Yes, there are probably too many options to choose from in the GPT 5.6 release. In the context of reasoning models, though, I find it interesting how these choices map onto training-time and inference-time scaling. If we loosely map the options onto the classic o1 plots, Sol, Terra, and Luna stand in for three model sizes and training budgets along the training-compute axis. The effort settings then sit on the inference-time-compute axis. Figure 1. A loose mapping from OpenAI's classic o1 scaling plots to the GPT 5.6 choices. The three model options represent different model sizes and training budgets, while the effort setting represents inference-time compute.
将 Sol、Terra、Luna 视为模型规模和训练预算，努力级别视为推理算力。 Identifies Sol, Terra, Luna as model sizes/training budgets, effort levels as inference compute.
提出性能与成本的权衡，例如 Luna 配合 Extra High 努力可能优于 Sol 配合 Medium。 Suggests performance-cost trade-offs, e.g., Luna with Extra High effort may beat Sol with Medium.
提供实用的默认推荐：Luna 配合 High 努力作为平衡选择。 Provides a practical default recommendation: Luna with High effort as a balanced choice.
强调前沿模型配置选择的复杂性。 Highlights the complexity of configuration choices in frontier models.
局限 · Limitations
分析基于单一基准（Artificial Analysis Coding Agent Index v1.1）。 Analysis based on a single benchmark (Artificial Analysis Coding Agent Index v1.1).
默认推荐可能无法泛化到多样化的任务或领域。 Default recommendation may not generalize across diverse tasks or domains.
成本和性能数据来自特定发布帖子，可能具有时效性。 Cost and performance data are from a specific release post, potentially time-sensitive.
与 o1 缩放图的映射是松散的，可能过度简化实际训练动态。 The mapping to o1 scaling plots is loose and may oversimplify actual training dynamics.
文章未涉及推理质量或安全性方面的潜在权衡。 The article does not address potential trade-offs in reasoning quality or safety.