GPT-5.6 Sol and Terra: Where they fit for coding agents and code review

OpenAI's release of GPT-5.6 introduces three models—Sol, Terra, and Luna—each designed for different coding tasks within engineering teams. Sol, the flagship model, excels in long-horizon coding tasks and thorough reviews, offering persistence and completion of complex tasks, while Terra provides a cost-effective option for scoped implementations and initial reviews. Luna, the fastest and most economical, is suited for high-volume, low-reasoning tasks. Sol stands out for its practical execution capabilities, showing a higher pass rate in coding tasks and identifying more issues in code reviews compared to its counterparts, although it requires filtering to manage its verbose output. Terra, while less precise than Sol, offers a quieter review lane, making it suitable for triage and less critical tasks. Despite these advancements, architectural planning and high-level judgment still benefit from using models like Fable 5 and Sonnet 5, which provide more nuanced decision-making and cleaner review comments, respectively. The strategic use of these models allows engineering teams to optimize their workflow by selecting the appropriate model for each stage of the software development lifecycle based on task requirements, cost considerations, and the need for precision or comprehensiveness.

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