GPT-6 Astra and GPT-5.6 Luna compared for code review and efficiency

Comparative analysis of AI models GPT-5.6 Luna and GPT-6 Astra reveals significant differences in cost and performance for code review tasks. In testing across 50 public benchmark pull requests, Luna proved to be substantially more affordable, costing approximately $0.0041 per review compared to $0.113 for Astra—a 28x cost difference.

While Luna is effective for identifying everyday correctness bugs, it demonstrated lower accuracy in security and verification. Luna identified 69 verified bugs, whereas Astra identified 92. Specifically, Luna had a higher error rate in findings, with 24 of its 93 findings failing verification, compared to only 4 for Astra. Consequently, Luna is considered suitable for general correctness but is not recommended for reviewing sensitive authentication or permission code.

Regarding operational efficiency, GPT-6 Astra introduces new capabilities for managing token usage. Unlike previous Codex workflows, Astra is designed to be aware of usage limits and can orchestrate cheaper subagents. By reshaping workflows—such as assigning explicit budgets and delegating narrow tasks—users can potentially reduce token usage by up to 91.75%.