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WebXPRT 5 source code access is on the way

Justin Greene
By Justin Greene
on March 12, 2026

Recently, a member of the tech press asked us if we were planning to offer a way for users to set up an offline version of WebXPRT 5 for locally hosted tests. The short answer is “yes.”

The long answer is that the question provides us with a good opportunity to talk about XPRT source code access and let new users know how it works.

Since the early days of the BenchmarkXPRT Development Community, we’ve provided free access to the benchmark source code. We believe that by publishing XPRT code and allowing interested parties to access and review that code, we’re doing our part to encourage transparency and honesty in the benchmarking industry.

While we offer free access to the XPRT source code, our approach to derivative work differs from some traditional open-source models that encourage developers to alter products and even take them in substantially different directions. Because benchmarking requires a product that remains static to enable valid comparisons, we prioritize maintaining the integrity and consistency of the benchmark over time. So, we allow people to download the source, but we also reserve the right to control derivative works. This approach discourages a situation where someone publishes an unauthorized version of the benchmark and calls it an “XPRT.”

For WebXPRT 5, we’ll offer the code in the form of a build package—containing all the necessary files and instructions—that will be available upon request. By offering the code upon request, as opposed to posting a permanent download link, we can prevent bots or other malicious actors from downloading it. This method also lets us engage with users interested in the source code and answer their questions.

With the WebXPRT 5 build package, you’ll be able to set up your own WebXPRT 5 instance for purposes of review, internal testing, or experimentation. We do ask that you publish only test results from the official version of WebXPRT 5 that we host at WebXPRT.com.

We expect to have the build package ready within the next few weeks. When it’s available, we’ll let readers know here in the blog, and we’ll provide more details about the access and setup process.

If you have any questions about accessing the WebXPRT 5 source code, please let us know!

Justin

Posted in benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, browser performance, Browser-based benchmarks, Collaborative benchmark development, Performance benchmarking, Source code, tech press, Web-based testing, WebXPRT, WebXPRT 5 | Also tagged , , , , , , , ,

WebXPRT 5 is live!

Justin Greene
By Justin Greene
on February 26, 2026

The big day has finally arrived—WebXPRT 5 is now available!

You can access the benchmark at WebXPRT.com or WebXPRT5.com. For longtime WebXPRT users, the WebXPRT 5 UI will have an all-new look but a very familiar feel. The general process for kicking off both manual and automated tests is the same as with WebXPRT 4, so the transition to WebXPRT 5 testing will be straightforward. For legacy testing purposes, we will continue to make WebXPRT 4 available on our site.

Here is a quick overview of the differences between WebXPRT 4 and WebXPRT 5:

General changes

  • We’ve updated the aesthetics of the WebXPRT UI to make WebXPRT 5 visually distinct from older versions. We did not significantly change the flow of the UI.
  • We’ve updated content in some of the workloads to reflect changes in everyday technology, such as upgrading most of the photos in the photo processing workloads to higher resolutions.
  • We’ve updated the base calibration system for score calculations and adjusted the scoring scale. WebXPRT 5 scores will be in a lower numerical range than WebXPRT 4 scores. You should not compare these results to scores from previous versions of WebXPRT.

The workloads

WebXPRT 5 includes the following seven workloads:

  • Video Background Blur with AI. Blurs the background of a video call using an AI-powered segmentation model.
  • Photo Effects. Applies a filter to six photos using the Canvas API.
  • Detect Faces with AI. Detects faces and organizes photos in an album using computer vision (OpenCV.js with Caffe Model).
  • Image Classification with AI. Labels images in an album using machine learning (OpenCV.js and ML Classify with the SqueezeNet model).
  • Document Scan with AI. Scans a document image and converts it to text using ML-based OCR (Wasm with LSTM).
  • School Science Project. Processes a DNA sequencing task using Regex and String manipulation.
  • Homework Spellcheck. Spellchecks a document using Typo.js and Web Workers.

We’re thankful for all of the feedback we received during the WebXPRT 5 development process and Preview period, and we look forward to seeing your WebXPRT 5 results. If you have any questions about WebXPRT, please feel free to contact us!

Justin

Posted in AI, benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, browser performance, Browser-based benchmarks, Caffe, Cross-platform benchmarks, face detection, Future of performance evaluation, HTML5, image classification, image processing, inference, JavaScript, Machine learning, object detection, on-device AI, ONNX Runtime Web, Performance benchmarking, results, results submission, SqueezeNet, Wasm, WebAssembly, WebXPRT, WebXPRT 4, WebXPRT 5 | Also tagged , , , , , , , , , , , , , , , , , , , , , , ,

Get your WebXPRT 5 Preview results on WebXPRT.com: how to submit them

Justin Greene
By Justin Greene
on January 15, 2026

The WebXPRT 5 Preview has been available for only a few weeks, but users have already started submitting test results for us to review for publication in the WebXPRT 5 Preview results viewer. We’re excited to receive those submissions, but we know that some of our readers are either new to WebXPRT or may never have submitted a test result. In today’s post, we’ll cover the straightforward process of submitting your WebXPRT 5 Preview test results for publication in the viewer.

Unlike sites that automatically publish all results submissions, we publish only results that meet a set of evaluation criteria. Those results can come from OEM labs, third-party labs, tech media sources, or independent user submissions. What’s important to us is that the scores must be consistent with general expectations, and for sources outside of our labs and data centers, each must include enough detailed system information that we can determine whether the score makes sense. That said, if your scores are different from what you see in our database, please don’t hesitate to send them to us; we may be able to work it out together.

The actual result submission process is simple. On the end-of-test results page that displays after a test run, click the Submit your results button below the overall score. Then, complete the short submission form that pops up, and click Submit.

When filling in the system information fields in the submission form, please be as specific as possible. Detailed device information helps us assess whether individual scores represent valid test runs.

That’s all there is to it!

Figure 1 below shows the end-of-test results screen and the Submit your results button below the overall score.

Figure 1: A screenshot of the WebXPRT 5 Preview end-of-test results screen, showing the Submit your results button below the overall score.

Figure 2 below shows how the results submission form would look if I filled in the necessary information and submitted a score at the end of a recent WebXPRT 5 Preview run on one of the systems here in our lab.

Figure 2: A screenshot of the WebXPRT 5 Preview results submission pop-up window after filling in the email address and system information fields.

After you submit your test result, we’ll review the information. If the test result meets the evaluation criteria, we’ll contact you to confirm how we should display its source in our database. For that purpose, you can choose one of the following:

  • Your first and last name
  • “Independent tester” (if you wish to remain anonymous)
  • Your company’s name, if you have permission to submit the result under that name. If you want to use a company name, please provide a valid corresponding company email address.

As always, we will not publish any additional information about you or your company without your permission.

We look forward to seeing your scores! If you have questions about WebXPRT 5 Preview testing or results submission—or you’d like to share feedback on WebXPRT 5—please let us know!

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, browser performance, Browser-based benchmarks, Cross-platform benchmarks, Performance benchmarking, WebXPRT, WebXPRT 5 | Also tagged , , , , , , , , , ,

WebXPRT 5: The workload lineup

Justin Greene
By Justin Greene
on December 8, 2025

The WebXPRT 5 development process heading into the final stretch, so we’d like to share more information about the workloads you’re likely to see in the WebXPRT 5 Preview release—and when that release may be available. We’re still actively testing candidate builds, studying results from multiple system tests, and so on, so some details could change. That said, we’re now close enough to provide a clearer picture of the workload lineup.

Core workloads

WebXPRT 5 will likely include the following seven workloads:  

  • Video Background Blur with AI. Blurs the background of a video call using an AI-powered segmentation model.
  • Photo Effects. Applies a filter to six photos using the Canvas API.
  • Detect Faces with AI. Detects faces and organizes photos in an album using computer vision (OpenCV.js with Caffe Model).
  • Image Classification with AI. Labels images in an album using machine learning (OpenCV.js and ML Classify with the SqueezeNet model).
  • Document Scan with AI. Scans a document image and converts it to text using ML-based OCR (Wasm with LSTM).
  • School Science Project. Processes a DNA sequencing task using Regex and String manipulation.
  • Homework Spellcheck. Spellchecks a document using Typo.js and Web Workers.

The sub-scores for each of these tests will contribute to WebXPRT 5’s main overall score. (We’ll discuss scoring in future blogs.)

Experimental workloads

We’re currently planning to include an experimental workload section, something we’ve long discussed, in WebXPRT 5. Workloads in this section will use cutting-edge browser technologies that may not be compatible with the same broad range of platforms and devices as the technologies in WebXPRT 5’s core workloads. For that reason, we will not include the scores from the experimental section—in the Preview build and future releases—in WebXPRT 5’s main overall score.

In addition, WebXPRT 5’s experimental workloads will be completely optional.

Moving forward, WebXPRT’s experimental workload section will provide users with a straightforward way to learn how well certain browsers or systems handle new browser-based technologies (e.g., new web apps or AI capabilities). We’ll benefit from the ability to offer workloads for large-scale testing and user feedback before committing to including them as core WebXPRT workloads. Because future experimental workloads will run independently of the main test, we can add them without affecting the main WebXPRT score or requiring users to repeat testing to obtain comparable scores. We think it will be a win-win scenario in many respects.  

We’re still evaluating whether we can finish the first experimental workload in time to include it in the WebXPRT 5 Preview release, but we will definitely have at least the section and the framework for adding such a workload. When we are confident that an experimental workload is ready to go, we’ll share more information here in the blog and be all set up to incorporate it.

Timeline

If all goes well, we hope to publish the WebXPRT 5 Preview very soon, followed by a general release in early 2026. If that timeline changes significantly, we’ll provide an update here in the blog as soon as possible.

What about an “AI score”?

We’re still discussing the concept of a stand-alone WebXPRT 5 “AI score,” and we go back and forth on it. That score would combine WebXPRT’s AI-related subscores into a single score for use in AI capability comparisons. Because we’re just now beefing up WebXPRT’s AI capabilities, we’ve definitely decided not to include an AI score right now. We would love your feedback on the concept as we plan WebXPRT’s future. If that’s something that you would be interested in, please let us know!

If you have any questions about the WebXPRT 5 details we’ve shared above, please feel free to ask!

Justin

Posted in AI, benchmark, BenchmarkXPRT, BenchmarkXPRT development community, browser performance, Browser-based benchmarks, Caffe, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, image classification, image processing, JavaScript, LAMP, on-device AI, Performance benchmarking, Performance of computing devices, SqueezeNet, Wasm, Web AI, web API, Web-based testing, WebXPRT, WebXPRT 4, WebXPRT 5 | Also tagged , , , , , , , , , , , , , , , , , , , , ,

Multi-tab testing in a future version of WebXPRT?

Justin Greene
By Justin Greene
on September 4, 2025

In previous posts about our recommended best practices for producing consistent and reliable WebXPRT scores, we’ve emphasized the importance of “clean” testing. Clean testing involves minimizing the amount of background activity on a system during test runs to ensure stable test conditions. With stable test conditions, we can avoid common scenarios in which startup tasks, automatic updates, and other unpredictable processes contribute to high score variances and potentially unfair comparisons.

Clean testing is a vital part of accurate performance benchmarking, but it doesn’t always show us what kind of performance we can expect in typical everyday conditions. For example, while a browser performance test like WebXPRT can provide clean testing scores that serve as a valuable proxy for overall system performance, an entire WebXPRT test run involves only two open browser tabs. Most of us will have many more tabs open at any given time during the day. Those tabs—and any associated background services, extensions, plug-ins, or renderers—have the potential to require CPU cycles and frequently consume memory resources. Depending on the number of tabs you leave open, the performance impact on your system can be noticeable. Even with modern browser tab management and resource-saving features, a proliferation of tabs can still have a significant impact on your computing experience.

To address this type of computing, we’ve been considering the possibility of adding one or more multi-tab testing features to a future version of WebXPRT. There are several ways we could do this, including the following options:

  • We could open each full workload cycle in a new tab, resulting in seven total tabs.
  • We could open each individual workload iteration in a new tab, resulting in 42 total tabs.
  • We could allow users to run multiple full tests back-to-back while keeping the tabs from the previous test(s) open.

If we do decide to add multi-tab features to a future version of WebXPRT, we could integrate them into the main score or make them optional and thus not affect traditional WebXPRT testing. We’re looking at all these options.

Whenever we have multiple choices, we seek your input. We want to know if a feature like this is something you’d like to see. Below, you’ll find two quick survey questions that will help us gauge your interest in this topic. We would appreciate your input!

Would you be interested in using future WebXPRT multi-tab testing features?

How many browser tabs do you typically leave open at one time?

If you’d like to share additional thoughts or ideas related to possible multi-tab features, please let us know!

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, Benchmarking computing devices, BenchmarkXPRT, browser performance, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, Performance benchmarking, WebXPRT, WebXPRT 4 | Also tagged , , , , , , , , , , , ,

Browser-based AI tests in WebXPRT 4: face detection and image classification

Justin Greene
By Justin Greene
on June 5, 2025

I recently revisited an XPRT blog entry that we posted from CES Las Vegas back in 2020. In that post, I reflected on the show’s expanded AI emphasis, and I wondered if we were reaching a tipping point where AI-enhanced and AI-driven tools and applications would become a significant presence in people’s daily lives. It felt like we were approaching that point back then with the prevalence of AI-powered features such as image enhancement and text recommendation, among many others. Now, seamless AI integration with common online tasks has become so widespread that many people unknowingly benefit from AI interactions several times a day.

As AI’s role in areas like everyday browser activity continues to grow—along with our expectations for what our consumer devices should be able to handle—reliable AI-oriented benchmarking is more vital than ever. We need objective performance data that can help us understand how well a new desktop, laptop, tablet, or phone will handle AI tasks.

WebXPRT 4 already includes timed AI tasks in two of its workloads: the “Organize Album using AI” workload and the “Encrypt Notes and OCR Scan” workload. These two workloads reflect the types of light browser-side inference tasks that are now fairly common in consumer-oriented web apps and extensions. In today’s post, we’ll provide some technical information about the Organize Album workload. In a future post, we’ll do the same for the Encrypt Notes workload.

The Organize Album workload includes two different timed tasks that reflect a common scenario of organizing online photo albums. The workload utilizes the AI inference and JavaScript capabilities of the WebAssembly (Wasm) version of OpenCV.js—an open-source computer vision and machine learning library. In WebXPRT 4, we used OpenCV.js version 4.5.2.

Here are the details for each task:

  • The first task measures the time it takes to complete a face detection job with a set of five 720 x 480 photos that we sourced from commercial photo sites. The workload loads a Caffe deep learning framework model (res10_300x300_ssd_iter_140000_fp16.caffemodel) using the commands found here. 
  • The second task measures the time it takes to complete an image classification job (labeling based on object detection) with a different set of five 718 x 480 photos that we sourced from the ImageNet computer vision dataset. The workload loads an ONNX-based SqueezeNet machine learning model (squeezenet.onnx v 1.0) using the commands found here.

To produce a score for each iteration of the workload, WebXPRT calculates the total time that it takes for a system to organize both albums. In a standard test, WebXPRT runs seven iterations of the entire six-workload performance suite before calculating an overall test score. You can find out more about the WebXPRT results calculation process here.

We hope this post will give you a better sense of how WebXPRT 4 measures one kind of AI performance. As a reminder, if you want to dig into the details at a more granular level, you can access the WebXPRT 4 source code for free. In previous blog posts, you can find information about how to access and use the code. You can also read more about WebXPRT’s overall structure and other workloads in the Exploring WebXPRT 4 white paper.

If you have any questions about this workload or any other aspect of WebXPRT 4, please let us know!

Justin

Posted in AI, benchmark, Benchmark metrics, Benchmarking, Benchmarking computing devices, BenchmarkXPRT, browser performance, Browser-based benchmarks, Caffe, CES, Collaborative benchmark development, computer vision, Consumer Electronics Show, Cross-platform benchmarks, face detection, image classification, ImageNet, inference, JavaScript, Las Vegas, object detection, OCR, On-premise, ONNX Runtime Web, Performance benchmarking, SqueezeNet, Wasm, WebAssembly, WebXPRT, WebXPRT 4 | Also tagged , , , , , , , , , , , , , ,

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