SWE-agent vs Devin
Detailed side-by-side comparison to help you choose the right tool
SWE-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.
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Starting Price
FreeDevin
🔴DeveloperAI Coding
Devin is an autonomous AI software engineer by Cognition that plans, executes, and reports on complex engineering tasks without constant human input.
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Starting Price
$500/moFeature Comparison
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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
Devin - Pros & Cons
Pros
- ✓Genuinely autonomous — handles multi-step tasks without constant prompting
- ✓Parallel agents allow multiple tasks to run simultaneously
- ✓Documented enterprise case studies with real efficiency numbers (12x at Nubank)
- ✓Core plan entry price dropped from $500 to $20 in 2026, much more accessible
- ✓Works inside existing GitHub/Slack/CI workflows
- ✓Can tackle migrations and test generation at scale that would be prohibitively manual
Cons
- ✗ACU costs add up fast on longer tasks — real monthly spend can reach $300-500
- ✗Struggles with ambiguous or architecture-level tasks that require deep context
- ✗Output still needs human review before merging PRs
- ✗Not an in-editor experience — separate from Cursor, VS Code workflows
- ✗Requires clear task specifications to produce good output
- ✗Enterprise features (VPC, SSO) only available at custom pricing tiers
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