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.
I don't understand how people measure how much more or less work they need to do. It's not that gpt-4o was incapable of exuding enormous amounts of code quickly, it's that the tokens were relativ garbage.
How do you have an opinion on 4.6/4.7 here? It's less clear but I could totally see that 4.7 or beyond leads to project completion 20% faster, by removing dead ends, foot guns, less backtracking, etc.
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
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.
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.
For me it was pretty clear from the start that costs will have to increase. It is the classical drug dealer model: first you hook them with cheap supply, maybe even free, then you slowly jack the prize up to a level that can (just) be sustained. Then you decrease the quality of the product by diluting it so you get more bucks for each gram you bought. You could also call it enshittification if you like.
The goal of every company that needs to make ever more money for investors is to earn more money while spending less. There are many ways of doing this without reducing the quality of the product, e.g. using less staff to do more, getting more compute out of same the energy, using cheaper or free energy, optimizing algorithms in ways that do not degrade quality or you grow because you gain more customers and break into new markets etc. And once you made all these optimizations and the market is saturated, then the only optimizations left are the ones where the quality goes down or the risk is increased. Quality in that sense, is what you can get away with without customers jumping ship. So you will also work on locking customers in and make jumping ship look very hard and complicated.
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).
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.
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.
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.
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.
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.
At this point, I still don't see a reason to use Opus. I'm happy with Sonnet's performance for a third of the price. Tried several times with not a big gain.
I'm mostly surprised that people found the output quality of Opus 4.6 good enough... 4.7 so far is a pretty sizable improvement for the stuff I care about. I don't really care how cheap 4.6 was per task when 90% of the tasks weren't actually being done correctly. Or maybe it's that people like the LLM agreeing with them blindly while sneakily doing something else under the hood? Did people enjoy Claude routinely disregarding their instructions? Not really sure I understand, I truly found 4.6 immensely frustrating (from the getgo, not just the "pre-nerf" version, whatever that means). 4.7 is a buggy mess, it's slow, and it costs a lot per token. It's also a huge breath of fresh air because it actually seems to make a good faith effort at doing the thing you asked it to do, and doesn't waste your time with irrelevant nonsense just to make it look busy or because it thinks you want that nonsense (I mean, it still does all of these things to some extent, but so far it seems like it does them much less than 4.6 did).
Disclaimer: I'm always running on max and don't really have token limits so I am in a position not to care about cost per token. But I am not surprised by the improved benchmark results at all, 4.6 was really not nearly as strong of a model as people seem to remember it being.
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.
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
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.
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.
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.
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.
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.
(Submitted title was "Claude Opus 4.7 costs 20–30% more per session". We've since changed it to a (more neutral) version of what the article's title says.)
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.
The cost of intelligence is non-linear, with slightly dumber models costing much less. For a growing surface of problems you do not need frontier intelligence. You should use frontier intelligence for situations where you would otherwise require human intervention throughout the workflow, which is much more expensive than any model.
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?
gemma4 and qwen3.6 are pretty capable but will be slower and wrong more often than the larger models. But you can connect gemma4 to opencode via ollama and it.. works! it really can write and analyze code. It's just slow. You need serious hardware to run these fast, and even then, they're too small to beat the "frontier" models right now. But it's early days
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[ 3.7 ms ] story [ 69.5 ms ] threadGiven 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.
How do you have an opinion on 4.6/4.7 here? It's less clear but I could totally see that 4.7 or beyond leads to project completion 20% faster, by removing dead ends, foot guns, less backtracking, etc.
How to tell / measure effectively? No clue.
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.
But it ignores completely the fact that the same intelligence is dropping by an order of magnitude (at least) every 12 months.
GPT o1 launched at $600/M output tokens and GPT4.5 launched at $150/M.
Opus 4.7 is $25/M for more intelligence
The goal of every company that needs to make ever more money for investors is to earn more money while spending less. There are many ways of doing this without reducing the quality of the product, e.g. using less staff to do more, getting more compute out of same the energy, using cheaper or free energy, optimizing algorithms in ways that do not degrade quality or you grow because you gain more customers and break into new markets etc. And once you made all these optimizations and the market is saturated, then the only optimizations left are the ones where the quality goes down or the risk is increased. Quality in that sense, is what you can get away with without customers jumping ship. So you will also work on locking customers in and make jumping ship look very hard and complicated.
1. https://github.com/juliusbrussee/caveman
I'm already at 27% of my weekly limit in ONE DAY.
https://news.ycombinator.com/item?id=47799256
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.
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.
And it's not that they "don't notice" it's that they physically can't distinguish finer angular separation.
Until it's making 100k decisions a day and many are dependent on previous results.
Disclaimer: I'm always running on max and don't really have token limits so I am in a position not to care about cost per token. But I am not surprised by the improved benchmark results at all, 4.6 was really not nearly as strong of a model as people seem to remember it being.
[0] https://she-llac.com/claude-limits
[1] https://xcancel.com/bcherny/status/2044839936235553167
Re-ran the bake-off with 4.7 authoring and… gpt5.4 still clearly winning. Same skills, same prompts, same agents.md.
> 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
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.
Surely you can see the first lab that solves this gains a massive advantage?
And if it's not good enough for coding, what kind of money, if any, would make it good enough?