KernelGenBench-MS (Pass@5) - cuBLAS Operators: leaderboard
KernelGenBench asks models and coding agents to write Triton kernels that drop into PyTorch in place of an existing operator. A kernel counts only when it passes numerical checks across a combinatorial suite of shapes, data types and layouts and clears anti-hack interception that blocks calls back to the reference implementation. This board covers the cuBLAS routines on an NVIDIA A100, generated by five zero-shot samples per operator with no execution feedback, counting an operator solved when any sample passes.
Metric: Clean pass rate (%; share of the 50 cuBLAS routines whose generated Triton kernel passes numerical validation on every test case of its combinatorial shape, dtype and layout suite and every anti-hack check, as a drop-in replacement; direct zero-shot generation, solved when any of five samples at temperature 0.8 passes, no execution feedback; NVIDIA A100; 30-minute budget per operator). Source: arxiv.org. Saturation forecast: Around February 2027. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.6 | 74 | #102 |
| 2 | GLM-5 | 20 | #260 |
| 3 | Qwen 3.5 27B | 8 | #359 |
| 4 | MiniMax-M2.5 | 2 | #582 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=kernelgenbench-ms-pass-5-cublas-operators · How It Works · Data refreshed daily, snapshot 2026-11-13.