SWE-bench
BENCHMARKEvaluates LLMs on real-world GitHub issues from 15+ Python repositories
View on GitHubOverview
SWE-bench: a free, copy-ready benchmark on OpenRuna. Evaluates LLMs on real-world GitHub issues from 15+ Python repositories
What this benchmark does
Looking for a dependable benchmark? "SWE-bench" gives you a tested starting point instead of a blank prompt box. Evaluates LLMs on real-world GitHub issues from 15+ Python repositories It is catalogued next to similar resources on OpenRuna, so the rest of the toolkit you need is close by. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.
Use cases
- Use "SWE-bench" when you need a repeatable benchmark for professional work without rewriting instructions every time.
- Hand "SWE-bench" 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 "SWE-bench" 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. A short follow-up turn usually tightens the result to exactly what you need.
Tips by platform
Claude
With Claude, drop this benchmark into Project knowledge so every chat in the project inherits it. Ask Claude to restate the goal first, then run — it catches edge cases early.
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
Cursor users can store this benchmark as a project rule so Agent mode applies it automatically. Mention it with @ when you want it scoped to a single task.
Frequently asked questions
- What is "SWE-bench"?
- It is a benchmark listed on OpenRuna — Evaluates LLMs on real-world GitHub issues from 15+ Python repositories You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
- Is "SWE-bench" 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 "SWE-bench" 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 "SWE-bench"?
- 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.
Related resources
- 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
- Benchmark
PinchBench
Benchmarking system for evaluating LLM models as OpenClaw coding agents. Built with Rust by the kilo.ai team. MIT licensed
- Benchmark
AgentBench (THUDM)
Comprehensive benchmark to evaluate LLMs as agents across 8 diverse environments including household, web shopping, OS interaction, and database tasks. ICLR 2024. Apache 2.0 licensed
- Benchmark
SWE-rebench (Nebius)
Continuously updated benchmark with 21,000+ real-world SWE tasks for evaluating agentic LLMs. Decontaminated, mined from GitHub
- Benchmark
Vectara Hallucination Leaderboard
Leaderboard comparing LLM performance at producing hallucinations when summarizing short documents. Systematic evaluation of factual consistency across major models. Apache 2.0 licensed
- Benchmark
VLMEvalKit
Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard. Apache 2.0 licensed
- Benchmark
MLPerf Training
Industry-standard ML training benchmarks from MLCommons. Reference implementations for training AI models at scale across image classification, object detection, NLP, and recommendation tasks. Apache 2.0 licensed
- Benchmark
MLPerf Inference
Industry-standard ML inference benchmarks with reference implementations for AI accelerators
