Capture One M1 Benchmark Test Results

Recently Capture One was updated to natively support M1 Processors; DT took this as an opportunity to develop a standardized benchmark for Capture One using Applescript. We’re glad to share the initial results below, with special thanks to our many customers who ran the benchmark on their own systems.

Selection Score, Exposure Score, and Process Score

Before reviewing the raw numbers keep in mind that benchmarking is an imperfect art, so it’s best to squint at the results rather than carefully scrutinize them. While we provided instructions for best practices (e.g. close all other apps, start a new session) almost any difference in the setup of a computer can affect the results of a benchmark. Our benchmark runs each operation many times and averages the results, but there is still run-to-run variation. 

Finally, the benchmark rounded to the nearest 0.1 of a second, which made sense on the slower/older computer we developed the benchmark on but decreased our precision on some of the faster computers. We plan to release a new version of our benchmark as an open-source project later this month – it will remove that rounding and add some new operations for a more holistic benchmark; stay tuned by subscribing to our newsletter here!

Technical Explanation:

Longer bars mean faster/better performance. The benchmark use Applescript to complete a set of tasks that represent typical usage in Capture One: selecting/browsing, adjusting, and processing. This is done on a standard set of 20 Phase One IQ4 150mp raw files, packed as EIPs.

  • Selections – The script deselects, and then selects each image. This is repeated 10 times and the average result is taken.
  • Adjustments – The exposure of each image is set to 1, and then set to 0. This is repeated 10 times and the average result is taken.
  • Processing – Each image is processed as a 16-bit TIFF. This is repeated twice. Note that since we wait for each file to complete before processing the next one, this will be slightly slower than processing them all at once.

These raw execution times are then translated into scores by inverting their value and normalizing their values to each other for easy comparison.

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