MacBook Air M5 (15-inch), 16GB vs MacBook Pro M5 Pro (16-inch), 64GB for local AI
The MacBook Pro M5 Pro (16-inch), 64GB holds 32 of the 48 current models here, and the MacBook Air M5 (15-inch), 16GB holds 12. The MacBook Air M5 (15-inch), 16GB costs $2,500 less. On Gemma 4 12B, the strongest model both hold, the MacBook Pro M5 Pro (16-inch), 64GB is about 2.1× faster: 29 tok/s against 14, both estimated from memory bandwidth. The MacBook Air M5 (15-inch), 16GB pays for itself sooner, in 118 years against 269 years at 500k tokens a day, though that is each machine on its own strongest model rather than on the same one.
| MacBook Air M5 (15-inch), 16GB | MacBook Pro M5 Pro (16-inch), 64GB | |
|---|---|---|
| Price | $1,499 | $3,999 |
| Memory | 16 GB | 64 GB |
| Usable by the GPU | 10.5 GB | 48 GB |
| Memory bandwidth | 153 GB/s | 307 GB/s |
| Power under load | 65 Wstand-in | 140 Wstand-in |
| Models that fit | 12 | 32 |
| Best model it runs | Gemma 4 12B | Qwen3.8 27B |
| Speed on that model | 14 tok/s estimatedGemma 4 12B | 12 tok/s estimatedQwen3.8 27B |
| Pay-back on that model | Pays back in 118 years | Pays back in 269 years |
Neither power figure above is measured on the machine beside it. Both are stand-ins borrowed from the nearest hardware the data does have, so the gap between them is not a difference between these two machines. Each machine's page names the figure it borrows and why. Every pay-back figure on this page prices its electricity from these numbers.
Run the numbers on the MacBook Air M5 (15-inch), 16GB · or the MacBook Pro M5 Pro (16-inch), 64GB
Jump to: Side by side on Gemma 4 12B · How much use it takes to pay back · What the extra memory buys · The assumptions behind both columns
Side by side on Gemma 4 12B
The table above gives each machine the strongest model it can hold, and those are not the same model, so the two speeds in it are not a race. Gemma 4 12B is the strongest model both machines hold, so this is the pair running the same work.
| MacBook Air M5 (15-inch), 16GB | MacBook Pro M5 Pro (16-inch), 64GB | |
|---|---|---|
| Speed | 14 tok/s estimated | 29 tok/s estimated |
| Pay-back | Pays back in 118 years | Pays back in 318 years |
Run Gemma 4 12B on the MacBook Air M5 (15-inch), 16GB · or on the MacBook Pro M5 Pro (16-inch), 64GB
How much use it takes to pay back
Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both machines on the same model, at the five levels of use the calculator names. The MacBook Air M5 (15-inch), 16GB pays back sooner at every level of use, so this is not a choice that turns on how hard you work it.
| A day's use | MacBook Air M5 (15-inch), 16GB | MacBook Pro M5 Pro (16-inch), 64GB |
|---|---|---|
| 50ka few chats a day | 1,177 years | 3,184 years |
| 200klight assistant use | 294 years | 796 years |
| 1Ma moderate coding-assistant day | 59 years | 159 years |
| 4Mheavy coding with an agent | 15 years | 40 years |
| 20Magents running most of the day | 3.0 yearsits ceiling | 8.0 years |
On Gemma 4 12B the MacBook Air M5 (15-inch), 16GB generates at most 19.8M tokens a day, so its figure at 20M tokens a day is for the most it can do, not for the whole of what was asked.
Run the MacBook Air M5 (15-inch), 16GB at 20M tokens a day · or the MacBook Pro M5 Pro (16-inch), 64GB
What the extra memory buys
The MacBook Pro M5 Pro (16-inch), 64GB holds 20 models the MacBook Air M5 (15-inch), 16GB cannot at 32k of context. The strongest of them are what the difference in memory actually buys.
| Model | Weights | Needs at 32k | On the MacBook Pro M5 Pro (16-inch), 64GB |
|---|---|---|---|
| Qwen3.8 27BSonnet-class | 16 GB | 19 GB | 12 tok/s estimated |
| Qwen3.6 27BHaiku-class | 17 GB | 19 GB | 12 tok/s estimated |
| Nemotron 3.5 Bolt 74B-A9BHaiku-class | 22 GB | 22 GB | 32 tok/s estimated |
| Qwen3.6 35B-A3BHaiku-class | 22 GB | 23 GB | 37 tok/s estimated |
| Muse Glimmer 30BHaiku-class | 17 GB | 18 GB | 13 tok/s estimated |
| Gemma 4 26B-A4BHaiku-class | 17 GB | 18 GB | 27 tok/s estimated |
14 more, on the MacBook Pro M5 Pro (16-inch), 64GB page.
The extra memory buys context as well. Of the 12 current models both machines hold at 32k, 7 run to a longer window on the MacBook Pro M5 Pro (16-inch), 64GB: the weights are a fixed size and the KV cache is not, so what the weights leave spare is what a longer context grows into. Ministral 3 8B reaches 256k there against 32k on the MacBook Air M5 (15-inch), 16GB, each the longest window the calculator offers that the machine still holds it at.
The MacBook Air M5 (15-inch), 16GB is also head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · DGX Spark, 128GB · GMKtec EVO-X2, 128GB · MacBook Air M5 (13-inch), 16GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB · Radeon AI PRO R9700, 32GB.
The MacBook Pro M5 Pro (16-inch), 64GB is also head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · DGX Spark, 128GB · GMKtec EVO-X2, 128GB · Beelink GTR9 Pro, 128GB · Minisforum MS-S1 Max, 128GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB · Radeon AI PRO R9700, 32GB. With the same machine at another memory size: 24GB · 48GB.
The assumptions behind both columns
Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Change any of it in the calculator.
More head to head: everything the MacBook Air M5 (15-inch), 16GB runs · everything the MacBook Pro M5 Pro (16-inch), 64GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier