Geekbench

What's new in this version:

- Geekbench 7, the latest version of Primate Labs’ cross-platform benchmark, has arrived and features both new and improved workloads to measure the performance of your CPUs and GPUs.

New Media Workloads:
Geekbench 7 includes new media workloads that measure how well your CPU handles audio and video encoding, decoding, and processing. The workloads model the tasks behind video conferencing, screen sharing, and everyday content consumption. These new workloads:
- Encode screen-sharing video with the AV1 codec, modeling the screen-sharing features in video conferencing apps
- Compress music and spoken-word audio with the Opus codec, modeling voice memo and podcast apps
- Decode video and audio while generating live captions with the Whisper speech recognition model, modeling video playback with automatic subtitles enabled
- Alongside the new media workloads, Geekbench 7 adds a Game Physics workload built on the Jolt Physics engine used in popular video games, expands the Photo Editor workload with a richer set of real-world edits, and updates the Photo Library workload to support importing and processing modern image formats such as JPEG XL and DNG.

A Smarter Multi-Core Benchmark:
- The multi-core benchmark in Geekbench 7 has been redesigned to better reflect how real applications behave. Not every task in the real world is multi-threaded, and pretending otherwise distorts scores without telling you anything useful about your device.
- In Geekbench 7, a workload only runs in multi-threaded mode if the task it models actually runs multi-threaded in real applications. For example, the HTML5 Browser test isn’t included in the multi-threaded suite because web browsers are single-threaded (or lightly threaded).
- The result is a multi-core score that’s a more accurate, more useful, and more relevant measure of how your device performs the work you actually do.

A Refreshed GPU Benchmark:
- The GPU benchmark has a new focus on the machine learning and content creation applications that increasingly define GPU performance. For machine learning, the GPU benchmark includes workloads that:
- Track faces and apply real-time filter effects to video, modeling the face filters in social media apps.
- Upscale images with machine learning, modeling super resolution features in content creation apps.
- Blur backgrounds in video conferencing streams, modeling virtual background features in video conferencing apps.
- Geekbench 7 also introduces new GPU image editing and synthesis workloads, including RAW image processing, LUT-based video color grading, path tracing, and fluid simulation.
- And by popular demand, CUDA joins OpenCL, Vulkan, and Metal as a supported API in the GPU Benchmark. You can now measure your NVIDIA GPU using the API that powers its most demanding applications.

Larger Data Sets:
Since the release of Geekbench 6, the tasks people perform have become more strenuous, and the data sets they use have grown larger and more demanding. To reflect this, we’ve updated the data sets the workloads process so they’re more challenging for your device and better reflect the files people work with today. This includes:
- More (and more varied) archives in the File Compression workload, spanning source code, object code, and text documents
- More (and more varied) documents in the PDF Viewer workload, from park maps to technical documents to academic papers
- More assets and different image formats throughout the developer and image processing workloads