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MLE-bench (OpenAI)

BENCHMARK

Benchmark for measuring how well AI agents perform at machine learning engineering. Evaluates agents on 75 Kaggle competitions covering diverse ML tasks. MIT licensed

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Overview

Free benchmark — MLE-bench (OpenAI). Benchmark for measuring how well AI agents perform at machine learning engineering. Evaluates agents on 75 Kaggle competitions covering diverse ML tasks. MIT licensed

What this benchmark does

Looking for a dependable benchmark? "MLE-bench (OpenAI)" gives you a tested starting point instead of a blank prompt box. Benchmark for measuring how well AI agents perform at machine learning engineering. Evaluates agents on 75 Kaggle competitions covering diverse ML tasks. MIT licensed It is catalogued next to similar resources on OpenRuna, so the rest of the toolkit you need is close by. Paste it straight into a chat, drop it into a system prompt, or store it as a reusable skill.

Use cases

  • Use "MLE-bench (OpenAI)" when you need a repeatable benchmark for professional work without rewriting instructions every time.
  • Hand "MLE-bench (OpenAI)" to a new teammate so their benchmark output matches your team's quality bar from day one.
  • Combine it with related tools and prompts in the same OpenRuna category to build an end-to-end workflow.
  • Reach for it during planning or review sessions when you want consistent, AI-assisted structure.

Example output

Ask the model to apply "MLE-bench (OpenAI)" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Add one example of your own and the output quality jumps noticeably.

Tips by platform

Claude

In Claude, paste the full benchmark as your first message or add it to Project instructions, then ask Claude to confirm assumptions before it executes. For longer benchmarks, iterate inside the artifact panel.

ChatGPT

For ChatGPT, save this benchmark as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.

Cursor

Add this benchmark to your Cursor rules and invoke it from Agent mode for repeatable results. Link back to its OpenRuna page in the rule so the source stays discoverable.

Frequently asked questions

What is "MLE-bench (OpenAI)"?
It is a benchmark listed on OpenRuna — Benchmark for measuring how well AI agents perform at machine learning engineering. Evaluates agents on 75 Kaggle competitions covering diverse ML tasks. MIT licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "MLE-bench (OpenAI)" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this benchmark?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "MLE-bench (OpenAI)" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "MLE-bench (OpenAI)"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

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