19 Months of ChatGPT Pro....or a Mac Studio?

0:00 On September 29th, Open AI halved what new subscribers get for $200 a month of Chat GPT Pro. 19 months of that is $3,799, almost to the dollar. That's also the

0:12 price of a Mac Studio with Apple's M5 Max and 64 gigs of memory. If you're paying that $200 every month or you were about to start, the math just moved under you. Same rent, smaller apartment.

0:24 So, do you stop renting and buy the box? It comes down to what fits in 64 gigs, how fast it writes code, and how long it takes to pay for itself. One puzzle on the way. On this exact chip, a model

0:36 with 122 billion parameters writes almost three times faster than one with 27 billion. Bigger wins. Sounds backwards, and the reason behind it decides which models this box can really

0:46 use. And one warning up front, so it doesn't ambush you at minute 12. A local model in the 27 to 35 billion range isn't GPT-6 Pro, and it isn't Astra. You're trading capability for ownership.

0:59 Start with the meter. Until the 29th, $200 of Pro got you 20 times the plus allowance in Codex and Chat GPT work. New subscribers now get 10 times. GPT-6 Pro messages in chat dropped from

1:12 200 a week to 100. Put another way, if you're new, every unit of Codex work now costs you twice what it did before the cut. Two outlets, same numbers. The dates are the tricky part. New

1:23 subscribers get the smaller allowance straight away. If you already pay, you keep your current limits until October 29th. After that, existing subscribers move down to the new level two. In

1:33 exchange, they get a one-time $2,500 credit, which expires on December 31st. Generous and also in a hurry. Open AI gave this one a warm-up act. On September 10th, it paused new sign-ups

1:46 and upgrades to the 20 times plan, citing demand for Astra. Existing subscriptions kept renewing, and the $100 Pro plan stayed available the whole time. 19 days later, on the 29th,

1:58 sign-ups reopened. Door's back. Room behind it shrank. Same day, OpenAI added a $500 pro tier. In Gadget reports it includes an ultra-fast mode at up to 300 tokens a second. A token's a chunk of

2:11 text, roughly a word. Keep that 300 in your pocket. If you'd been eyeing that $500 tier, the box cost about 7 and 1/2 months of it. OpenAI also announced GPT 6.1 Soul, which it says roughly matches

2:24 GPT 6.0 Astra at coding for about a fifth of the cost. OpenAI's own claim, not independently checked. In Gadget frames the whole plan change around OpenAI's increasingly efficient models.

2:36 Rent that can shrink overnight makes a one-time price tag look friendly. So, what's actually inside? Inside is a Mac Studio with the M5 Max, 64 gigs of memory, and a terabyte of

2:47 storage. I'm working from a price of $3,799. Reported pricing gets you most of the way there. $3,099 for the 40-core model, plus $400 to go

2:58 from 48 to 64 gigs. A storage bump covers the rest, though it's exact price isn't confirmed. Why does a GPU core count matter here? Apple's spec page has a rule tucked into it.

3:09 You can only get 64 gigs with the 40-core GPU version, and that version moves memory at 614 GB a second. With 32 cores, you'd get 460. So, choosing 64 gigs also chooses the faster memory. For

3:23 running language models, 614 is the number on that sheet that matters most. For contrast, the M5 Ultra version starts at $5,499, and the one Ars Technica reviewed cost

3:35 $12,299, according to write-ups of that review. Ars pitches the Studio at people running open-weight models at their desk. At 12 grand, your payback chart needs a second

3:46 page. A fair comparison is the laptop artificial analysis just benchmarked. A MacBook Pro with the M5 Pro, also 64 gigs, priced at $3,700. It's memory runs at 307 GB a second.

4:00 Double that and you get the Studio's 614. To see why that doubling matters, picture memory as a pipe. On one end is the shelf where the model's weight sit. On the other is the chip doing the

4:11 maths. Every time a model writes one token, it needs its weights. For an ordinary dense model, that means all of them. So, each new token means dragging the

4:20 entire model through the pipe, then doing it all again for the next one. Now, you can do napkin maths. Qwen 3.5 27B at 6-bit is a 26 GB file. A 614 GB a second pipe can move 26 gigs about 23

4:35 and 1/2 times a second. So, your ceiling is roughly 23 and 1/2 tokens a second. Hardware Corner measured that exact model on this chip. Their machine was a 128 gig MacBook Pro, not a Studio, but

4:48 the 40-core GPU and the 614 pipe are the same. Result, 23.6 tokens a second, right on the napkin number. Landing that close is partly luck, since the 26 is rounded and real runs almost never use

5:01 the full pipe. Still, it tells you the pipe is setting the pace. One lesson covers the whole box. More memory lets you hold a bigger model. A wider pipe lets you move it faster. Two different

5:12 upgrades, and while the model's writing, width is the one you feel. Those bit numbers, 6-bit and 4-bit, are how many bits each weight gets. Fewer bits means a smaller file and a shorter trip

5:23 through the pipe at some cost in precision. So, you'll hear the same model quoted at different sizes. What's 23.6 tokens a second like? A 300-line file is roughly 3,000 tokens. So, call

5:35 it 2 minutes to write out. Fine for one file. A long time to sit and watch an agent rewrite a file you never asked it to touch. For scale, the same model on an RTX 5090, a dedicated graphics card,

5:47 did 49.1 tokens a second, about twice as fast. And the MacBook with half the pipe, same napkin, 307 / 26 is under 12. So, you'd expect roughly half the Studio speed on dense models. Call that a

6:01 bandwidth expectation, not a measurement. Artificial Analysis did test that MacBook on actual agent work, eight coding tasks, 168 turns, 4-bit models with the conversation growing to

6:13 about 56,000 tokens by the end. That's 21 turns per task. For a coding agent, that's getting straight to the point. On two models, the MacBook finished within 2 to 9% of AMD's Ryzen AI Halo, a 35

6:26 billion model from Qwen with an odd trick we'll get to shortly, and Qwen 3.8 27B. On a small 9 billion parameter model, it was 21% slower. So, a 64 gig Mac holds its own on agent coding with

6:40 the very models you'd run. What that test can't give you is Studio speed. Artificial Analysis hasn't benchmarked an M5 Max yet, so for that, the pipe maths and Hardware Corner are what we've

6:50 got. Now, that puzzle. On the same M5 Max, Hardware Corner ran Qwen 3.5 122B at 4-bit, a 69.6 gig file. Out came 65.9 tokens a second, almost three times the dense 27B. Run the napkin. If you moved

7:08 all 69.6 gigs through a 614 pipe every time, you'd top out under nine tokens a second. Measured 65.9. No pipe that size carries that. So, the model can't be moving the

7:20 whole thing. What you're looking at is a mixture of experts model. Picture the weights split into lots of small specialists. For each token, only a few of them get called on, and only those go

7:30 through the pipe. Everything else stays on the shelf. Big shelf, small trip. Your dense 27B hauls all 26 gigs for every token. Qwen's 122B stores about 70 gigs and carries a sliver each time. So,

7:44 bigger came out faster. Small trips also explained the pushback on X to Apple's pitch of four times faster for LLM inference. Dense models like the 27B land around 23 tokens a

7:55 second, well below what that claim implies. While mixture of experts models run much faster. Same chip, different trip size. Which brings us to the catch. Qwen's 122B is 69.6 gigs. Your shelf

8:08 holds 64. Faster or not, it doesn't go on. Same with Qwen 3 CodeR next at 8-bit, which Hardware Corner clocked at 48 to 79 tokens a second. Size 84.7 gigs.

8:21 So, this chip's headline speeds come from models a 64 gig box can't hold. 64 gigs sounds roomy right up until a single coding model asked for 85 of them. And the model isn't alone up

8:32 there. macOS and your editor share the same 64 gigs. So, something that barely squeezes in on paper leaves you no elbow room in practice. So, what does fit? Qwen 3.5 27B at 6-bit, 26 gigs with room

8:46 to spare. Plus the two models Artificial Analysis ran at 4-bit on its 64 gig MacBook. Qwen 3.8 27B and Qwen 3.6 the 35B with A3B in its name. They ran on 64 gigs there, so

9:02 they'll run on 64 gigs here. That A3B is the odd trick I promised. Only about 3 billion of its 35 billion parameters go through the pipe for each token. Small trips again.

9:13 Of Hardware Corner's results, the one that applies directly to a 64 gig Studio is the dense 27B at 23.6 tokens a second. Going by the pipe, the 35B mixture of experts model should beat it,

9:26 but that's an expectation until someone measures it. So, the realistic shelf for this box is the 27 to 35 billion class. A useful coding tier and also where the distance to the frontier shows.

9:38 Engadget describes Astra class models as Open AI's top coding models. A local 27B isn't in that class. Treat the quality gap as real, but unscored. These sources don't pit local

9:49 quan against Astra on the same tasks. Speed's easier to put a number on. Remember that 300? That's the $500 tiers ultra fast mode, and it's an up to. Your dense model on the desk does about 24.

10:02 So, at its best, the $500 tier writes about 12 times faster than your desk. What you get back is ownership. Your code never leaves your desk. There's no weekly message cap to watch, and your

10:13 allowance can't get halved on a Tuesday. In practice, I think that makes the box best at the unglamorous stuff. Writing tests, refactors, boilerplate, the 10th pass over the same kind of change. Work

10:25 where a good enough model running all day with no meter beats a brilliant one on a meter. If you do buy, I'd start with the two models Artificial Analysis already put through real agent work on

10:34 64 gigs, then time a normal day's tasks before trusting any spreadsheet, mine included. Now, the rent maths, which is mine, so it's on screen as an estimate. $3,799

10:47 divided by $200 a month comes to 19 months. Well, 18.995. Apple ending the price in 99 is the only thing standing between you and a perfectly round number. Electricity

10:59 isn't in there yet. Apple rates the Studio's maximum continuous power at 480 watts, but that's a ceiling, not a typical draw. Ours measured the M5 Studio's averaging about 75 watts in a

11:11 video transcoding test. Transcoding isn't a language model run, so treat 75 as a hint. So, I'll pick an assumption and label it. 100 watts, 8 hours a day at 30 cents a kilowatt hour. Call it

11:24 about $7 a month. Take that off the $200 you're saving, and payback stretches to roughly 20 months. Swap in your own rate. My maths also leaves out things that cut both ways. Resale value helps

11:35 the box. Sales tax hurts it, and the box keeps working in month 21 while the subscription keeps billing. Change the comparison and the answer changes. Against the $100 Pro plan, the same box

11:47 takes 38 months to pay back. Over 3 years in a field where every figure I've quoted is dated late September. And if you're already on the $200 plan, don't cancel today. Your old limits last

11:59 until October 29th, and you get a one-time $2,500 credit that expires December 31st. Treat that as a use it or lose it window. I'd use that window as a test. Push your hardest real work

12:11 through Pro while it's generous, and keep a note of which tasks needed Astra and which a smaller model could have handled. Your buying decision is on that list.

12:20 Scoreboard time. Box $3,799. Rent $200, now buying half as much for new subscribers. Payback 19 months on paper, about 20 with my power guess. Pipe 614.

12:35 Shelf 64 gigs, which means the 27 to 35 billion class at roughly 24 tokens a second for a dense model. My verdict splits you three ways. First, buy the studio if your coding is heavy, private,

12:47 and repetitive, and a 27 to 35 billion model already handles it well. You're the one that have meter hurts most, and 19 to 20 months is a fair payback on a box you keep. Second, keep

12:59 paying for Pro if you need frontier reasoning, nasty debugging, architecture calls, problems where Astra class models are the point. A local 27B won't replace that, and at 24 tokens a second, it

13:11 won't race the $500 tiers ultra fast mode either. Third, if your usage is light, try the $100 plan or pay as you go API access. If you rarely hit the old limits, having them may not touch you,

13:24 and $3,799 is a lot of hardware to own for occasional prompts. If I were buying today, I'd get the 64-gig Studio only after that notepad

13:34 says most of my work is 27B-shaped. For the right workload, it pays for itself in under 2 years. For the wrong one, it's a $3,799 way to still be paying $200 a month,

13:46 which leads one open question. If Soul really does match Astra at coding for a fifth of the cost, how long before the $200 meter start to buying more than the box does?