MLOps-Bench - Codex - Test Pass: leaderboard
MLOps-Bench (2026), practical ML engineering tasks in reproducible environments; Codex harness, published evaluation split with separate development tasks. Task completion requires every task test to pass; test pass averages partial correctness. The three agent harnesses are separate boards; effort and API snapshots not supplied by the paper are not inferred. Rate (%); higher is better.
Metric: Rate (%). Source: arxiv.org. 1 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.5 | 68.7 | #31 |
Interactive version: theaggregate.ai/benchmark?slug=mlops-bench-codex-test-pass · How It Works · Data refreshed daily, snapshot 2026-10-20.