This is what I realized, can you provide more detail on how you've observed this? The /usage screen does not make it clear.

How so?

This.

The exact opposite, actually.

Sonnet is slower due to much higher output and reasoning token generation.

I'm personally in the "they keep releasing shameless lobbying papers disguised as thinly veiled research or essay-coded content, push anticompetitive walled-garden practices, show little else but contempt for their non-enterprise customer base, refuse to communicate about anything and choose public silence as their baseline, seemingly force their employees into vows of public silence as well, actively degrade their products across the board with their vibeslop approach with measurable impacts on customers, openly attack not only open weights models but open source software, and all while pretending they're the 'public benefit corporation' formed by a valiant group of heroes escaping from a duplicitous snake and who, even in light of their own massively duplicitous behavior as of late, should apparently be trusted to be the some sort of arbiter over what this tech should get to be and how it should get to be used while they could hardly be more gleeful about how we're all going to be replaced in 6 months from now perpetually" camp.

Which is a bit of a bummer considering they do genuinely make the best model that's most pleasant to work with in my opinion.

It seems to be more them losing goodwill combined with their marketing.

I don't agree with your framing that all negativity is from crazies

Yeah you're overthinking it. Their product releases and their general approach to business is harming their business.

"OpenAI models are better, cheaper, and more reliable" - rational people

Most of these are good points though with the right framing.

"Real and unprecedented revenue growth"

Bro that is financial engineering, not real revenue growth. They engineered the switch to usage based pricing and a price hike timed the quarter before they wanted to go public, long enough to juice their numbers but not long enough for them not to be able to manage backlash and have to walk things back. Then they tried to extrapolate that manufactured bump to make it look like they have record shattering revenue growth.

Am i missing something? Because your making my point. Its only worth it compared to Opus 4.8, if the tasks your running requires Opus 4.8 low (or non-existing lower).

For the rest the gap in pricing vs efficiency is so small, that there is no point in using Sonnet. I am looking at their own cost comparisons vs efficiency...

> Claude Code is still a meme

Claude Code generates more revenue than OpenAI...It appears to be a nice meme.

maybe https://outyet.ai/models/claude-sonnet-5?

Not a US citizen. Identity verification is not going to help me.

I think they mean per dollar in the perf/$charts, not per marketing class. I.e. the new model is a complete Pareto failure in said perf/$ charts with the sole exception of Sonnet 5 low, which is dumb enough to not have comparison at all. Opus 4.8 delivers a better outcome per dollar, regardless what the underlying size of the models is.

I'd generously assume this is something about the specific category of agentic task presented in the chart... but it does raise the question "then why is that category the one they chose to highlight here".