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

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

The WebXPRT 5 Preview is here!

We’re excited to announce that the WebXPRT 5 Preview is now available!

The Preview is available to everyone. You can access it at www.WebXPRT5.com or through a link on WebXPRT.com.

You are free to publish scores from testing with this Preview build; in fact, we encourage it. We want to know how it is performing for you, so we love to see both test scores and any feedback you would like to give.

We may still tweak a few things in the benchmark between this Preview and the final release. We plan to limit any potential changes, however, to areas like the UI and other features, things we do not expect to affect test scores.

Longtime WebXPRT users will notice that while the WebXPRT 5 Preview UI has a new look and feel, the basic layout has not changed. The general process for kicking off both manual and automated tests is the same as with WebXPRT 4, so the transition from WebXPRT 4 to WebXPRT 5 testing should be straightforward.

We also encourage you to check out our recent XPRT blog post on the WebXPRT 5 workload lineup for more details about what’s new in the Preview release—including more AI-oriented scenarios than ever before!

After you try the WebXPRT 5 Preview, please send us your comments. Thanks, and happy testing!

Justin

WebXPRT 5: The workload lineup

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

WebXPRT 5: Starting to assemble the pieces

In our last blog post, we shared the exciting news that we’re currently working on WebXPRT 5. In that post, we described some of the ways that WebXPRT may evolve with the release of WebXPRT 5. In today’s post, we’ll revisit some of the points of emphasis from the last post and focus on potential workload changes in a bit more detail.

With any benchmark development project, there are always technical challenges you need to iron out. That is especially true with a cross-platform, browser-based benchmark like WebXPRT. Because we’re in the middle of exploring the technical feasibility of a few of the options we’ll mention, we’re not yet ready to say for certain that all these features will be available in the initial WebXPRT 5 release. We can, however, now paint a clearer picture of the overall direction we’re headed.

In the section below, you’ll find updated info on where we stand with respect to some of the key development focal points we discussed in our last post. If there’s an item from that post or previous posts that we didn’t mention below—such as updating the test harness—it doesn’t mean that we’re dropping that goal. We’re just focusing on workloads today.

One of our key goals with WebXPRT 5 is providing more AI-related workloads. In past blog posts, we’ve discussed the growing importance of local, browser-side AI. With WebXPRT 5, we’re investigating two ways that we can expand WebXPRT’s AI portfolio: 1) updating existing WebXPRT 4 AI-oriented workloads, and 2) adding all-new AI workloads.

Here are some possible ways those AI-related changes may play out in both categories:

Updating existing WebXPRT 4 AI-oriented workloads

  • Splitting the existing Organize Album using AI workload’s timed tasks—face detection and image classification—into two independent workloads.
  • Updating the face detection and image classification tasks with the latest versions of the OpenCV.js computer vision and machine learning libraries.
  • Updating the Caffe deep learning framework for the face detection task.
  • Updating the ONNX-based SqueezeNet machine learning model for the image classification tasks.
  • Updating the version of the Tesseract.js OCR engine that WebXPRT uses in the Encrypt Notes and OCR Scan workload. 

Potentially adding all-new AI workloads (either core or experimental workloads)

  • We’re exploring the idea of including a workload that uses an AI-powered segmentation model to blur the background of a video call.
  • We’re exploring the feasibility of including a local LLM chat workload.
  • We would eventually like to include a WebGPU-based web AI framework for a computer vision workload.

In addition to the goal of adding more AI, we previously discussed the possibility of adding non-AI WebGPU workloads. As a web API, WebGPU enables web-based applications—such as image-based GenAI and inference workloads—to directly access the graphics rendering and computational capabilities of a system’s GPU. In the future, WebXPRT 5 could use that technology to execute complex 3D rendering workloads.

We hope today’s post gives you a better sense of where WebXPRT 5 may be headed. We want to reemphasize that while we are actively investigating the possible changes mentioned above, nothing is set in stone. As the pieces start to fall into place, we’ll provide more information here in the blog.

If you have any questions or comments about WebXPRT 5, please feel free to contact us!

Justin

Multi-tab testing in a future version of WebXPRT?

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

Browser-based AI tests in WebXPRT 4: optical character recognition

In our previous blog post, we discussed the rapidly expanding influence of AI-enhanced technologies in areas like everyday browser activity—and the growing need for objective performance data that can help us understand how well new consumer devices will handle AI tasks. We noted that WebXPRT 4 already includes timed AI tasks in two of its workloads—the “Organize Album using AI” and “Encrypt Notes and OCR Scan”—and we provided some technical details for the Organize Album workload. In today’s post, we’ll focus on the Encrypt Notes workload.

The Encrypt Notes workload includes two separate scenarios that reflect common web-based productivity app tasks. The first scenario syncs a set of encrypted notes, and the second scenario uses AI-based optical character recognition (OCR) to scan a receipt, extract data, and then add that data to an expense report.

Here are the details for each scenario:

  • The encrypt notes scenario downloads a set of notes, encrypts that data, temporarily stores it in the browser’s localStorage object (the localStorageDB.js database layer), and then decrypts and renders it for display. This scenario measures HTML5 Local Storage, JavaScript, AES encryption, and WebAssembly (Wasm) performance. 
  • The OCR scan scenario uses a Wasm-based version of Tesseract.js (tesseract-core.wasm.js v2.20) to scan an expense receipt. Tesseract.js is a JavaScript port of the Tesseract OCR engine—a popular open-source C/C++ library that extracts text from images and PDFs. The scenario then adds the receipt to an expense report. This scenario measures HTML5 Local Storage, JavaScript, and Wasm performance. 

We mention this test under the AI umbrella in part because people sometimes use the term “OCR” to refer to a spectrum of AI and non-AI technologies. In this case, though, the specifics make this workload clearly have an AI component. The Wasm-based Tesseract library that we use in WebXPRT 4 is based on a version of C/C++ (v4.x) that uses Long Short-Term Memory (LSTM). LSTM is a type of recurrent neural network (RNN) that is particularly well-suited for processing and predicting sequential data. As such, it is clearly an AI component of the Encrypt Notes and OCR Scan workload.

To produce a score for each iteration of the workload, WebXPRT calculates the total time that it takes for a system to sync (encrypt, decrypt, and render) the notes, use OCR to scan the receipt, and add the scanned data to an expense report. 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.

Along with our post on the Organize Album workload, we hope this information provides a deeper understanding of WebXPRT 4’s AI-equipped workloads. As we mentioned last time, if you want to explore the structure of these workloads in more detail, you can check out previous blog posts for information about how to access and use the WebXPRT 4 source code for free. 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 WebXPRT 4, please let us know!

Justin

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