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0 pointsTopfi4mo ago0 comments

My issue is that we haven't even gotten the release version of 3.0, that is also still in Preview, so may stick with 3.0 till that has been deemed stable.

Basically, what does the word "Preview" mean, if newer releases happen before a Preview model is stable? In prior Google models, Preview meant that there'd still be updates and improvements to said model prior to full deployment, something we saw with 2.5. Now, there is no meaning or reason for this designation to exist if they forgo a 3.0 still in Preview for model improvements.

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xnx4mo ago· 3 in thread

Given the pace AI is improving and that it doesn't give the exact same answers under many circumstances, is the the [in]stability of "preview" a concern?

GMail was in "beta" for 5 years.

TopfiOP4mo ago

Should have clarified initially what I meant by stable, especially because it isn't that known how these terms are defined for Gemini models. Not talking about getting consistent output from a not-deterministic model, but stable from a usage perspective and in the way Google uses the word "stable" to describe their model deployments [0]. "Preview" in regard to Gemini models means a few very specific restrictions including far stricter rate limits and a very tight 14 day deprecation window, making them models one cannot build on.

That is why I'd prefer for them to finish the role out of an existing model before starting work on a dedicated new version.

[0] https://ai.google.dev/gemini-api/docs/models

verdverm4mo ago

ChatGPT 4.5 was never released to the public, but it is widely believed to be the foundation the 5.x series is built on.

Wonder how GP feels about the minor bumps for other model providers?

TopfiOP4mo ago

Minor version bumps are good and I want model providers to communicate changes. The issue I am having is that Gemini "preview" class models have different deprecation timelines and rate limits, making them impossible to rely on for professional use cases. That's why I'd prefer they finish the 3.0 role out prior to putting resources into deploying a second "preview" class model.

For a stable deployment, Google needs a sufficient amount of hardware to guarantee inference and having two Pro models running makes that even more challenging: https://ai.google.dev/gemini-api/docs/models

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