Steampunk Spotter compares AI models for Ansible accuracy

Summary

Steampunk Spotter presents an AI model leaderboard focused on Ansible automation. The comparison runs real-world prompts, including nginx deployments and network automation, through GPT-5.6-Terra, DeepSeek-V4-Flash, and Claude Sonnet 5. The models are tested across three Ansible scenarios with increasing complexity. Spotter checks each output for errors, warnings, and hints, and records whether the task was completed correctly on the first attempt. Across all three models and scenarios, the system reports 140 errors, 94 warnings, and 221 hints. One model produced the highest error count in two scenarios, while another surpassed it in the most difficult scenario; the page does not identify those models in the extracted results. In the simplest scenario, only one model produced errors, with 12 errors recorded, while the other two were clean. The most frequent issue was a missing fully qualified module name, accounting for 96 errors, followed by missing or invalid collection versions in requirements.yml with 28 errors and invalid module parameters with 10. Steampunk offers the complete comparison in a downloadable report.