Apple Foundation Models
platform.claude.com399 points by MehrdadKhnzd 13 hours ago
399 points by MehrdadKhnzd 13 hours ago
This is Apple commoditizing LLMs while keeping control of the UX.
They are a hardware company and will keep selling the best machine for AI use. Well done.
Benedict Evans may be right after all; frontier models look more and more like telecom companies in the 90s. Billions and billions of investment in infrastructure while others further up the stack captured all the value.
There will be frontier models that are non-commoditized, but they'll be kept guarded and hidden away, and you'll only get the final result, so that they can't be distilled and their harness can't be reverse engineered. They'll be billed like employees, rather than like a tool.
The non-commodity network services of the early 1990’s and the non-commodity 3d graphics hardware of the mid-1990s made the same argument.
Isn't that what they are doing already? The model is already guarded and hidden and i only get to send it what i want. Talk with it to clarify my requirements. And i can switch to a different provider for cheaper/better results.
I doubt that. What stops the Chinese labs from figuring it out? It’s not like these models are fundamentally different from each other
If all you have is the starting point and the finishing point, the lack of the path taken from one point to another limits your ability to train models that can efficiently recreate the work, and increases its cost enough that it's possible the US labs can progress capabilities faster than Chinese labs can distill that behavior.
> lack of the path taken from one point to another limits your ability to train models that can efficiently recreate the work
Isn’t this the problem inference (training) a model is designed to solve :)))
It is!
And it's a hard problem.
What's an easier form of training is being able to see the intermediate results and train to imitate them.
That’s already the case. Chinese ingenuity allowed them to achieve what they did without access to reasoning outputs
I think this will be isolated to highly specialized fields where training data will need to be selectively curated.
It is much better. Imagine if the whole Manhattan project could have been outsourced and costs you nothing. I expect in a short time that open source models will be almost or almost parity by 2030 and running on consumer devices.
Market phenomena like this are a bit like the Manhattan project in that you pay for it, and make use of it, whether you want to or not. It's functionally very similar to the government doing something.
In spite of their deeper pockets, massive datacenters, colosal amounts of user data, and hundreds of thousands of top developers, even Amazon, Meta, Microsoft, and Google are well behind.
I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)
>I think Evans is completely wrong.
I wish there was a case where I find Evans is wrong. As far as my memory served me, I failed to record a single one.
I disagree that Amazon, Meta, Microsoft, and Google are "well" behind. If anything the frontier model advantage seems to be at best 6 - 9 months. And that the Chinese model are all doing well.
One of Steve Jobs's line, "It is a feature, not a product." Even if Apple were a generation behind or 1 year behind frontier model. The advantage of default is enough to hold a lot of its user.
To put it simply, even if OpenAI or Anthropic were better, there is zero chances they would topple Apple in hardware sales, user or ecosystem. On the other hand, even if Apple's AI were 6 - 9 months or a generation behind, most user would settle for it and damage OpenAI / Anthropic.
> On the other hand, even if Apple's AI were 6 - 9 months or a generation behind,
Do you mean Google's AI with Apple wrappers? Apple's in-house AI is further behind Google, amd very far from the frontier according to your ranking. IMO, Google is on the frontier - I recall Altman calling for an OpenAI all-hands-on deck when Gemini was released because of how good it was compared to ChatGPT. I also suspect Google has the lowest operating expenses due to scale, experience and luck/planning (TPUs), there will come a time when AI investments will slow down, and the cost of revenue will become more important.
Even their own employees get frustrated if they can't use Claude or Codex. 6-9 months is a big difference and I think it's closer to 9 than 6. And never mind the harness etc are also many months behind.
This is just wishful thinking. I am sure someone from gossip media will also find Apple employees who are ready to leave job if Apple disallows Claude usage.
If anything Apple should notice it is Anthropic has got a really good marketing team and it would be no shame if they pick a trick or two from them.
Remember the implicit “pareto” in “frontier models”.
Anthropic and OpenAI are far behind state of the art for the entire curve except the “extremely expensive for barely measurable improvements” part.
GLM is probably the third most expensive frontier model (benchmarks and reviews will say for sure), and is apparently ~Opus 4.6 for 10% the inference cost.
The last I checked, qwen was still owning the 24-32GiB RAM range (it runs reasonably without a GPU!) and somewhere around 3.5-4 generation models.
Also, even anthropic says Mythos ~= ChatGPT 5.5, so it’s unlikely either one is leaving the other behind. The big problem they both have is they asked for the government to gate keep model releases and use cases, and their wish was granted.
That’s knocked them back 6 months already. Anthropic’s only frontier offering has been taken down.
I use both Claude and Codex and don’t see any meaningful difference between the two. My use case is modeling semi complex physical processes (energy and manufacturing) in code for simulations. I also have to do a good fair of automation via scripting in Python or PowerShell for manipulating data as well as legacy code analysis (C, Fortran, COBOL). Given I provide the models with the information and documentation they need, both perform very similarly. I recently did a full codebase review (for design patterns and vulnerabilities) and both Codex and Fable agreed 100% about the most critical findings. I do very little front end development, although some of my automation scripts have TUIs and again no problem with either Claude or Codex generating them for me. At this point I go with the less expensive, which seems to be Codex. With the $100 plan I rarely hit the limits. With Claude I max out my plan in about 4-6 hours of work.
Did you find much of a difference between Fable and Opus?
Yes. Fable is much more organized and consistent at taking small bites of the (sorry) apple when solving a problem. Specifically I'm talking about a machine learning problem I'd been working on for awhile with Opus and it was (and is, again) constantly stating that all the signal is exploited, everything is now overfit, etc, etc, etc. The first day I pointed Fable at the situation I got a 10% improvement by paying attention to the little details that Opus instead took slightly negative results and extrapolated to "fully exploited". I've had to drop back, again, to forcing Opus to explain what it's looked at and the detail it has quietly assumed away.
It's like the difference to talking to two smartest kids in a class, but one really belongs a grade higher - and the other hasn't learned yet to ask the questions that encourage it to dig in that little bit more for the additional multi-order effects.
Had a very similar experience. Opus went "look, t-sne shows your features are neatly clustered" (it didn't) and left it at that. Fable didn't fully explore the problem/data, but it did go much further, implementing models to check for correlations and adjust feature clusters. Opus was able to finish the job after Fable was cut, but required much prodding (doing exactly what you described: pointing it towards things that look off and asking it, are you sure that's all there is to this?).
I constantly hit safety blocks in Fable (I’m trying to write secure software, which is equivalent to finding security holes, so banned).
I didn’t use it on big enough tasks to notice any improvement.
I had been hitting plan limits pretty regularly, but fixed it by changing my workflow. That also increased the success rate of claude by an order of magnitude.
I think it's highly likely that there will remain one or two companies on the very bleeding edge of AI development for the foreseeable future.
But what I think a lot of people miss is that the market for the truly bleeding edge (developing bio-tech, building the most sophisticated software stacks (probably with a tilt towards simulation, GPU kernel optimization, etc)) is not the whole market.
There's a plethora of use-cases for models that are not on the bleeding edge. If I can solve my relatively simple problems with an off-the-shelf model for a minuscule fraction of the cost of the frontier, I'm going to.
Anecdotal case in point, but writing mostly enterprise CRUD in C#, I've gotten plenty of mileage out of Sonnet, very rarely do I need to use Opus.
Its somewhat of a myth that you need the most advanced, expensive model for software development.
I'm perfectly happy at claude opus 4.6. All improvements since then have not meaningfully improved my day to day. If i can get 4.6 on my laptop for 5-10k, i'd gladly start shifting my ~1k/month Anthropic spend over.
Some of the harness even let you run a local model for most things, and only pay for the latest frontier models when needed, which cuts down cost drastically.
Is Google behind? The general opinions I read suggest Gemini is very competitive with Anthropic and OpenAI's top models.
> I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)
Truly fascinating ecosystem and community in general, as experiences differ so wildly. Anthropic's models seems far behind OpenAI to me, especially when you get into "Pro" territory, and there doesn't seem to be any worthy competition to Pro Mode available at all.
And this is said with someone who use both platforms, and spend a lot of my day interacting with agents and LLMs in various ways. The interesting part is that probably so do you too, and probably your experience and what you share lines up with what you experience! Yet we come away with basically opposite takeaways :) I don't think either of us are wrong either, somehow.
I agree with what you're saying. I have a Claude plan for work and I prefer using Claude more than any other LLM I've tried. Having recently tried the Codex 100€ plan with GPT-5.5 in high/xhigh, I don't think it's worse that the Opus models, just different.
I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.
Just my two cents.
> I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.
Yeah, exact prompting matters a lot, seemingly more than people think. There is definitely tradeoffs between how literal the models takes the prompts, on one hand it's useful for the model to ignore their own instinct when you know better, so they don't go chasing geese randomly, but on the other hand it's useful sometimes when they self-direct, when you misworded something and it's obvious you meant something different because of the context, and similar things. They're basically good at different things.
Really agree every model isn't equal and they aren't as interchangeable without adjusting how you prompt them as people seem to think.
People use a model as their daily driver, get very familiar with it and it's behavior, and then go and use another model and have a hard time. It's very difficult to separate "the model is bad" from "the model works differently".
> It's very difficult to separate "the model is bad" from "the model works differently"
At which point it’s fair to reject the commoditization label.
Also missing from these discussions are e.g. Qwen, which is at least as good as one back from OpenAI or Anthropic’s frontiers.
> Also missing from these discussions are e.g. Qwen, which is at least as good as one back from OpenAI or Anthropic’s frontiers.
They're missing in the discussion because the ones you can run locally, aren't actually "one step away from other closed-source labs" in practice when you use them. They might benchmark as such, but they're sadly far away from measuring up to those scores except for very specific use cases, even when you have say 96GB of VRAM available to run the bigger models even most (at home) consumers won't be able to run.
> the ones you can run locally, aren't actually "one step away from other closed-source labs"
And they probably won’t be for at least another decade. Comparing like with like, flagship model running on the best hardware it can run on, Qwen is close.
> Qwen is close
I wish so badly this was true, but sadly today it just isn't.