· ai-devtools
Ted Sanders explains OpenAI model versioning, size impacts performance, cost, and recall
The way these large language models perform is remarkably complex, meaning there is no fixed hierarchy where the model excelling at mathematics will automatically be the one best suited for humor.
Nevertheless, a significant portion of the performance differences across models can be traced back to just one factor: the model's scale. Generally speaking, larger models tend to exhibit several traits: they become more capable and dependable, they incur a higher expense per token, and they possess superior memory for obscure pieces of information.
Regarding how OpenAI structures its releases, Ted Sanders elaborated on the model naming conventions, noting that version numbers currently convey several pieces of information simultaneously. For instance, a designation like 6.1 suggests that the release followed 5.6, might share certain characteristics in terms of its training data or temperament with other 6.1 iterations (hence its designation is not 5.7), and does not represent a massive breakthrough warranting the fanfare associated with a 7. He also mentioned that their models are arranged in a specific sequence: Luna [...].
Why it matters
Understanding these conventions helps developers predict model capabilities and operational costs when selecting the right LLM for a project. This provides context for interpreting new model releases and version updates.
