Interestingly, I found the original nano banana also has the best latency/quality trade-off that new versions can't beat. This might be domain/prompt specific though. I wonder if there is some truth in the saying that something is either new or improved by never "new and improved".
This is the problem with cloud models, you build a "predictable" workflow then they remove it with a new and improved one that is less deterministic and often costs more. If you use a local model discontinuation is no longer a thing to worry about.
> clearly benchmark and optimise for a specific model over millions of datapoints > new model comes out > get to do it all over again. At this point just become Cursor and get paid for it.
I am more concerned about the cost step up from Gemini 2.5 Flash to 3.5 Flash, with the latter being roughly 3x more expensive. I thought the intention of the Flash models was to be relatively low-latency and more affordable compared to Pro, but the newer Flash models aren’t being priced as such. Then again, the era of cheap and plentiful AI might be coming to an end…
Agree with the observation others have made. The only true solve if a specific model version is critical to your application or workflow, you need to host the model yourself so you have control over it. You don't want to be stuck getting rug-pulled by a model provider.
And as another commenter pointed out - in particular for Google of all companies - expect that the rug pull can and will happen. They're not known for keep anything around for very long.
I love how there is a "Please do not discontinue gemini-2.0-flash[-lite], 2.5 is NOT an equivalent" from Feb 20th. Getting too attached to models is a smell.
It's not a smell. Why should these developers rebuild a core piece of their stack every few months. Switching out a model requires a new round of testing and validation when we should be able to rely on a piece of software the behave the same way since the last time we touched it.
In the post the issue is performance. Are you saying that getting too attached to performance is a smell? That sounds very odd.
It's not because a model performs better in some applications (often by fine-tuning to get better scores at specific tests) that it is better across the board or that we have to believe the company releasing the model with a high number 3 > 2 so that it is commonly accepted as better.
Pushing the reasonnning further: f you need an Opus level performance then not accepting GPT 3 isn't a smell.
I built some BigQuery workflows on 2.0 and 2.5 flash lite that are something like 6x more expensive with 3.1 flash lite.
I tried 3 flash for months and it didn’t work using Googles own vertexai integration because it’s been in preview mode for months.
Not wanting to pay significantly more and do a bunch of rework isn’t a smell.
They left a large gap in their new pricing vs the prior generation, and if you had a working use case that sucks. The model is >99% reliable for my use case so there’s nothing to gain from a smarter model.
It's such a good model for the price, for a lot of tasks it outperforms gpt5 at 3x the speed and 1/5 the price. The price jump from 2.5->3->3.5 has been so high.
Where do people get ideas like this? In what world does this make sense?
You have several choices:
1. Work with a supplier and sign a contract guaranteeing support for whatever period of time you want at a mutually agreeable price
2. Host your own stack to depend on and support it for however long you want
3. Accept that you're paying for a service and that it can go away at any time.
Companies aren't obligated to support things forever and they aren't obligated to open them up when they no longer feel it's worth supporting them. Claiming they should is absurd.
There are plenty of open weights models available already. If the ability to keep running the same model is important to you, then choose one of those.
So you’re telling me, these people have workflows thats so tightly integrated to gemini-2.5-flash that no other model matches it’s performance? Really?
Have they really looked at all alternatives and found none to be a viable option?
I might have underestimated how good 2.5-flash was. I understand the issue with pricing though.
This is why I believe, for a company, to never be reliant on closed-weight models.
UGH why are they killing this model? This is one of the best models you can use in an API for a large swath of tasks. It's kind of the perfect trifecta of fast, cheap, and smart enough.
I was going to reply that Anthropic, which supposedly is the most capacity constrained of the leading AI labs, still provides access to models as old as Opus 3.
Yes, Gemini 2.5 Flash is well balanced model that meets sweet spot of price vs performance trade-off which is good enough for non-reasoning tasks and offer at competitive price.
Is it really? A 9B model is equivalent? Honest question, as I haven't spent that much time with the 9B variant or Flash 2.5. But that seems like a pretty bold claim for such a small model. I assumed Flash 2.5 was considerably larger, but maybe I'm wrong?
Interestingly, I found the original nano banana also has the best latency/quality trade-off that new versions can't beat. This might be domain/prompt specific though. I wonder if there is some truth in the saying that something is either new or improved by never "new and improved".
This is the problem with cloud models, you build a "predictable" workflow then they remove it with a new and improved one that is less deterministic and often costs more. If you use a local model discontinuation is no longer a thing to worry about.
> clearly benchmark and optimise for a specific model over millions of datapoints > new model comes out > get to do it all over again. At this point just become Cursor and get paid for it.
I am more concerned about the cost step up from Gemini 2.5 Flash to 3.5 Flash, with the latter being roughly 3x more expensive. I thought the intention of the Flash models was to be relatively low-latency and more affordable compared to Pro, but the newer Flash models aren’t being priced as such. Then again, the era of cheap and plentiful AI might be coming to an end…
If they don't want to host it maybe they could open source it. This would probably be a win-win situation.
Agree with the observation others have made. The only true solve if a specific model version is critical to your application or workflow, you need to host the model yourself so you have control over it. You don't want to be stuck getting rug-pulled by a model provider.
And as another commenter pointed out - in particular for Google of all companies - expect that the rug pull can and will happen. They're not known for keep anything around for very long.
I love how there is a "Please do not discontinue gemini-2.0-flash[-lite], 2.5 is NOT an equivalent" from Feb 20th. Getting too attached to models is a smell.
It's not a smell. Why should these developers rebuild a core piece of their stack every few months. Switching out a model requires a new round of testing and validation when we should be able to rely on a piece of software the behave the same way since the last time we touched it.
In the post the issue is performance. Are you saying that getting too attached to performance is a smell? That sounds very odd.
It's not because a model performs better in some applications (often by fine-tuning to get better scores at specific tests) that it is better across the board or that we have to believe the company releasing the model with a high number 3 > 2 so that it is commonly accepted as better.
Pushing the reasonnning further: f you need an Opus level performance then not accepting GPT 3 isn't a smell.
I built some BigQuery workflows on 2.0 and 2.5 flash lite that are something like 6x more expensive with 3.1 flash lite.
I tried 3 flash for months and it didn’t work using Googles own vertexai integration because it’s been in preview mode for months.
Not wanting to pay significantly more and do a bunch of rework isn’t a smell.
They left a large gap in their new pricing vs the prior generation, and if you had a working use case that sucks. The model is >99% reliable for my use case so there’s nothing to gain from a smarter model.
That's like saying 'getting attached to locked dependencies for your app is a smell'.
But this could be framed as 'getting attached to an API revision when a new one is available'...
I can see it both ways, tbh.
It's such a good model for the price, for a lot of tasks it outperforms gpt5 at 3x the speed and 1/5 the price. The price jump from 2.5->3->3.5 has been so high.
Why not a "stop killing AI" movement?
If a company deploys a paid AI model and makes people depend on it, they need to dump the weights at EOL.
Where do people get ideas like this? In what world does this make sense?
You have several choices:
1. Work with a supplier and sign a contract guaranteeing support for whatever period of time you want at a mutually agreeable price
2. Host your own stack to depend on and support it for however long you want
3. Accept that you're paying for a service and that it can go away at any time.
Companies aren't obligated to support things forever and they aren't obligated to open them up when they no longer feel it's worth supporting them. Claiming they should is absurd.
There are plenty of open weights models available already. If the ability to keep running the same model is important to you, then choose one of those.
gemini 2 and 2.5 were great models for quick-and-dirty OCR
It was fine to lose 2, but 2.5 will be dearly missed as it hit the sweet spot in terms of cost-performance :/
So you’re telling me, these people have workflows thats so tightly integrated to gemini-2.5-flash that no other model matches it’s performance? Really?
Have they really looked at all alternatives and found none to be a viable option?
I might have underestimated how good 2.5-flash was. I understand the issue with pricing though.
This is why I believe, for a company, to never be reliant on closed-weight models.
UGH why are they killing this model? This is one of the best models you can use in an API for a large swath of tasks. It's kind of the perfect trifecta of fast, cheap, and smart enough.
Why does Google constantly kill off good things?
because they keep these models loaded, and they can't just arbitrarily load up whatever models you want.
but it's more likely just a business case: they need you buying higher tier model output. They know whose doing what, so someone needs their 3Q bonus.
I was going to reply that Anthropic, which supposedly is the most capacity constrained of the leading AI labs, still provides access to models as old as Opus 3.
But then I realized Opus 3 is an outlier, and Anthropic has removed access to relatively more recent models. https://platform.claude.com/docs/en/about-claude/model-depre...
I wonder what the deal is with Opus 3.
I believe a lot of people prefer it for "creative" writing.
the writing is on the wall for it, I have switched to gemma-4-26b-a4b.
At least in benchmarks, it scores higher and is faster.
I really like Gemini 2.5 Flash Lite because it's a dirt cheap model that support every input modalities.
At least now MiMo v2.5 exists and can be used as another dirt cheap multimodal model.
Yes, Gemini 2.5 Flash is well balanced model that meets sweet spot of price vs performance trade-off which is good enough for non-reasoning tasks and offer at competitive price.
Can't run Qwen 3.6 35B A3B? Even Qwen 3.5 9B is comparable.
Is it really? A 9B model is equivalent? Honest question, as I haven't spent that much time with the 9B variant or Flash 2.5. But that seems like a pretty bold claim for such a small model. I assumed Flash 2.5 was considerably larger, but maybe I'm wrong?
"Do not get rid of GPT 4o"
Isn't asking Google to not discontinue a product a bit like asking the tide to not rise?
That, and while you’ve chained yourself to the floor of the littoral zone previously.