LLMs exist on a logaritmhic performance/cost frontier. It's not really clear whether Opus 4.5+ represent a level shift on this frontier or just inhabits place on that curve which delivers higher performance, but at rapidly diminishing returns to inference cost.
To me, it is hard to reject this hypothesis today. The fact that Anthropic is rapidly trying to increase price may betray the fact that their recent lead is at the cost of dramatically higher operating costs. Their gross margins in this past quarter will be an important data point on this.
I think the tendency for graphs of model assessment to display the log of cost/tokens on the x axis (i.e. Artificial Analysis' site) has obscured this dynamic.
> It's not really clear whether Opus 4.5+ represent a level shift on this frontier or just inhabits place on that curve which delivers higher performance, but at rapidly diminishing returns to inference cost.
I think we're reaching the point where more developers need to start right-sizing the model and effort level to the task. It was easy to get comfortable with using the best model at the highest setting for everything for a while, but as the models continue to scale and reasoning token budgets grow, that's no longer a safe default unless you have unlimited budgets.
I welcome the idea of having multiple points on this curve that I can choose from. depending on the task. I'd welcome an option to have an even larger model that I could pull out for complex and important tasks, even if I had to let it run for 60 minutes in the background and made my entire 5-hour token quota disappear in one question.
I know not everyone wants this mental overhead, though. I predict we'll see more attempts at smart routing to different models depending on the task, along with the predictable complaints from everyone when the results are less than predictable.
I meant reference Toby Ord's work here. I think his framing of the performance/cost frontier hasn't gotten enough attention https://www.tobyord.com/writing/hourly-costs-for-ai-agents
They're also getting closer to IPO and have a growing user base. They can't justify losing a very large number of billions of other people's money in their IPO prospectus.
So there's a push for them to increase revenue per user, which brings us closer to the real cost of running these models.
They're also getting into cloud compute given you can use the desktop app to work in a temporary sandbox that they provision for you.
I was about to call it reselling but so many startups with their fingers in the tech startup pie offer containerised cloud compute akin to a loss leader. Harking back to the old days of buying clock time on a mainframe except you're getting it for free for a while.
I agree, and I'm also quite skeptical that Anthropic will be able to remain true to its initial, noble mission statement of acting for the global good once they IPO.
At that point you are beholden to your shareholders and no longer can eschew profit in favor of ethics.
Unfortunately, I think this is the beginning of the end of Anthropic and Modei being a company and CEO you could actually get behind and believe that they were trying to do "the right thing".
It will become an increasingly more cutthroat competition between Anthropic and OpenAI (and perhaps Google eventually if they can close the gap between their frontier models and Claude/GPT) to win market share and revenue.
Perhaps Amodei will eventually leave Anthropic too and start yet another AI startup because of Anthropic's seemingly inevitable prioritization of profit over safety.
I think the pivot to profit over good has been happening for a long time. See Dario hyping and salivating over all programming jobs disappearing in N months. He doesn't care at all if it's true or not. In fact he's in a terrible position to even understand if this is possible or not (probably hasn't coded for 10+ years). He's just in the business of selling tokens.
And worse, he (eventually) has to sell tokens above cost - which may have so much "baggage" (read: debt to pay Nvidia) that it'll be nearly impossible; or a new company will come to play with the latest and greatest hardware and undercut them.
Just how if Boeing was able to release a supersonic plane that was also twice as efficient tomorrow; it'd destroy any airline that was deep in debt for its current "now worthless" planes.
Skeptical is a light way to put it. It is essentially a forgone conclusion that once a company IPOs, any veil that they might be working for the global good is entirely lifted.
A publicly traded company is legally obligated to go against the global good.
It’s not really, companies like GM used to boast about how well they treated their employees and communities. It was Jack Welch and a legion of like-minded arseholes who decided they should be increasingly richer no matter who or what paid for it.
See also HP. Pretty much only Costco left.
Fair point.
Call me an optimist, but I'm still holding out hope that Amodei is and still can do the right thing. That hope is fading fast though.
« Don’t be evil »
The problem is that people equate money to power and power to evil.
So no matter what, if you do something lots of people like (and hence compensate you for), you will be evil.
It's a very interesting quirk of human intuition.
A reasonable conclusion, considering that money and power seem to have their own gravity, so people with more of both end up getting even more of both, and vice versa.
Can't blame someone who comes to such a conclusion about money and power.
The "real cost" of running near-SOTA models is not a secret: you can run local models on your own infrastructure. When you do, you quickly find out that typical agentic coding incurs outsized costs by literal orders of magnitude compared to the simple Q&A chat most people use AI for. All tokens are very much not created equal, and the typical coding token (large model, large noisy context) costs a lot even under best-case caching scenarios.
I mean, the signs have been there that the costs to run and operate these models wasn't as simple as inference costs. And the signs were there (and, arguably, are still there) that it costs way, way more than many people like to claim on the part of Anthropic. So to me this price hike is not at all surprising. It was going to come eventually, and I suspect it's nowhere near over. It wouldn't surprise me if in 2-3 years the "max" plan is $800 or $2000 even.
> The fact that Anthropic is rapidly trying to increase price may betray the fact that their recent lead is at the cost of dramatically higher operating costs.
Or they are just not willing to burn obscene levels of capital like OpenAI.
IMHO there is a point where incremental model quality will hit diminishing returns.
It is like comparing an 8K display to a 16K display because at normal viewing distance, the difference is imperceptible, but 16K comes at significant premium.
The same applies to intelligence. Sure, some users might register a meaningful bump, but if 99% can't tell the difference in their day-to-day work, does it matter?
A 20-30% cost increase needs to deliver a proportional leap in perceivable value.
Whenever we get the locally runnable 4k models things are going to get really awkward for the big 3 labs. Well at least Google will still have their ad revenue I guess.
Given how little claude usage they've been giving us on the "pro" plan lately, I've started doing more with the various open Qwen3.* models. Both Qwen3-coder-next and Qwen3.5-27b have been giving me good results and their 3.6 models are starting to be released. I think Anthropic may be shooting themselves in the foot here as more people start moving to local models due to costs and/or availability. Are the Qwen models as good as Claude right now? No. But they're about as good as Claude was 9 months ago (prior to 4.5). If I need some complex planning I save that for claude and have the Qwen models do the implementation.
I've also been using the Qwen3.5-27B and the new Qwen3.6 locally, both at Q6. I don't agree that they're as good as pre-Opus Claude. I really like how much they can do on my local hardware, but we have a long way to go before we reach parity with even the pre-Opus Claude in my opinion.
I was thinking the exact same thing just now as I load up qwen3.6 into hermes agent and all while fantasizing that it will replace opus 4.7. It might not actually but seems like we're on the verge of that.
Lately I've been wondering too just how large these proprietary "ultra powerful frontier models" really are. It wouldn't shock me if the default models aren't actually just some kind of crazy MoE thing with only a very small number of active params but a huge pool of experts to draw from for world knowledge.
They're not perfect but the local model game is progressing so quickly that they're impossible to ignore. I've only played around with the new qwen 3.6 models for a few minutes (it's damn impressive) but this weekend's project is to really put it through its paces.
If I can get the performance I'm seeing out of free models on a 6-year-old Macbook Pro M1, it's a sign of things to come.
Frontier models will have their place for 1) extensive integrations and tooling and 2) massive context windows. But I could see a very real local-first near future where a good portion of compute and inference is run locally and only goes to a frontier model as needed.
I've had really good results form qwen3-coder-next. I'm hoping we get a qwen3.6-coder soon since claude seems to get less-and-less available on the pro plan.
If the apple silicon keeps making the gains it makes, a mac studio with 128gb of ram + local models will be a practical all-local workflow by say 2028 or 2030. OpenAI and Anthropic are going to have to offer something really incredible if they want to keep subscription revenue from software developers in the near future, imo
Depends a lot on the task demands. "Got 95% of the way to designing a successful drug" and "Got 100% of the way" is a huge difference in terms of value, and that small bump in intelligence would justify a few orders of magnitude more in cost.
But that objective measure is exactly what we’re lacking in programming: There is often many ways to skin a cat, but the model only takes one. Without knowing about those it didn’t take, how do you judge the quality of a new model?
It probably depends what you're using the models for. If you use them for web search, summarizing web pages, I can imagine there's a plateau and we're probably already hitting it.
For coding though, there is kind of no limit to the complexity of software. The more invariants and potential interactions the model can be aware of, the better presumably. It can handle larger codebases. Probably past the point where humans could work on said codebases unassisted (which brings other potential problems).
I'm seeing a lot of sentiment, and agree with a lot of it, that opus 4.6 un-nerfed is there already and for many if not most software use cases there's more value to be had in tooling, speed, and cost than raw model intelligence.
I agree, but also the model intelligence is quite spikey. There are areas of intelligence that I don't care at all about, except as proxies for general improvement (this includes knowledge based benchmarks like Humanity's Last Exam, as well as proving math theorems etc). There are other areas of intelligence where I would gladly pay more, even 10X more, if it meant meaningful improvements: tool use, instruction following, judgement/"common sense", learning from experience, taste, etc. Some of these are seeing some progress, others seem inherent to the current LLM+chain of thought reasoning paradigm.
yeah thats is my biggest issue - im okay with paying 20-30% more but what is the ROI? i dont see an equivalent improvement in performance. Anthropic hasnt published any data around what these improvements are - just some vague “better instruction following"
The other thing is most people don't really care about price per token or whatever but how much it will cost to execute (successfully) a task they want.
It doesn't matter if a model is e.g. 30% cheaper to use than another (token-wise) but I need to burn 2x more tokens to get the same acceptable result.
Longer version of the comment https://www.linkedin.com/pulse/imperceptible-upgrade-petko-d...
> IMHO there is a point where incremental model quality will hit diminishing returns.
It feels like we're there to me, to be honest.
Does anyone here use 8k display for work? Does it make sense over 4k?
I was always wondering where that breaking point for cost/peformance is for displays. I use 4K 27” and it’s noticeably much better for text than 1440p@27 but no idea if the next/ and final stop is 6k or 8k?
> IMHO there is a point where incremental model quality will hit diminishing returns.
You mean a couple of years ago?
>IMHO there is a point where incremental model quality will hit diminishing returns.
It's not necessary a single discrete point I think. In my experience, it's tied to the quality/power of your harness and tooling. More powerful tooling has made revealed differences between models that were previously not easy to notice. This matches your display analogy, because I'm essentially saying that the point at which display resolution improvements are imperceptible matters on how far you sit.
The "multiplier" on Github Copilot went from 3 to 7.5. Nice to see that it is actually only 20-30% and Microsoft wanting to lose money slightly slower.
https://docs.github.com/fr/copilot/reference/ai-models/suppo...
Yep, and I just made a recommendation that was essentially "never enable Opus 4.7" to my org as a direct result. We have Opus 4.6 (3x) and Opus 4.5 (3x) enabled currently. They are worth it for planning.
At 7.5x for 4.7, heck no. It isn't even clear it is an upgrade over Opus 4.6.
in copilot I find it hard to justify using opus at even 3x vs just using GPT 5.4 high at 1x
The title is a misdirection. The token counts may be higher, but the cost-per-task may not be for a given intelligence level. Need to wait to see Artificial Analysis' Intelligence Index run for this, or some other independent per-task cost analysis.
The final calculation assumes that Opus 4.7 uses the exact same trajectory + reasoning output as Opus 4.6. I have not verified, but I assume it not to be the case, given that Opus 4.7 on Low thinking is strictly better than Opus 4.6 on Medium, etc., etc.
yep, ran a controlled experiment on 28 tasks comparing old opus 4.6 vs new opus 4.6 vs 4.7, and found that 4.7 is comparable in cost to old 4.6, and ~20% more expensive then new 4.6 (because new 4.6 is thinking less)
Very unlikely that the article is wrong. the 4.7 intelligence bump is not that big, plus most of the token spend is in inputs/tool calls etc, much of which won't change even with this bump.
im running some experiments on this but based on what i have seen on my own personal data - I dont think this is true
"given that Opus 4.7 on Low thinking is strictly better than Opus 4.6 on Medium, etc., etc.”
Opus 4.7 in general is more expensive for similar usage. Now we can argue that is provides better performance all else being equal but I haven’t been able to see that
A question I've been asking alot lately (really since the release of GPT-5.3) is "do I really need the more powerful model"?
I think a big issue with the industry right now is it's constantly chasing higher performing models and that comes at the cost of everything else. What I would love to see in the next few years is all these frontier AI labs go from just trying to create the most powerful model at any cost to actually making the whole thing sustainable and focusing on efficiency.
The GPT-3 era was a taste of what the future could hold but those models were toys compare to what we have today. We saw real gains during the GPT-4 / Claude 3 era where they could start being used as tools but required quite a bit of oversight. Now in the GPT-5 / Claude 4 era I don't really think we need to go much further and start focusing on efficiency and sustainability.
What I would love the industry to start focusing on in the next few years is not on the high end but the low end. Focus on making the 0.5B - 1B parameter models better for specific tasks. I'm currently experimenting with fine-tuning 0.5B models for very specific tasks and long term I think that's the future of AI.
Many people were hoping that Sonnet 4.6 was "Opus 4.5 quality but with Sonnet speed/cost" but unfortunately that didn't pan out.
I agree, and yet here i am using it... However, I think the industry IS going multiple directions all at once with smaller models, bigger models etc. I need to try out Google's latest models but alas what can one person do in the face of so many new models...
It appears that they are testing using Max. For 4.7 Anthropic recognizes the high token usage of max and recommends the new xhigh mode for most cases. So I think the real question is whether 4.7 xhigh is “better” than 4.6 max.
> max: Max effort can deliver performance gains in some use cases, but may show diminishing returns from increased token usage. This setting can also sometimes be prone to overthinking. We recommend testing max effort for intelligence-demanding tasks.
> xhigh (new): Extra high effort is the best setting for most coding and agentic use cases
Ref: https://platform.claude.com/docs/en/build-with-claude/prompt...
Inserting an xhigh tier and pushing max way out has very “these go to 11” vibes.
Just yesterday I was happy to have gotten my weekly limit reset [1]. And although I've been doing a lot of mockup work (so a lot of HTML getting written), I think the 1M token stuff is absolutely eating up tokens like CRAZY.
I'm already at 27% of my weekly limit in ONE DAY.
I'm seeing the opposite. With Opus 4.7 and xhigh, I'm seeing less session usage , it's moving faster, and my weekly usage is not moving that much on a Team Pro account.
Four day workweek!
> I'm already at 27% of my weekly limit in ONE DAY.
Ouch, that's very different than experience. What effort level? Are you careful to avoid pushing session context use beyond 350k or so (assuming 1m context)?
yeah similar for me - it uses a bunch more tokens and I haven’t been able to tell the ROI in terms of better instruction following
it seems to hallucinate a bit more (anecdotal)
I had it hallucinate a tool that didn't exist, it was very frustrating!
Anthropic intruduces fake tool calls to prevent distillation of their models. Others still distill. Anthropic distils third party models. Claude now hallucinates tools.
Brilliant.
Iam at 22%, just two task. A bug fixing and a Scalar integration.
I tried to do my usual test (similar to pelican but a bit more complex) but it ran out of 5 hour limit in 5 minutes. Then after 5 hours I said "go on" and the results were the worst I've ever seen.
On actual code, I see what you see a 30% increase in tokens which is in-line with what they claim as well. I personally don't tend to feed technical documentation or random pros into llms.
Given that Opus 4.6 and even Sonnet 4.6 are still valid options, for me the question is not "Does 4.7 cost more than claimed?" but "What capabilities does 4.7 give me that 4.6 did not?"
Yesterday 4.6 was a great option and it is too soon for me to tell if 4.7 is a meaningful lift. If it is, then I can evaluate if the increased cost is justified.
Yeah that was an interesting discovery in a development meeting. Many people were chasing after the next best model and everything, though for me, Sonnet 4.6 solves many topics in 1-2 rounds. I mainly need some focus on context, instructions and keeping tasks well-bounded. Keeping the task narrow also simplifies review and staying in control, since I usually get smaller diffs back I can understand quickly and manage or modify later.
I'll look at the new models, but increasing the token consumptions by a factor of 7 on copilot, and then running into all of these budget management topics people talk about? That seems to introduce even more flow-breakers into my workflow, and I don't think it'll be 7 times better. Maybe in some planning and architectural topics where I used Opus 4.6 before.
haven't people been complaining lately about 4.6 getting worse?
People complain about a lot of things. Claude has been fine:
https://marginlab.ai/trackers/claude-code-historical-perform...
I will be the first to acknowledge that humans are a bad judge of performance and that some of the allegations are likely just hallucinations...
But... Are you really going to completely rely on benchmarks that have time and time again be shown to be gamed as the complete story?
My take: It is pretty clear that the capacity crunch is real and the changes they made to effort are in part to reduce that. It likely changed the experience for users.
While that's a nice effort, the inter-run variability is too high to diagnose anything short of catastrophic model degradation. The typical 95% confidence interval runs from 35% to 65% pass rates, a full factor of two performance difference.
Moreover, on the companion codex graphs (https://marginlab.ai/trackers/codex-historical-performance/), you can see a few different GPT model releases marked yet none correspond to a visual break in the series. Either GPT 5.4-xhigh is no more powerful than GPT 5.2, or the benchmarking apparatus is not sensitive enough to detect such changes.
That performance monitor is super easy to game if you cache responses to all the SWE bench questions.
You dramatically overestimate how much time engineers at hypergrowth startups have on their hands
No we increased our plans
How long will they host 4.6? Maybe longer for enterprise, but if you have a consumer subscription, you won't have a choice for long, if at all anymore.
I was trying to figure out earlier today how to get 4.6 to run in Claude Code, as part of the output it included "- Still fully supported — not scheduled for retirement until Feb 2027." Full caveat of, I don't know where it came up with this information, but as others have said, 4.5 is still available today and it is now 5, almost 6 months old.
I'm still using 4.5 because it gets the niche work I'm using it for where 4.6 would just fight me.
Opus 4.5 is still available
Wow, they hosted it for 6 months. Truly LTS territory :)
Claude code seems to be getting worse on several fronts and better on others. I suspect product is shifting from 'make it great' to 'make it make as much money for us as possible and that includes gathering data'.
Recently it started promoting me for feedback even though I am on API access and have disabled this. When I did a deep dive of their feedback mechanism in the past (months ago so probably changed a lot since then) the feedback prompt was pushing message ids even if you didn't respond. If you are on API usage and have told them no to training on your data then anything pushing a message id implies that it is leaking information about your session. It is hard to keep auditing them when they push so many changes so I am now 'default they are stealing my info' instead of believing their privacy/data use policy claims. Basically, my level of trust is eroding fast in their commitment to not training on me and I am paying a premium to not have that happen.
It doesn't look good for Anthropic, especially considering they are burning billions in investor money.
Looks like they lost the mandate of heaven, if Open AI plays it right it might be their end. Add to that the open source models from China.
OpenAI is dealing with exactly the same energetic and financial constraints as Anthropic. That will become apparent soon.
I was sort of hoping that the peak is something like $15 per hour of vibe help (yes I know some of you burn $15 in 12milliseconds), and that you can have last year's best or the current "nano/small" model at $1 per hour.
But it looks like it's just creeping up. Probably because we're paying for construction, not just inference right now.
Claude seems so frustrating lately to the point where I avoid and completely ignore it. I can't identify a single cause but I believe it's mostly the self-righteousness and leadership that drive all the decisions that make me distrust and disengage with it.
What do you mean by this? What are you frustrated by?
You're offended by their political beliefs, so you don't like the way the model works?
using dumber models to own the libs
Definitely experimenting with less expensive ones. I have a few versions of my settings.json
I also wonder if token utilization has or will ever find its way to employee performance reviews as these models go up in price.
It does cost more but I found the quality of output much higher. I prefer it over the dumbing of effort/models they were doing for the last two months. They have to get users used to picking the appropriate model for their task (or have an automatic mode - but still let me force it to a model).
Some broad assumptions are being made that plans give you a precise equivalent to API cost. This is not the case with reverse engineering plan usage showing cached input is free [0]. If you re-run the math removing cached input the usage cost is ~5-34% more. Was the token plan budget increase [1] proportional to account for this? Can’t say with certainty. Those paying API costs though the price hike is real.
Yeah. I just did a day with 4.7 and I won't be going back for a while. It is just too expensive. On top of the tokenization the thinking seems like it is eating a lot more too.
yeah i am still not clear why there are 5 effort modes now on top of more expensive tokenization
Once you've seen a few results of an LLM given too much sway over product decisions, 5 effort modes expressed as various english adjectives is pretty much par for the course
Yeah I noticed today, I had it work up a spreadsheet for me and I only got 3 or 4 turns in the conversation before it used up all my (pro) credits. It wasn't even super-complicated or anything, only moderately so.
Well, LLMs are priced per token, and most of the tokens are just echoing back the old code with minimal changes. So, a lot of the cost is actually paying for the LLM to echo back the same code.
Except, it's not that trivial to solve. I tried experimenting with asking the model to first give a list of symbols it will modify, and then just write the modified symbols. The results were OK, but less refined than when it echoes back the entire file.
The way I see it is that when you echo back the entire file, the process of thinking "should I do an edit here" is distributed over a longer span, so it has more room to make a good decision. Like instead of asking "which 2 of the 10 functions should you change" you're asking it "should you change method1? what about method2? what about method3?", etc., and that puts less pressure on the LLM.
Except, currently we are effectively paying for the LLM to make that decision for *every token*, which is terribly inefficient. So, there has to be some middle ground between expensively echoing back thousands of unchanged tokens and giving an error-ridden high-level summary. We just haven't found that middle ground yet.
I think the ideal way for these LLMs to work will be using AST-level changes instead of "let me edit this file".
grit.io was working on this years ago, not sure if they are still alive/around, but I liked their approach (just had a very buggy transformer/language).
>and most of the tokens are just echoing back the old code with minimal changes
I thought coding harnesses provided tools to apply diffs so the LLM didn't have to echo back the entire file?
They can, but this reduces the quality. The LLM has a harder time picking the first edit, and then all subsequent work is influenced by that one edit. Like first creating an unnecessary auxiliary type, and then being stuck modifying the rest of the code to work with it.
So, in practice, many tools still work on the file level.
Interesting because I already felt like current models spit out too much garbage verbose code that a human would write in a far more terse, beautiful and grokable way
I had a case yesterday where Claude wrote me a series of if/elses in python. I asked it if it could use some newer constructs instead, and it told me that I was on a new enough python version that I could use match/case. Great!
And then it proceeded to rewrite the block with a dict lookup plus if-elses, instead of using match/case. I had to nag it to actually rewrite the code the way it said it would!
yeah opus 4.7 feels a lot more verbose - i think they changed the system prompt and removed instructions to be terse in its responses
Just hit my quota with 20x for the first time today…
We noticed this two weeks ago where we found some of our requests are unexpected took more tokens than measured by count_tokens call. At the end they were Anthropic's A/B testing routing some Opus 4.6 calls to Opus 4.7.
https://matrix.dev/blog-2026-04-16.html (We were talking to Opus 4.7 twelve days ago)
Not only that but they seem to have cut my plan ability to use Sonnet too. I have a routine that used to use about 40% of my 5 hour max plan tokens, then since yesterday it gets stopped because it uses the whole 100%. Anyone else experience this?
yeah it seems like sonnet 4.6 burns thru tokens crazy fast. I did one prompt, sonnet misunderstood it as 'generate an image of this' and used all of my free tokens.
For me there is no point in using Claude Opus 4.7, it's too expensive since it does not do 100% of the job. Since AI can anyway only do 90% of most tasks, I can use another model and do the remaining 15-30% myself.
This is the backdoor way of raising prices... just inflate the token pricing. It's like ice cream companies shrinking the box instead of raising the price
News like this always makes me wonder about running my own model, something I've never done. A couple thousand bucks can get you some decent hardware, it looks like, but is it good for coding? What is your all's experience?
And if it's not good enough for coding, what kind of money, if any, would make it good enough?
I want to give give you realistic expectations: Unless you spend well over $10K on hardware, you will be disappointed, and will spend a lot of time getting there. For sophisticated coding tasks, at least. (For simple agentic work, you can get workable results with a 3090 or two, or even a couple 3060 12GBs for half the price. But they're pretty dumb, and it's a tease. Hobby territory, lots of dicking around.)
Do yourself a favor: Set up OpenCode and OpenRouter, and try all the models you want to try there.
Other than the top performers (e.g. GLM 5.1, Kimi K2.5, where required hardware is basically unaffordable for a single person), the open models are more trouble than they're worth IMO, at least for now (in terms of actually Getting Shit Done).
We need more voices like this to cut through the bullshit. It's fine that people want to tinker with local models, but there has been this narrative for too long that you can just buy more ram and run some small to medium sized model and be productive that way. You just can't, a 35b will never perform at the level of the same gen 500b+ model. It just won't and you are basically working with GPT-4 (the very first one to launch) tier performance while everyone else is on GPT-5.4. If that's fine for you because you can stay local, cool, but that's the part that no one ever wants to say out loud and it made me think I was just "doing it wrong" for so long on lm studio and ollama.
Not sure why all the other commentors are failing to mention you can spend considerably less money on an apple silicon machine to run decent local models.
Fun fact: AWS offers apple silicon EC2 instances you can spin up to test.
Unless you use H100 or 4x 5090 you won't get a decent output.
The best bang for the buck now is subcribing to token plans from Z.ai (GLM 5.1), MiniMax (MiniMax M2.7) or ALibaba Cloud (Qwen 3.6 Plus)
Running quantized models won't give you results comparable to Opus or GPT.
You should be aware that any model you can run on less than $10k worth of hardware isn't going to be anywhere close to the best cloud models on any remotely complex task.
Many providers out there host open weights models for cheap, try them out and see what you think before actually investing in hardware to run your own.
The latest Qwen3.6 model is very impressive for its size. Get an RTX 3090 and go to https://www.reddit.com/r/LocalLLaMA/ to see the latest news on how to run models locally. Totally fine for coding.
i think the new qwen models are supposed to be good based on some the articles that i read
Contrary to people here who feel the price increases, reduction of subscription limits etc are the result of the Anthropic models being more expensive to run than the API & subscription revenue they generate I have a theory that Anthropic has been in the enshittification & rent seeking phase for a while in which they will attempt to extract as much money out of existing users as possible.
Commercial inference providers serve Chinese models of comparable quality at 0.1x-0.25x. I think Anthropic realised that the game is up and they will not be able to hold the lead in quality forever so it's best to switch to value extraction whilst that lead is still somewhat there.
> Commercial inference providers serve Chinese models of comparable quality…
"Comparable" is doing some heavy lifting there. Comparable to Anthropic models in 1H'25, maybe.
Benchmarks suggests they are comparable: https://artificialanalysis.ai/?models=claude-opus-4-6-adapti...
But let's say for the sake of discussion Opus is much better - still doesn't justify the price disparity especially when considering that other models are provided by commercial inference providers and anthropics is inhouse.
> Benchmarks suggests they are comparable
The problem here is people think AI benchmarks are analogous to say, CPU performance benchmarks. They're not:
* You can't control all the variables, only one (the prompt).
* The outputs, BY DESIGN, can fluctuate wildly for no apparent reason (i.e., first run, utter failure, second run, success).
* The biggest point, once a benchmark is known, future iterations of the model will be trained on it.
Trying to objectively measure model performance is a fool's errand.
Every time a new model comes out, I'm left guessing what it means for my token budget in order to sustain the quality of output I'm getting. And it varies unpredictably each time. Beyond token efficiency, we need benchmarks to measure model output quality per token consumed for a diverse set of multi-turn conversation scenarios. Measuring single exchanges is not just synthetic, it's unrealistic. Without good cost/quality trade-off measures, every model upgrade feels like a gamble.
The company I work for provides all engineering employees with a Claude subscription. My job isn't writing (much) code, and we have Copilot with MS Office, plus multiple internal AI tools on top of that. So I'm free to do low-stakes experiments on Claude without having to worry about hitting my monthly usage limit.
I am finding that for complex tasks, Claude's quality of output varies _tremendously_ with repeated runs of the same model and prompt. For example, last week I wrote up (with my own brain and keyboard) a somewhat detailed plain english spec of a work-related productivity app that I've always wanted but never had the time to write. It was roughly the length of an average college essay. The first thing I asked Claude to do was not write any code, but come up with a more formal design and implementation plan based on the requirements that I gave. The idea was to then hand _that_ to Claude and say, okay, now build it.
I used Opus 4.6 with High reasoning for all of this and did not change any model settings between runs.
The first run was overall _amazing_. It was detailed, well-written, contained everything that I asked for. The only drawback was that I was ambiguous on a couple of points which meant that the model went off and designed something in a way that I wasn't expecting and didn't intend. So I cleared that up in my prompt, and instead of keeping the context and building on what was already there, I started a new chat and had it start again from scratch.
What it wrote the second time was _far_ less impressive. The writing was terse, there was a lot less detail, the pretty dependency charts and various tables it made the first time were all gone. Lots of stuff was underspecified or outright missing.
New chat, start again. Similar results as the second run, maybe a bit worse. It also started _writing code_ which was something I told it NOT to do. At this point I'm starting to panic a little because I'm sure I didn't add, "oh, and make it crappy" to the prompt and I was a little angry about not saving the first iteration since it was fairly close to what I had wanted anyway.
I decided to try one last time and it finally gave me back something within about 95% of the first run in terms of quality, but with all the problems fixed. So, I was (finally) happy with that, and it used that to generate the application surprisingly well, with only a few issues that should not be too hard to fix after the fact.
So I guess 4th time was a charm, and the fare was about $7 in tokens to get there.
That’s the joy of purchasing an intangible and non-deterministic product. The profit margin is completely within the vendor’s control and quality is hard for users to measure.
So intelligence has turned into a utility per Sam Altman et al., and now the same companies get to hike the price of accessing it by 20–30%, right as it’s becoming the backbone of how teams actually ship work. People are pushing out so much, so fast that last week’s output is already a blur. I’ve got colleagues who refuse to go back to writing any of this stuff by hand.
And now maintaining that pace means absorbing arbitrary price increases, shrugged off with “we were operating at a loss anyway.”
It stops being “pay to play” and starts looking more like pay just to stay in the ring, while enterprise players barely feel the hit and everyone else gets squeezed out.
Market maturing my butthole... it’s obviously a dependency being priced in real time. Tech is an utter shit show right now, compounded by the disaster of the unemployment market still reeling from the overhiring of 2020.
save up now and career pivot. pick up gardening.
> So intelligence has turned into a utility.
"Utility" is close, but "energy source" may be closer. When it becomes the thing powering the pace of work itself, raising prices is less about charging for access and more about taxing dependency.
Like every startup ever, they were selling it to you at a loss to compete for market share and are slowly increasing pricing. Duh.
But the unique thing about AI is that the "world" is depending on it like water, oil, gas, etc. Not just a specific use case.
Anthropic must be loving it. It's free money.
`/model claude-opus-4-6`
This is the reality I'm seeing too. Does this mean that the subscriptions (5x, 10x, 20x) are essentially reduced in token-count by 20-30%?
yeah thats the part that is unclear to me as well - if our usage capacity is now going to run out faster.
This is probably an adjacent result of this (from anthropic launch post):
> In Claude Code, we’ve raised the default effort level to xhigh for all plans.
Try changing your effort level and see what results you get
effort level is separate from tokenization. Tokenization impacts you the same regardless.
I find 5 thinking levels to be super confusing - I dont really get why they went from 3 -> 5
Claude's tokenizers have actually been getting less efficient over the years (I think we're at the third iteration at the least since Sonnet 3.5). And if you prompt the LLM in a language other than English, or if your users prompt it or generate content in other languages, the costs go higher even more. And I mean hundreds of percent more for languages with complex scripts like Tamil or Japanese. If you're interested in the research we did comparing tokenizers of several SOTA models in multiple languages, just hit me up.
I would encourage you to post a link here, and also to submit to HN if you haven't already. :)
Good reminder to choose model-agnostic tooling!
Pretty funny that this article was clearly written by Claude.
4.7 one-shot rate is at least 20-30% higher for me
How are you able to track this as you use it? A bit stumped atm
Purely empirical
Don't forget that the model doesn't have an incentive to give right solution the first time. At least with Opus 4.6 after it got nerfed, it would go round in circles until you tell it to stop defrauding you and get to correct solution. That not always worked though. I found starting session again and again until less nerfed model was put on the request. Still all points to artificially make customer pay more.
I'm still using Sonnet 4.6 with no issues.
How does this solve the issue? 4.6 will be disabled after one or more release like any other legacy model.
Won't the thing that replaces 4.6 come down in token cost?
Talked to Claude earlier today and Opus 4.7 cost up to 35% more.
In my “repo os” we have an adversarial agent harness running gpt5.4 for plan and implementation and opus4.6 for review. This was the clear winner in the bake-off when 5.4 came out a couple months ago.
Re-ran the bake-off with 4.7 authoring and… gpt5.4 still clearly winning. Same skills, same prompts, same agents.md.
As a regular listener of Ed Zitron this comes as absolutely no surprise. Once you understand the levels of obfuscation available to anthro / OAI you will realize that they have almost certainly hit a model plateau ~1 year ago. All benchmark improvements since have come at a high compute cost. And the model used when evaluating said benchmarks is not the same model you get with your subscription.
This is already becoming apparent as users are seeing quality degrade which implies that anthropic is dropping performance across the board to minimize financial losses.
Because those braniacs added 20-30% more system prompt
I can’t stand reading this. One article. Many words. Not written by a human.
Feels like LLMs are devolving into having a single, instantly recognizable and predictable writing style.
I don't know anything about tokens. Anthropic says Pro has "more usage*", Max has 5x or 20x "more usage*" than Pro. The link to "usage limits" says "determines how many messages you can send". Clearly no one is getting billed for tokens.
anthropic’s pricing is all based on token usage
https://platform.claude.com/docs/en/about-claude/pricing
So if you are generating more tokens, you are eating up your usage faster
The fundamental problem with these frontier model companies is that they're incentivized to create models that burn through more tokens, full stop. It's a tale as old as capitalism: you wake up every day and choose to deliver more value to your customers or your shareholders, you cannot do both simultaneously forever.
People love to throw around "this is the dumbest AI will ever be", but the corollary to that is "this is the most aligned the incentives between model providers and customers will ever be" because we're all just burning VC money for now.
> but the corollary to that is "this is the most aligned the incentives between model providers and customers will ever be" because we're all just burning VC money for now.
Please say this louder for everyone to hear. We are still at the stage where it is best for Anthropic's product to be as consumer aligned (and cost-friendly) as possible. Anthropic is loosing a lot of money. Both of those things will not be true in the near future.
Their bigger incentive is to deliver the best product in the cheapest way, because there is tight competition with at least 2 other companies. I know we all love to hate on capitalism but it's actually functioning fine in this situation, and the token inflation is their attempt to provide a better product, not a worse one.
The compute is expensive, what is with this outrage? People just want free tools forever?
are you okay with paying more for your services without any perceived improvement in the service itself?
That's been a constant for my entire adult life.
> The compute is expensive, what is with this outrage?
Gamblers (vibe-coders) at Anthropic's casino realising that their new slot machine upgrade (Claude Opus) is now taking 20%-30% more credits for every push of the spin button.
Problem is, it advertises how good it is (unverified benchmarks) and has a better random number generator but it still can be rigged (made dumber) by the vendor (Anthropic).
The house (Anthropic) always wins.
> People just want free tools forever?
Using local models are the answer to this if you want to use AI models free forever.
And what about with Caveman[1]?
Can we have one thread about Claude without people trying to shovel Caveman?
Much of the token usage is in reasoning, exploring, and code generation rather than outputs to the user.
Does making Claude sound like a caveman actually move the needle on costs? I am not sure anymore whether people are serious about this.
To me, caveman sounds bad and is not as easy to understand compared to normal English.
Caveman doesn't and cannot change the tokenizer, so the relative token count differences by input category will remain unchanged.
isn’t caveman a joke? why would you use it for real work?