# MLE-bench

> MLE-bench scores rose from 16.9% in October 2024 with o1-preview plus AIDE to 64.4% in February 2026 with Gemini 3 using retrieval and agent scaffolding — a system-level figure, not a bare-model one.

Machine-learning engineering tasks derived from real Kaggle competitions.

- **Category:** Coding
- **2026 frontier:** 16.9% in Oct 2024 (o1-preview + AIDE) to 64.4% in Feb 2026 (Gemini 3 with retrieval and agent scaffolding)
- **Credibility:** The 64.4% figure is achieved with retrieval and agent scaffolding, so it measures the system rather than the model. Note that MLE-bench Lite is a separate, higher-scoring configuration and should not be quoted interchangeably.
- **Verification:** Verified
- **Source:** [MLE-bench (OpenAI paper) / reporting](https://aiwiki.ai/wiki/mle_bench)
- **Last updated:** 2026-10-04