We'll be keeping an eye on open models (of which we already make good use of). I think that's the way forward. Actually it would be great if everybody would put more focus on open models, perhaps we can come up with something like the "linux/postgres/git/http/etc" of the LLMs: something we all can benefit from while it not being monopolized by a single billionarie company. Wouldn't it be nice if we don't need to pay for tokens? Paying for infra (servers, electricity) is already expensive enough
One of two main reasons why I'm wary of LLMs. The other is fear of skill atrophy. These two problems compound. Skill atrophy is less bad if the replacement for the previous skill does not depend on a potentially less-than-friendly party.
It was an experiment to see if I could enter a mature codebase I had zero knowledge of, look at it entirely through an AI, and come to understand it.
And it worked! Even though I've only worked on the codebase through Claude, whenever I pick up a ticket nowadays I know what file I'll be editing and how it relates to the rest of the code. If anything, I have a significantly better understanding of the codebase than I would without AI at this point in my onboarding.
I've worked with people who will look at code they don't understand, say "llm says this", and express zero intention of learning something. Might even push back. Be proud of their ignorance.
It's like, why even review that PR in the first place if you don't even know what you're working with?
A good dev would've read deeper into the concern and maybe noticed potential flaws, and if he had his own doubts about what the concern was about, would have asked for more clarification. Not just feed a concern into AI and fling it back. Like please, in this day and age of AI, have the benefit of the doubt that someone with a concern would have checked with AI himself if he had any doubts of his own concern...
And no, I don't understand them at all. Taking responsibility for something, improving it, and stewarding it into production is a fantastic feeling, and much better than reading the comment section. :)
We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs.
I am learning at an incredible rate with LLMs.
But I’m so much more detached of the code, I don’t feel that ‘deep neural connection’ from actual spending days in locked in a refactor or debugging a really complex issue.
I don’t know how a feel about it.
Sure, you don't know the code by heart, but people debugging code translated to assembly already do that.
The big difference is being able to unleash scripts that invalidate enormous amount of hypothesis very fast and that can analyze the data.
Used to do that by hand it took hours, so it would be a last resort approach. Now that's very cheap, so validating many hypothesis is way cheaper!
I feel like my "debugging ability" in terms of value delivered has gone way up. For skill, it's changing. I cannot tell, but the value i am delivering for debugging sessions has gone way up
But if you don't and there's no PR process (side projects), the motivation to form that connection is quite low.
You very much decide how you employ LLMs.
Nobody are keeping a gun to your head to use them. In a certain way.
Sonif you use them in a way that increase you inherent risk, then you are incredibly wrong.
I understand why a designer might read this post and not be happy about it. If you don't think your management values or appreciates design skill, you'd worry they're going to glaze over the bullet points about design productivity, and jump straight to the one where PMs and marketers can build prototypes and ignore you. But that's not what the sales pitch is focused on.
This all bumps up against the fact that most people default to “you use the tool wrong” and/or “you should only use it to do things where you already have firm grasp or at least foundational knowledge.”
It also bumps against the fact that the average person is using LLM’s as a replacement for standard google search.
What an interesting paradox-like situation.
Well, if internet is down, so is our revenue buddy. Engineering throughput would be the last of our concerns.
I don't believe it. Having something else do the work for you is not learning, no matter how much you tell yourself it is.
Having other people do work for you is how people get to focus on things they actually care about.
Do you use a compiler you didn't write yourself? If so can you really say you've ever learned anything about computers?
Open your eyes, and you might become a believer.
It does seem like there is a cult of people who categorically see LLMs as being poor at anything without it being founded in anything experience other than their 2023 afternoon to play around with it.
Could you do it again without the help of an LLM?
If no, then can you really claim to have learned anything?
And yes. If LLMs disappear, then we need to hire a lot of people to maintain the infrastructure.
Which naturally is a part of the risk modeling.
It’s quite possible to be deep into solving a problem with an LLM guiding you where you’re reading and learning from what it says. This is not really that different from googling random blogs and learning from Stack Overflow.
Assuming everyone just sits there dribbling whilst Claude is in YOLO mode isn’t always correct.
Not everyone learns at the same pace and not everyone has the same fault tolerance threshold. In my experiencd some people are what I call "Japanese learners" perfecting by watching. They will learn with AI but would never do it themselves out of fear of getting something wrong while they understand most of it, others that I call "western learners" will start right away and "get their hands dirty" without much knowledge and also get it wrong right away. Both are valid learning strategies fitting different personalities.
And not even just understanding, but verifying that they’ve implemented the optimal solution.
It’s like saying clothing manufacturers are paying the “loom tax” tax when they could have been weaving by hand…
I've said it before and I'll say it again, local models are "there" in terms of true productive usage for complex coding tasks. Like, for real, there.
The issue right now is that buying the compute to run the top end local models is absurdly unaffordable. Both in general but also because you're outbidding LLM companies for limited hardware resources.
You have a $10K budget, you can legit run last year's SOTA agentic models locally and do hard things well. But most people don't or won't, nor does it make cost effective sense Vs. currently subsidized API costs.
So my point is: If you have the attitude that unless it is the bleeding edge, it may have well not exist, then local models are never going to be good enough. But truth is they're now well exceeding what they need to be to be huge productivity tools, and would have been bleeding edge fairly recently.
But it requires that one does not do something stupid.
Eg. For recurring tasks: keep the task specification in the source code and just ask Claude to execute it.
The same with all documentation, etc.
Training and inference costs so we would have to pay for them.
model elo $/M
---------------------------------------
glm-5.1 1538 2.60
glm-4.7 1440 1.41
minimax-m2.7 1422 0.97
minimax-m2.1-preview 1392 0.78
minimax-m2.5 1386 0.77
deepseek-v3.2-thinking 1369 0.38
mimo-v2-flash (non-thinking) 1337 0.24
My manager doesn't even want us to use copilot locally. Now we are supposed to only use the GitHub copilot cloud agent. One shot from prompt to PR. With people like that selling vendor lock in for them these companies like GitHub, OpenAI, Anthropic etc don't even need sales and marketing departments!
I hit my 5 hour limit within 2 hours yesterday, initially I was trying the batched mode for a refactor but cancelled after seeing it take 30% of the limit within 5 minutes. Had to cancel and try a serial approach, consumed less (took ~50 minutes, xhigh effort, ~60% of the remaining allocation IIRC), but still very clearly consumed much faster than with 4.6.
It feels like every exchange takes ~5% of the 5 hour limit now, when it used to be maybe ~1-2%. For reference I'm on the Max 5x plan.
For now I can tolerate it since I still have plenty of headroom in my limits (used ~5% of my weekly, I don't use claude heavily every day so this is OK), but I hope they either offer more clarity on this or improve the situation. The effort setting is still a bit too opaque to really help.
https://artificialanalysis.ai/?intelligence-efficiency=intel...
Looking at their cost breakdown, while input cost rose by $800, output cost dropped by $1400. Granted whether output offsets input will be very use-case dependent, and I imagine the delta is a lot closer at lower effort levels.
I’ve noticed 4.7 cycling a lot more on basic tasks. Though, it also seems a bit better at holding long running context.
Though, from my limited testing, the new model is far more token hungry overall
My workflow is to give the agent pretty fine-grained instructions, and I'm always fighting agents that insist on doing too much. Opus 4.5 is the best out of all agents I've tried at following the guidance to do only-what-is-needed-and-no-more.
Opus 4.6 takes longer, overthinks things and changes too much; the high-powered GPTs are similarly flawed. Other models such as Sonnet aren't nearly as good at discerning my intentions from less-than-perfectly-crafted prompts as Opus.
Eventually, I quit experimenting and just started using Opus 4.5 exclusively knowing this would all be different in a few months anyway. Opus cost more, but the value was there.
But now I see that 4.7 is going to replace both 4.5 and 4.6 in VSCode Copilot, and with a 7.5x modifier. Based on the description, this is going to be a price hike for slower performance — and if the 4.5 to 4.6 change is any guide, more overthinking targeted at long-running tasks, rather than fine-grained. For me, that seems like a step backwards.
And yes, Claude models are generally more fun to use than GPT/Codex. They have a personality. They have an intuition for design/aesthetics. Vibe-coding with them feels like playing a video game. But the result is almost always some version of cutting corners: tests removed to make the suite pass, duplicate code everywhere, wrong abstraction, type safety disabled, hard requirements ignored, etc.
These issues are not resolved in 4.7, no matter what the benchmarks say, and I don't think there is any interest in resolving them.
After just ~4 prompts I blew past my daily limit. Another ~7 more prompts & I blew past my weekly limit.
The entire HTMl/CSS/JS was less than 300 lines of code.
I was shocked how fast it exhausted my usage limits.
With enterprise subscription, the bill gets bigger but it's not like VP can easily send a memo to all its staff that a migration is coming.
Individuals may end their subscription, that would appease the DC usage, and turn profits up.
Sticking with codex. Also GPT 5.5 is set to come next week.
And even then... why can't they write a novel? Or lowering the bar, let's say a novella like Death in Venice, Candide, The Metamorphosis, Breakfast at Tiffany's...?
Every book's in the training corpus...
Is it just a matter of someone not having spent a hundred grand in tokens to do it?
Our default topology is a two-agent pair: one implementer and one reviewer. In practice, that usually means Opus writing code and Codex reviewing it.
I just finished a 10-hour run with 5 of these teams in parallel, plus a Codex run manager. Total swarm: 5 Opus 4.7 agents and 6 Codex/GPT-5.4 agents.
Opus was launched with:
`export CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=35 claude --dangerously-skip-permissions --model 'claude-opus-4-7[1M]' --effort high --thinking-display summarized`
Codex was launched with:
`codex --dangerously-bypass-approvals-and-sandbox --profile gpt-5-4-high`
What surprised me was usage: after 10 hours, both my Claude Code account and my Codex account had consumed 28% of their weekly capacity from that single run.
I expected Claude Code usage to be much higher. Instead, on these settings and for this workload, both platforms burned the same share of weekly budget.
So from this datapoint alone, I do not see an obvious usage-efficiency advantage in switching from Opus 4.7 to Codex/GPT-5.4.
Maybe I missed it, but it doesn’t tell you if it’s more successful for less overall cost?
I can easily make Sonnet 4.6 cost way more than any Opus model because while it’s cheaper per prompt it might take 10x more rounds (or never) solve a problem.
Is Opus 4.7 that significantly different in quality that it should use that much more in tokens?
I like Claude and Anthropic a lot, and hope it's just some weird quirk in their tokenizer or whatnot, just seems like something changed in the last few weeks and may be going in a less-value-for-money direction, with not much being said about it. But again, could just be some technical glitch.
Having a taste of unnerfed Opus 4.6 I think that they have a conflict of interest - if they let models give the right answer first time, person will spend less time with it, spend less money, but if they make model artificially dumber (progressive reasoning if you will), people get frustrated but will spend more money.
It is likely happening because economics doesn't work. Running comparable model at comparable speed for an individual is prohibitively expensive. Now scale that to millions of users - something gotta give.
To me this seems more that it's trained to be concise by default which I guess can be countered with preference instructions if required.
What's interesting to me is that they're using a new tokeniser. Does it mean they trained a new model from scratch? Used an existing model and further trained it with a swapped out tokeniser?
The looped model research / speculation is also quite interesting - if done right there's significant speed up / resource savings.
I'm surprised that it's 45%. Might go down (?) with longer context answers but still surprising. It can be more than 2x for small prompts.
It was on the higher end of Anthropics range - closer to 30-40% more tokens
https://www.claudecodecamp.com/p/i-measured-claude-4-7-s-new...
If tech companies convince Congress that AI is an existential issue (in defense or even just productivity), then these companies will get subsidies forever.
And shafting your customers too hard is bad for business, so I expect only moderate shafting. (Kind of surprised at what I've been seeing lately.)
If I can have Claude write up the plan, and the other models actually execute it, I'd get the best of both worlds.
(Amusingly, I think Codex tolerates being invoked by Claude (de facto tolerated ToS violation), but not the other way around.)
This gives me hope that even if future versions of Opus continue to target long-running tasks and get more and more expensive while being less-and-less appropriate for my style, that a competitor can build a model akin to Opus 4.5 which is suitable for my workflow, optimizing for other factors like cost.
Plenty of OSS models being released as of late, with GLM and Kimi arguably being the most interesting for the near-SOTA case ("give these companies a run for their money"). Of course, actually running them locally for anything other than very slow Q&A is hard.
That's an incentive difficult to reconcile with the user's benefit.
To keep this business running they do need to invest to make the best model, period.
It happens to be exactly what Anthropic's strategy is. That and great tooling.
The difference here is Opus 4.7 has a new tokenizer which converts the same input text to a higher number of tokens. (But it costs the same per token?)
> Claude Opus 4.7 uses a new tokenizer, contributing to its improved performance on a wide range of tasks. This new tokenizer may use roughly 1x to 1.35x as many tokens when processing text compared to previous models (up to ~35% more, varying by content), and /v1/messages/count_tokens will return a different number of tokens for Claude Opus 4.7 than it did for Claude Opus 4.6.
> Pricing remains the same as Opus 4.6: $5 per million input tokens and $25 per million output tokens.
ArtificialAnalysis reports 4.7 significantly reduced output tokens though, and overall ~10% cheaper to run the evals.
I don't know how well that translates to Claude Code usage though, which I think is extremely input heavy.
If the models don't get to a higher level of 'intelligence' and still struggle with certain basic tasks at the SOTA while also getting more expensive, then the pitch is misleading and unlikely to happen.
So yes, I expect the price to go down.
It's going to be a very expensive game, and the masses will be left with subpar local versions. It would be like if we reversed the democratization of compilers and coding tooling, done in the 90s and 00s, and the polished more capable tools are again all proprietary.
Oh well
OpenAI was built as you say. Google had a corporate motto of "Don't be evil" which they removed so they could, um, do evil stuff without cognitive dissonance, I guess.
This is the other kind of enshitification where the businesses turn into power accumulators.
You could call it a rug pull, but they may just be doing the math and realize this is where pricing needs to shift to before going public.
What I've been doing is running a dual-model setup — use the cheaper/faster model for the heavy lifting where quality variance doesn't matter much, and only route to the expensive one when the output is customer-facing and quality is non-negotiable. Cuts costs significantly without the user noticing any difference.
The real risk is that pricing like this pushes smaller builders toward open models or Chinese labs like Qwen, which I suspect isn't what Anthropic wants long term.
There are 2 things to consider:
* Time to market.
* Building a house on someone else's land.
A smaller builder might reconsider (re)acquiring relevant skills and applying them. We don't suddenly lose the ability to program (or hire someone to do it) just because an inference provider is available.
This is going to be blunt, but this business model is fundamentally unsustainable and "founders" don't get to complain their prospecting costs went up. These businesses are setting themselves up to get Sherlocked.
The only realistic exit for these kinds of businesses is to score a couple gold nuggets, sell them to the highest bidder, and leave.
The whole magic of (pre-nerfed) 4.6 was how it magically seemed to understand what I wanted, regardless of how perfectly I articulated it.
Now, Anth says that needing to explicitly define instructions are as a "feature"?!
In my opinion, we've reached some ceiling where more tokens lead only to incremental improvements. A conspiracy seems unlikely given all providers are still competing for customers and a 50% token drives infra costs up dramatically too.
Claude design on the other hand seemed to eat through (its own separate usage limit) very fast. Hit the limit this morning in about 45 mins on a max plan. I assume they are going to end up spinning that product off as a separate service.
To be clear, I'm not saying that it's a good thing, but it does seem to be going in this direction.
And junior devs have never added much value. The first two years of any engineer’s career is essentially an apprenticeship. There’s no value add from have a perpetually junior “employee”.
Under the hood, what was happening is that older models needed reminders, while 4.7 no longer needs it. When we showed these reminders to 4.7 it tended to over-fixate on them. The fix was to stop adding cyber reminders.
More here: https://x.com/ClaudeDevs/status/2045238786339299431
latest claude still fails the car wash test