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.
How SWE-bench works
The benchmark pulls real issues from repositories like Django, Flask, scikit-learn, and other production Python codebases. Each task includes:
- The issue description from GitHub
- The repository codebase at the commit before the fix
- A test suite that passes after the correct fix is applied
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.
Why SWE-bench matters
SWE-bench tests skills that HumanEval doesn't touch:
- Codebase navigation: Finding the right files in a large repository
- Bug comprehension: Understanding what's broken from an issue description
- Multi-file patches: Changes that span multiple files and functions
- Test awareness: The fix must pass existing tests without breaking anything
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.
Current leaderboard
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.
The limitations are real, though. 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.
So if you're choosing a model for a coding assistant, weight SWE-bench Verified over HumanEval: the spread between top models here reflects real engineering ability rather than noise. Then confirm on an agentic benchmark before trusting the model with multi-step work.
→ See all coding models ranked on the leaderboard · Full leaderboard
Reader questions
Frequently asked questions
01What is SWE-bench Verified?
SWE-bench Verified is a benchmark of 500 real GitHub issues from production Python repositories like Django, Flask, and scikit-learn. AI models must read the issue, navigate the codebase, and generate a code patch that passes the existing test suite. It is the most predictive public measure of real software engineering ability in 2026.
02Which model scores highest on SWE-bench Verified?
As of March 2026, GPT-5.3 Codex leads SWE-bench Verified with a score of 85, followed by GPT-5.4 (81), Claude Opus 4.6 (80), and GPT-5.2 (80). GPT-5.3 Codex is a specialized coding model — among general-purpose models, GPT-5.4 and Claude Opus 4.6 are the leaders.
03How is SWE-bench different from HumanEval?
HumanEval tests single-function generation from docstrings — it is saturated with frontier models scoring 91-95%. SWE-bench Verified tests real-world software engineering: navigating a codebase, understanding a bug report, writing multi-file patches, and passing existing tests. SWE-bench scores are much more predictive of actual coding assistant quality.
04What are SWE-bench's limitations?
SWE-bench Verified is Python-only and weighted toward Django and a few other repositories. It tests single-turn patch generation, not the iterative loop of writing, running, debugging, and retrying that real coding agents do. For agentic coding ability, pair SWE-bench with Terminal-Bench 2.0 and LiveCodeBench.
05What SWE-bench score do I need for a good coding assistant?
A SWE-bench Verified score above 75 indicates a model that can handle real software engineering tasks. The top models cluster in the 77-85 range. Anything below 60 will struggle with complex bug-fixing and multi-file changes. HumanEval scores above 90 are not enough on their own — always check SWE-bench for coding assistant selection.
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