TensorBench - Codegen: leaderboard

TensorBench (2026) tests coding agents (a published CLI harness with its model) on feature-addition and refactoring tasks in Scorch, a compiler-based tensor framework with dense and sparse tensors; a task passes when the patched repository passes its randomized regression tests and the tests the agent adds. This board is the pass rate in percent on the 7 code-generation tasks, such as OpenMP parallelism, SIMD vectorization and a CUDA backend; higher is better.

Metric: Pass rate (%) on the 7 code-generation tasks (OpenMP and atomic-scatter parallelism, SIMD vectorization, CUDA and out-of-core backends); each coding agent (a published CLI scaffold with its model) edits the open-source Scorch compiler-based tensor framework inside a per-task container, one run per task; a task passes when every test of the post-patch suite (pre-existing randomized regression tests plus the tests the agent adds) succeeds; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 7 models tracked.

Top models

#ModelScoreOverall rank
1Claude Opus 4.7 (xHigh)71.4#79 (Claude Opus 4.7)
2Claude Opus 4.657.1#102
3GPT-5.5 (xHigh)57.1#49 (GPT-5.5)
4GPT-5.3 Codex (xHigh)42.9#116 (GPT-5.3 Codex)
5Gemini 3.1 Pro (Preview)28.6#94
6Qwen 3 Coder 480B A35B Instruct14.3#643
7GPT-5.4 (xHigh)14.3#123 (GPT-5.4)

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=tensorbench-codegen · How It Works · Data refreshed daily, snapshot 2026-11-13.