OpenAI 的 Ted Sanders 分享挑选合适 GPT 模型的经验法则
原标题：OpenAI's Ted Sanders shares a rule of thumb for picking the right GPT-model
OpenAI 的 Ted Sanders 对新模型 GPT-6.1 Terra 进行了分类，并解释了 OpenAI 的模型命名方式。他表示模型性能是多维的，模型大小、推理力度和多智能体系统是不同维度，合并为单一“智商分数”会掩盖细节；模型大小也非一切，更新更小的模型可凭更好的训练数据或架构超越更旧更大的模型。据 Sanders 说法，GPT-6.1 Terra 在技术写作上可能优于更大的 Astra，在图像处理和电脑操作上大致持平且价格低得多，但多数其他领域不及；选型建议是只看质量选 Astra，略在意价格选 Sol，非常在意价格选 Terra 或 Luna。
为什么值得看OpenAI 员工解释了 GPT 模型命名背后的尺寸、推理力度与多智能体维度，并给出按价格敏感度选型的具体建议。
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原文
OpenAI's Ted Sanders has categorized the new GPT-6.1 Terra and explained how the company names its language models. According to Sanders, model performance is multidimensional. A model that's great at math won't automatically be good at jokes. Still, most performance gaps come down to one variable: model size. Sanders says OpenAI's model names are difficult to simplify because model size, reasoning effort, and multi-agent systems are distinct dimensions. Combining them into a single "IQ score" would obscure details.
And size doesn't explain everything. Newer, smaller models can outperform older, larger ones through better training data or improved architectures. GPT-6.1 Terra is likely better than the larger Astra at technical communication and roughly matches it in image processing and computer use at a much lower price point, but falls short in most other areas, Sanders says. "If you care about quality only, pick Astra. If you're a bit price sensitive, pick Sol. If you're very price sensitive, pick Terra or Luna."
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