Best background coding agent

Combined ranking

1. 1
   GitHub Copilot Coding AgentGrade ↗Visit ↗▲115 pts

   GPT #4Claude #3Gemini #1Grok #1

   Unmatched workflow integration inside the GitHub ecosystem. Its structured "plan-first" visual interface allows developers to inspect, modify, and validate the agent's plan before execution, offering the right balance of steerability and autonomy.

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   Gemini Unmatched workflow integration inside the GitHub ecosystem. Its structured "plan-first" visual interface allows developers to inspect, modify, and validate the agent's plan before execution, offering the right balance of steerability and autonomy.

   Grok Deep GitHub integration lets it take issues directly, work asynchronously in background via Actions, make multi-file changes, run tests, and open draft PRs natively—seamless for typical GitHub users with strong real-world adoption and ecosystem maturity. Assumption: most practitioners use GitHub.

   Claude Tickets already live in GitHub Issues for most teams, and this is the only agent where "assign the issue to Copilot" is a native one-click act — it works in a firewalled Actions runner, opens a draft PR, responds to review comments, and inherits org policy/audit controls; lowest adoption friction of anything on this list.

   GPT Best-integrated ticket-to-PR workflow and strong value for GitHub-centric practitioners, with issue and Jira assignment, review-comment iteration, Actions-based testing, security scanning, auditability, and conservative branch permissions.

   Where it falls short

   per GPT Execution quality remains less dependable than the top three on ambiguous, architectural, or complex cross-file work.

   per Claude Quality ceiling trails the frontier labs' own agents on hard tasks, and running inside GitHub Actions means slower iterations, session time limits, and awkward fits for teams whose tickets live outside GitHub.

   per Gemini Complete ecosystem dependency; it is strictly bound to GitHub, making it far less effective or unusable for teams utilizing GitLab, Bitbucket, or self-hosted repository setups.

   per Grok Tied to GitHub platform and subscription; less flexible for non-GitHub workflows or self-hosted repos.

2. 2
   DevinGrade ↗Visit ↗▲211 pts

   GPT #5Claude #4Gemini #2Grok #2

   Leading raw agentic autonomy with a fully sandboxed browser, terminal, and editor. Can ingest a ticket, research the code, write/run tests, and debug in an isolated environment to deliver ready-to-merge PRs. (NEAR-TIE WITH FACTORY.AI: Devin wins on raw multi-tool capability and developer independence, whereas Factory.ai is vastly superior for enterprise guardrails).

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   Gemini Leading raw agentic autonomy with a fully sandboxed browser, terminal, and editor. Can ingest a ticket, research the code, write/run tests, and debug in an isolated environment to deliver ready-to-merge PRs. (NEAR-TIE WITH FACTORY.AI: Devin wins on raw multi-tool capability and developer independence, whereas Factory.ai is vastly superior for enterprise guardrails).

   Grok True cloud sandbox autonomy for end-to-end tasks like features/bug fixes/migrations, can handle longer-horizon work and open PRs with strong enterprise traction (e.g., large refactors).

   Claude The most mature end-to-end autonomous workflow for ticket-driven teams — native Slack/Linear/Jira assignment, parallel Devin fleets, persistent machine snapshots, and organizational knowledge/playbooks that improve repeat tasks; post-Windsurf-acquisition pricing ($20 entry) fixed its old value problem.

   GPT The most complete autonomous-engineering workflow, with persistent environments, browser use, CI repair, reusable knowledge, automations, and first-class GitHub, GitLab, Bitbucket, Azure DevOps, Jira, Linear, Slack, and Teams integrations.

   Where it falls short

   per GPT Its high and sometimes difficult-to-predict compute cost is poor value for most individuals and small teams unless deep autonomy and integrations are heavily used.

   per Claude ACU-based costs still balloon at scale and reliability variance on large, ambiguous tasks remains its reputation drag — it is not for teams unwilling to invest in scoping tickets tightly and building playbooks.

   per Gemini High operational costs and latency; it is prone to getting stuck in circular agent loops and racking up compute bills on complex or loosely scoped tasks.

   per Grok Expensive for routine use and may overstep without sufficient scoping/review; better for supervised complex tasks than lightweight tickets.

3. 3
   OpenAI CodexGrade ↗Visit ↗incumbent▼210 pts

   GPT #1Claude #1Gemini —Grok —

   Best overall for a typical GitHub team: consistently strong real-world PR acceptance, capable long-running cloud sandboxes, parallel delegation, direct issue assignment, test execution, and review-driven iteration. Assumes tickets are well scoped and repositories have reproducible setup.

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   GPT Best overall for a typical GitHub team: consistently strong real-world PR acceptance, capable long-running cloud sandboxes, parallel delegation, direct issue assignment, test execution, and review-driven iteration. Assumes tickets are well scoped and repositories have reproducible setup.

   Claude The most polished ticket-to-PR pipeline as of 2026 — cloud sandboxes that run many tasks in parallel, GitHub integration for assigning work and auto-opening PRs, strong code-review mode, and GPT-5-Codex-class models tuned specifically for long autonomous runs; bundled into ChatGPT Plus/Pro/Team plans, so the typical practitioner gets it at effectively marginal cost. Rank assumes the practitioner wants a hosted, low-setup agent rather than self-hosted control.

   Where it falls short

   per GPT Private dependencies and service-heavy environments require substantial sandbox configuration, while token-based cloud-task costs can vary.

   per Claude Weakest at deep integration with non-GitHub ticket systems (Jira/Linear flows are clunkier than Devin's), and its sandboxed environment setup for complex monorepos with private dependencies still takes real configuration effort.

4. 4
   Claude CodeGrade ↗Visit ↗incumbent▼18 pts

   GPT #2Claude #2Gemini —Grok —

   Near-tied with Codex; particularly strong on feature work, multi-file refactors, codebase reasoning, and clean, reviewable patches, with ticket-to-PR operation through GitHub and Claude Code Actions.

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   GPT Near-tied with Codex; particularly strong on feature work, multi-file refactors, codebase reasoning, and clean, reviewable patches, with ticket-to-PR operation through GitHub and Claude Code Actions.

   Claude The strongest underlying agentic coding models (top of SWE-bench-class evals through late 2025), and its GitHub Actions integration gives a genuine ticket→PR loop — @claude an issue and it opens a PR — plus web/cloud background sessions; excels on long, multi-file refactors where other agents rabbit-hole. Near-tie with Codex; Codex edges it on the packaged background-agent product surface, Claude Code wins on raw task completion quality.

   Where it falls short

   per GPT Long autonomous runs can consume expensive or rate-limited usage, and the turnkey background workflow is less unified than Codex’s.

   per Claude More assembly required as a team-wide background agent — the Actions workflow, permissions, and environment are yours to configure, and heavy autonomous use gets expensive on API/Max-plan token budgets.

5. 5
   OpenHandsGrade ↗Visit ↗▲44 pts

   GPT —Claude —Gemini #5Grok #3

   Leading open-source agent framework (ex-OpenDevin) with strong SWE-bench performance, self-hostable, model-agnostic, supports full agentic loops for issue resolution and PR creation; versatile for teams wanting control/customization.

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   Grok Leading open-source agent framework (ex-OpenDevin) with strong SWE-bench performance, self-hostable, model-agnostic, supports full agentic loops for issue resolution and PR creation; versatile for teams wanting control/customization.

   Gemini The premier open-source autonomous agent framework. Allows complete data privacy, integration with local models, and customized sandboxing inside self-hosted CI/CD pipelines, removing vendor lock-in and allowing extensive customization.

   Where it falls short

   per Gemini High setup and maintenance overhead; requires teams to manage their own sandboxed environments, model API costs, and system orchestration to achieve a production-grade workflow.

   per Grok Requires setup and model choice (performance varies); more framework than polished turnkey product for casual users.

6. 6
   Cursor Cloud AgentsGrade ↗Visit ↗▼13 pts

   GPT #3Claude —Gemini —Grok —

   Excellent bug-fixing performance, strong model choice, parallel remote agents, configurable environments, and unusually smooth handoff between background work, PR review, and the Cursor IDE.

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   GPT Excellent bug-fixing performance, strong model choice, parallel remote agents, configurable environments, and unusually smooth handoff between background work, PR review, and the Cursor IDE.

   Where it falls short

   per GPT Internet-enabled agents automatically execute commands with repository write access, making it unsuitable for sensitive code without strict network and secret controls.

7. 7
   FactoryGrade ↗Visit ↗▼13 pts

   GPT —Claude —Gemini #3Grok —

   Enterprise-first architecture that uses specialized "Droids" (Code, Test, Reliability) instead of a generic loop. It draws context from Jira/Linear, Slack, Sentry, and CI/CD to handle ticket-to-PR pipelines with strong governance. (NEAR-TIE WITH DEVIN: Devin wins on raw multi-tool capability, whereas Factory.ai is vastly superior for enterprise guardrails and team-level metrics).

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   Gemini Enterprise-first architecture that uses specialized "Droids" (Code, Test, Reliability) instead of a generic loop. It draws context from Jira/Linear, Slack, Sentry, and CI/CD to handle ticket-to-PR pipelines with strong governance. (NEAR-TIE WITH DEVIN: Devin wins on raw multi-tool capability, whereas Factory.ai is vastly superior for enterprise guardrails and team-level metrics).

   Where it falls short

   per Gemini Heavy enterprise-only entry barrier; tailored exclusively for large enterprise setups, requiring heavy upfront integration and configuration that makes it inaccessible for individuals or small startups.

8. 8
   EllipsisGrade ↗Visit ↗▼12 pts

   GPT —Claude —Gemini #4Grok —

   Highly efficient for small-to-medium tasks, integrating seamlessly into GitHub and Linear to turn issues into code. Its distinct value lies in its dual-agent reviewer/coder flow that performs deep, automated code reviews and self-correction directly within PR comments.

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   Gemini Highly efficient for small-to-medium tasks, integrating seamlessly into GitHub and Linear to turn issues into code. Its distinct value lies in its dual-agent reviewer/coder flow that performs deep, automated code reviews and self-correction directly within PR comments.

   Where it falls short

   per Gemini Limited reasoning scope; it struggles with broad, multi-file architectural refactors or highly complex feature additions, making it best suited for bug fixes and routine maintenance.

9. 9
   SWE-AgentGrade ↗Visit ↗new2 pts

   GPT —Claude —Gemini —Grok #4

   Clean, research-backed architecture excels at autonomously resolving real GitHub issues with high benchmark scores in minimal setup; influential design used in many agents.

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   Grok Clean, research-backed architecture excels at autonomously resolving real GitHub issues with high benchmark scores in minimal setup; influential design used in many agents.

   Where it falls short

   per Grok More minimal/research-oriented; lacks some enterprise polish/multi-agent features of OpenHands for production team scaling.

10. 10
    Google JulesGrade ↗Visit ↗▼21 pts

    GPT —Claude #5Gemini —Grok —

    The value pick — generous free tier, simple GitHub repo connection, async cloud VMs that produce PRs with audible/readable diffs and plans, and Gemini 3-era model upgrades closed much of the quality gap for small-to-medium tasks.

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    Claude The value pick — generous free tier, simple GitHub repo connection, async cloud VMs that produce PRs with audible/readable diffs and plans, and Gemini 3-era model upgrades closed much of the quality gap for small-to-medium tasks.

    Where it falls short

    per Claude Still noticeably behind Codex and Claude Code on complex multi-file changes and has the thinnest ticket-system and enterprise-controls story, so it suits solo devs and side projects more than teams running it as production infrastructure.

Rank history

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By model

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Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand