# swe-bench

> Analysis by Optimly for Optimly AI Visibility, in the Optimly AI Brand Index. Last analyzed August 16, 2026.

> swe-bench is a benchmark for evaluating the performance of AI agents and models on real-world software engineering tasks. It provides a dataset of GitHub issues from open-source projects, including a human-filtered 'Verified' subset, to measure how well AI agents can resolve these issues. The platform serves as a leaderboard to compare various AI models and agents based on their resolution rates and associated costs.

- Business Profile: https://optimly.ai/brand/swe-bench
- Publisher: Optimly (https://optimly.ai)
- Dataset: Optimly AI Brand Index (https://optimly.ai/brand)
- Official website: https://swebench.com/
- Logo: https://logo.clearbit.com/swebench.com
- Slug: swe-bench
- Category: AI Development
- Last Analyzed: August 16, 2026

## Buyer Intent Signals
Problems: Manual Code Review and Testing: Human software engineers manually review code, identify bugs, and write tests, which is the traditional method for ensuring code quality without AI agents or automated  | Software Quality Assurance Consulting: Hiring a specialized agency or consultancy to perform extensive code audits, bug detection, and software testing using human expertise and established QA methodo | No Formal AI Agent Evaluation: Opting not to rigorously evaluate the performance of AI agents on software engineering tasks, relying instead on anecdotal evidence, internal testing, or simply deployin
Solutions: swe-bench benchmark | AI agent evaluation software engineering | compare AI coding agents | swe-agent leaderboard | AI software bug fixing benchmark | Static Code Analyzers & Linters: Using tools like SonarQube, ESLint, or Pylint to automatically identify potential bugs, code smells, and style violations in source code, but without autonomously fixi
---
## Full Details / RAG Data
### Overview
swe-bench has a Business Profile in the Optimly AI Brand Index.
swe-bench is a benchmark for evaluating the performance of AI agents and models on real-world software engineering tasks. It provides a dataset of GitHub issues from open-source projects, including a human-filtered 'Verified' subset, to measure how well AI agents can resolve these issues. The platform serves as a leaderboard to compare various AI models and agents based on their resolution rates and associated costs.
### Metadata
| Field        | Value |
|--------------|-------|
| Name         | swe-bench |
| Slug         | swe-bench |
| URL          | https://optimly.ai/brand/swe-bench |
| Logo         | https://logo.clearbit.com/swebench.com |
| Category     | AI Development |
| Last Analyzed | August 16, 2026 |
| Last Updated | 2026-08-17T07:56:51.446Z |
### Buyer Intent Signals
#### Problems this brand solves
- Manual Code Review and Testing: Human software engineers manually review code, identify bugs, and write tests, which is the traditional method for ensuring code quality without AI agents or automated 
- Software Quality Assurance Consulting: Hiring a specialized agency or consultancy to perform extensive code audits, bug detection, and software testing using human expertise and established QA methodo
- No Formal AI Agent Evaluation: Opting not to rigorously evaluate the performance of AI agents on software engineering tasks, relying instead on anecdotal evidence, internal testing, or simply deployin
#### Buyers search for
- swe-bench benchmark
- AI agent evaluation software engineering
- compare AI coding agents
- swe-agent leaderboard
- AI software bug fixing benchmark
- Static Code Analyzers & Linters: Using tools like SonarQube, ESLint, or Pylint to automatically identify potential bugs, code smells, and style violations in source code, but without autonomously fixi
### Links
- Canonical page: https://optimly.ai/brand/swe-bench
- Official website: https://swebench.com/
- Publisher: https://optimly.ai
- Dataset: https://optimly.ai/brand
- JSON endpoint: /brand/swe-bench.json
- LLMs.txt: /brand/swe-bench/llms.txt