OpenCL vs. CUDA vs. Vulkan: Hardware Benchmark Differences

This article explains the fundamental differences between OpenCL, CUDA, and Vulkan compute scores when benchmarking computer hardware. While all three Application Programming Interfaces (APIs) measure a processor's ability to handle non-gaming tasks like 3D rendering, simulations, and data processing, they interact with your hardware in vastly different ways. We will explore how CUDA maximizes performance exclusively on Nvidia hardware, why OpenCL offers broad compatibility at the cost of peak efficiency, and how Vulkan provides modern, low-overhead performance across multiple platforms, helping you understand which benchmark score actually matters for your specific workload.

What is a Compute Benchmark?

Before comparing the three APIs, it is important to understand what a compute benchmark measures. Unlike gaming benchmarks that test how fast a Graphics Processing Unit (GPU) can draw frames on a screen, compute benchmarks test General-Purpose computing on Graphics Processing Units (GPGPU). This measures how well the hardware handles heavy math, physics simulations, video encoding, and artificial intelligence tasks.

OpenCL, CUDA, and Vulkan are the translators between the benchmarking software and the hardware. Because each translator works differently, the exact same graphics card will produce three completely different scores depending on which API you test it with.

CUDA: Maximum Performance for Nvidia

CUDA (Compute Unified Device Architecture) is a proprietary API developed by Nvidia. It only works on Nvidia GPUs.

How it affects scores: If you benchmark an Nvidia GPU using CUDA, OpenCL, and Vulkan, the CUDA score will almost always be the highest.
Why it happens: Because Nvidia controls both the hardware and the CUDA software, they are tailored to work together seamlessly. CUDA allows the benchmark to bypass software bottlenecks and talk directly to specific parts of the Nvidia chip, such as Tensor Cores for AI or RT Cores for ray tracing.
What the score means: A CUDA score represents the absolute maximum compute potential of an Nvidia graphics card. However, it cannot be used to compare an Nvidia card to an AMD or Intel card, because those brands cannot run CUDA natively.

OpenCL: The Universal Baseline

OpenCL (Open Computing Language) is an open-source standard maintained by the Khronos Group. It is designed to run on almost any hardware, including GPUs and CPUs from AMD, Intel, Nvidia, and Apple.

How it affects scores: OpenCL scores are generally lower than CUDA scores on Nvidia hardware, and often lower than Vulkan scores on modern AMD and Intel hardware.
Why it happens: OpenCL is an older API designed for maximum compatibility. Because the code has to be able to run on anything from a smartphone chip to a massive workstation GPU, it cannot take advantage of the unique architectural shortcuts of a specific graphics card. Furthermore, hardware manufacturers (especially Nvidia) often prioritize updating their other drivers over OpenCL, leading to less efficient performance.
What the score means: OpenCL scores provide a level playing field to compare different brands (like AMD vs. Nvidia) using the exact same test. It reflects how hardware performs in older, cross-platform professional applications.

Vulkan: Modern, Low-Overhead Efficiency

Vulkan is also maintained by the Khronos Group. While it is primarily known as a gaming and graphics API, "Vulkan Compute" is heavily used for non-gaming workloads. It was built from the ground up to replace older APIs and match the efficiency of modern multi-core processors.

How it affects scores: Vulkan compute scores usually sit between OpenCL and CUDA on Nvidia hardware, and frequently produce the highest scores for AMD and Intel GPUs.
Why it happens: Vulkan is a "low-overhead" API, meaning it gets out of the way and lets the software control the hardware more directly, similar to how CUDA works, but without being locked to one brand. It manages system memory and multi-threaded workloads much better than OpenCL.
What the score means: A Vulkan compute score shows how well a piece of hardware handles modern, optimized, cross-platform tasks. It is the most accurate indicator of raw compute power when comparing modern AMD, Intel, and Nvidia GPUs head-to-head.

Why the Same GPU Gets Different Scores

If you run a benchmark tool like Geekbench on a single computer, you can run the test three times—once for each API—and get three vastly different numbers. This comes down to three factors:

API Overhead: OpenCL requires the computer's CPU to do a lot of translation work before the GPU can start crunching numbers. CUDA and Vulkan reduce this translation time, resulting in higher scores.
Driver Optimization: AMD invests heavily in Vulkan optimization, while Nvidia invests heavily in CUDA. A benchmark score is testing the quality of the company's software drivers just as much as the physical silicon.
Hardware Utilization: An OpenCL test might only use the standard shading cores of a GPU, while a CUDA test might engage specialized AI cores on the same GPU, massively inflating the final score.

Which Score Should You Care About?

When looking at hardware reviews or testing your own system, you should only pay attention to the API that matches the software you actually use in the real world.

Look at CUDA scores if: You are buying an Nvidia GPU for 3D rendering (Blender, Octane, V-Ray), machine learning, AI generation, or heavy video editing in programs like Adobe Premiere Pro, which explicitly use CUDA acceleration.
Look at OpenCL scores if: You use macOS, rely on older cross-platform productivity software, or use specific scientific computing applications that have not migrated to Vulkan. Also useful if you use Adobe applications or DaVinci Resolve with an AMD graphics card.
Look at Vulkan scores if: You are comparing AMD, Intel, and Nvidia cards strictly for their raw, modern hardware capability, or if you use modern cross-platform software, emulators, and lightweight AI models that utilize Vulkan to run on non-Nvidia hardware.