
Geekbench AI is a cross-platform benchmark tool from Primate Labs that measures how well a device’s CPU, GPU, and NPU handle real-world AI and machine learning tasks. It helps users and developers understand on-device AI performance across phones, tablets, laptops, and desktops.
What Is Geekbench AI?
GeekbenchAI (previously known as Geekbench ML) is a benchmarking suite designed to test AI workloads. It runs ten different machine learning tasks covering computer vision and natural language processing. These tests model everyday AI applications rather than synthetic workloads.
The benchmark evaluates performance using three data types:
- Single Precision (FP32)
- Half Precision (FP16)
- Quantized (INT8)
This gives a more complete picture of how hardware handles modern AI tasks like image recognition, language processing, and on-device inference.
Higher scores mean better performance. Scores are calibrated against a baseline (Intel Core i7-10700 at 1,500 points), so double the score equals roughly double the performance.
Geekbench AI Features
Key features include:
- Cross-platform support for Android, iOS, Windows, macOS, and Linux
- Tests CPU, GPU, and NPU (Neural Processing Unit) performance
- Real-world AI workloads instead of just theoretical numbers
- Separate scores for different precision levels
- Accuracy measurement incorporated into results
- Results uploaded to the public Geekbench Browser for comparisons
Geekbench AI Download and How to Use It
GeekbenchAI Download is simple and free for basic use:
- Android: Available on Google Play Store
- iOS / iPadOS: Download from the Apple App Store
- Windows / macOS / Linux: Download from the official Geekbench website (geekbench.com/ai)
How to Use Geekbench AI:
- Install and open the app on your device.
- Choose to test CPU, GPU, or NPU if options are available.
- Run the benchmark (it takes a few minutes).
- View your Single Precision, Half Precision, and Quantized scores.
- Optionally upload results to the Geekbench Browser to compare with other devices.
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Geekbench AI Scores and Results
GeekbenchAI provides three main scores:
- Single Precision Score — Best for accuracy-heavy tasks
- Half Precision Score — Common for faster AI inference
- Quantized Score — Used for efficient, low-power AI on mobile devices
Geekbench AI on Different Platforms
- Android & iOS: Popular for testing smartphones and tablets. Focuses heavily on mobile NPUs and TensorFlow Lite / Core ML.
- Windows & macOS: Useful for laptops and desktops to check AI readiness for tools like Copilot, local LLMs, or creative software.
- Results vary significantly depending on whether the test runs on CPU, GPU, or dedicated NPU.
Suggested Image Placement Ideas:
- Hero image: GeekbenchAI app interface showing test in progress or final score screen.
- Under “What Is GeekbenchAI?”: Diagram explaining Single, Half, and Quantized precision.
- Under “How to Use GeekbenchAI”: Step-by-step screenshots of the app on Android/iOS/Windows.
- Under “GeekbenchAI Scores”: Example comparison chart from the Geekbench Browser.
- Under platform sections: Device-specific benchmark result visuals (e.g., iPhone vs Android phone scores).
FAQs
What does Geekbench AI measure?
It measures on-device AI and machine learning performance across CPU, GPU, and NPU using real-world vision and language tasks.
Is Geekbench AI free?
Yes, the basic version is free to download and run. Corporate or pro licenses are available for teams and automated testing.
How accurate are Geekbench AI scores?
Scores provide a good comparative view of AI performance, but real-world results can vary depending on the specific AI app and optimizations used.
Can I run Geekbench AI on Windows?
Yes, GeekbenchAI is available for Windows, macOS, and Linux in addition to Android and iOS.
What is a good Geekbench AI score?
It depends on the device category. New flagship phones and laptops with strong NPUs often score several thousand points on Neural Engine or NPU tests. Compare your device on the public benchmark chart.
Is Geekbench AI better than other AI benchmarks?
It offers good cross-platform consistency and real-world task focus. It complements tools like MLPerf but is easier for everyday users to run on consumer devices.