SWE-bench Verified tests AI models on resolving real GitHub issues from Django, Flask, and scikit-learn. Here's how it works, why it matters, and which models score highest.
SWE-bench Verified is the closest thing we have to a benchmark that measures real software engineering ability. Instead of toy problems, it gives AI models actual GitHub issues from popular open-source repositories and asks them to generate patches that fix the bugs.
The benchmark pulls real issues from repositories like Django, Flask, scikit-learn, and other production Python codebases. Each task includes:
The model must read the issue, understand the codebase, identify the relevant files, and produce a code patch. That patch is applied and the test suite runs. If the tests pass, it's a success.
SWE-bench Verified is a human-curated subset of 500 tasks from the original SWE-bench dataset, filtering out ambiguous or poorly defined issues.
SWE-bench tests skills that HumanEval doesn't touch:
This is much closer to what a developer actually does daily. It's why SWE-bench scores have become the primary metric for evaluating AI coding agents like Cursor, Copilot, and Claude Code.
According to BenchLM.ai data, the top models on SWE-bench Verified are:
| Rank | Model | Score |
|---|---|---|
| 1 | GPT-5.3 Codex | 85 |
| 2 | GPT-5.4 | 81 |
| 3 | Claude Opus 4.6 | 80 |
| 4 | GPT-5.2 | 80 |
| 5 | Grok 4.1 | 77 |
Full leaderboard: SWE-bench Verified scores
The spread here is much wider than HumanEval. An 85 vs 75 on SWE-bench represents a meaningful difference in real-world coding ability.
SWE-bench is Python-only. It doesn't test JavaScript, TypeScript, Rust, Go, or any other language. The tasks are weighted toward Django and a few other repositories, so models that have been heavily fine-tuned on those codebases may have an advantage.
It also tests single-turn patch generation. The iterative loop of writing code, running tests, fixing errors, and trying again — which is how AI coding agents actually work — isn't captured by SWE-bench alone.
SWE-bench Verified is the gold standard for evaluating AI coding ability in 2026. If you're choosing a model for a coding assistant, SWE-bench scores are more predictive than HumanEval scores. Compare models head-to-head on our comparison pages.
Data sourced from BenchLM.ai. Last updated March 2026.
On this page
New model releases, benchmark scores, and leaderboard changes. Every Friday.
Free. Unsubscribe anytime.
AIME and HMMT are high school math olympiad competitions now used to benchmark AI. Frontier models score 95-99% — competition math is effectively solved. Here's what that means.
We ranked every major LLM by coding benchmarks — HumanEval, SWE-bench Verified, and LiveCodeBench. Here's which model actually comes out on top, and why the answer depends on what you're building.
Chatbot Arena ranks AI models using Elo ratings from blind human preference votes. Here's how the system works, what the scores mean, and how Elo compares to standardized benchmarks.