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What is SWE-bench?

Learn what SWE-bench measures, why it matters for AI coding agents, and how software-engineering benchmarks connect to AI compute demand.

Plain-English definition

SWE-bench is a software-engineering benchmark that evaluates whether AI systems can resolve real GitHub issues by producing changes to real repositories that satisfy evaluation tests.

Memory trick: SWE-bench is closer to “fix this repo issue” than “write this function.”

Why it matters

Repository-level repair is closer to deployed coding-agent work than short completions. If such workflows become reliable, developers can generate longer, repeated inference demand for debugging, patching, and validation.

  • Repository-level repair mirrors deployed coding-agent work more than short completions do.
  • Reliable repair workflows generate repeated inference for debugging, patching, and validation.
  • That repetition is what turns a benchmark gain into sustained token demand.

Simple example

A task can give an agent a code repository plus an issue description, then evaluate whether the submitted patch resolves the problem under its tests. Tool access and agent scaffold affect both score and cost.

  • A task supplies a repo plus an issue and checks whether the submitted patch passes tests.
  • Tool access and agent scaffold change both the score and the compute consumed.
  • The same model can post very different numbers under different scaffolds.

Example figures are illustrative calculations, not current quoted market prices.

Current example

Primary source

The official SWE-bench repository describes a benchmark for resolving real-world GitHub issues and its variants, including SWE-bench Verified. Its September 2026 update also made SWE-bench Multimodal v2 available as open source. Last checked: Sep 16, 2026.

No leaderboard performance claim is made here; consult the official benchmark configuration before comparing systems.

Common mistake

Do not compare SWE-bench values without checking the subset, scaffold, tools, test-time compute, and evaluation date.

Practical takeaway

What you can do with this

Use SWE-bench as capability evidence, then measure your own repository tasks by cost per accepted patch and engineer review burden.

  • Confirm the subset (such as Verified), scaffold, tools, and date before comparing values.
  • Treat the public number as capability evidence, not a cost estimate.
  • Measure cost per accepted patch and reviewer burden on your own issues.

Decision check: do two SWE-bench numbers you are comparing share subset, scaffold, tools, and test-time compute?

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