CUDAHercules - Single-Kernel Mean Speed (Self-Refine): 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 20 architecture-general single-kernel tasks, after up to 10 revision rounds with compile and execution feedback: 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 20 architecture-general Class 1 single-kernel tasks, a task whose code fails the validator counting 0, so 1 matches the expert; self-refine@10: up to 10 revision rounds with compile and execution feedback (CUDAForge)). Source: arxiv.org. Saturation forecast: Around June 2027. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.4 | 0.31 | #123 |
| 2 | Claude Opus 4.6 | 0.27 | #102 |
| 3 | Qwen 3.5 122B A10B | 0.24 | #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-single-kernel-mean-speed-self-refine · How It Works · Data refreshed daily, snapshot 2026-11-13.