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.
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SWE-bench Verified gives AI models real GitHub bugs to fix. The model must navigate a production codebase, write a patch, and pass the test suite. GPT-5.3 Codex leads at 85; the top general-purpose models (GPT-5.4, Claude Opus 4.6) both score 80-81. It is the most predictive coding benchmark for real-world use in 2026.
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. For that, pair it with Terminal-Bench 2.0.
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.
→ See all coding models ranked on the leaderboard · Full leaderboard
What is SWE-bench Verified? SWE-bench Verified is a benchmark of 500 real GitHub issues from production Python repos (Django, Flask, scikit-learn). AI models must navigate the codebase, write a patch, and pass the test suite. It is the standard for measuring real-world software engineering in 2026.
Which model scores highest on SWE-bench Verified? GPT-5.3 Codex leads at 85, followed by GPT-5.4 (81), Claude Opus 4.6 (80), and GPT-5.2 (80). See the SWE-bench leaderboard for current rankings.
How is SWE-bench different from HumanEval? HumanEval tests single-function generation — it is saturated with top models scoring 91-95%. SWE-bench tests real software engineering: codebase navigation, bug comprehension, multi-file patches, and test suite compliance. SWE-bench is far more predictive of coding assistant quality.
What are SWE-bench's limitations? SWE-bench is Python-only and weighted toward a few repositories. It tests single-turn patch generation, not the iterative debugging loop real coding agents use. Pair it with Terminal-Bench 2.0 and LiveCodeBench for a fuller picture.
What SWE-bench score do I need for a good coding assistant? Above 75 indicates a model that handles real software engineering. The top models cluster in the 77-85 range. Anything below 60 will struggle with complex bug-fixing. Always check SWE-bench alongside HumanEval when selecting a coding model.
Data sourced from BenchLM.ai. Last updated March 2026.
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