I recently learned about some insights shared by a member of the OpenAI team. They indicated that the models everyone can access are actually quite close to the advanced versions that OpenAI keeps for internal use. This raises a question in my mind about the real differences between what we have access to and what they operate with in private.
For those of us working with these public APIs, what does this mean? Are our capabilities really almost on par with the internal tools used by OpenAI? Or is there still a notable gap in terms of functionality and performance?
Has anyone come across other comments from OpenAI staff regarding this? I’m eager to understand more about the timeline that connects their internal model work to the public version launches. It feels like the lag might be less extensive than what is commonly thought.
honestly this makes sense from a buisness perspective - why would they hold back their best tech for months when competitors are breathing down thier neck? the gap is probably more about fine-tuning and safety checks rather than huge capability differences. most companies do this
From what I’ve observed working with the API over the past year, there’s definitely some truth to this. The performance jumps between model releases suggest they’re not sitting on dramatically superior versions for extended periods. However, I suspect the real differences lie in computational resources and specialized fine-tuning rather than core capabilities. Internal versions likely have access to more compute power, custom training data, and experimental features that haven’t passed safety evaluations yet. The timeline aspect is interesting though - it seems like they’re pushing updates faster than before, probably due to competitive pressure. My guess is we’re seeing models that are maybe 3-6 months behind their absolute cutting edge, not the year-plus gaps that some people assume.