Apple Mac mini review: The new M6 impresses, but the price hike is rough

Apple has released the new M6 Mac mini, and if you are looking for a sleek new chassis, you will be disappointed. The design remains identical to previous generations, focusing entirely on internal upgrades. This is the standard Apple playbook for mid-cycle refreshes, prioritizing silicon over aesthetics. It is a safe, predictable move that usually keeps enthusiasts happy with raw power gains.

However, the story takes a sharp turn when you look at the pricing. According to recent reports, the base model now starts at $899. This represents a significant $300 increase over the M4 Mac mini that launched in 2024 with the same 16GB of RAM and 256GB of storage. For years, Apple offered a compelling entry point for professionals and hobbyists alike. That era appears to be ending abruptly.

The culprit here is not Apple’s greed but a global memory crunch. The semiconductor industry is facing unprecedented supply constraints that are driving up the cost of RAM and storage components. These hardware costs are now being passed directly to consumers. This is a macroeconomic reality that affects every tech company, not just Cupertino.

This price hike has serious implications for local AI workflows. Large language models and image generators require substantial memory to run efficiently on-device. The old $599 entry point was the sweet spot for running quantized models locally. At $899, that barrier to entry just became much steeper. You are paying a premium for the same baseline resources you could get cheaper just two years ago.

Despite the cost, the M6 chip itself is impressive. It offers the kind of performance headroom that makes local inference smoother and faster. If you are already invested in the Apple ecosystem, the upgrade path is logical. The efficiency gains are real and noticeable in daily tasks. But the value proposition has shifted from affordable power to premium performance.

This trend signals a broader change in how we access AI capabilities. As hardware costs rise, the gap between cloud and local processing may narrow again. Users might find it more economical to subscribe to cloud AI services rather than buying expensive local hardware. The dream of a cheap, powerful desktop AI hub is becoming more expensive to realize.

What this means for you is that you need to reassess your hardware strategy. If you are building a local AI lab, wait for prices to stabilize or look at used enterprise gear. For now, the Mac mini is a powerful tool but a less accessible one. Try this prompt to analyze your current setup: "Compare the cost-per-token of running Llama 3.1 8B on an M6 Mac mini versus using a cloud API, factoring in electricity and subscription fees." This will help you decide if local hardware still makes financial sense for your specific use case.