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Which AI model should your team use for Ansible

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.

read1 min views1 publishedSep 23, 2026
Which AI model should your team use for Ansible
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We 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.

Download the full report

How this comparison was run

This leaderboard tests the currently most popular models, GPT-5.6-Terra, DeepSeek-V4-Flash, and Claude Sonnet 5, across three Ansible scenarios of increasing complexity. Each model's output was checked by Spotter for errors, warnings, and how often it completed the task correctly on the first try.

100+ errors across all three models #

Spotter found 140 errors, 94 warnings, and 221 hints across the three models and three scenarios.

Download the full report

One model led in errors in two of the three scenarios #

One 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

The same model produced 12 errors in the simplest scenario #

One model was the only one to trip any errors on the easiest scenario, while the other two came back completely clean.

Download the full report

Most common error types across all scenarios and models #

More than two-thirds of errors were caused by missing fully qualified module names.

Get the full report #

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