> a Swift package that makes Claude available as a server-side language model in Apple's Foundation Models framework
Ahh I was hoping for the opposite: all of the existing features of Claude Code but somehow running locally on my laptop's neural engine. A pipe dream on an M2 with 8 GB of RAM, but I had a flicker of hope there.
Check out this WWDC session. Obviously not going to compete with the frontier models (and I think 8GB is too small anyways), but Apple did demo MLX + OpenCode.
If only we could buy 1TB of unified memory in a Mac for $1k-$2k in total hardware costs. Apple would basically be able to extinguish the entirety of the market cap for Nvidia, OpenAI, Anthropic, and others all at once.
In 10 years, I hope my MacBook Pro can run today's frontier models and has 1TB of unified Memory.
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.
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)
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 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.
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.
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 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.
> 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".
You mean the model that was available for a whole of three days? No, I had played around with it a tiny bit, but not much than that. I guess time will tell if it gets close.
Maybe I’m alone in thinking this but I think the long term victor will be the one that works out pricing best.
Fable might well be a better model but it’s too expensive for everyday AI use. Definitely if we’re talking about the kind of stuff you’re going to want to do on your phone. Even for coding, I’m not going to reach for Fable (well, when I can…) for 95% of the work I do.
I don’t believe a mature AI industry is going to have a one size fits all, single winner.
Yes, and pricing is one of the features of a commodity, because users can jump back and forth between services, it becomes a pricing race to the bottom. Agree also that you don’t need the best model all the time. You could have the most powerful model draft the design, requirements, guidelines, policies or whatnot then get the lower tier models execute it. Then again you can have the most powerful model do the testing and review, and give back feedback, rinse and repeat. Just like in the real world you don’t need an entire staff of lead engineers.
Extremely tangential, but this is my favourite upshot of AI. For decades, companies have been walling off their services and forcing us into their fuckass UIs. Now over the course of the last twelve months, suddenly everything has an MCP and I can use it through my command line chat interface.
Any company that doesn't adapt gets so hammered by people's AI-DIY web scrapers that they have no choice but to cave.
The play here seems pretty evidence, if I may assume. Apple creates an interface that is generalized enough so you can easily swap models, and while Claude is preferred by Apple today, it may be any provider or even local models in the future, and the APIs the developers use remain the same, so "migration" becomes easier.
for the on-device model, yes it runs on the Neural Engine (at the moment) so a newer chip means faster, cheaper local inference. For the server side path this Claude package is about your machine is irrelevant since it's a network call. The same API covers both, so "best machine for AI" only bites when the session is actually local.
But we can imagine that the balance of what's on-device vs what's remote will move continuously towards the former as time, improved HW and improved local models keep progressing
It's been clear for years now that eventually ai will be embedded at the os level. Apple even recognized it way back when they first introduced Apple Intelligence. Yes they're commoditizing llms or whatever. But this has been a user facing feature they've been iterating on for years now
The betas of the next OS's include a Siri AI chatbot, and the AI features are built into various parts of the OS. A user has no idea what model is powering any of it - Apple controls the UX.
The article is about (from the eyes of a user) white-labeled usage of Claude models on Apple devices, this subthread is about white-labeled usage of LLMs on Apple devices, how is it not relevant?
> The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.
That's the point! That's the whole "white-labeling" part, and what the commentator earlier is talking about. You're very close in understanding the context here!
I think you're taking the written words a bit too literally here. Read it with a more lax filter and less literal word-meaning, and I think the original comment will become a bit clearer.
You know what, I've been a bit too snipe-y in my previous comments, and it led to to discussion devolving in unproductive ways.
I'd genuinely like to understand where you're coming from more.
I think we're all in agreement that this framework is very much about letting developers swap the models easily, and treat them as commodities. That seems pretty obvious.
I do however still don't see how this has anything to do with controlling the UX (or the new Siri for that matter! The new Siri doesn't use Anthropic models, and there are no extensions point for it to do so — that's pretty much the whole reason why it won't be available in the EU).
I don't know if it helps. One way to look at it is branding product. Apple is branding the product. So they supposedly have more value to customers as it stands for quality, awareness, trust etc. As oppose to 100 little components in computer which maybe from different brands, and Apple may switch brand year to year without user noticing. So those components makers have little power over Apple.
Same is happening to Claude software package as it would stand behind branded Apple foundation models. From pure software developer thinking this is exactly what Claude offered here so where is the issue? Issue is in larger space where Apple could take steps to block Claude out of their ecosystem if they so wish at some point and there is little Claude / Anthropic would do if Apple Foundation is the only thing that Apple consumers would know about.
I can't reply to your child comment for whatever reason, but Siri is part of the Apple Foundation Models framework. The idea is that no matter what backend the developer uses, the end user will always say "Hey Siri." This is analogous to controlling the UX. Siri is independent of whichever model the app developer uses.
No, Siri is entirely separate from this framework.
Are you thinking about Intents? That lets Siri interact with data (and perform some actions in them) from your apps, but it is something completely different.
You can definitely expose things from your app via Intents that will end up calling an external arbitrary LLM somewhere, but it does not require using Foundation Models API whatsoever.
I think that's what they are trying to avoid. If you need on-device intelligence, their pitch was "The model the device already has is best", and if you need something more specific an adapter (aka, a fine-tune/lora) is best.
They were wrong when their on-device model was way behind. They still might be right in the long term.
While multiple app I use might need Gemma 4 E4B, I use dozens of apps and app devs can choose from hundreds of models. A shared cache might reduce size a little when there's overlap, but the core problem still exists. If each app chooses a model disk and memory-swapping explode.
Its probably be better for device manufacturers to bake in a default. I'm not proposing they limit you from using others, but one shared default might be best developer/user experience for 99% of apps.
- Being warm in memory is the single biggest perf speedup you can get, and a default is much more likely to be warm.
- "Best model" is usually "best model for this device" given both RAM and compute. A developer can't test every device but Apple can/will.
- Each model needs to be optimized for the hardware (what's running on ANE, what's running on Metal, what's running on CPU). The default gets optimized.
- If you need custom model, a Lora is probably best (30MB, benefits from all of the above)
You could say the default should be swappable, but that's more a linux ideal than an Apple one so I doubt we ever see that. Plus there are real downsides: intentional or not, prompts end up optimized to the model they are developed for, so swapping the default system model would degrade every app.
I see an id based ability suggesting `modelId`. but in current docs I cannot find any context to it. The other limit is that it suggests Swift Packages. but I'm not seeing any model management hints similar to Docker/Ollama/etc where:
- Application can ask for specific model, if available use it. if not, ask to download it (or try some fallback / alternative)
- User can manage models. So as a user I can clean unused models (and for non-techie have something similar to offloading apps when unused for some period of time).
The apps can use the system provided on-device model using the same framework and APIs; but there's no affordances to deduplicate custom models between apps.
I have a Mac with 4TB of storage but it’s still annoying when every new AI app I try installs its own virtual environment with a fresh copy of Python, PyTorch, other duplicate libraries, and then models on top of that.
As an occasional python user I'm always amazed and frustrated that it seems that the only way to be able to use/build anything is to create a whole separate environment.
And now given everybody now does this I guess the incentive to stop breaking stuff reduces even further.
The meme phrase “it’s fractally wrong” applies to the entire python ecosystem, IMHO. Virtual environments are just another layer of this fractal wrongness in the layer cake of ecosystem awfulness.
I have a couple small apps that have a (non-LLM) model, and originally the models and code were in PyTorch, built by Python devs.
The original plan was to ship Python. However I found out I can migrate them to CoreML, and now it's a model file + Swift code. I got some massive performance improvements as well.
Of course, this doesn't work at all for non-Mac environments, but it was nice to be able to do it. (Also doesn't solve the duplicate large models problem)
First Microsoft has broken keyfabe by putting "Copilot is for entertainment purposes only" in the Copilot terms of use and putting warnings in copilot for excel "avoid using COPILOT for ... any task requiring accuracy or reproducibility ... Tasks with legal, regulatory or compliance implications".
Then Apple quietly refuses to participate by not investing tens or hundreds of billions in creating a competing LLM. Sure, they resell Claude for the marks or utilize Gemini to placate the gullible fools but they know what's up.
Is this Apple encouraging developers to go through their api abstraction layer to use LLMs so that when they launch their own (which I think we’ve heard they’ve been spending lots of money on training and might be somehow involved with Siri or current Apple AI?) that they can easily help devs make a seamless transition? Or is it just a developer nicety or something else?
Apple has some clever mechanics to protect user data. I had to work with App tracking stuff lately and their approach to keeping user details private with anonymized cohorts (SKAN, Differential Privacy) before reporting tracking events to third party platforms was surprisingly well thought out. There is value in having them in your loop if you care about privacy.
My read of the ATT stuff is basically that it forced all the apps to use meta ad tracking because they’re the only ones who figured out how to serve relevant ads despite it.
This is support for a new framework that ships with reality/mac/iPad/watch/tv/iOS 27 (and that they've promised to open-source later in the year, so presumably you'll also be able to lean on this if you ship Swift on your backend).
The framework's whole deal is that it lets you use the same API to target either the device built-in models, the Apple-hosted online models (Private Cloud Computer), or write your own shims to call out to arbitrarily hosted online models.
You can then dynamically route your calls to a different kind of model/provider, using system APIs, without having to write your own abstraction layer over "I want to use local model for this, but I want to use Claude for that", or having to integrate your own API integration with Anthropic/OpenAI APIs.
It abstracts things like tool calling in one place; and has a bunch of other niceties/oddities (it keeps the same "transcript" going, even if you dynamically switch providers/models during a session) and some other things.
A dark, but not totally unfair take: It makes it easier for Apple to take payment for the models others provide, and even allows Apple, if they want to, to use the data to build a dataset for training their own models based on how users use third party models. It's only on Apple devices this API is used, so they split up the market by not letting developers use the same system if they want things to work on iOS, locking users even more in.
The cynic (or realist?) in my thinks this abstraction layer is Apple's way of making sure that users give their own Apple Intelligence credit for the underlying LLM functionality, even if another company is actually providing the LLM.
Yeah, Apple just designs and writes the SoC, CPU, graphics unit, neural unit, compiler (Swift), OS, graphics layer, 3D API, core libs from graphics to persistence, filesystem, broadband chip, and a few more things besides...
Maybe they plan to have the providers pay for being the default model? So basically, what Google is doing right now for search engines. The difference however is that Google is making money with additional search requests while AIs are (as of now) losing money with additional requests. I don't see the business case for them yet though.
I think this is just Apple planning for their on-device models getting better, which makes sense given they have access to Gemini now. If developers use this for all their code calling an external LLM, then as Apple's model becomes more capable and covers more use cases it'll be easy to switch to it at individual call sites. That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut of.
> That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut of.
With other words, it's unlikely to happen as there is no money in it. Better for Apple to create some new subscription "AI" and "AI-lite" plans people can subscribe to, and since Apple is a company and we all know what those care about, it's unlikely to become a utopia of local models running on your phone.
How can you practically use this in software if you're to deploy this to users? Asking a user to create and enter their own API key is a bar too high for good UX.
The even bigger hurdle is selling token based pricing to normal (non-dev) users.
"You pay an indeterminant amount of money to ask a question and you might not even get the response you want without spending even more money" doesn't appeal to most people who aren't gamblers and explaining how "thank you" at the end of a long exchange can be expensive due to context is an even harder thing for an average person to swallow.
Token cost going up/down like a yo-yo also doesn't help. Normal users NEED fixed costs and don't want to expend energy constantly keeping up with the AI meta. "My subscription lasted much longer last month" isn't a winning problem either.
I think Apple is correct that Local LLM for most things is the future.
Ugh. It really is. I have allihat.com which is the only safari extension (i think still) that talks to claude. And it's well sought for. But you as a user have to enter a friggin claude api key. :( And I still don't grok their TOS around this. Like you can still type: ```setup-token Set up a long-lived authentication token (requires Claude subscription)``` but this seems like a trap? :) Whose using this? Doesn't this like insta break their TOS if you use that anywhere?
Right now for allihat.com I just let people use the Apple model locally if you don't feel like using the claude key. And my conversions to paying user shot up like 3x! But it really isn't a replacement obviously to claude. I was hoping Apple would make proxying to Claude some kind of thing they do for me so I also don't have to proxy to my own server just to try and manage API to Claude usage.
I think Apple has a fairly good plan for supplying a common API and default on device models.
What confuses me about this article is: The code examples Python, Ruby, etc.) look to me like the original Anthropic APIs, not Apple’s abstraction. Did I miss something?
Coding agent itself an imposed layer. Now they are adding one more layer? Many times I think of coding agent as the vendor supervisor from the body shops of the 90's who promise the customer everything under the sky and thrash the poor contractor to deliver. Coding agents consume 10x more tokens just like how body shops charged their customers vs how they paid the contractors. For a simple test, the same task that makes the model to go out of context length when used via a coding agent, runs fine when prompted directly.
Layers are luxury and remove control and transparency.
From app developer standpoint why would anyone ship claude keys like that ... or am I missing something? From consumer standpoint - I guess they can use their own keys but it is not something that is very user friendly as you can imagine.
For production, route requests through your own back end with .proxied. The relay at baseURL adds the Claude API credential server-side, so the app ships no key. The headers you provide are sent on every request so your proxy can authorize the caller.
I’m surprised to see the model names hardcoded as an enum (e.g. `.sonnet4_6`), instead of a string with model discovery so that the user can select their preferred model without having to get a new app version through the App Store to support newer models.
>Model identifiers are values of ClaudeModel. Use a compiled-in constant, or construct one with explicit capabilities for an ID that isn't compiled in yet (see Capabilities):
Special emphasis on the "isn't compiled in yet" and "or construct one" bit.
This was expected.
Apple will carefully choose what & how people can use AI in their ecosystem and will make sure of it. I hope "Apple Foundation Models" Eco-system grows with support from major model providers.
This seems smart. Apple, despite not really leading in AI themselves, are right on the hot path of where developers are going to yolo slop into the ecosystem. Make a tonne of sense to define a nice clean API that places like Anthropic can build on top of and expose to developers.
It's also smart for them to make sure the billing is going direct from Anthropic to the developer. The initial thought is "That means Apple's not taking a cut", but from the other side of it, developers who use this API are going to have to expose that cost to customers somehow, and that translates to subscription/InAppPurchase etc. on top of which Apple will get it's 30%.
> A key bundled into an app is extractable from the shipping binary, and anyone who extracts it can make requests billed to your account. Use .apiKey for development only, and switch to a proxy before release.
I don't like this model. Then all the user data is visible to the proxy.
Far better would be some kind of micro payment architecture where a wallet is on the users device and coins are attached to each request.
We just need to live in the alternate universe where micro payments succeeded.
Apple's Foundation Models framework (shipping in iOS 27 / macOS 27 this fall) is the standard Swift API for on-device AI — the same API Apple uses for their own small model. This package makes Claude plug into that same API as a drop-in swap.
// Apple's on-device model
let session = LanguageModelSession(model: SystemLanguageModel.default)
// Claude — same API, just different model constructor
let session = LanguageModelSession(model: ClaudeLanguageModel(name: .sonnet4_6, auth: auth))
One API, two tiers. You write your app once against the Foundation Models protocol. On-device model handles fast/free/private tasks; Claude handles heavy reasoning, long context, or capability gaps — you swap the model, not your code.
You don't call the Anthropic API directly. Apple's framework handles streaming, tool calling, and structured output (@Generable) — you just get Claude's capability through it.
What I'm curious about is whether this is actually on-device. Apple's framework caps local models around 3B params last I looked, and Claude is way bigger than that. So either there's some hybrid setup I haven't seen documented, or this is mostly a Claude SDK in FM clothing. Anyone tried it on a plane?
> a Swift package that makes Claude available as a server-side language model in Apple's Foundation Models framework
Ahh I was hoping for the opposite: all of the existing features of Claude Code but somehow running locally on my laptop's neural engine. A pipe dream on an M2 with 8 GB of RAM, but I had a flicker of hope there.
Check out this WWDC session. Obviously not going to compete with the frontier models (and I think 8GB is too small anyways), but Apple did demo MLX + OpenCode.
https://developer.apple.com/videos/play/wwdc2026/232/ https://www.youtube.com/watch?v=wykPErJ8M-8
I've found most of the frontier coding models require somewhere between 300GB to 1TB to run with full capabilities.
If only we could buy 1TB of unified memory in a Mac for $1k-$2k in total hardware costs. Apple would basically be able to extinguish the entirety of the market cap for Nvidia, OpenAI, Anthropic, and others all at once.
In 10 years, I hope my MacBook Pro can run today's frontier models and has 1TB of unified Memory.
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.
I think this will be isolated to highly specialized fields where training data will need to be selectively curated.
Everything can be distilled, it will just become more painful
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)
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 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.
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?
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 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.
When you say "Pro" territory, do you include Fable?
You mean the model that was available for a whole of three days? No, I had played around with it a tiny bit, but not much than that. I guess time will tell if it gets close.
Maybe I’m alone in thinking this but I think the long term victor will be the one that works out pricing best.
Fable might well be a better model but it’s too expensive for everyday AI use. Definitely if we’re talking about the kind of stuff you’re going to want to do on your phone. Even for coding, I’m not going to reach for Fable (well, when I can…) for 95% of the work I do.
I don’t believe a mature AI industry is going to have a one size fits all, single winner.
Yes, and pricing is one of the features of a commodity, because users can jump back and forth between services, it becomes a pricing race to the bottom. Agree also that you don’t need the best model all the time. You could have the most powerful model draft the design, requirements, guidelines, policies or whatnot then get the lower tier models execute it. Then again you can have the most powerful model do the testing and review, and give back feedback, rinse and repeat. Just like in the real world you don’t need an entire staff of lead engineers.
> while keeping control of the UX.
Extremely tangential, but this is my favourite upshot of AI. For decades, companies have been walling off their services and forcing us into their fuckass UIs. Now over the course of the last twelve months, suddenly everything has an MCP and I can use it through my command line chat interface.
Any company that doesn't adapt gets so hammered by people's AI-DIY web scrapers that they have no choice but to cave.
Does “the best machine for AI use” apply here considering these models are still server-side?
The play here seems pretty evidence, if I may assume. Apple creates an interface that is generalized enough so you can easily swap models, and while Claude is preferred by Apple today, it may be any provider or even local models in the future, and the APIs the developers use remain the same, so "migration" becomes easier.
for the on-device model, yes it runs on the Neural Engine (at the moment) so a newer chip means faster, cheaper local inference. For the server side path this Claude package is about your machine is irrelevant since it's a network call. The same API covers both, so "best machine for AI" only bites when the session is actually local.
But we can imagine that the balance of what's on-device vs what's remote will move continuously towards the former as time, improved HW and improved local models keep progressing
I would think so, as “use” doesn’t specify implementation. If you use a word processor it may be running locally or remotely.
From a user’s perspective, it doesn’t matter.
It's been clear for years now that eventually ai will be embedded at the os level. Apple even recognized it way back when they first introduced Apple Intelligence. Yes they're commoditizing llms or whatever. But this has been a user facing feature they've been iterating on for years now
Now we only need to commoditize the hardware.
How is this Apple keeping control of the UX?
The betas of the next OS's include a Siri AI chatbot, and the AI features are built into various parts of the OS. A user has no idea what model is powering any of it - Apple controls the UX.
I'm aware. How is this relevant to the posted article?
The article is about (from the eyes of a user) white-labeled usage of Claude models on Apple devices, this subthread is about white-labeled usage of LLMs on Apple devices, how is it not relevant?
Because that's not what the article is about; this is about a unified API for the _app developers_ to access different kind of models.
That API has no user-facing components, and has no influence over UX of what the end-users are interacting with.
The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.
> The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.
That's the point! That's the whole "white-labeling" part, and what the commentator earlier is talking about. You're very close in understanding the context here!
I’m sorry, so your position now is that “being completely invisible to the users” is “controlling the UX”?
I think you're taking the written words a bit too literally here. Read it with a more lax filter and less literal word-meaning, and I think the original comment will become a bit clearer.
You know what, I've been a bit too snipe-y in my previous comments, and it led to to discussion devolving in unproductive ways.
I'd genuinely like to understand where you're coming from more.
I think we're all in agreement that this framework is very much about letting developers swap the models easily, and treat them as commodities. That seems pretty obvious.
I do however still don't see how this has anything to do with controlling the UX (or the new Siri for that matter! The new Siri doesn't use Anthropic models, and there are no extensions point for it to do so — that's pretty much the whole reason why it won't be available in the EU).
Help me see your point of view!
I don't know if it helps. One way to look at it is branding product. Apple is branding the product. So they supposedly have more value to customers as it stands for quality, awareness, trust etc. As oppose to 100 little components in computer which maybe from different brands, and Apple may switch brand year to year without user noticing. So those components makers have little power over Apple.
Same is happening to Claude software package as it would stand behind branded Apple foundation models. From pure software developer thinking this is exactly what Claude offered here so where is the issue? Issue is in larger space where Apple could take steps to block Claude out of their ecosystem if they so wish at some point and there is little Claude / Anthropic would do if Apple Foundation is the only thing that Apple consumers would know about.
I can't reply to your child comment for whatever reason, but Siri is part of the Apple Foundation Models framework. The idea is that no matter what backend the developer uses, the end user will always say "Hey Siri." This is analogous to controlling the UX. Siri is independent of whichever model the app developer uses.
No, Siri is entirely separate from this framework.
Are you thinking about Intents? That lets Siri interact with data (and perform some actions in them) from your apps, but it is something completely different.
You can definitely expose things from your app via Intents that will end up calling an external arbitrary LLM somewhere, but it does not require using Foundation Models API whatsoever.
It's Apple, so it's some revolutionary big brained play, and not just yet another llm sdk.
While I'm happy with Apple introducing this abstraction. my main concern was with local models.
I'd love using Gemma4 as an example. but thinking of a user. if 10 Apps each uses same model and downloads it, the phone will be bloated.
I still didn't understand if Apple provided a way for multiple apps uses same on-device model (without tricky namespaces and permissions).
I didn't see anything suggesting that's the case.
I think that's what they are trying to avoid. If you need on-device intelligence, their pitch was "The model the device already has is best", and if you need something more specific an adapter (aka, a fine-tune/lora) is best.
They were wrong when their on-device model was way behind. They still might be right in the long term.
While multiple app I use might need Gemma 4 E4B, I use dozens of apps and app devs can choose from hundreds of models. A shared cache might reduce size a little when there's overlap, but the core problem still exists. If each app chooses a model disk and memory-swapping explode.
Its probably be better for device manufacturers to bake in a default. I'm not proposing they limit you from using others, but one shared default might be best developer/user experience for 99% of apps.
- Being warm in memory is the single biggest perf speedup you can get, and a default is much more likely to be warm.
- "Best model" is usually "best model for this device" given both RAM and compute. A developer can't test every device but Apple can/will.
- Each model needs to be optimized for the hardware (what's running on ANE, what's running on Metal, what's running on CPU). The default gets optimized.
- If you need custom model, a Lora is probably best (30MB, benefits from all of the above)
You could say the default should be swappable, but that's more a linux ideal than an Apple one so I doubt we ever see that. Plus there are real downsides: intentional or not, prompts end up optimized to the model they are developed for, so swapping the default system model would degrade every app.
That's a great opportunity for Apple to provide a universal unique model ID protocol and some shared storage space to allow devs to register models.
Check out “Bring an LLM provider to the Foundation Models framework” - https://developer.apple.com/videos/play/wwdc2026/339
I see an id based ability suggesting `modelId`. but in current docs I cannot find any context to it. The other limit is that it suggests Swift Packages. but I'm not seeing any model management hints similar to Docker/Ollama/etc where:
- Application can ask for specific model, if available use it. if not, ask to download it (or try some fallback / alternative)
- User can manage models. So as a user I can clean unused models (and for non-techie have something similar to offloading apps when unused for some period of time).
The apps can use the system provided on-device model using the same framework and APIs; but there's no affordances to deduplicate custom models between apps.
Do you guys not have phones (with at least 1TB of storage)?
Who’s “you guys” a developer from Bay Area? A student with a MacBook Neo? Or John Appleseed who bought basic iPhone 17e?
https://knowyourmeme.com/memes/do-you-guys-not-have-phones
I'm guessing it's a BlizzCon joke.
I have a Mac with 4TB of storage but it’s still annoying when every new AI app I try installs its own virtual environment with a fresh copy of Python, PyTorch, other duplicate libraries, and then models on top of that.
As an occasional python user I'm always amazed and frustrated that it seems that the only way to be able to use/build anything is to create a whole separate environment.
And now given everybody now does this I guess the incentive to stop breaking stuff reduces even further.
Might as well have static binaries.
The meme phrase “it’s fractally wrong” applies to the entire python ecosystem, IMHO. Virtual environments are just another layer of this fractal wrongness in the layer cake of ecosystem awfulness.
It’s a nice language though.
I have a couple small apps that have a (non-LLM) model, and originally the models and code were in PyTorch, built by Python devs.
The original plan was to ship Python. However I found out I can migrate them to CoreML, and now it's a model file + Swift code. I got some massive performance improvements as well.
Of course, this doesn't work at all for non-Mac environments, but it was nice to be able to do it. (Also doesn't solve the duplicate large models problem)
If you use uv, python apps use a shared cache which helps a lot.
No? iPhones don't come standard with that much storage.
https://knowyourmeme.com/memes/do-you-guys-not-have-phones
Sounds ripe for block-level deduplication. :D Or an API that lets you request a model and handles caching.
First Microsoft has broken keyfabe by putting "Copilot is for entertainment purposes only" in the Copilot terms of use and putting warnings in copilot for excel "avoid using COPILOT for ... any task requiring accuracy or reproducibility ... Tasks with legal, regulatory or compliance implications".
Then Apple quietly refuses to participate by not investing tens or hundreds of billions in creating a competing LLM. Sure, they resell Claude for the marks or utilize Gemini to placate the gullible fools but they know what's up.
https://www.microsoft.com/en-us/microsoft-copilot/for-indivi...
https://support.microsoft.com/en-US/Excel/copilot-function
Is this Apple encouraging developers to go through their api abstraction layer to use LLMs so that when they launch their own (which I think we’ve heard they’ve been spending lots of money on training and might be somehow involved with Siri or current Apple AI?) that they can easily help devs make a seamless transition? Or is it just a developer nicety or something else?
Apple has some clever mechanics to protect user data. I had to work with App tracking stuff lately and their approach to keeping user details private with anonymized cohorts (SKAN, Differential Privacy) before reporting tracking events to third party platforms was surprisingly well thought out. There is value in having them in your loop if you care about privacy.
My read of the ATT stuff is basically that it forced all the apps to use meta ad tracking because they’re the only ones who figured out how to serve relevant ads despite it.
It would be cool if they offered some kind of prompt sanitation option.
This is support for a new framework that ships with reality/mac/iPad/watch/tv/iOS 27 (and that they've promised to open-source later in the year, so presumably you'll also be able to lean on this if you ship Swift on your backend).
The framework's whole deal is that it lets you use the same API to target either the device built-in models, the Apple-hosted online models (Private Cloud Computer), or write your own shims to call out to arbitrarily hosted online models.
You can then dynamically route your calls to a different kind of model/provider, using system APIs, without having to write your own abstraction layer over "I want to use local model for this, but I want to use Claude for that", or having to integrate your own API integration with Anthropic/OpenAI APIs.
It abstracts things like tool calling in one place; and has a bunch of other niceties/oddities (it keeps the same "transcript" going, even if you dynamically switch providers/models during a session) and some other things.
A dark, but not totally unfair take: It makes it easier for Apple to take payment for the models others provide, and even allows Apple, if they want to, to use the data to build a dataset for training their own models based on how users use third party models. It's only on Apple devices this API is used, so they split up the market by not letting developers use the same system if they want things to work on iOS, locking users even more in.
Call it Intelligence Store and charge… wait for it… 30%.
The cynic (or realist?) in my thinks this abstraction layer is Apple's way of making sure that users give their own Apple Intelligence credit for the underlying LLM functionality, even if another company is actually providing the LLM.
This is clearly because they plan to monetise AI in the future, and they don't want competition.
Assembled in Cupertino once more. ;)
Yeah, Apple just designs and writes the SoC, CPU, graphics unit, neural unit, compiler (Swift), OS, graphics layer, 3D API, core libs from graphics to persistence, filesystem, broadband chip, and a few more things besides...
Notably good models are not on that list.
Neither are other capex heavy items like chip fabs.
Yeah, they also don't mine their own steel and copper. Such mere assemblers!
Yeah, that totally makes them merely assemblers then /s
There are already on-device models that you can use through this framework as a developer. Claude would just be an additional one.
Maybe they plan to have the providers pay for being the default model? So basically, what Google is doing right now for search engines. The difference however is that Google is making money with additional search requests while AIs are (as of now) losing money with additional requests. I don't see the business case for them yet though.
> which I think we’ve heard they’ve been spending lots of money on training and might be somehow involved with Siri or current Apple AI
Lol bro this is literally it this is the model they've been training (was Apple Foundation model not a big enough hint?)
I think this is just Apple planning for their on-device models getting better, which makes sense given they have access to Gemini now. If developers use this for all their code calling an external LLM, then as Apple's model becomes more capable and covers more use cases it'll be easy to switch to it at individual call sites. That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut of.
> That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut of.
With other words, it's unlikely to happen as there is no money in it. Better for Apple to create some new subscription "AI" and "AI-lite" plans people can subscribe to, and since Apple is a company and we all know what those care about, it's unlikely to become a utopia of local models running on your phone.
How does using Gemini lead to better on-device models?
Apple is distilling models from gemini
How can you practically use this in software if you're to deploy this to users? Asking a user to create and enter their own API key is a bar too high for good UX.
The even bigger hurdle is selling token based pricing to normal (non-dev) users.
"You pay an indeterminant amount of money to ask a question and you might not even get the response you want without spending even more money" doesn't appeal to most people who aren't gamblers and explaining how "thank you" at the end of a long exchange can be expensive due to context is an even harder thing for an average person to swallow.
Token cost going up/down like a yo-yo also doesn't help. Normal users NEED fixed costs and don't want to expend energy constantly keeping up with the AI meta. "My subscription lasted much longer last month" isn't a winning problem either.
I think Apple is correct that Local LLM for most things is the future.
Ugh. It really is. I have allihat.com which is the only safari extension (i think still) that talks to claude. And it's well sought for. But you as a user have to enter a friggin claude api key. :( And I still don't grok their TOS around this. Like you can still type: ```setup-token Set up a long-lived authentication token (requires Claude subscription)``` but this seems like a trap? :) Whose using this? Doesn't this like insta break their TOS if you use that anywhere?
Right now for allihat.com I just let people use the Apple model locally if you don't feel like using the claude key. And my conversions to paying user shot up like 3x! But it really isn't a replacement obviously to claude. I was hoping Apple would make proxying to Claude some kind of thing they do for me so I also don't have to proxy to my own server just to try and manage API to Claude usage.
> For production, route requests through your own back end with .proxied
Apple is offering developers with less than 2 million downloads free AI models via their servers https://techcrunch.com/2026/06/08/apple-bets-cheaper-ai-will...
The same way you did it before — by proxying the requests to your backend.
I think Apple has a fairly good plan for supplying a common API and default on device models.
What confuses me about this article is: The code examples Python, Ruby, etc.) look to me like the original Anthropic APIs, not Apple’s abstraction. Did I miss something?
> Requests go directly from your app to the Claude API; Apple is not in the request path and does not see prompts or responses.
I know this is from a developer perspective. But as a consumer this is just funny.
Why?
Coding agent itself an imposed layer. Now they are adding one more layer? Many times I think of coding agent as the vendor supervisor from the body shops of the 90's who promise the customer everything under the sky and thrash the poor contractor to deliver. Coding agents consume 10x more tokens just like how body shops charged their customers vs how they paid the contractors. For a simple test, the same task that makes the model to go out of context length when used via a coding agent, runs fine when prompted directly.
Layers are luxury and remove control and transparency.
You wouldn't use this when building a coding agent.
From app developer standpoint why would anyone ship claude keys like that ... or am I missing something? From consumer standpoint - I guess they can use their own keys but it is not something that is very user friendly as you can imagine.
it says:
Proxy (production)
For production, route requests through your own back end with .proxied. The relay at baseURL adds the Claude API credential server-side, so the app ships no key. The headers you provide are sent on every request so your proxy can authorize the caller.
https://platform.claude.com/docs/en/cli-sdks-libraries/libra...
I’m surprised to see the model names hardcoded as an enum (e.g. `.sonnet4_6`), instead of a string with model discovery so that the user can select their preferred model without having to get a new app version through the App Store to support newer models.
>Model identifiers are values of ClaudeModel. Use a compiled-in constant, or construct one with explicit capabilities for an ID that isn't compiled in yet (see Capabilities):
Special emphasis on the "isn't compiled in yet" and "or construct one" bit.
the github repo: https://github.com/anthropics/ClaudeForFoundationModels
I didn’t understand what they were doing with Apple Foundation Models until this. It made it sound like they were training their own. Good strat tho!
> It made it sound like they were training their own.
They are.
This was expected. Apple will carefully choose what & how people can use AI in their ecosystem and will make sure of it. I hope "Apple Foundation Models" Eco-system grows with support from major model providers.
This seems smart. Apple, despite not really leading in AI themselves, are right on the hot path of where developers are going to yolo slop into the ecosystem. Make a tonne of sense to define a nice clean API that places like Anthropic can build on top of and expose to developers.
It's also smart for them to make sure the billing is going direct from Anthropic to the developer. The initial thought is "That means Apple's not taking a cut", but from the other side of it, developers who use this API are going to have to expose that cost to customers somehow, and that translates to subscription/InAppPurchase etc. on top of which Apple will get it's 30%.
So where does the api key reside? You can’t ship it on the iOS client since anyone can read and abuse it
https://platform.claude.com/docs/en/cli-sdks-libraries/libra...
it says put into your API layer and proxy it.
> Usage is billed to your Anthropic account at standard API pricing.
While expected, it’s still a bummer.
The pricing squeezes will continue until token spend improves!
Can someone explain me what it means in the context of Apple and ChatGPT/Claude/Mistral...?
Does "Apple Intelligence" need to be Turned On for this as well?
> A key bundled into an app is extractable from the shipping binary, and anyone who extracts it can make requests billed to your account. Use .apiKey for development only, and switch to a proxy before release.
I don't like this model. Then all the user data is visible to the proxy.
Far better would be some kind of micro payment architecture where a wallet is on the users device and coins are attached to each request.
We just need to live in the alternate universe where micro payments succeeded.
So actually the most successful AI was OpenRouter Intelligence? Pronounced as OÏ.
Associated blog post: https://claude.com/blog/claude-for-foundation-models
Misleading title. This is about Claude for Apple Foundation Models, not about Apple Foundation Models
I'm not sure if I want to touch anything Anthropic anymore.
What it is
Apple's Foundation Models framework (shipping in iOS 27 / macOS 27 this fall) is the standard Swift API for on-device AI — the same API Apple uses for their own small model. This package makes Claude plug into that same API as a drop-in swap.
One API, two tiers. You write your app once against the Foundation Models protocol. On-device model handles fast/free/private tasks; Claude handles heavy reasoning, long context, or capability gaps — you swap the model, not your code.You don't call the Anthropic API directly. Apple's framework handles streaming, tool calling, and structured output (@Generable) — you just get Claude's capability through it.
What I'm curious about is whether this is actually on-device. Apple's framework caps local models around 3B params last I looked, and Claude is way bigger than that. So either there's some hybrid setup I haven't seen documented, or this is mostly a Claude SDK in FM clothing. Anyone tried it on a plane?
Read the linked article? It is absolutely a cloud service. Neither Apple nor Anthropic is suggesting otherwise
Why would I want a nerfed model?