Measuring Real-World Coding Performance

SWE-Bench has become the gold standard for evaluating AI's ability to solve actual software engineering problems from open-source repositories.

How It Works

The benchmark methodology:

• Real GitHub Issues: 2,294 actual bugs from 12 repos

• Full Repository Context: Models must understand entire codebases

• Automated Verification: Solutions checked against test suites

• Diverse Challenges: From simple fixes to complex features

Current Leaderboard

Top performers on SWE-Bench Verified:

• Claude 3.5 Sonnet with scaffolding: 49%

• GPT-4o with SWE-Agent: 33.2%

• Devin: 13.86% (fully autonomous)

• Base models without tools: under 5%

"SWE-Bench shows us where we are and where we need to go. Solving 50% of real issues autonomously seemed impossible two years ago." — Benchmark creator

What the Numbers Mean

These aren't toy problems—they're actual bugs that challenged human developers. AI systems solving half of them signals real capability.

Future Iterations

Harder benchmarks are coming: SWE-Bench Multi-repo, longer-horizon tasks, and challenges requiring architectural decisions.