SWE-bench Wars: How AI Coding Benchmarks Hit 80%

SWE-bench and related benchmarks have become the standard for evaluating AI coding systems. This guide explains what they measure and how to interpret the numbers.

Quick Answer

SWE-bench tests whether AI systems can fix real GitHub issues and pass project tests. Leading models now reach high pass rates (e.g. 80%+ in some evals). Scores are useful but not sufficient for choosing a tool—workflow, integration, and your own testing matter more. We avoid citing specific percentages here; methodology and versions change. See the SWE-bench project for official results.

What SWE-bench Measures

No human in the loop; fully automated. SWE-bench Verified uses a stricter subset of issues.

How to Interpret Results

Other Coding Benchmarks

• HumanEval: Function completion from docstrings.
• MBPP: Python programming problems.
• DS-1000: Data science code generation.
• Vendor evals: Tool-specific benchmarks (e.g. Copilot, Cursor).

Each measures different skills. No single benchmark covers everything.

What Benchmarks Miss

• Editor integration: UX, shortcuts, diff review.
• Your codebase: Style, architecture, conventions.
• Team workflow: PR flow, review, approval.
• Cost and speed: Benchmarks rarely include these.

Practical Takeaway

Use benchmarks to narrow the field, not to pick a winner. Try top tools on your own tasks. Cursor, Claude Code, Windsurf, and OpenAI Codex all perform well in evals; your preference will depend on workflow and integration. See our tool directory for comparisons.