Sounds like they did. IMO, building your business on anything but open-weight models is a bad idea. Unless you're on the s&p 500 you are an insect to google, anthropic, grok (ew), and openai - and they could crush you at any time without even noticing.
As an alternative, it's not a bad idea to first convert each document to markdown via a thinking or agentic LLM. Embedded figures can even be embedded as readable tables or Latex or Mermaid. Do record the name and parameters of the model that performs the conversion. You can then query the markdown using any model with an input token cost that is exactly equal to the encoding of the markdown. For multiple queries you can also use input caching.
Might be missing something —- are there any issues with Gemini 3.1 Pro that aren’t there in 2.5?
I agree, though. 2.5 Pro is a great model. Very competent, knows a lot, and can process tons of text (and videos, and images, and audio too iirc?). Basically unlimited access to it too via AI Studio. I used it for processing and transforming bucketloads of data, ingesting masses of transcripts and converting them to flashcards, etc. I’ll be sad to see it go. None of the newer, cheaper, but obviously less intelligent benchmaxxed smaller models really seem to hold a candle to it for lots of things.
Nope. The projects I'm on where we use it, we're carefully migrating to the newer models. Where we can we test with evals to try and get an understanding of how the models have changed.
It's not all roses -- I've seen some regressions -- but generally the 3.x Flash models are pretty great for our use cases.
The great thing about LLMs though is it's incredibly easy to diversify and have fallbacks. But of course that means additional costs, mostly centered around engineering efforts to test and integrate them.
Sounds like they did. IMO, building your business on anything but open-weight models is a bad idea. Unless you're on the s&p 500 you are an insect to google, anthropic, grok (ew), and openai - and they could crush you at any time without even noticing.
As an alternative, it's not a bad idea to first convert each document to markdown via a thinking or agentic LLM. Embedded figures can even be embedded as readable tables or Latex or Mermaid. Do record the name and parameters of the model that performs the conversion. You can then query the markdown using any model with an input token cost that is exactly equal to the encoding of the markdown. For multiple queries you can also use input caching.
Sunsetting a model with a two-month notice is exactly why the open-weight argument keeps winning. The API is a dependency you don't control.
2.5 flash was also good to use with as the llm layer for voice products.
but google gonna google.
Might be missing something —- are there any issues with Gemini 3.1 Pro that aren’t there in 2.5?
I agree, though. 2.5 Pro is a great model. Very competent, knows a lot, and can process tons of text (and videos, and images, and audio too iirc?). Basically unlimited access to it too via AI Studio. I used it for processing and transforming bucketloads of data, ingesting masses of transcripts and converting them to flashcards, etc. I’ll be sad to see it go. None of the newer, cheaper, but obviously less intelligent benchmaxxed smaller models really seem to hold a candle to it for lots of things.
It's still 'preview' and not generally available, so can't run it for US restricted workloads.
Flash is the new Pro, try it first
Nope. The projects I'm on where we use it, we're carefully migrating to the newer models. Where we can we test with evals to try and get an understanding of how the models have changed.
It's not all roses -- I've seen some regressions -- but generally the 3.x Flash models are pretty great for our use cases.
The great thing about LLMs though is it's incredibly easy to diversify and have fallbacks. But of course that means additional costs, mostly centered around engineering efforts to test and integrate them.
Check model garden on vertex ai for other models that you can access
Models you can download and use elsewhere if Google nixes access