- 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.
by tylertreat
1 subcomments
- 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…
by whycombinetor
1 subcomments
- I feel this way about gpt-5-nano (EOL December 2026). It seems like the open weight models have progressed a long way since these old models were released though. Deepseek V4 Flash is even cheaper than gpt-5-nano. I'm still going to pay a cloud provider to run it for me, I'm not local inference pilled yet, but I _can_ run it myself in the future if worse comes to worst.
Objectively testable evals are one thing, but how does one judge whether a new model is adequately reproducing the subjective "writing style" of an old model that you've gotten accustomed to the feel of?
by mips_avatar
1 subcomments
- 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.
by _doctor_love
0 subcomment
- 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.
- If they don't want to host it maybe they could open source it. This would probably be a win-win situation.
- 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.
- 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.
- I don't know how many times people will need to learn this: Do not use Google in Production.
by kilroy123
1 subcomments
- 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?
by ryukoposting
0 subcomment
- I feel the same way about qwen-2.5-coder. Work yanked it from our internally-availbale models one day, breaking a couple tools that depended on it heavily. I haven't found another model that performs as well for the specific tasks I was doing with it. Like yeah, I could throw some gargantuan model at it but then it would take eons to get the same result that used to take 3 seconds.
I've settled on deepseek-v4-flash as a replacement. Results are just as good, but it's slower.
- 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.
- > 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.
by uproarchat
1 subcomments
- I wonder if some day we might see Archive.org organizations preserving older models as operating costs go down
by low_tech_punk
0 subcomment
- 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".
- 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.
- 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.
- Maybe there's also a security aspect to this, older models are probably worse against prompt injection, etc.
- there was a community of people who used (famously sycophantic) gpt 4o as—for lack of a better word—a friend, who were devastated when it was shut down
I suppose at least in this case the loss is not an emotional one?
- There will be such a massive shift to Qwen VL when Google shoots itself in the foot retiring Gemini 2.5 Flash just because a $1 million/yr L7 wanted to show initiative to become a $1.2 million/yr L8
- Can't run Qwen 3.6 35B A3B? Even Qwen 3.5 9B is comparable.
- 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.
- 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 :/
by throw_m239339
0 subcomment
- "Don't discontinue Google RSS reader!"
How about you stop relying on Google products? You've learned nothing after all these years?
- Isn't asking Google to not discontinue a product a bit like asking the tide to not rise?
- "Do not get rid of GPT 4o"
- sucks we use 2.5 flash/lite in our company it handles millions of requests a day
theres nothing in its price range that provides the same all around perf
as noted, gemini 3 flash is expensive
really not liking google these days they are not hungry anymore
- [flagged]
by m_m_carvalho
0 subcomment
- [flagged]
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