{"slug": "which-ai-model-should-your-team-use-for-ansible", "title": "Which AI model should your team use for Ansible", "summary": "Spotter's AI leaderboard benchmark of GPT-5.6-Terra, DeepSeek-V4-Flash, and Claude Sonnet 5 on three Ansible scenarios of increasing complexity found 140 errors, 94 warnings, and 221 hints across the models, with more than two-thirds of errors caused by missing fully qualified module names. One model posted the highest error count in two of the three scenarios and was the only model to produce errors — 12 of them — on the simplest scenario, while the other two returned clean output. The full report is available as a PDF download.", "body_md": "# Which AI model should your team use for Ansible\n\nWe ran real-world Ansible prompts, from nginx deployments to network automation, through the most capable AI models on the market. Then logged every error, warning, and security flag each one produced.\n\n[Download the full report](#ai-report)\n\n#### How this comparison was run\n\nThis leaderboard tests the currently most popular models, **GPT-5.6-Terra, DeepSeek-V4-Flash,\nand\nClaude Sonnet 5**, across three Ansible scenarios of increasing complexity. Each model's output\nwas\nchecked by Spotter for errors, warnings, and how often it completed the task correctly on the first\ntry.\n\n## 100+ errors across all three models\n\nSpotter found 140 errors, 94 warnings, and 221 hints across the three models and three scenarios.\n\n[Download the full report](#ai-report)\n\n## One model led in errors in two of the three scenarios\n\nOne model posted the highest error count in two out of three scenarios, losing that spot only on the hardest one, where a different model overtook it\n\n## The same model produced 12 errors in the simplest scenario\n\nOne model was the only one to trip any errors on the easiest scenario, while the other two came back completely clean.\n\n[Download the full report](#ai-report)\n\n## Most common error types across all scenarios and models\n\nMore than two-thirds of errors were caused by missing fully qualified module names.\n\n## Get the full report\n\n### Processing, please wait...\n\n### Something went wrong.\n\nPlease try again later.\n\n### Your report is ready — download it now or check your inbox for a copy.\n\n[Download](https://steampunk.si/pdf/XLAB_Steampunk_Spotter_Report_AI_Leaderboard.pdf)", "url": "https://wpnews.pro/news/which-ai-model-should-your-team-use-for-ansible", "canonical_source": "https://steampunk.si/spotter/ai-leaderboard/", "published_at": "2026-09-23 06:25:21+00:00", "updated_at": "2026-09-23 06:53:47.278524+00:00", "lang": "en", "topics": ["ai-tools", "large-language-models", "developer-tools"], "entities": ["Spotter", "GPT-5.6-Terra", "DeepSeek-V4-Flash", "Claude Sonnet 5", "Ansible"], "alternates": {"html": "https://wpnews.pro/news/which-ai-model-should-your-team-use-for-ansible", "markdown": "https://wpnews.pro/news/which-ai-model-should-your-team-use-for-ansible.md", "text": "https://wpnews.pro/news/which-ai-model-should-your-team-use-for-ansible.txt", "jsonld": "https://wpnews.pro/news/which-ai-model-should-your-team-use-for-ansible.jsonld"}}