If you find a flaw in our scoring, file an issue on GitHub. We ship the patch the same week. This page documents the entire 9bench composite score, including the parts we can't measure perfectly.
We run a 1024×1024 FP32 matrix multiplication compute shader on
your GPU. The shader executes 2 × N³ floating-point
operations per pass. Best-of-3 measurement with adaptive warm-up
so laptop GPUs that idle at low power get a fair reading.
Output is GFLOPS (billions of FP operations per second).
Single-core: SHA-256 hash chain on the main thread via
Web Crypto API. Multi-core: same workload across N Web Workers
where N = navigator.hardwareConcurrency. Output is
hashes per second. Multi-core scaling efficiency is also reported.
Sequential reads, sequential writes, and a random-access pointer-chase on a 256 MB Float32Array. Reports GB/s read, GB/s write, and ns latency. Browser sandboxing caps this at 30-50% of native — we never pretend otherwise.
Every component score is a simple linear or logarithmic transform of the raw measurement. There is no proprietary "magic" — anyone can reproduce these numbers.
round(GFLOPS × 3) Calibration: a typical 2024 mid-range GPU (~500 GFLOPS in browser) scores ~1500. RTX 4070-class hits ~3000 in browser (well below native ceiling).
round(SHA_per_sec_single / 300) Calibration: Apple Silicon high-end ~1300, modern x86 ~1000-1500.
round(SHA_per_sec_multi / 600) Multi-core counts cores divided by 600. Browser Web-Worker scaling is structurally below native (~3-5× speedup typical for 16-core instead of native ~12×).
round(((read_GBs + write_GBs) / 2) × 60) Average of read + write bandwidth × 60. Browser-sandbox-capped — see section 5 for limits.
The 9bench overall score is a weighted geometric mean of the four component scores. Geometric mean prevents a single weak component from being masked by strong ones — the "no shortcut" version.
overall = exp(0.35 × ln(GPU) + 0.45 × ln(CPU·M) + 0.20 × ln(RAM)) We don't include CPU single-core in the composite because it correlates strongly with multi-core for in-browser SHA workloads. Including it would double-count.
Tiers are descriptive labels for sharing, not gospel. We use letter-tiers (S/A/B) for the top half because that's Reddit-shareable, and word-labels for the bottom half because no one shares "D-tier".
| Tier | Score | Description |
|---|---|---|
| S-tier | ≥ 1386 | Enthusiast / workstation |
| A-tier | ≥ 900 | Power user |
| Solid daily driver | ≥ 600 | Comfortable for most work |
| Working machine | ≥ 300 | Office class |
| Patient & honest | ≥ 0 | It still gets the job done |
Browsers cap absolute throughput on RAM workloads via the JavaScript memory model (no SIMD-vectorized memcpy, no DMA, no direct mmap). Browser RAM scores typically run at 30-50% of native. We disclose this on every result page.
We also cannot measure: thermal throttling under sustained load, disk/SSD I/O, network bandwidth, GPU memory bandwidth (only compute), peak-vs-sustained turbo behavior. For those, install a native benchmark.
What we can measure: relative ranking across machines. 9bench is calibrated for cross-platform fairness — a 2024 MacBook Pro and a 2024 Windows desktop running the same browser produce comparable composite scores within ±5-10%.
9bench is MIT-licensed. You can fork it, ship your own variant, run it commercially. Truth-series tools should not be locked behind proprietary licenses. Source on GitHub.
Built independently. Sister project of Toololis. Author identity.