YouTube Explained: Test & Compare vs A/B vs Split Test (2024)

Article last updated on:
March 12, 2024

You’re probably here because you:

  • Heard all of these YouTube terms
  • And they got you all confused
  • So you want to understand what they mean

We’re here to clear things up once and for all.

Comparison Table: YouTube Testing

Essentially, all 3 of terms refer to the same feature, which does this: you test two or more versions of something to see which one performs better.

Here’s a table to make things clear:

Test And CompareA/B TestingSplit Testing
Specific UseThumbnailsTitles, ThumbnailsTitles, Thumbnails
VariationsUp to 3 thumbnails2 variations2+ variations
FocusOptimize for better CTRIdentify differences between variationsIdentify differences between variations
ManagementYT StudioThumbnail TestThumbnail Test
Data AnalysisBasic metricsExtra AnalysisExtra Analysis
Differences: YouTube Testing Methods

Every type of testing, explained

Let’s break down each.

1. Test and Compare

Test and Compare is a new feature on YouTube that allows creators to A/B test different thumbnails for their videos.

Screenshot from Creator Insider on YouTube: How YouTube Split Testing Works
  • Benefits: Grabs viewers’ attention, helps pick the most effective thumbnail, boosts video views.
  • Availability: Rolled out gradually in 2023, expected to be available to all creators “by late 2024”.
  • How it Works: During video upload, you can choose “Test and Compare” (under the thumbnail upload section) to compare up to 3 thumbnails. YouTube will then show these thumbnails to different audiences and measure which one gets the most watch time.
  • Outcome: The thumbnail that leads to the highest average view duration wins the test.

Read more: All about Test & Compare.

2. A/B Testing

A/B Testing is a method used by YouTubers to experiment with different thumbnails and titles for their videos.

The goal is, as always, to find the most performant version of each video.

Illustration of thumbnail testing on YouTube

Benefits of A/B Testing:

  • Improved video performance: By testing different elements, you can identify what grabs viewers’ attention and keeps them engaged. This can lead to more views, likes, comments, watch time, and ultimately, a more successful channel.
  • Data-driven decisions: A/B testing takes the guesswork out of what works for your target audience. You get concrete data to see which variations resonate better, allowing you to tailor your content for maximum impact.
  • Increased audience engagement: When viewers find your thumbnails and titles compelling, they’re more likely to click on your videos. A/B testing helps you optimize these elements to spark curiosity and draw viewers in.

Read more: A/B Testing on YouTube.

3. Split Testing

Split testing on YouTube is a method creators use to experiment with different elements of their videos and see which ones resonate best with viewers.

It’s not a specific built-in feature like “Test and Compare” for thumbnails, but a broader strategy.

Benefits of split testing on YouTube:

  • Data-driven decisions: Helps you choose the most effective elements for your videos based on actual audience response.
  • Improved performance: Leads to higher click-through rates, watch time, and overall engagement.
  • Content optimization: Allows you to refine your content strategy based on what resonates with your viewers.

Read more: YouTube Split Testing, Explained.

When to use which term?

In many cases, you might use these terms interchangeably. But if you need to be precise, consider the following

  • Use “test & compare” for the general concept of testing variations.
  • Use “A/B testing” when you’re specifically comparing exactly two variations.
  • Use “split testing” when you’re comparing more than two variations, or the changes being tested are more complex.

Hope that explains!

Frequently Asked Questions

1. What is the difference between all these terms?

All three methods involve testing variations of something to see which performs best.

  • YouTube Test & Compare is specifically designed for comparing thumbnails…
  • While A/B testing + Split Testing can be used for any marketing element or website/app feature.

2. Which method should I use to test?

Here’s our take:

  • Test & Compare – if you’re unsure which thumbnail will perform better for your video.
  • A/B testing or Split Testing – for more complex scenarios where you want to test different ad copy, landing pages, website features, etc.

3. Are all these options available?

Not entirely right now. YouTube released this feature just to their beta testers, but not to the general public.

An external app like Thumbnail Test is your best bet:

It is a tool that offers a specific tool for A/B testing thumbnails and titles for your YouTube videos.

  • Focus: Optimizes video click-through rate (CTR) by testing different thumbnails and titles.
  • Functionality: Lets you upload and test multiple variations of thumbnails and titles for your YouTube videos. You can choose to run the test hourly or daily.
  • Benefits: Helps identify which thumbnail and title combination generates the most clicks for your video, potentially leading to more views and engagement. offers more flexibility for testing compared to YouTube’s limited 3 thumbnail option.


This guide just looked into the 3 terms that refer to testing your video’s quality on YouTube.

In short, they’re all very close to each other, so you don’t have to worry about the differences.

Key Takeaways

  • YouTube Test & Compare is a simplified version of A/B testing specifically designed for thumbnails.
  • A/B testing and split testing are broader terms encompassing testing any element that can be varied.
  • A/B testing and split testing typically require more technical knowledge and resources to set up and analyze compared to YouTube Test & Compare.

Thank you for reading this,

About the author

David is the head of the editing team at ThumbnailTest. With his help, the editorial team is able to provide you with the best free guides related to YouTube thumbnails and A/B testing.