CUDAHercules - Module-Level Mean Speed (One-Shot): leaderboard
CUDAHercules (Li et al., 2026) asks models to write CUDA against expert reference implementations, with correctness checked by the validator of each task over several problem sizes or randomized inputs. This board is mean expert-relative speed on the 43 architecture-general module-level tasks (a module or kernel family such as attention or FFT), from one sample given the task, hardware metadata and few-shot exemplars: the expert runtime divided by the generated runtime, averaged over all tasks with failed tasks counting zero, so 1 matches the expert code; higher is better.
Metric: Mean expert-relative speed (the expert reference runtime divided by the generated code's runtime, averaged over the 43 architecture-general Class 2 module-level tasks, a task whose code fails the validator counting 0, so 1 matches the expert; one-shot: task specification, hardware metadata and few-shot exemplars, one sample, no execution feedback). Source: arxiv.org. Saturation forecast: Around July 2027. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.4 | 0.43 | #123 |
| 2 | Claude Opus 4.6 | 0.28 | #102 |
| 3 | Qwen 3.5 122B A10B | 0.01 | #331 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=cudahercules-module-level-mean-speed-one-shot · How It Works · Data refreshed daily, snapshot 2026-11-13.