Devin vs Devika: Closed vs Open-Source AI Software Engineers Compared (2026)

Pros

• Full autonomous execution: browses web, writes code, runs tests, debugs iteratively
• SWE-Bench Verified: ~45%+ — among highest autonomous task completion rates
• Built-in secure sandbox: shell, browser, and code editor in one agent environment
• Can be assigned GitHub issues and will open PRs with working solutions
• Asynchronous: runs in background while you work on other tasks

Cons

• High cost: ~$500/mo — only justified for teams with significant engineering throughput needs
• Closed-source — no self-hosting, no model transparency
• Non-deterministic: same task can produce different results across runs
• Fails on highly context-dependent codebases without sufficient documentation

Pros

• Fully open-source — inspect, modify, and self-host the entire agent
• Free to run with your own OpenAI/Anthropic/local model keys
• Good for understanding how multi-step coding agents work under the hood
• Active community contributions and forks
• No data leaves your infrastructure if using local models

Cons

• Task completion rate significantly lower than Devin on complex tasks
• Requires manual setup: Python environment, API keys, browser driver
• Less reliable on multi-step tasks that require long-horizon planning
• No official support — community-maintained