OpenAI 的 Ted Sanders 分享挑选合适 GPT 模型的经验法则
OpenAI's Ted Sanders shares a rule of thumb for picking the right GPT-model
AI 导读
OpenAI 的 Ted Sanders 对 GPT-6.1 Terra 做了分类，并解释了公司如何为语言模型命名。
摘要与补充说明
他表示模型性能是多维的，擅长数学的模型未必擅长讲笑话，但多数性能差距归结为一个变量：模型规模。由于模型规模、推理投入和多智能体系统是不同维度，把三者合并成单一“IQ 分数”会掩盖细节；较新的小模型也能靠更好的训练数据或架构改进超越旧的大模型。Sanders 称 GPT-6.1 Terra 在技术沟通上可能优于规模更大的 Astra，在图像处理和计算机使用上大致相当且价格低得多，但多数其他领域落后。他的选择建议是：只看质量选 Astra，对价格略敏感选 Sol，非常在意价格则选 Terra 或 Luna。
正文
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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