What are the key differences between the GPT-4o-mini model and the full GPT-4o model in terms of performance, capabilities, efficiency, dataset size, and intended use cases? Does GPT-4o-mini use a smaller or older dataset compared to GPT-4o? How do their training methodologies, response accuracy, and processing power compare?
Hi @Abdul_Moiz !
The exact innards are unknown to us (for neither model). My assumption is that both of them are distilled versions of their much larger ancestors (GPT-4 Turbo) with some additional finetuning and extra logic on token sampling (like token masking and constrained sampling for structured outputs).
In terms of the difference between mini and its big sibling, again purely speculative, but I would say it’s the model size (number of params), possibly different stopping times (the mini has possibly seen less tokens over few epochs - possibly).
In practical terms - I would use mini for summarization, both data I provide it in-context, as well as when using via ChatGPT Web Search. For essentially all else (e.g. extracting certain types of information, classification tasks, etc) I use gpt-4o. For reasoning tasks I opt for reasoning. Oddly enough gpt-4o is actually cheaper on image tasks than the mini version.
Hope that helps!
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Thank you platypus, but if I use gpt-4o-mini for only the Q/A chatbot that’s enough or not ?
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Difficult to say, depends on what kind of data/context it’s dealing with. But what I always say to people - try with the mini, evaluate, and then decide if you need to step it up to larger models.
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