OpenHands vs Devin

These two tools sit on opposite ends of the "autonomous coding agent" spectrum. Both claim to do what most other AI coding tools will not: accept a task description, then go do the whole thing — research, code, test, open a pull request — with minimal supervision. The difference is how they deliver that promise and what it costs.

OpenHands (formerly OpenDevin) is an open-source agent framework under MIT license. You clone the repo, run it in Docker, bring your own LLM API key, and point it at a task. Architecturally it resembles Devin's pitch: a sandboxed VM with browser, shell, and code editor, driven by a planning loop that breaks work into steps and executes them with tool calls. Because it is open source, you can inspect every prompt, fork the orchestrator, and wire it to your own CI. The project spun out of a community response to Devin's initial demo in March 2024 and has since become the reference implementation for what an autonomous coding agent looks like in public.

Devin, from Cognition, is the managed SaaS original. You talk to it in a web UI (or Slack), it spins up a cloud sandbox, and it reports progress through the session. The interface is polished: a live terminal, a browser pane, a plan view, and integrations into GitHub, Slack, Linear, and Jira. Devin is a product, not a framework — you do not bring your own model, you do not touch the orchestrator, you pay per session.

What they actually do well

OpenHands is where the benchmark numbers live. On SWE-bench Verified (real-world GitHub bug fixes), OpenHands configurations with Claude Sonnet 4.6 have hit the mid-50s, putting it competitive with the best agent runs published anywhere. It is also the tool you reach for if you need to write your own agent — the codebase is readable, the tool-use loop is well documented, and the Apache/MIT licensing means you can ship derivatives commercially.

Devin's win is the experience. The live browser pane lets it research a library, read docs, and show you what it found. The GitHub integration is genuinely smooth — you can @mention Devin in an issue and a pull request shows up. For non-engineering stakeholders (product managers filing bug reports, designers requesting small fixes), Devin's chat interface is easier to approach than a JSON-configured self-hosted agent. Devin's strength is not raw capability, it is packaging.

Where the gap is real

Real-world performance has been Devin's weak point. The initial launch showed end-to-end feature builds that looked magical in video; third-party and customer evaluations have generally landed in the 14–20% solve rate range on SWE-bench, with longer runs and higher costs than alternatives. The tool has improved through 2025 and 2026, but the gap between demo and daily reality has been wide enough that some early enterprise customers have migrated to OpenHands or Claude Code subagents for actual work.

OpenHands requires you to operate it. There is no one-click deploy. You run Docker, manage API keys, observe cost, handle sandbox security, update the repo when releases ship. For a team with a platform engineer this is trivial; for a non-technical buyer this is the whole blocker.

Model access is a clean difference. OpenHands lets you route to Anthropic, OpenAI, Google, DeepSeek, local Ollama, or any LiteLLM-supported provider. When a new SOTA model ships on a Friday, you have it on Monday. Devin is locked to Cognition's stack, which means you wait for them to integrate, tune, and release.

Integration quality flips the other way. Devin ships polished GitHub, Slack, Linear, and Jira integrations. OpenHands ships the agent; integrations you build or assemble from community examples. For a team that lives in Slack and wants zero friction, Devin is closer to done.

When Devin is worth it

Despite the cost and benchmark gap, Devin has a legitimate buyer: organisations where no one on the team will ever want to operate a Docker stack, where the procurement preference is managed SaaS, and where the tasks thrown at the agent are broad product-manager-style requests ("add export to CSV on the reports page") rather than narrow engineering problems. In that context, Devin's UI, Slack integration, and "just send it a Linear ticket" flow do save meaningful time. The question is whether that saves enough to justify $500/month per seat when OpenHands plus Sonnet 4.6 would do the same work for a fraction of the API cost.

For most engineering teams reading this, the answer is no. For a growth-stage company where the head of engineering wants a PM-facing tool and does not want to staff an AI platform, Devin has a case.

The cost gap between these two is the largest of any pair in this comparison.

OpenHands costs only what you spend on LLM API tokens. A typical task run with Claude Sonnet 4.6 costs between $0.50 and $5 depending on length; a full SWE-bench run is in the $10–$30 range per batch of tasks. Self-hosting compute for the sandbox is marginal (a $20/month VPS handles dozens of concurrent runs). Total monthly spend for a single developer doing daily agent work lands around $30–$100. A team of ten might spend $500–$1,500 per month combined.

Devin starts at $20/month for the Core plan but that tier is heavily rate-limited and aimed at evaluation. Real use lives on the Team plan, which is custom-priced and typically lands in the $500/seat/month range based on public reports, with usage overages on long-running sessions. A team of ten using Devin for daily work is commonly a $5,000–$10,000/month line item, not counting the procurement cycle.

The math: if Devin and OpenHands produced identical output, you are paying roughly 10x for Devin's UI and integrations. In practice, OpenHands produces better benchmark results in public testing, so the premium is even less defensible on capability grounds alone. Hidden cost to watch on Devin: long-running sessions (the agent gets stuck and burns hours) inflate the usage line unexpectedly. On OpenHands, the equivalent failure mode is the agent loop consuming API tokens on repeat tool calls; cap it by setting a max-iterations ceiling and a per-run budget.