[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-best-ai-coding-agent-2026-ranked-benchmarks-en":3,"article-related-best-ai-coding-agent-2026-ranked-benchmarks-en":30,"series-tools-8008013b-982a-4d2d-879f-7010a7fe4c14":79},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":29},"8008013b-982a-4d2d-879f-7010a7fe4c14","best-ai-coding-agent-2026-ranked-benchmarks-en","Best AI Coding Agent 2026, Ranked by Benchmarks","\u003Cp data-speakable=\"summary\">Codex CLI with GPT-5.5 leads Terminal-Bench 2.1, while \u003Ca href=\"\u002Ftag\u002Fclaude-code\">Claude Code\u003C\u002Fa> and opencode win on depth and open-source adoption.\u003C\u002Fp>\u003Cp>Codex CLI with GPT-5.5 hit 83.4% on Terminal-Bench 2.1, and Claude Code with Opus 4.8 followed at 78.9%. On the pricing side, \u003Ca href=\"\u002Ftag\u002Fgithub-copilot\">GitHub Copilot\u003C\u002Fa> Pro starts at $10 a month, while \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fcodex\" target=\"_blank\" rel=\"noopener\">OpenAI Codex CLI\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopencode-ai\u002Fopencode\" target=\"_blank\" rel=\"noopener\">opencode\u003C\u002Fa> draw very different lines around model access, subscriptions, and BYOK setups.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Agent\u003C\u002Fth>\u003Cth>Default model\u003C\u002Fth>\u003Cth>Top score\u003C\u002Fth>\u003Cth>Entry price\u003C\u002Fth>\u003Cth>Source\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Codex CLI\u003C\u002Ftd>\u003Ctd>GPT-5.5\u003C\u002Ftd>\u003Ctd>83.4% Terminal-Bench 2.1\u003C\u002Ftd>\u003Ctd>Free\u003C\u002Ftd>\u003Ctd>Apache-2.0, 94,277 stars\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Claude Code\u003C\u002Ftd>\u003Ctd>Opus 4.8\u003C\u002Ftd>\u003Ctd>78.9% Terminal-Bench 2.1\u003C\u002Ftd>\u003Ctd>$20\u002Fmo Pro\u003C\u002Ftd>\u003Ctd>Proprietary, 134,868 stars\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>opencode\u003C\u002Ftd>\u003Ctd>BYOK\u003C\u002Ftd>\u003Ctd>n\u002Fa public pair score\u003C\u002Ftd>\u003Ctd>Free\u003C\u002Ftd>\u003Ctd>MIT, 180,312 stars\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GitHub Copilot\u003C\u002Ftd>\u003Ctd>Haiku 4.5 \u002F GPT-5 mini\u003C\u002Ftd>\u003Ctd>n\u002Fa public pair score\u003C\u002Ftd>\u003Ctd>$10\u002Fmo Pro\u003C\u002Ftd>\u003Ctd>Proprietary\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Windsurf (Devin Desktop)\u003C\u002Ftd>\u003Ctd>SWE 1.6 + OSS models\u003C\u002Ftd>\u003Ctd>n\u002Fa public pair score\u003C\u002Ftd>\u003Ctd>Free\u003C\u002Ftd>\u003Ctd>Proprietary, Cognition\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>Terminal-Bench 2.1 is the score that matters here\u003C\u002Fh2>\u003Cp>Terminal-Bench 2.1 matters because it tests the whole loop: editing files, running commands, fixing failures, and keeping state across a messy terminal session. That is much closer to real coding work than a single-shot coding prompt, and it explains why the same model can rank differently inside different agents.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782730991658-n99x.png\" alt=\"Best AI Coding Agent 2026, Ranked by Benchmarks\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The public leaderboard at \u003Ca href=\"https:\u002F\u002Ftbench.ai\" target=\"_blank\" rel=\"noopener\">tbench.ai\u003C\u002Fa> gives a clean read on usable pairings. As of June 28, 2026, the top entries include Codex CLI plus GPT-5.5 at 83.4%, Claude Code plus Opus 4.8 at 78.9%, and Terminus 2 plus GPT-5.5 at 78.2%.\u003C\u002Fp>\u003Cul>\u003Cli>Codex CLI + GPT-5.5: 83.4%\u003C\u002Fli>\u003Cli>Claude Code + Opus 4.8: 78.9%\u003C\u002Fli>\u003Cli>Gemini CLI + Gemini 3.1 Pro: 70.7%\u003C\u002Fli>\u003Cli>Claude Code + Opus 4.7: 69.7%\u003C\u002Fli>\u003C\u002Ful>\u003Cp>That spread is big enough to matter in daily use. A 4 to 8 point difference on a terminal \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> often means fewer dead ends, fewer broken edits, and less babysitting when the agent has to recover from a failed command.\u003C\u002Fp>\u003Ch2>Claude Code is the strongest paid option for hard problems\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa> is the agent I would pick when the job is hard reasoning inside a terminal, not just autocomplete in an editor. With \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-opus-4-8\" target=\"_blank\" rel=\"noopener\">Claude Opus 4.8\u003C\u002Fa>, it posts 78.9% on Terminal-Bench 2.1 and 69.2% on \u003Ca href=\"\u002Ftag\u002Fswe-bench\">SWE-bench\u003C\u002Fa> Pro, which is strong enough to keep it near the top even after the Codex CLI result.\u003C\u002Fp>\u003Cblockquote>\"Claude Code is Anthropic’s terminal-first coding assistant.\" — Anthropic\u003C\u002Fblockquote>\u003Cp>The product also has the kind of workflow extras that matter once you use it every day: MCP support, sub-agents, background and cloud sessions, CLAUDE.md memory, hooks, and skills. That makes it feel less like a chat box and more like a tool you can actually shape around a team’s habits.\u003C\u002Fp>\u003Cp>Pricing is straightforward, but the limits are not trivial. Claude Pro costs $20 per month, or $17 per month on annual billing, and the same subscription covers Claude Code plus Claude.ai and Claude Desktop inside a five-hour rolling session window with a weekly cap. Max starts at $100 per month, and Max 20x reaches $200 per month.\u003C\u002Fp>\u003Ch2>Open source is crowded, and opencode leads by adoption\u003C\u002Fh2>\u003Cp>If your main filter is source code and community traction, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopencode-ai\u002Fopencode\" target=\"_blank\" rel=\"noopener\">opencode\u003C\u002Fa> is the biggest name in the open-source camp. The repo has 180,312 GitHub stars and an MIT license, which puts it ahead of \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa> at 134,868 stars, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fgoogle-gemini\u002Fgemini-cli\" target=\"_blank\" rel=\"noopener\">Gemini CLI\u003C\u002Fa> at 105,641 stars, and \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fcodex\" target=\"_blank\" rel=\"noopener\">OpenAI Codex\u003C\u002Fa> at 94,277 stars.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782730993490-owsh.png\" alt=\"Best AI Coding Agent 2026, Ranked by Benchmarks\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That star count does not tell you which agent is best at fixing bugs, but it does tell you where developers are spending attention. opencode, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fcline\u002Fcline\" target=\"_blank\" rel=\"noopener\">Cline\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpaul-gauthier\u002Faider\" target=\"_blank\" rel=\"noopener\">Aider\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FKilo-Org\u002Fkilocode\" target=\"_blank\" rel=\"noopener\">Kilo Code\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fzed.dev\" target=\"_blank\" rel=\"noopener\">Zed\u003C\u002Fa> all appeal to people who want to bring their own model and keep control over cost.\u003C\u002Fp>\u003Cul>\u003Cli>opencode: 180,312 stars, MIT\u003C\u002Fli>\u003Cli>Claude Code: 134,868 stars, proprietary\u003C\u002Fli>\u003Cli>Gemini CLI: 105,641 stars, Apache-2.0\u003C\u002Fli>\u003Cli>OpenAI Codex: 94,277 stars, Apache-2.0\u003C\u002Fli>\u003Cli>Zed: 86,147 stars, OSS Rust\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The trade-off is simple. Open-source agents are free as tools, but you pay for model usage yourself. That can be cheaper for heavy users with the right API mix, or more expensive if you pick a pricey frontier model and run long sessions all day.\u003C\u002Fp>\u003Ch2>Pricing tells a different story than benchmarks\u003C\u002Fh2>\u003Cp>Benchmarks reward capability, while pricing rewards restraint. \u003Ca href=\"https:\u002F\u002Fcursor.com\" target=\"_blank\" rel=\"noopener\">Cursor\u003C\u002Fa> starts at $20 per month for Pro, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffeatures\u002Fcopilot\" target=\"_blank\" rel=\"noopener\">GitHub Copilot\u003C\u002Fa> starts at $10 per month for Pro, and \u003Ca href=\"https:\u002F\u002Fwindsurf.com\" target=\"_blank\" rel=\"noopener\">Windsurf\u003C\u002Fa> now points users into \u003Ca href=\"https:\u002F\u002Fdevin.ai\" target=\"_blank\" rel=\"noopener\">Devin\u003C\u002Fa> after Cognition folded Windsurf into Devin Desktop.\u003C\u002Fp>\u003Cp>That Windsurf move matters because it changed the meaning of a familiar free tier. The old Windsurf editor is now the Devin Free tier at $0 per month, with unlimited Tab completions and inline edits, a light agent quota, and limited model availability. Devin Pro costs $20 per month and adds full model availability, free use of SWE 1.6 and leading open-source models, plus Devin Cloud agents.\u003C\u002Fp>\u003Cp>Here is the practical comparison for people choosing a default today:\u003C\u002Fp>\u003Cul>\u003Cli>Cheapest paid default: GitHub Copilot Pro at $10\u002Fmonth\u003C\u002Fli>\u003Cli>Best IDE-first flow: Cursor Pro at $20\u002Fmonth\u003C\u002Fli>\u003Cli>Best terminal-first paid agent: Claude Code Pro at $20\u002Fmonth\u003C\u002Fli>\u003Cli>Best free open-source route: opencode, Cline, or Aider with your own API key\u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Ca href=\"\u002Fnews\u002Fclaude-code-vs-codex\" target=\"_blank\" rel=\"noopener\">Claude Code vs Codex\u003C\u002Fa> is the real head-to-head if you want a terminal agent, while the editor crowd will keep comparing Cursor, Copilot, and Devin Desktop. The right answer depends on whether you care more about raw benchmark score, monthly spend, or how much control you want over the model underneath.\u003C\u002Fp>\u003Ch2>The model behind the agent still decides the ceiling\u003C\u002Fh2>\u003Cp>Even the best agent cannot outrun the model it calls. That is why the same article has to mention \u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fdeepseek.com\" target=\"_blank\" rel=\"noopener\">DeepSeek\u003C\u002Fa> alongside the tools that wrap them.\u003C\u002Fp>\u003Cp>On the self-reported SWE-bench Pro leaderboard, Claude Opus 4.8 scores 69.2%, GPT-5.5 scores 58.6%, and Gemini 3.1 Pro scores 54.2%. On \u003Ca href=\"\u002Ftag\u002Fswe-bench-verified\">SWE-bench Verified\u003C\u002Fa>, GPT-5.5 posts 88.7% and Opus 4.8 posts 88.6%, which is one reason the model debate keeps splitting by benchmark.\u003C\u002Fp>\u003Cp>That split is not a contradiction. Terminal-Bench asks whether an agent can drive a terminal end to end. SWE-bench asks whether a model can fix real GitHub issues. Those are related tasks, but they reward different habits.\u003C\u002Fp>\u003Cp>The open-weight side matters too. DeepSeek V4, GLM-5.2, Qwen3.7 Max, MiniMax M3, and Kimi K2.6 give teams more room to self-host or buy by the token, which is why cost-sensitive teams keep testing them against the closed models.\u003C\u002Fp>\u003Ch2>What I would pick today\u003C\u002Fh2>\u003Cp>If I wanted the best terminal agent for hard work, I would start with \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fcodex\" target=\"_blank\" rel=\"noopener\">Codex CLI\u003C\u002Fa> plus GPT-5.5, then test whether \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa> feels better on my own codebase. If I wanted the best free path with control over models, I would pick \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopencode-ai\u002Fopencode\" target=\"_blank\" rel=\"noopener\">opencode\u003C\u002Fa> and bring my own provider.\u003C\u002Fp>\u003Cp>The next thing to watch is whether the gap between terminal agents and IDE agents keeps widening as teams move more work into long-running sessions. If Codex keeps its lead on Terminal-Bench while Devin Desktop keeps absorbing older products like Windsurf, the market will split even harder between people who want scoreboards and people who want a polished editor workflow.\u003C\u002Fp>\u003Cp>For now, the clean takeaway is simple: pick the agent by the job, not by the brand. If you want the highest Terminal-Bench number, start with Codex CLI. If you want the strongest paid reasoning assistant, choose Claude Code. If you want the most visible open-source project, install opencode and bring your own model.\u003C\u002Fp>","Codex CLI leads Terminal-Bench 2.1, while Claude Code wins on depth and opencode leads open source by stars.","www.morphllm.com","https:\u002F\u002Fwww.morphllm.com\u002Fai-coding-agent",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782730991658-n99x.png","tools","en","054589c7-78d8-4835-a5e4-4413a6146970",[17,18,19,20,21],"AI coding agents","Terminal-Bench","Claude Code","Codex CLI","opencode",[23,24,25],"Codex CLI with GPT-5.5 leads Terminal-Bench 2.1 at 83.4%.","Claude Code is the strongest paid terminal agent for hard reasoning tasks.","opencode leads open source by GitHub stars, while pricing favors Copilot and BYOK tools.",0,"2026-06-29T11:02:39.121798+00:00","2026-06-29T11:02:39.116+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":31,"relatedLang":38,"relatedPosts":42},[32,34,36],{"name":19,"slug":33},"claude-code",{"name":17,"slug":35},"ai-coding-agents",{"name":37,"slug":21},"OpenCode",{"id":15,"slug":39,"title":40,"language":41},"best-ai-coding-agent-2026-ranked-benchmarks-zh","2026 最佳 AI 寫碼代理排名","zh",[43,49,55,61,67,73],{"id":44,"slug":45,"title":46,"cover_image":47,"image_url":47,"created_at":48,"category":13},"426e735b-aedc-45a9-bf1c-7e84ece9493e","codex-deepseek-v4-pro-moark-setup-en","Codex 接入 DeepSeek-V4-Pro，三步可用","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782738173484-wn38.png","2026-06-29T13:02:25.248526+00:00",{"id":50,"slug":51,"title":52,"cover_image":53,"image_url":53,"created_at":54,"category":13},"3fb3a982-e726-4b72-af23-5fa3294d18bc","devin-ai-alternatives-real-workflows-en","Devin AI Alternatives That Fit Real Workflows","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782732808399-w5eg.png","2026-06-29T11:32:58.823843+00:00",{"id":56,"slug":57,"title":58,"cover_image":59,"image_url":59,"created_at":60,"category":13},"2d074071-d7aa-454e-bdee-da0a52c0ea66","claude-code-turns-agent-setup-into-terminal-work-en","Claude Code turns agent setup into terminal work","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782731910708-9ol7.png","2026-06-29T11:18:02.20016+00:00",{"id":62,"slug":63,"title":64,"cover_image":65,"image_url":65,"created_at":66,"category":13},"ab601a41-618a-4ce3-80a5-51be58465863","openclaw-bailian-qwen37-max-config-template-en","OpenClaw配置百炼Qwen3.7-Max接入模板","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782715689491-easw.png","2026-06-29T06:47:44.970402+00:00",{"id":68,"slug":69,"title":70,"cover_image":71,"image_url":71,"created_at":72,"category":13},"c93a893b-79b9-4a7c-b1ad-474ad3aaf94a","mistral-ocr-4-citation-ready-structured-output-en","Mistral OCR 4 turns scans into citation-ready data","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782702193433-o2ts.png","2026-06-29T03:02:47.761739+00:00",{"id":74,"slug":75,"title":76,"cover_image":77,"image_url":77,"created_at":78,"category":13},"c0e1cb25-3bed-460f-8d23-bae34cec2075","codex-app-april-upgrade-agent-work-units-en","Codex App 4月升级，把 Agent 拆成工作单元","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782660768212-t813.png","2026-06-28T15:32:23.853571+00:00",[80,85,90,95,100,105,110,115,120,125],{"id":81,"slug":82,"title":83,"created_at":84},"8008f1a9-7a00-4bad-88c9-3eedc9c6b4b1","surepath-ai-mcp-policy-controls-en","SurePath AI's New MCP Policy Controls Enhance AI Security","2026-03-26T01:26:52.222015+00:00",{"id":86,"slug":87,"title":88,"created_at":89},"27e39a8f-b65d-4f7b-a875-859e2b210156","mcp-standard-ai-tools-2026-en","MCP Standard in 2026: Integrating AI Tools","2026-03-26T01:27:43.127519+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"165f9a19-c92d-46ba-b3f0-7125f662921d","rag-2026-transforming-enterprise-ai-en","How RAG in 2026 is Transforming Enterprise AI","2026-03-26T01:28:11.485236+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"6a2a8e6e-b956-49d8-be12-cc47bdc132b2","mastering-ai-prompts-2026-guide-en","Mastering AI Prompts: A 2026 Guide for Developers","2026-03-26T01:29:07.835148+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"3ab2c67e-4664-4c67-a013-687a2f605814","garry-tan-open-sources-claude-code-toolkit-en","Garry Tan Open-Sources a Claude Code Toolkit","2026-03-26T08:26:20.245934+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"66a7cbf8-7e76-41d4-9bbf-eaca9761bf69","github-ai-projects-to-watch-in-2026-en","20 GitHub AI Projects to Watch in 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