alt Hacker NewsApple Silicon costs more than OpenRouter
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Unless I'm misunderstanding, this is counting the entire laptop in the cost of generating tokens. The calculation seems to omit that, in addition to receiving LLM output, you have also received a laptop in exchange for your money. If you intend to put this machine in a dark corner and run it solely as a token-munching server, a laptop would be an exceptionally poor choice of technology for this purpose. But if you intend to use the laptop as a laptop, having a laptop is a pretty big benefit over not having a laptop.
You also get the benefit of privacy, freedom from censorship, and control over the model used (i.e. it will not be rugpulled on you in three months after you've built a workflow around a specific model's idiosyncrasies).
Frontier AI companies are selling at a loss.
Excusing everything else that u/bastawhiz said[0]; the obvious fact here is that Claude, OpenAI, Gemini et al. are quite literally burning through 100's of billions of dollars and selling it back to you for pennies on the dollar in the hopes that they get to be the only one left.
If I spend $10 growing Oranges and sell them to you for $1; then of course it's more expensive for you to do the growing.
I feel like I'm taking crazy pills. These models will become more expensive over time, it's functionally impossible for them not to, they just want to capture the market before they have to stop selling at a huge loss.
If you want a good dense model, use qwen3.6 27B instead, speed will be up, and if you don't take my word for it being smarter, take openrouter's prices of it against the bigger, slower and less memory-efficient gemma do the talking.
If you want a faster model, go for qwen3.6 35B (or gemma 4 26B if gemma models perform better for your tasks). There is a reason why people (myself included) haven't shut up about those two (especially the 27B). Its small enough to run at a decent speed (especially with the built in MTP that finally has official llama.cpp support) and for many workloads (every benchmark I have ever thrown at it) it is matching or surpassing models it has no right to.
A couple of days ago I woke up with my internet being down, started 27B in pi, told it to diagnose whats wrong by giving it my router's password, went to grab a coffee and by the time I got back, i had a full report with suggestion on how to proceed. I love openrouter and I use it for many things, but it is not cheaper.
Subjectivity and opinions based on personal experience with all those models implied naturally, I assume the 31B gemma has cases in which it edges out, I've just failed finding any and I have been running all 4 models mentioned since hours after each of them dropped nonstop for different tasks. Hell, for my hermes, I've started getting better results once I switched from gemma 4 26B to qwen3.5 9B, not even the massively improved 3.6 series. It just feels outdated/ cherrypicked to not use what by many accounts is the current consumer hardware SOTA if doing such an analysis.
A lot of comments here are about the issues with the analysis in OP’s post but much of them are “a distinction without a difference” with respect to the broader conclusion. When we look at purely cost and performance (setting aside privacy) then it’s better for individual devs to pay for hosted then for self hosting. Employers are paying for tokens on the job and most devs are finding the $PREFERRED_PROVIDER’s $20/$100/$200/month subscription sufficient outside of work. Most devs don’t fall in the conditions under which running local models make sense purely on the basis of cost vs performance.
More critically, in practice, setting up local models seems more like a hobby, an educational exercise, or an act of privacy control than it is for cost cutting or productivity.
Mmmm, nope if you do the smart thing. MacBook M5 max 128gb is a premium laptop at 6k, but with it you can do many things and is your good main driver for the day. Then, it can also run DeepSeek V4 flash and perform non trivial tasks locally, without censorship or limitations, even without an internet connection and on very privacy sensitive data. That's a good deal. If you buy 25k for a dual Mac Studio 512gb to abandon OpenAI and company you are going to be disappointed by both performance and cost.
The author only compared output token costs -- but for typical agentic workloads, input tokens dominate the costs by a large margin. Running inference locally, input tokens are, to first order, free. (They only generate implicit costs through higher time-to-first-token, higher power use, and lower token output speed).
OP is comparing against Gemma everywhere but concludes paying Anthropic make more sense. Anthropic is $15 per million output token which is 30-35x more expensive even in openrouter .
This is like comparing e-bike at home with e-bike rental and concluding therefore we need to rent Toyota since it can go at similar speeds. Getting tired of bad posts getting much attention .
Everything is currently heavily subsidized. If the AI companies don't improve efficiency, they'll eventually have to start charging what it actually costs to offer the service, which is a multiple of what the currently charge.
I expect self-hosted to be quite competitive pretty soon. Github Copilot is already wildly more expensive than it was last month. People are going from spending a few bucks to a few thousand for that same usage. So, if it doesn't get a lot more efficient (like 3x the tokens, or more, from the same infrastructure), the prices will have to go up quite a lot to keep the lights on. Everything in AI is running partly on investors money, everyone is trying to buy a monopoly and insurmountable lead and some way to lock people into a specific model and ecosystem, but so far that hasn't happened (except for people who voluntarily lock themselves into a specific ecosystem, but even in those cases, it's usually easy to get the AI to help move to another, there are no truly unique features in AI that at least one, and probably three or four, other players don't also offer).
Article is seriously wrong, because it makes a huge mistake in the last part. You can't simply look at the produced tokens and that is your cost. In agentic coding there are lots of turns meaning you not only pay for the output tokens you also pay for all the input tokens sent each time (even if a lot cheaper, like 10x when cached). So this calculation does not accurately represent the api cost at all.
Second thing is you can starkly upgrade the token generation locally if you use agent teams. Single conversations are memory bandwidth bound and don't fully make use of your compute. If you can batch tokens from multiple agents you can easily 5x token generation.
I have free electricity from solar and an old Macbook Pro M1 Max that has depreciated to zero and has no other use. Now how do the economics work out?
This is not surprising at all. The biggest benefit of cloud model in terms of energy efficiency is that when running more than 1 requests, the power consumption of said GPU roughly stayed the same. The more concurrency requests the server can handle, the less power each request consume. The server GPU is already likely more energy efficient than local GPU, concurrency make the cost structure unbeatable by local hardware. It is generally assumed the local hardware only run 1 request, but if the local engine is meant to serve a small business with meaningful concurrency, the economy might still work out.
I simply can't go back to cloud AI. Privacy and full control are more important to me than speed and SOTA models.
"Accelerated depreciation (if any) from shortening the lifespan of the device will be more expensive than the electricity"
Shortening the lifespan?
For me, the value in local inference is getting your hands dirty and goofing around. That is to say, learning.
So we shouldn’t be comparing it to the cost of open router api access at all, we should be comparing it to the cost of a 4 credit university course.
I suppose folks here already know this but it deserves a mention: subscription pricing is 10-20x cheaper than API pricing at Anthropic for example and it will be a far better experience (better models, faster responses, as much parallelism as you want, etc) so if it works for you there's no economic argument to buy a machine for inference at the moment.
In my testing, qwen-3.6-27b in full precision is well below sonnet, but above claude haiku in coding tasks. Gemma is not even close to qwen, it’s much, much worse.
I like that the numbers were crunched, but the answer to these is always a bit of a foregone conclusion.
* Industrial power pricing
* Wholesale hardware pricing
* Utilization density of shared API
means API always wins a cost shootout.
Privacy & tinkering is cool too though
Slightly different slice into this a very similar situation (local vs OpenRouter AI inference).
But in _every_ metric other than privacy it was better to run via OpenRouter than a local model, and not by a small amount.
Direct link to the comparison charts:
https://sendcheckit.com/blog/ai-powered-subject-line-alterna...
I don't hear people debating which is cheaper, local or cloud run models. The conversation, at least what I hear, is a lot of the time users are not utilizing an awful lot of tickets all the time, those providers will be paid if you never use them. If 80% - 90% of the work I and my team are doing with Ai is grunt work, write tests for this, implement a FFT here, write the dB query for X. Nothing exhausting. Those who are using AI for whole cloth "vibe coded" applications and services are definitely better suited to cloud. If a work laptop can run my local models and get my works needed performance for development, why wouldn't I as a company prefer that?
Add to that the privacy improvements and data protection and potentially further specific inferance if needed it's a no brainer.
Again, Ai is a tool, and the right tool for the job, I would wager with no evidence looked up, is that the majority of Devs would be happy with 10-30 per second locally.
Local LLMs aren’t about cost, but control.
I run the latest 20b-30b models on a MacBook Air... running inference with an MoE (25 tps) for like 2 hours is like 10% battery.. (look me up on huggingface to download my models)
also you gotta realize frontier models have massive "system prompts" that clog up the context window with garbage.
being able to write your own system prompts gives you a MASSIVE edge..
Even if a Mac mini at home was slightly cheaper per token I still use OpenRouter because I want to out source the heat generation and noise to a datacenter.
Right now, local inference only make sense for privacy reasons.
This is common when processing PII. Lawyers, doctors our similar should not be using cloud solutions.
Also it's harder to setup and always more expensive than any cloud solution.
> "run a model like Gemma 4 31b, which is almost anthropic sonnet levels of performance"
I wish people stopped deluding themselves — I regularly try (and benchmark for my purposes) local models and they are NOWHERE near the huge models like Sonnet or Opus. Nowhere. Yes, you can sometimes get plausibly-looking output for simple tasks, but for anything even remotely requiring thinking there is simply no comparison.
Local models are useful. I use them for spam filtering, and soon intend to use them for image tagging and OCR. But let's stop saying they can get us "anthropic sonnet levels of performance", because that's just not true.
Consider deepseek as well. About 50 cents per 1M tokens, for >1T model
One important difference is that costs are bounded on your own machine. Like with cloud providers, I'm always worried that cost may accidentally explode if I launch an agent swarm wrong.
Now, it looks like the providers I use have good limits. But I do worry about this.
This doesn't compare like for like, since its comparing the total cost for the local machine with the usage cost for the cloud service, despite the cloud service also needing a local machine to be useful.
Author forgot that after 3 years when hardware no longer decent for inference you can still resell it for 25-50% of price.
Obviously if RAM apocalypse passes by then high-end configurations preserve resale value worse than base models, but still it's hefty bonus of Apple hardware that might change math a lot.
So I did the India-specific analysis for a tier-3 city. Here, electricity costs 1/3rd of the US version, and you also get solar subsidy up to a certain amount.
tldr;
Hardware deprecation costs are the major factor.
But, if we assume ZERO hardware deprecation (not realistic), then local inference becomes super cheap.. roughly, 90%+ cheaper.
Third case: the break-even happens only if we can get at the very very very least, 8.7 years of useful hardware life. A more realistic number, however, when working 8 hrs/day and not of 24 hrs/day, is around 25 years.
So, for now, local inference is preferable if you deeply care about privacy. From cost perspective, it's still not there.
For me, the appeal of local compute is first and foremost confidentiality and having the possibility to run my 200K documents through an LLM just to see what happen without having to consider the cost.
And this all assumes OpenRouter costs and availability will persist.
> Let's round up to $0.20 per kWh.
Next paragraph
> At ~50-100 watts and $0.18/kWh that's $0.009 or $0.018 per hour. $0.02 per hour. $0.48 cents per day for the electricity to be running inference at 100%.
lol
I've dug into this previously for one simple reason: NVidia segments the market by capping VRAM and Apple silicon uses a shared memory model that could challenge that but it currently doesn't. And I really wonder if Apple realizes the potential of what they have or if they even care.
So, for comparison, a 5090 has 32GB of VRAM and you can get one for ~$3000 maybe. To go beyond that memory with current generation (ie Blackwell) GPUs, you have to go to the RTX 6000 Pro w/ 96GB of VRAM and that's almost $10,000 for the GPU by itself. Beyond that you're in the H100/H200 GPUs and you're talking much bigger money.
Part of the problem here is the author is looking at laptops. That's the only place you'll find the M5 Max currently. The real problem here is that the Mac Studios haven't been updated in almost 2 years. There were configs of those with 256/512GB of RAM but they've been discontinued, possibly because of the RAM shortage and possibly because of they're reaching EOL. Apple hasn't said why. They never do.
Many expect M5 Ultra Mac Studios in Q3 and the M5 Ultra may well have >1TB/s of memory bandwidth (for comparison, the 5090 is 1.8TB/s). Memory bandwidth isn't the only issue. A 5090 will still have more compute power (most likely) but being able to run large models without going to a $10k+ GPU could be huge.
But yes, it's hard to compete with the scales and discounted electricity of a data center. Even H200 compute hours are kinda cheap if you consider the capital cost of what you're using.
I've looked into getting a 128GB M5 Max 16" MBP. That retails for $6k. You might be able to get it for $5400. But I don't think the value proposition is quite there yet. It's close though.
Open router doesn't cost money per say, it depends on the providers pricing
Will this cost structure always be this way and are there other benefits to not running your LLM on the cloud?
E.g.
Privacy
Uptime
Future cost structure controls
This is a field that has moved very quickly. And it has moved in a direction to try to trap users into certain habits. But these habits might not best align with what best benefits end users today or some time in the future.
Except I already have a local Mac to run Xcode. OpenRouter cannot help with that, at any price.
> 64 gigs should run a model like Gemma 4 31b
No, it can run anything in the 70B range. It's a notable quality upgrade from the 30B, which isn't obvious because the famous flurry of April releases didn't contain any 70Bs.
It can also run 120B in UD-Q3. Or 230B disk-streamed.
I'm even surprised people ignorantly talking about advantages of buying very expensive device , run it only sometimes and aiming to beat cloud vendors.
If small model is great it will be hosted with good electricity cost and will be utilized 24/7.
Isn't it 2+2 of economics ?
CPU is a commodity, and we are still buying cpu and ram from vendors for same reason
Apple services are ~27% of revenue and growing double-digits. The chip is a moat for that flywheel, not a standalone compute bet.
Isn't this just saying cloud AI providers are heavily subsidizing the true cost of the service.
The true advantage of locally self-hostable, open weight models isn't about monetary cost at all, it's about the CIA triad.
Running locally, you get confidentiality of knowing your tokens are only ever being processed by your own hardware. You get the integrity of knowing your model isn't being secretly or silently quantized differently behind the scenes, or having it's weights updated in ways you don't want. And you get the availability of never having to worry about an API outage, or even an internet outage, for local inference capacity.
And this isn't even starting to address the whole added world of features and tunability you get when you control the inference stack. Sampling parameters, caching mechanisms, interpretability etc.
OpenRouter may be cheaper than frontier labs, but you still lose all of these benefits from open weight models the moment you decide to rely on someone else's hardware for your processing.
Your laptop AI costs too much? Speculative investors can help!
> Throwing money at anthropic makes more sense in this context.
But you are dependent on them, which is the biggest factor IMO, there was a website posted here before of people getting banned from using it over silly reasons, not to mention price hikes, or privacy concerns. Maybe now it’s more expensive or slower to run locally, but you are in full control of everything.
What is the security of OpenRouter? I have a feeling user has no idea where their data is going and how it will be used or am I wrong?
When I see so many options, that looks like it would take months to audit whether it actually makes sense and is safe to use. But I guess some people are fine with YOLO-ing it.
It should not at all be surprising that running models at home is more expensive than commodity providers. That's just generally true of running your own stuff. Even if the cost in money isn't higher, the cost in time is often _significantly_ higher.
This is why the idea that the AI labs are in trouble because inference will be a commodity is _completely backwards_. Some of the largest and most powerful companies in the world sell commodities. They compete on scale and efficiency, and you are never going to be able to compete with the big labs on either.
What would really elevate an article like this is if we could somehow quantify human brain’s equivalent outputs and compare the costs with local LLM and cloud LLMs.
Bizarre running local models have nothing to do with cost. It's about privacy first and foremost
Local isn’t (just) about cost, it’s control and trust.
This isn't a good analysis, and it's because it keeps rounding everything up. He rounds up the cost of electricity by 10%. He has a range of power use, takes the high end (which is 2x the low end) and multiplies it by the inflated electricity cost.
But then they talk about using a newly purchased Mac to do the inference, running at full capacity, 24/7. Why would you do that? Apple silicon is fast but the author points out: you're only getting 10-40 tokens per second. It's not bad, but it's not meant for this!
It's comparing apples to oranges. Yeah, data centers don't pay residential electricity rates. Data centers use chips that are power efficient. Data centers use chips that aren't designed to be a Mac.
Apple silicon works out pretty good if you're not burning tokens 24/7/365 and you're not buying hardware specifically to do it. I use my Mac Studio a few times a week for things that I need it for, but I can run ollama on it over the tailnet "for free". The economics work when I'm not trying to make my Mac Studio behave like a H100 cluster with liquid cooling. Which should come as no surprise to anyone: more tokens per watt on hardware that's multi tenant with cheap electricity will pretty much always win.