OpenAI's Ted Sanders shares a rule of thumb for picking the right GPT-model
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OpenAI's Ted Sanders has provided guidance on selecting the appropriate GPT model, emphasizing that performance is multidimensional and cannot be reduced to a single metric. He explains that while model size is a primary factor in performance gaps, it does not account for everything, as newer smaller models can outperform older larger ones due to better training data or architecture. Sanders categorizes the new GPT-6.1 Terra alongside other models like Astra, Sol, Luna, and O3, noting that specific strengths vary across tasks such as math, humor, and technical communication. He advises users who prioritize quality above all else to choose Astra, those with moderate price sensitivity to select Sol, and highly budget-conscious users to opt for Terra or Luna. This naming convention reflects distinct dimensions including model size, reasoning effort, and multi-agent systems rather than a simplified IQ score. The article highlights that GPT-6.1 Terra offers competitive image processing and computer use capabilities at a lower price point compared to the larger Astra model. However, it falls short in most other areas when compared to its larger counterpart. Sanders' insights aim to help developers navigate the complex landscape of OpenAI's diverse model offerings.
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