Primate Labs has launched Geekbench 7, a major update to its popular performance testing software. This new version adds real-world CPU workloads, video and audio encoding tests, AI-focused benchmarks, and for the first time, support for Nvidia’s CUDA platform.
If you’ve ever seen a phone or laptop review mention a “Geekbench score,” that figure comes from this app. Geekbench has become the standard for measuring processor performance under load, and version 7 marks a significant shift in how those tests are designed.
What’s Actually New
More Realistic CPU Tests
Earlier Geekbench versions used synthetic workloads—tasks created to stress hardware rather than reflecting real-world usage. Geekbench 7 now focuses on real-world workloads. This means the tests better mirror tasks you actually perform, like editing photos, compressing files, or making video calls.
The multi-core test, which evaluates how well a chip manages multiple tasks simultaneously, has been completely revamped. Primate Labs also expanded the datasets for testing, making them larger and more challenging. This change ensures that modern high-performance chips can’t breeze through the tests as easily as before.
Video and Audio Benchmarks
Geekbench 7 introduces specialized video and audio encoding and decoding tests. Encoding compresses media into smaller files—think exporting a video from iMovie or sharing a clip on YouTube. Decoding is the opposite: unpacking that compressed file for playback. These tasks are common for processors, and earlier Geekbench versions didn’t measure them directly.
AI Performance Testing
This version adds benchmarks focusing on AI inference, which involves running an AI model to produce results. For example, this happens when your phone automatically recognizes faces in photos or when your laptop runs a local AI assistant. As AI features become more integrated into apps and operating systems, measuring how quickly a chip can handle these tasks is increasingly important.
CUDA Support for Nvidia GPUs
On the GPU front, Geekbench 7 now supports CUDA, Nvidia’s proprietary computing platform. Think of it as the language Nvidia GPUs use for tasks beyond just graphics. With this update, Geekbench can benchmark Nvidia graphics cards for compute performance, not just rendering. For anyone with a gaming PC or workstation using an Nvidia card, this opens up a new way to compare scores.
| Feature | Detail |
|---|---|
| Developer | Primate Labs |
| New test categories | Video encoding/decoding, audio encoding/decoding, AI inference |
| GPU additions | CUDA support (Nvidia), redesigned compute benchmarks |
| Dataset size | Larger than previous versions (exact size not disclosed) |
| Multi-core test | Fully redesigned from Geekbench 6 |
| Platforms | macOS, Windows, iOS, Android |
Why Geekbench Scores Have Been Controversial
Benchmark software often faces skepticism, and Geekbench is no different. Critics have claimed that older versions favored chips that excelled in narrow, artificial tasks, missing the broader picture of everyday performance. For example, Apple’s chips consistently achieve impressive Geekbench scores, but real-world performance comparisons with Windows machines can be more complex than those numbers suggest.
The move toward real-world workloads in Geekbench 7 aims to bridge that gap. Now, the score you see in a review should more accurately reflect what you’ll experience while using the device.
What This Means for You
If you pay attention to tech reviews, you’ll notice Geekbench scores frequently pop up when comparing phones, laptops, and tablets. With Geekbench 7, those scores should hold more significance since they’re based on tasks that are closer to what you actually do.
For everyday users, the most practical change is likely the video encoding test. Whether you’re exporting from a video editor, saving a screen recording, or using an app that compresses clips, this is a common bottleneck. Now, there’s a standardized way to compare how different devices manage it.
The AI benchmarks look towards the future. Currently, most people don’t run AI models locally (on-device rather than in the cloud), but that’s changing quickly. Features like Apple Intelligence, Microsoft’s Copilot, and Google’s on-device AI tools are pushing AI processing onto your device’s chip. Having a benchmark for this now gives reviewers a consistent way to compare devices as these features evolve.
For PC builders and enthusiasts with Nvidia graphics cards, CUDA support means Geekbench can finally provide a GPU score that reflects the card’s compute capabilities, not just its graphics performance.
Community Reaction
“Finally. The old multi-core test was a joke on anything with efficiency cores. Curious to see how this changes the Apple vs. AMD standings.”
u/throckmorton_builds, Reddit r/hardware
“The video encoding benchmark is what I’ve been waiting for. Geekbench scores never matched what I saw in actual Handbrake exports. Hopefully, this is more honest.”
YouTube comment on The Verge’s Geekbench 7 coverage
What To Watch
- New comparison scores incoming: Since Geekbench 7 scores aren’t directly comparable to Geekbench 6 numbers, expect major tech sites to rerun benchmarks on popular devices soon. This could change how certain chips are viewed, especially Apple Silicon and AMD’s latest Ryzen processors.
- AI benchmark rankings: As more devices come with dedicated AI accelerators designed for AI tasks, the new inference tests will become crucial. Keep an eye on Apple, Qualcomm, and AMD as they highlight or contest those numbers.
- Nvidia CUDA leaderboards: With CUDA now supported, Nvidia’s GPU compute rankings on Geekbench’s public results database will be populated for the first time. This offers a fresh point of comparison for RTX cards alongside AMD’s offerings.
Sources: MacRumors | The Verge | Tom’s Hardware
Maya Torres
Maya Torres is the Consumer Tech Editor at Explosion.com with 7 years covering product launches for major technology publications. She has reviewed over 300 devices across smartphones, laptops, wearables, and smart home products. Maya specializes in translating spec sheets into real-world buying advice and attends CES, MWC, and Apple keynotes as press. Her reviews focus on helping readers decide what to buy, not just what specs look good on paper.
