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.
That post doesn't address the human factor of cost, and I don't mean that in a good way. Even if AI costs more than a human, it's tireless, doesn't need holidays, is never going to have to go to HR for sexual harassment issues, won't show up hungover or need an advance to pay for a dying relative's surgery. It can be turned on and off with the flip of a switch. Hire 30 today, fire 25 of them next week. Spin another 5 up just before the trade show demo needs to go out and fire them with no remorse afterwards.
The cost to hire a human is highly predictable. The cost of AI isn't. I, as a human, need food and shelter, which puts a ceiling to my bargaining power. I can't withdraw my labour indefinitely.
The power dynamics are also vastly against me. I represent a fraction of my employer's labour, but my employer represents 100% of my income.
That dynamic is totally inverted with AI. You are a rounding error on their revenue sheet, they have a monopoly on your work throughput. How do you budget an workforce that could turn 20% more expensive overnight?
By continuously testing competitors and local LLMs? The reason for rising prices is that they (Anthropic) probably realized that they have reached a ceiling of what LLMs are capable of, and while it's a lot, it is still not a big moat and it's definitely not intelligence.
> Anything but the simplest tooling is not transferable between model generations, let alone completely different families.
It is transferable-yes, you will get issues if you take prompts and workflows tuned for one model and send them to another unchanged. But, most of the time, fixing it is just tinkering with some prompt templates
People port solutions between models all the time. It takes some work, but the amount of work involved is tractable
Plus: this is absolutely the kind of task a coding agent can accelerate
The biggest risk is if your solution is at the frontier of capability, and a competing model (even another frontier model) just can’t do it. But a lot of use cases, that isn’t the case. And even if that is the case today, decent odds in a few more months it won’t be
Yep. My approach has been, if I can’t reliably get something to 90+% with a flash / nano / haiku, then it’s not viable for any accuracy critical work. (I don’t know of or have the luck of having any other work.) Starting out with the pro / opus for any production classification work has always been a trick.
Ha. Sounds a lot like the one 10x vs. predictable mediocre guys with a scaffolding of processes. Aim high and hit or miss or try to grind predictably and continuously. Same with humans and depends on the loss you can afford.
If you're talking about APIs and SDKs, whether direct API calls or driving tools like Claude code or codex with human out of the loop, I think that's actually fairly straightforward to switch between the various tools.
If you're talking about output quality, then yeah, that's not as easy. But for product outputs (building a customer service agent or something like that), having a well-designed eval harness and doing testing and iteration can get you some degree of convergence between the models of similar generations. Coding is similar (iterate, measure), but less easy to eval.
For most tasks, at some future date, isn't there going to be some ambient baseline of capabilities you can get per $/tok, starting at ~0 for OSS models, such that eventually all tooling gets trivially transferable?
It's not that hard to make it generic. It does take a little work, but really it boils down to figuring out how to make things work with the "dumbest" model in your set.
Note that it is very likely this market can't sustain this level of competition for long. We are all still chasing the carrot of AGI, while hardware costs skyrocket.
This is why there are a ton of corps running the open source models in house... Known costs, known performance, upgrade as you see fit. The consumer backlash against 4o was noted by a few orgs, and they saw the writing on the wall... they didnt want to develop against a platform built on quicksand (see openweb, apps on Facebook and a host of other examples).
There are people out there making smart AI business decisions, to have control over performance and costs.
That was a great promise before the models starting becoming "moody" due to their proprietors arbitrarily modifying their performance capabilities and defaults without transparency or recourse.
I think it's difficult to say agentic and human developer labor are fungible in the real world at this point. Agents may succeed in discrete tasks, like those in a benchmark assessment, but those requiring a larger context window (i.e. working in brownfield systems, which is arguably the bulk of development work) favor developers for now. Not to mention that at this point a lot of necessary context is not encoded in an enterprise system, but lives in people's heads.
I'd also flip your framing on its head. One of the advantages of human labor over agents is accountability. Someone needs to own the work at the end of the day, and the incentive alignment is stronger for humans given that there is a real cost to being fired.
> 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.
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.
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.
That sounds very plausible. But it implies they could offer even higher performance models at much higher costs if they chose to; and presumably they would if there were customers willing to pay. Is that the case? Surely there are a decent number of customers who’d be willing to pay more, much more, to get the very best LLMs possible.
Like, Apple computers are already quite pricey -- $1000 or $2000 or so for a decent one. But you can spec up one that’s a bit better (not really that much better) and they’ll charge you $10K, $20K, $30K. Some customers want that and many are willing to pay for it.
Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train?
FWIW, Artificial Analysis has a "Intelligence vs Cost" plot on their front page that shows models' score vs cost to run the benchmark, which should be more fair in this sense. According to that one, Opus 4.7 (max) is slightly cheaper than 4.6 (though still very expensive).
Yeah. Combine this with much of Corpos right now using a “burn as many tokens as you need” policy on AI, the incentive is there for them to raise price and find an equilibrium point or at least reduce the bleed.
I find it interesting that folks are so focused on cost for AI models. Human time spent redirecting AI coding agents towards better strategies and reviewing work, remains dramatically more expensive than the token cost for AI coding, for anything other than hobby work (where you're not paying for the human labor). $200/month is an expensive hobby, but it's negligible as a business expense; SalesForce licenses cost far more.
The key question is how well it a given model does the work, which is a lot harder to measure. But I think token costs are still an order of magnitude below the point where a US-based developer using AI for coding should be asking questions about price; at current price points, the cost/benefit question is dominated by what makes the best use of your limited time as an engineer.
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.
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.
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.
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.
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.
In Kolkata, sweet sellers was struggling with cost management after covid due to increased prices of raw materials. But they couldn't increase the price any further without losing customers. So they reduced the size of sweets instead, and market slowly reduced expectations. And this is the new normal now.
Human psychology is surprisingly similar, and same pattern comes across domains.
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
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 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.
I did some work yesterday with Opus and found it amazing.
Today we are almost on non-speaking terms.
I'm asking it to do some simple stuff and he's making incredible stupid mistakes:
This is the third time that I have to ask you to remove the issue that was there for more than 20 hours. What is going on here?
and at the same time the compacting is firing like crazy. (What adds ~4 minute delays every 1 - 15 minutes)
| # | Time | Gap before | Session span | API calls |
|---|----------|-----------|--------------|-----------|
| 1 | 15:51:13 | 8s | <1m | 1 |
| 2 | 15:54:35 | 48s | 37m | 51 |
| 3 | 16:33:33 | 2s | 19m | 42 |
| 4 | 16:53:44 | 1s | 9m | 30 |
| 5 | 17:04:37 | 1s | 17m | 30 |
# — sequential compaction event number, ordered by time.
Time — timestamp of the first API call in the resumed session, i.e. when the new context (carrying the compaction summary) was first sent to the
model.
Gap before — time between the last API call of the prior session and the first call of this one. Includes any compaction processing time plus user
think time between the two sessions.
Session span — how long this compaction-resumed session ran, from its first API call to its last before the next compaction (or end of session).
API calls — total number of API requests made during this resumed session. Each tool use, each reply, each intermediate step = one request.
Bottomline, I will probably stay on Sonnet until they fix all these issues.
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.
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.
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.
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
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.
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
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.
Taking the article's 5% accuracy improvement at face value: if true, then it's more than worth the token inflation IMO. That's because of tool call chains, where errors compound and accumulate, and small improvements in accuracy get greatly magnified.
Again, the article's numbers are likely a rather crude approximation, but taking 85% accuracy (claude 4.6) vs 90% (4.7) as inputs:
Compounded, small improvements really move the needle downstream. 1.4x doesn't seem worth it for 5% better, but 10 calls in, that's more than a 40% improvement.
At this point, as an experienced developer, unless they can promise consistent very high quality, which they can't, I would rather lean towards almost as good but faster. At this point, that compromise is Codex.
I would rather steer quickly, get ideas because I'm moving quickly, do course correction quickly - basically I'm not happy blocking my chain of thought/concentration and fall prey to distractions due to Claude's slowness and compaction cycles. Sometimes I don't even notice that Codex has compacted.
For architectural discussions, sure I'll pick Claude. I'm mentally prepared for that. But once we are in the thick of things, speed matters. I would they rather focus on improving Sonnet's speed.
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.
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?
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.
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.
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.
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?
Asked Opus 4.7 to extend an existing system today. After thorough exploration and a long back and forth on details it came up with a plan. Then proceeded to build a fully parallel, incompatible system from scratch with the changes I wanted but everything else incompatible and full of placeholders
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.
"One session" is not a very interesting unit of work. What I am interested in is how much less work I am required to do, to get the results I want.
This is not so much about my instructions being followed more closely. It's the LLM being smarter about what's going on and for example saving me time on unnecessary expeditions. This is where models have been most notably been getting better to my experience. Understanding the bigger picture. Applying taste.
It's harder to measure, of course, but, at least for my coding needs, there is still a lot of room here.
If one session costs an additional 20% that's completely fine, if that session gets me 20% closer to a finished product (or: not 20% further away). Even 10% closer would probably still be entirely fine, given how cheap it is.
FYI: Anthropic increased people's subscription quotas to counteract the token cost change. In classic Anthropic fashion this is only announced via X post and not any official announcement.
However, if you are using API costs then I guess you're left holding the bag.
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 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.
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).
> The model will not silently generalize an instruction from one item to another.
I am clearly missing something but wouldn't this be an ideal thing to do? Surely if it was optimised it would use less tokens while not losing anything from the instructions ?
Am I dumb, or are they not explaining what level thinking they're using? We all read the Anthropic blog post yesterday - 4.7 max consumes/produces an incredible number of tokens and it's not equivalent to 4.6 max; xhigh is the new "max".
I've been using 4.6 models since each of them launched.
Same for 4.5.
4.6 performers worse or the same in most of the tasks I have.
If there is a parameter that made me use 4.6 more frequently is because 4.5 get dumber and not because 4.6 seemed smarter.
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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.
(* explained at https://news.ycombinator.com/item?id=26998308)
The power dynamics are also vastly against me. I represent a fraction of my employer's labour, but my employer represents 100% of my income.
That dynamic is totally inverted with AI. You are a rounding error on their revenue sheet, they have a monopoly on your work throughput. How do you budget an workforce that could turn 20% more expensive overnight?
> Anything but the simplest tooling is not transferable between model generations, let alone completely different families.
It is transferable-yes, you will get issues if you take prompts and workflows tuned for one model and send them to another unchanged. But, most of the time, fixing it is just tinkering with some prompt templates
People port solutions between models all the time. It takes some work, but the amount of work involved is tractable
Plus: this is absolutely the kind of task a coding agent can accelerate
The biggest risk is if your solution is at the frontier of capability, and a competing model (even another frontier model) just can’t do it. But a lot of use cases, that isn’t the case. And even if that is the case today, decent odds in a few more months it won’t be
If you're talking about output quality, then yeah, that's not as easy. But for product outputs (building a customer service agent or something like that), having a well-designed eval harness and doing testing and iteration can get you some degree of convergence between the models of similar generations. Coding is similar (iterate, measure), but less easy to eval.
> The cost of AI isn't.
This is why there are a ton of corps running the open source models in house... Known costs, known performance, upgrade as you see fit. The consumer backlash against 4o was noted by a few orgs, and they saw the writing on the wall... they didnt want to develop against a platform built on quicksand (see openweb, apps on Facebook and a host of other examples).
There are people out there making smart AI business decisions, to have control over performance and costs.
> How do you budget an workforce that could turn 20% more expensive overnight?
Like, say, oil or DRAMs?
This is an architecture that people are increasing begging to give network connectivity that can't differentiate its system prompt from user input
I'd also flip your framing on its head. One of the advantages of human labor over agents is accountability. Someone needs to own the work at the end of the day, and the incentive alignment is stronger for humans given that there is a real cost to being fired.
> 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.
So there's a push for them to increase revenue per user, which brings us closer to the real cost of running these models.
Like, Apple computers are already quite pricey -- $1000 or $2000 or so for a decent one. But you can spec up one that’s a bit better (not really that much better) and they’ll charge you $10K, $20K, $30K. Some customers want that and many are willing to pay for it.
Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train?
The key question is how well it a given model does the work, which is a lot harder to measure. But I think token costs are still an order of magnitude below the point where a US-based developer using AI for coding should be asking questions about price; at current price points, the cost/benefit question is dominated by what makes the best use of your limited time as an engineer.
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.
https://docs.github.com/fr/copilot/reference/ai-models/suppo...
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.
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.
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.
Human psychology is surprisingly similar, and same pattern comes across domains.
> 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...
I'm already at 27% of my weekly limit in ONE DAY.
https://news.ycombinator.com/item?id=47799256
Today we are almost on non-speaking terms. I'm asking it to do some simple stuff and he's making incredible stupid mistakes:
and at the same time the compacting is firing like crazy. (What adds ~4 minute delays every 1 - 15 minutes) Bottomline, I will probably stay on Sonnet until they fix all these issues.https://matrix.dev/blog-2026-04-16.html (We were talking to Opus 4.7 twelve days ago)
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.
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.
Again, the article's numbers are likely a rather crude approximation, but taking 85% accuracy (claude 4.6) vs 90% (4.7) as inputs:
Compounded, small improvements really move the needle downstream. 1.4x doesn't seem worth it for 5% better, but 10 calls in, that's more than a 40% improvement.I would rather steer quickly, get ideas because I'm moving quickly, do course correction quickly - basically I'm not happy blocking my chain of thought/concentration and fall prey to distractions due to Claude's slowness and compaction cycles. Sometimes I don't even notice that Codex has compacted.
For architectural discussions, sure I'll pick Claude. I'm mentally prepared for that. But once we are in the thick of things, speed matters. I would they rather focus on improving Sonnet's speed.
[0] https://she-llac.com/claude-limits
[1] https://xcancel.com/bcherny/status/2044839936235553167
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.
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.
And if it's not good enough for coding, what kind of money, if any, would make it good enough?
Re-ran the bake-off with 4.7 authoring and… gpt5.4 still clearly winning. Same skills, same prompts, same agents.md.
This is not so much about my instructions being followed more closely. It's the LLM being smarter about what's going on and for example saving me time on unnecessary expeditions. This is where models have been most notably been getting better to my experience. Understanding the bigger picture. Applying taste.
It's harder to measure, of course, but, at least for my coding needs, there is still a lot of room here.
If one session costs an additional 20% that's completely fine, if that session gets me 20% closer to a finished product (or: not 20% further away). Even 10% closer would probably still be entirely fine, given how cheap it is.
However, if you are using API costs then I guess you're left holding the bag.
/model claude-opus-4-6But it looks like it's just creeping up. Probably because we're paying for construction, not just inference right now.
> The model will not silently generalize an instruction from one item to another.
I am clearly missing something but wouldn't this be an ideal thing to do? Surely if it was optimised it would use less tokens while not losing anything from the instructions ?
4.6 performers worse or the same in most of the tasks I have. If there is a parameter that made me use 4.6 more frequently is because 4.5 get dumber and not because 4.6 seemed smarter.