Devin vs OpenHands vs SWE-Agent: Autonomous AI Coders Compared

The jump from AI-assisted coding to AI-autonomous coding is not incremental. It is a category shift. Tools like Cursor, Windsurf, and Claude Code help you write code faster by sitting in your editor and responding to your prompts. Autonomous coding agents go further: you hand them a GitHub issue, walk away, and come back to a pull request with code changes, test updates, and a description of what was done and why. No babysitting required.

Three agents are leading this category. Devin, built by Cognition, is the commercial frontrunner that made headlines as the "first AI software engineer." OpenHands (formerly OpenDevin) is an open-source platform that lets you run autonomous agents locally or in the cloud with your choice of underlying model. SWE-Agent, created by researchers at Princeton, is the academic pioneer that proved language models could resolve real GitHub issues when given the right scaffolding. Each takes a meaningfully different approach to the same problem.

If you have already explored AI code editors compared and decided you want something that operates independently rather than assisting, this is the guide for you. We have tested all three on production codebases and tracked their success rates, costs, and failure modes over the past six months. The differences are significant, and the right choice depends heavily on your team's risk tolerance, budget, and infrastructure.

Cognition launched Devin in early 2024 with a bold claim: it was the first fully autonomous AI software engineer. The demo showed Devin navigating a codebase, writing code, debugging errors, deploying applications, and even completing real freelance jobs on Upwork. The hype was enormous. The reality, as always, turned out to be more nuanced, but Devin has matured considerably since that initial splash.

Architecture and Execution Model

Devin runs inside a full sandboxed compute environment. When you assign it a task, it gets its own virtual machine with a browser, terminal, and code editor. It can install packages, run test suites, browse documentation, search the web, and interact with APIs. This is fundamentally different from agents that just generate code and paste it into files. Devin executes, observes, and iterates in a feedback loop that closely mirrors how a human developer works.

The environment isolation is a double-edged sword. On one hand, Devin cannot accidentally trash your local development setup or leak credentials from your machine. On the other, it means Devin sometimes struggles with tasks that require access to your specific infrastructure, internal APIs, or proprietary toolchains that cannot be easily replicated in a sandbox.

SWE-bench Performance

On the SWE-bench Verified benchmark, which tests an agent's ability to resolve real GitHub issues from popular open-source projects, Devin scores between 25 and 30 percent depending on the configuration. That was groundbreaking when it first launched, though the field has caught up quickly. More telling than the benchmark number is how Devin performs on real-world tasks in your codebase: success rates drop on proprietary projects with custom frameworks, unusual build systems, or sparse documentation. Devin works best on well-structured codebases with good test coverage and standard tooling.

PR Generation and Code Review Integration

Devin generates pull requests directly against your repository. Each PR includes a description of the changes, the reasoning behind implementation choices, and links back to the original issue. It integrates with GitHub and GitLab, and you can configure it to request reviews from specific team members. The PR quality is generally good for straightforward bug fixes and small features. For larger changes, expect to iterate: Devin will respond to review comments and update the PR, but its ability to incorporate subtle architectural feedback is still limited.

Pricing

Devin uses a consumption-based pricing model. As of early 2031, the Team plan starts at $500/month for a pool of agent compute units (ACUs), with each task consuming units based on complexity and runtime. A simple bug fix might cost $2 to $5. A complex feature implementation can run $15 to $40. For teams running 200+ tasks per month, costs can scale to $2,000 to $5,000 monthly. There is also an Enterprise tier with volume discounts, SSO, and dedicated infrastructure. This is not cheap, but if Devin resolves issues that would otherwise take a junior developer 2 to 4 hours, the math works out for many teams.

OpenHands started as OpenDevin, an open-source project inspired by Cognition's Devin demo. The community rallied around a simple idea: if autonomous coding agents are going to reshape software engineering, the technology should not be locked behind a single vendor. OpenHands has since grown into a serious platform with hundreds of contributors, a modular architecture, and benchmark results that rival or exceed Devin's on SWE-bench.

Architecture and Execution Model

OpenHands runs agents inside Docker containers, giving each task an isolated environment with a shell, browser, and file system. You can run it on your own infrastructure, which means your code never leaves your network. This is the single biggest differentiator for teams with strict data governance requirements. The agent runtime supports multiple underlying LLMs: Claude, GPT-4o, Llama, and others. You pick the model that balances cost, capability, and privacy for your use case.

The platform exposes an event-driven architecture where every action the agent takes (reading a file, running a command, editing code) is logged as an event. You can replay these events to understand exactly what the agent did, which is invaluable for debugging failures and building trust. The sandbox is configurable: you can pre-install your project dependencies, mount volumes, and set environment variables so the agent starts with a realistic development environment rather than a blank slate.

SWE-bench Performance

OpenHands with Claude Sonnet 4 as the backend model scores approximately 53 percent on SWE-bench Verified. That is among the highest scores on the leaderboard and significantly ahead of Devin's published numbers. With GPT-4o, the score drops to around 38 percent. The model choice matters enormously: the same scaffolding produces dramatically different results depending on the reasoning capabilities of the underlying LLM. If you plan to use OpenHands, budget for a strong model. Running it with a cheap, small model will produce disappointing results.

PR Generation and Code Review Integration

OpenHands can generate pull requests through its GitHub integration. The PR workflow is less polished than Devin's, since you are responsible for configuring the integration and managing API tokens. But the flexibility is real: you can customize the PR template, add automated checks, and wire it into your existing CI/CD pipeline however you want. The agent can also respond to review comments, though this feature is newer and less battle-tested than the core issue-resolution flow.

Pricing

OpenHands itself is free and open-source. Your costs are the infrastructure to run it (a server with Docker, typically $50 to $200/month on a cloud provider) plus the API costs for the underlying LLM. Running Claude Sonnet 4 through the Anthropic API for a typical task costs $0.50 to $3.00 depending on the size of the codebase and the complexity of the issue. For a team running 200 tasks per month, expect $100 to $600 in API costs plus infrastructure. That is roughly one-third to one-fifth the cost of Devin for similar workloads. The tradeoff is that you own the operations: you are responsible for uptime, updates, and debugging when the agent fails.

SWE-Agent was built by John Yang and the Princeton NLP group to answer a research question: can language models resolve real software engineering tasks if you give them the right interface? The answer was a resounding yes, and SWE-Agent's design choices have influenced every autonomous coding agent that followed, including OpenHands and Devin.

Architecture and Execution Model

SWE-Agent takes a minimalist approach compared to Devin and OpenHands. It provides the LLM with a custom shell interface called the Agent-Computer Interface (ACI) that includes commands for navigating code, editing files, running tests, and searching repositories. The ACI is carefully designed to work within the token limits of language models: instead of dumping entire files into context, it shows the agent a scrollable window of code with line numbers, similar to how a human would use a text editor in the terminal.

This design is elegant and effective. By constraining how the agent interacts with the codebase, SWE-Agent avoids many of the failure modes that plague agents with unrestricted access. The agent cannot accidentally delete directories, overwrite unrelated files, or get lost in a rabbit hole of irrelevant code. Every action is deliberate and observable.

SWE-bench Performance

SWE-Agent was the first tool to demonstrate strong performance on SWE-bench, originally achieving around 12 percent on the full benchmark when it launched. With newer models, SWE-Agent with Claude Sonnet 4 scores approximately 43 percent on SWE-bench Verified. It trails OpenHands slightly, partly because OpenHands has a richer execution environment that handles dependency management and complex build systems more gracefully. But SWE-Agent's performance-per-dollar is excellent because the ACI keeps token usage low.

PR Generation and Workflow

SWE-Agent generates patches rather than full pull requests. It outputs a diff that resolves the issue, and it is up to you to apply the patch and create the PR. This is by design: SWE-Agent is a research tool first. Wrapping it in a production-grade PR workflow is something the community and downstream projects have done, but it requires integration work. If you want a turnkey "issue in, PR out" experience, OpenHands or Devin are better choices. If you want to understand how autonomous coding agents work under the hood and customize the behavior deeply, SWE-Agent's clean codebase is the best starting point.

Pricing

SWE-Agent is fully open-source and free to run. API costs per task are lower than OpenHands because the ACI design minimizes token consumption. A typical task with Claude Sonnet 4 costs $0.30 to $1.50 in API fees. Infrastructure requirements are modest: any machine that can run Python and Docker will work. For teams with engineering capacity to manage their own tooling, SWE-Agent offers the lowest cost-per-resolved-issue of the three options.

Every autonomous coding agent markets its SWE-bench score, and every team evaluating these tools asks the same question: do those numbers actually predict how well the agent will perform on my codebase? The honest answer is "partially." SWE-bench is useful, but it has limitations you need to understand.

The Numbers

On SWE-bench Verified (a curated subset of 500 real GitHub issues validated by human developers), the approximate scores as of early 2031 are:

• OpenHands + Claude Sonnet 4: ~53% resolved
• SWE-Agent + Claude Sonnet 4: ~43% resolved
• Devin: ~28% resolved
• OpenHands + GPT-4o: ~38% resolved
• SWE-Agent + GPT-4o: ~33% resolved

These scores measure resolution rate: the percentage of issues where the agent produces a patch that passes the existing test suite. Higher is better, but a 53 percent score does not mean the agent fails completely on the other 47 percent. Some of those "failures" produce partially correct patches that a human developer could finish in 15 minutes.

Why SWE-bench Does Not Tell the Whole Story

SWE-bench tests against popular open-source Python projects like Django, Flask, scikit-learn, and sympy. If your codebase is a TypeScript monorepo with a custom build system, these numbers have limited predictive value. The benchmark also assumes that the existing test suite is sufficient to validate a fix. In practice, many real-world issues require writing new tests, not just passing existing ones.

We tested all three agents on 40 issues from our own client projects (a mix of TypeScript, Python, and Go codebases) and found that resolution rates were roughly 30 to 40 percent lower than SWE-bench scores would suggest. OpenHands still led the pack, but the gap between agents narrowed. The main failure modes were: incorrect assumptions about project structure, inability to set up the development environment correctly, and misunderstanding the intent behind vaguely written issues.

The Model Matters More Than the Scaffolding

One of the clearest patterns across all benchmarks is that the underlying LLM accounts for roughly 60 to 70 percent of performance variation, while the agent scaffolding accounts for 30 to 40 percent. Swapping Claude Sonnet 4 for a weaker model in OpenHands drops the score by 15+ percentage points. This has a direct implication for your budget: skimping on the model to save on API costs will cost you more in failed tasks and manual cleanup. When we work with AI agent teams for product development, choosing the right model is always the first conversation.

Autonomous agents that can read, write, and execute code in your repository introduce security considerations that go far beyond what AI-assisted editors present. An autocomplete suggestion cannot exfiltrate your database credentials. An autonomous agent with shell access theoretically can.

Code and Data Security

Devin runs entirely on Cognition's infrastructure. Your code is uploaded to their sandboxed environment, processed, and the results are returned. Cognition provides SOC 2 compliance and data encryption, but your source code does leave your network. For teams building proprietary algorithms or handling regulated data, this is a non-trivial concern.

OpenHands can run entirely on your own infrastructure. Your code stays on your machines, and the only external call is to the LLM API. If you use a self-hosted model (like Llama running on your own GPUs), your code never leaves your premises at all. This is the strongest security posture available and the reason many enterprise teams gravitate toward OpenHands despite the operational overhead.

SWE-Agent has the same self-hosting advantage as OpenHands. The agent runs locally, and you control exactly what data flows to the LLM API. Both open-source options also let you use API providers that offer zero-retention policies, adding another layer of protection.

Cost Per Resolved Issue

This is the metric that actually matters for your budget. Not cost per task, but cost per successfully resolved issue. Based on our tracked data across 200+ tasks:

• OpenHands + Claude Sonnet 4: $3.50 average per resolved issue (including failed attempts)
• SWE-Agent + Claude Sonnet 4: $2.80 average per resolved issue
• Devin Team plan: $12.00 average per resolved issue

Devin's higher cost-per-resolution reflects both its pricing structure and a lower success rate on the types of issues we tested. If Devin's success rate improves (and it has been improving steadily), the cost gap will narrow. For now, the open-source options deliver better economics for teams willing to manage their own infrastructure.

Integration with Existing Workflows

Devin offers the smoothest out-of-the-box integration. Connect your GitHub repo, assign an issue, and Devin handles the rest. It takes about 30 minutes to set up. OpenHands requires more configuration: Docker setup, API key management, GitHub token configuration, and CI/CD integration. Budget half a day for a production-ready setup. SWE-Agent requires the most work to integrate into a production workflow, since it outputs patches rather than PRs. You will need custom scripting to turn it into an automated pipeline.

For teams already exploring how AI agents are reducing development costs, autonomous coding tools represent the next step: not just generating code faster, but resolving entire issues from start to finish.

After six months of running these tools on real client work, our recommendations are sharper than the generic "it depends" you will find elsewhere. Each agent has a clear sweet spot, and using the wrong one for your situation wastes time and money.

Choose Devin If:

You want a managed service where someone else handles infrastructure, updates, and reliability. Your team does not have a spare engineer to maintain an open-source agent platform. You are willing to pay a premium for polish and convenience. Devin is the right choice for engineering managers at mid-size companies who want to assign GitHub issues to an AI agent the same way they would assign them to a junior developer. The PR workflow, Slack integration, and web-based task monitoring make Devin the most accessible option for non-technical stakeholders who want visibility into what the AI is doing.

Choose OpenHands If:

You need the highest resolution rate and are comfortable managing your own infrastructure. Your security requirements prevent sending source code to third-party services. You want to choose your own LLM and switch models as the market evolves. OpenHands is our default recommendation for engineering teams with at least one person comfortable with Docker, APIs, and CI/CD configuration. The combination of top-tier benchmark scores, model flexibility, and self-hosting capability makes it the most versatile option. If your budget allows Claude Sonnet 4 as the underlying model, OpenHands consistently delivers the best results per dollar.

Choose SWE-Agent If:

You are a small team or solo developer who wants the lowest possible cost per resolved issue. You are comfortable writing scripts to integrate the agent into your workflow. You want to deeply understand how autonomous agents work and potentially customize the agent behavior for your specific codebase. SWE-Agent is the best learning tool in this category, and its ACI design is worth studying even if you ultimately deploy OpenHands in production.

The Practical Middle Ground

Most teams we advise start with OpenHands for bug fixes and small features, while keeping human developers on architectural work and complex cross-service changes. The success rate on well-defined, narrowly scoped issues (fixing a specific bug, adding a simple API endpoint, updating dependencies) is high enough that these agents save real time. The failure rate on ambiguous, large-scope tasks (redesigning a module, implementing a complex business rule with multiple edge cases) is still too high for unsupervised use.

Our recommended workflow: triage incoming issues into "agent-eligible" and "human-required" categories. Issues with clear reproduction steps, existing test coverage, and well-defined acceptance criteria go to the agent. Everything else goes to a developer. Over time, as the agents improve and your team builds intuition for what works, the "agent-eligible" category expands naturally.

What Comes Next

Autonomous coding agents are improving on a quarterly cadence. The SWE-bench Verified leaderboard has gone from 12 percent to over 50 percent in under two years. At that rate, we expect these tools to reliably resolve 70 to 80 percent of well-defined issues within the next 12 to 18 months. The teams that invest now in learning how to work alongside autonomous agents, defining good issue templates, building strong test suites, maintaining clear documentation, will have a meaningful productivity advantage over teams that wait.

The shift from AI-assisted to AI-autonomous coding does not eliminate the need for skilled developers. It changes what those developers spend their time on: less time on routine fixes and boilerplate, more time on architecture, product design, and the creative problem-solving that these agents still cannot do. If you want help evaluating which autonomous agents fit your team or building an AI-powered development workflow, book a free strategy call with us. We have been deploying these tools in production for over a year and can shortcut the experimentation phase for you.