Amazon now offers Apple’s 13-inch M1 MacBook Air 256GB for $799 shipped in three different colors. Normally fetching $999, today’s offer amounts to $200 in savings while marking a return to the lowest price of the year for one of the first times. This is also the best price to date overall, delivering some added savings over the latest releases in the macOS stable. While it isn’t that all-new version of Apple’s most portable MacBook, there is still something to be said for just how good of a value the original M1 MacBook Air still is, especially with as steep of a discount attached as you’ll find. Centered around a 13-inch Retina display with Apple Silicon under the hood, there’s also a pair of Thunderbolt ports supplemented by Wi-Fi 6 as well as 256GB of storage and 8GB of RAM. Get a better idea of its performance in our hands-on review, and then head below for more.
If you’re looking for more insight on why the M1 MacBook Air is worth considering over its newer M2 counterpart, we took a more in-depth look at how the two stack up over on 9to5Mac. Despite all of the love and fanfare around the newer version, our main takeaway was just how compelling the M1 MacBook Air still is for most users, especially those who want a more affordable package that’s even more portable than its predecessor. Especially at $300 below the M2 model.
This week has been all about previous-generation clearance on the Apple front, with several retailers beginning to offer new or matching all-time lows on iPads and accessories. Most notably, second-best prices have emerged on nearly every 12.9-inch M1 iPad Pro config, delivering some added value over the new M2 models with $200 in savings.
Apple 13-inch M1 MacBook Air features:
The Apple 13″ MacBook Air features Apple’s first chip designed specifically for Mac. The Apple M1 integrates the CPU, GPU, Neural Engine, I/O, and more into a single system on a chip (SoC). Tackle your projects with the fast 8-Core CPU and take on graphics-intensive apps and games with the 8-core GPU. Accelerate machine learning tasks with the 16-core Neural Engine.
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