Claude Opus 4.7 costs 20–30% more per session

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.

Or they are just not willing to burn obscene levels of capital like OpenAI.

This is also why I don't see the models getting commoditized anytime soon - the dimensionality of LLM output that is economically relevant keeps growing linearly for coding (therefore the possibility space of LLM outputs grows exponentially) which keeps the frontier nontrivial and thus not commoditized.

In contrast, there is not much demand for 100 page articles written by LLMs in response to basic conversational questions, therefore the models are basically commoditized at answering conversational questions because they have already saturated the difficulty/usefulness curve.

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).

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.

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?

You mean a couple of years ago?

At 7.5x for 4.7, heck no. It isn't even clear it is an upgrade over Opus 4.6.

I didn't buy Springles chips in years, even the box now is nothing like it was. Thinner. Shorter. I imagine how far from the top the slices stack up.

https://www.stet.sh/blog/opus-4-7-zod

https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-...

"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

If you can forgive the obviously-AI-generated writing, [CPUs Aren't Dead](https://seqpu.com/CPUsArentDead) makes an interesting point on AI progress: Google's latest, smallest Gemma model (Gemma 4 E2B), which can run on a cell phone, outperforms GPT-3.5-turbo. Granted, this factoid is based on `MT-Bench` performance, a benchmark from 2023 which I assume to be both fully saturated and leaked into the training data for modern LLMs. However, cross-referencing [Artificial Analysis' Intelligence Index](https://artificialanalysis.ai/models?models=gemma-4-e2b-non-...) suggests that indeed the latest 2B open-weights models are capable of matching or beating 175B models from 3-4 years ago. Perhaps more impressive, [Gemma 4 E4B matches or beats GPT-4o](https://artificialanalysis.ai/models?models=gemma-4-e4b%2Cge...) on many benchmarks.

If this trend continues, perhaps we'll have the capabilities of today's best models available to reasonably run on our laptops!

I personally think the whole "the newest model is crazy! You've gotta use X (insert most expensive model)" Is just FOMO and marketing-prone people just parroting whatever they've seen in the news or online.

it seems to hallucinate a bit more (anecdotal)

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)?

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.

2. Anthropic and OpenAI's financials are totally different. The former has nearly the same RRR and a fraction of the cash burn. There is a reason Anthropic is hot on secondary and OAI isn't

When I read these comments on Hacker News, I see a lot of people miffed about their personal subscription limits. I think this is a viewpoint that is very consumer focused, and probably within Anthropic they're seeing buckets of money being dumped on them from enterprises. They probably don't really care as much about the individual subscription user, especially power users.

You're offended by their political beliefs, so you don't like the way the model works?

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!

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.

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).

I thought coding harnesses provided tools to apply diffs so the LLM didn't have to echo back the entire file?

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).

I took the plan that I used from Codex and handed it to opencode with Qwen 3.5 running locally.

It created a library very similar to Codex but took 2x longer.

I haven't tried Qwen 3.6 but I hear it's another improvement. I'm confident with my AI skills that if/when cheap/subsidized models go away, I'll be fine running locally.

Fun fact: AWS offers apple silicon EC2 instances you can spin up to test.

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 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.

I find 5 thinking levels to be super confusing - I dont really get why they went from 3 -> 5

"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.

"Comparable" is doing some heavy lifting there. Comparable to Anthropic models in 1H'25, maybe.

https://platform.claude.com/docs/en/about-claude/pricing

So if you are generating more tokens, you are eating up your usage faster

That's one market segment - the high priced one, but not necessarily the most profitable one. Ferrari's 2025 income was $2B while Toyota's was $30B.

Maybe a more apt comparison is Sun Microsystems vs the PC Clone market. Sun could get away with high prices until the PC Clones became so fast (coupled with the rise of Linux) that they ate Sun's market and Sun went out of business.

There may be a market for niche expensive LLMs specialized for certain markets, but I'll be amazed if the mass coding market doesn't become a commodity one with the winners being the low cost providers, either in terms of API/subscriptions costs, or licensing models for companies to run on their own (on-prem or cloud) servers.

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.

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.

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.