Devin vs SWE-agent
Detailed side-by-side comparison to help you choose the right tool
Devin
🔴DeveloperAutonomous coding agent
Devin review covering cloud coding agents, desktop and CLI access, verified Free, Pro, Max and Teams pricing, use cases, limitations, pros, and cons.
Was this helpful?
Starting Price
$500/moSWE-agent
🔴DeveloperAI Development Assistants
Open-source autonomous coding agent from Princeton and Stanford researchers that resolves GitHub issues, detects cybersecurity vulnerabilities, and implements code changes using GPT-4o, Claude, or local LLMs — achieving state-of-the-art performance on SWE-bench benchmarks.
Was this helpful?
Starting Price
FreeFeature Comparison
Scroll horizontally to compare details.
Devin - Pros & Cons
Pros
- ✓Runs bounded tasks while developers do other work
- ✓Desktop, CLI, and cloud support varied delegation
- ✓Flex and full seats support mixed teams
- ✓Published individual tiers ease experimentation
Cons
- ✗Plausible pull requests may still contain subtle regressions
- ✗Cloud agents require scoped repository credentials
- ✗Allowances refresh daily and weekly rather than per task
- ✗Success depends heavily on setup, tests, and review
SWE-agent - Pros & Cons
Pros
- ✓Fully open-source under MIT license with an active community and ongoing research — over 17k GitHub stars and frequent releases from the Princeton NLP and Stanford teams
- ✓Model-agnostic architecture supports GPT-4o, Claude (Sonnet/Opus), DeepSeek, and local LLMs via Ollama or any OpenAI-compatible endpoint, avoiding vendor lock-in
- ✓State-of-the-art benchmark performance on SWE-bench (real GitHub issues) and on cybersecurity benchmarks like NYU CTF via the EnIGMA mode
- ✓Sandboxed Docker execution through SWE-ReX with scalable backends for AWS, Modal, and Kubernetes, enabling safe batch processing of many issues in parallel
- ✓Well-documented Agent-Computer Interface (ACI) with custom edit/search commands and linter feedback that meaningfully reduces LLM formatting errors on long tasks
- ✓Dual-purpose utility: same codebase handles software engineering (bug fixes, feature patches) and offensive security tasks (CTF, vulnerability discovery)
Cons
- ✗API costs add up quickly when using frontier models like GPT-4o or Claude Opus — a single SWE-bench run can consume significant tokens per issue
- ✗Initial setup is heavier than consumer tools: requires Docker, API key configuration, and YAML-based agent configs rather than a one-click install
- ✗No hosted UI out of the box — the primary interfaces are CLI, Python API, and an optional web demo, which is less accessible to non-developers
- ✗Python-centric benchmarking and tooling; while the agent can edit any language, its evaluation harness and examples lean heavily on Python repositories
- ✗Autonomy means it can make sweeping edits in a loop — without careful sandboxing and review, runs can waste compute or produce low-quality patches
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.