alt Hacker NewsIt's interesting that, of the large inference providers, Google has one of the most inconvenient policies around model deprecation. They deprecate models exactly 1 year after releasing them and force you to move onto their next generation of models. I had assumed, because they are using their own silicon, that they would actually be able to offer better stability, but the opposite seems to be true. Their rate limiting is also much stricter than OpenAI for example. I wonder how much of this is related to these TPU's, vs just strange policy decisions.
Replies
jbellis • today at 2:06 PM
Flash 2 isn't even at EOL until June but we started seeing ~90% error rates getting 429s over the weekend. (So we switched to GPT 5.4 nano.)
It's frustrating how cavalier they are about killing old Gemini releases. My read is that once a new model is serving >90% of volume, which happens pretty quickly as most tools will just run the latest+greatest model, the standard Google cost/benefit analysis is applied and the old thing is unceremoniously switched off. It's actually surprising that they recently extended the EOL date for Gemini 2.5. Google has never been a particularly customer-obsessed company...