>we don't want a hard dependency on another multi-billion dollar company just to write software

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.

>perhaps we can come up with something like the "linux/postgres/git/http/etc" of the LLMs

I fear that this may not be feasible in the long term. The open-model free ride is not guaranteed to continue forever; some labs offer them for free for publicity after receiving millions in VC grants now, but that's not a sustainable business model. Models cost millions/billions in infrastructure to train. It's not like open-source software where people can just volunteer their time for free; here we are talking about spending real money upfront, for something that will get obsolete in months.

Current AI model "production" is more akin to an industrial endeavor than open-source arrangements we saw in the past. Until we see some breakthrough, I'm bearish on "open models will eventually save us from reliance on big companies".

Another aspect I haven’t seen discussed too much is that if your competitor is 10x more productive with AI, and to stay relevant you also use AI and become 10x more productive. Does the business actually grow enough to justify the extra expense? Or are you pretty much in the same state as you were without AI, but you are both paying an AI tax to stay relevant?

My understanding is that the major part of the cost of a given model is the training - so open models depend on the training that was done for frontier models? I'm finding hard to imagine (e.g.) RLHF being fundable through a free software type arrangement.

I'm hopeful that new efficiencies in training (Deepseek et al.), the impressive performance of smaller models enhanced through distillation, and a glut of past-their-prime-but-functioning GPUs all converge make good-enough open/libre models cheap, ubiquitous, and less resource-intensive to train and run.

Open models keep closing the eval gap for many tasks, and local inference continues to be increasingly viable. What's missing isn't technical capability, but productized convenience that makes the API path feel like the only realistic option.

Frontier labs are incentivized to keep it that way, and they're investing billions to make AI = API the default. But that's a business model, not a technical inevitability.

The lock in is so incredibly poor. I could switch to whatever provider in minuets.

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.

What open models are truly competing with both Claude Code and Opus 4.7 (xhigh) at this stage?

> open models

Google just released Gemma 4, perhaps that'd be worth a try?

> I think that's the way forward. Actually it would be great if everybody would put more focus on open models,

I'm still surprised top CS schools are not investing in having their students build models, I know some are, but like, when's the last time we talked about a model not made by some company, versus a model made by some college or university, which is maintained by the university and useful for all.

It's disgusting that OpenAI still calls itself "Open AI" when they aren't truly open.

Any recommendations on good open ones? What are you using primarily?

Opencode go with open models is pretty good

I can recommend this stack. It works well with the existing Claude skills I had in my code repos:

1. Opencode

2. Fireworks AI: GLM 5.1

And it is SIGNIFICANTLY cheaper than Claude. I'm waiting eagerly for something new from Deepseek. They are going to really show us magic.

> we don't want a hard dependency on another multi-billion dollar company just to write software

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!

>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

Training and inference costs so we would have to pay for them.

Is that why they are racing to release so many products? It feels to me like they want to suck up the profits from every software vertical.

From what I understand you shouldn't wait more than 5min between prompts without compacting or clearing or you'll pay for reinitializing the cache. With compaction you still pay but it's less input tokens. (Is compaction itself free?)

For some real data, Artificial Analysis reported that 4.6 (max) and 4.7 (max) used 160M tokens and 100M tokens to complete their benchmark suite, respectively:

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.

Yes. I actually noticed my token usage go down on 4.6 when I started switching every session to max effort. I got work done faster with fewer steps because thinking corrected itself before it cycled.

I’ve noticed 4.7 cycling a lot more on basic tasks. Though, it also seems a bit better at holding long running context.

With AIs, it seems like there never is a comparison that is useful.

Why is it not useful? Input token pricing is the same for 4.7. The same prompt costs roughly 30% more now, for input.

absolutely!

Writing Opus 4.6 to 4.7 does make more sense for people who read left to right.

> you just limit the space to text

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?

You probably are.

The "small subset" argument is profoundly unconvincing, and inconsistent with both neurobiology of the human brain and the actual performance of LLMs.

The transformer architecture is incredibly universal and highly expressive. Transformers power LLMs, video generator models, audio generator models, SLAM models, entire VLAs and more. It not a 1:1 copy of human brain, but that doesn't mean that it's incapable of reaching functional equivalence. Human brain isn't the only way to implement general intelligence - just the one that was the easiest for evolution to put together out of what it had.

LeCun's arguments about "LLMs can't do X" keep being proven wrong empirically. Even on ARC-AGI-3, which is a benchmark specifically designed to be adversarial to LLMs and target the weakest capabilities of off the shelf LLMs, there is no AI class that beats LLMs.

Mirrors my sentiment. Those tools seem mostly useful for a Google alternative, scaffolding tedious things, code reviewing, and acting as a fancy search.

It seems that they got a grip on the "coding LLM" market and now they're starting to seek actual profit. I predict we'll keep seeing 40%+ more expensive models for a marginal performance gain from now on.

> ... but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results.

This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff.

My prior: it is 10X to 20X more likely Anthropic has done something other than shift to a short-term squeeze their customers strategy (which I think is only around ~5%)

What do I mean by "something other"? (1) One possibility is they are having capacity and/or infrastructure problems so the model performance is degraded. (2) Another possibility is that they are not as tuned to to what customers want relative to what their engineers want. (3) It is also possible they have slowed down their models down due to safety concerns. To be more specific, they are erring on the side of caution (which would be consistent with their press releases about safety concerns of Mythos). Also, the above three possibilities are not mutually exclusive.

I don't expect us (readers here) to agree on the probabilities down to the ±5% level, but I would think a large chunk of informed and reasonable people can probably converge to something close to ±20%. At the very least, can we agree all of these factors are strong contenders: each covers maybe at least 10% to 30% of the probability space?

How short-sighted, dumb, or back-against-the-wall would Anthropic have to be to shift to a "let's make our new models intentionally _worse_ than our previous ones?" strategy? Think on this. I'm not necessarily "pro" Anthropic. They could lose standing with me over time, for sure. I'm willing to think it through. What would the world have to look like for this to be the case.

There are other factors that push back against claims of a "short-term greedy strategy" argument. Most importantly, they aren't stupid; they know customers care about quality. They are playing a longer game than that.

Yes, I understand that Opus 4.7 is not impressing people or worse. I feel similarly based on my "feels", but I also know I haven't run benchmarks nor have I used it very long.

I think most people viewed Opus 4.6 as a big step forward. People are somewhat conditioned to expect a newer model to be better, and Opus 4.7 doesn't match that expectation. I also know that I've been asking Claude to help me with Bayesian probabilistic modeling techniques that are well outside what I was doing a few weeks ago (detailed research and systems / software development), so it is just as likely that I'm pushing it outside its expertise.

What's your reasoning effort set to? Max now uses way more tokens and isn't suggested for most usecases. Even the new default (xhigh) uses more than the old default (medium).

I haven't used Claude. Because I suspect this sort of things to come.

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.

Which plan are you on? I could see that happening with Pro (which I think defaults to Sonnet?), would be surprised with Max…

Are you using Claude subscription? Because that's not how it works there.

Same I was excited about 4.7 but seeing more anecdotes to conclude its not big of a boost to justify the extra tokenflatino

Sticking with codex. Also GPT 5.5 is set to come next week.

I haven't seen a noticeable difference BUT I've been always using the context mode plugin.

Running an open like Kimi constantly for an entire month will cost around 100-200$, being roughly equal to a pro-tier subscription. This is not my estimate so I’m more than open to hearing refutations. Kimi isn’t at all Opus-level intelligent but the models are roughly evenly sized from the guesses I’ve seen. So I don’t think it’s the infra being subsidized as much as it’s the training.

Tell that to oil and defense companies.

If tech companies convince Congress that AI is an existential issue (in defense or even just productivity), then these companies will get subsidies forever.

I've been assuming this for a while. If I have a complex feature, I use Opus 4.6 in copilot to plan (3 units of my monthly limit). Then have Grok or Gemini (.25-.33) of my monthly units to implement and verify the work. 80% of the time it works every time. Leave me plenty of usage over the month.

Not very encouraging for longer use, especially that the longer the conversation, the higher the chance the agent will go off the rails

I just switched fully into Codex today, off of Claude. The higher usage limits were one factor but I’m also working towards a custom harness that better integrates into the orchestrator. So the Claude TOS was also getting in the way.

Interesting. In conversational use, it's noticeably more verbose.

> is it really unthinkable that another oss/local model will be released by deepseek, alibaba, or even meta that once again give these companies a run for their money

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.

For my working style (fine-grained instructions to the agent), Opus 4.5 is basically ideal. Opus 4.6 and 4.7 seem optimized for more long-running tasks with less back and forth between human and agent; but for me Opus 4.6 was a regression, and it seems like Opus 4.7 will be another.

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.

I'm betting on a company like Taalas making a model that is perhaps less capable but 100x as fast, where you could have dozens of agents looking at your problem from all different angles simultaneously, and so still have better results and faster.

This regression put Anthropic behind Chinese models actually.

Nothing is unthinkable, I could think of Transformers.V2 that might look completely different, maybe iterations on Mamba turns out fruitful or countless of other scenarios.

Now that Anthropic have started hiding the chain of thought tokens, it will be a lot harder for them

Qwen released a new model the same day (3.6). The headline was kind of buried by Anthropic's release, though.

https://news.ycombinator.com/item?id=47792764

You can't accidentally retrain a model to use a different tokenizer. It changes the input vectors to the model.

Everything in AI moves super quickly, including the hivemind. Anthropic was the darling a few weeks ago after the confrontation with the DoD, but now we hate them because they raised their prices a little. Join us!

I doubt that’s the case. My guess is we’ll hit asymptomatic returns from transformers, but price-to-train will fall at moore’s law.

So over time older models will be less valuable, but new models will only be slightly better. Frontier players, therefore, are in a losing business. They need to charge high margins to recoup their high training costs. But latecomers can simply train for a fraction of the cost.

Since performance is asymptomatic, eventually the first-mover advantage is entirely negligible and LLMs become simple commodity.

The only moat I can see is data, but distillation proves that this is easy to subvert.

There will probably be a window though where insiders get very wealthy by offloading onto retail investors, who will be left with the bag.

If only there were an Open AI company who's mandate, built into the structure of the company, were to make frontier models available to everyone for the good of humanity.

Oh well

Yep, between this and the pricing for the code review tool that was released a couple weeks ago (15-25 a review), and the usage pricing and very expensive cost of Claude Design, I do wonder if Anthropic is making a conscious, incremental effort to raise the baseline for AI engineering tasks, especially for enterprise customers.

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.

Asking a seller to sell less.

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.

But with the same tokenizer, right?

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.

The long-term pitch of these AI companies is that the AI will essentially replace workers for low cost.

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.

I thought it would be to get better, to stay competitive with the competitors and free models.

The result is the same: they lose their brand of producing quality output. However the more clever the maneuver they try to pull off the more clear it is to their customers that they are not earning trust. That's what will matter at the end of this. Poor leadership at Claude.

That's the risk you take on.

There are 2 things to consider:

    * Time to market.
    * Building a house on someone else's land.

One could reconsider whether building your business on top of a model without owning the core skills to make your product is viable regardless.

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.

> if you're building on top of these models as a bootstrapped founder

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.

It must, if it now outputs more tokens than 4.6's tokenizer for the same input. I think the announcement and model cards provide a little more detail as to what exactly is different

Indeed, and if we accept the argument of this tech approaching AGI, we should expect that within x years, the subscription cost may exceed the salary cost of a junior dev.

To be clear, I'm not saying that it's a good thing, but it does seem to be going in this direction.

Hey, Boris from the Claude Code team here. People were getting extra cyber warnings when using old versions of Claude Code with Opus 4.7. To fix it, just run claude update to make sure you're on the latest.

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

4.7 is super variable in my one day experience - it occasionally just nails a task. Then I'm back to arguing with it like it's 2023.

Never attribute to incompetence what is sufficiently explained by greed.