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?
That's what they signed up for when established a hard dependency on an subscription online-only LLM model.
If you want to be sure to be in control, then host it yourself
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.
However, they are aggressively deprecating them (OpenAI is as well), and replacing with newer models. These newer models are all reasoning models, and importantly, only bear the flash name. They are not fast. And they are very expensive!
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.
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.
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've settled on deepseek-v4-flash as a replacement. Results are just as good, but it's slower.
If a company deploys a paid AI model and makes people depend on it, they need to dump the weights at EOL.
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.
Creating a legal obligation to release the weights of discontinued models doesn’t seem absurd. These models are built on existing publicly available information; a requirement that it be returned to the commons once it is no longer in commercial use hardly seems like a substantial regulatory burden.
You don't have to use Big Ai offerings, there are other options. Between deprecation and uncle sam, dependency/business risk appears to be increasing.
It's a calculation and choice that comes with consequences any way you land.
If you are paying an ongoing subscription for a service, I'd advise you not to rely on it too much or keep a list of alternatives.
If you were paying for an ongoing subscription for a service, that would be something different.
im always convinced people with takes on the open source models have never actually used them in a production agentic system
At least in benchmarks, it scores higher and is faster.
I suppose at least in this case the loss is not an emotional one?
This is one of the most dystopian subreddits:
Why does Google constantly kill off good things?
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.
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.
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.
At least now MiMo v2.5 exists and can be used as another dirt cheap multimodal model.
It was fine to lose 2, but 2.5 will be dearly missed as it hit the sweet spot in terms of cost-performance :/
Qwen 3.6 and Gemma 4 small models are in a league of their own.
How about you stop relying on Google products? You've learned nothing after all these years?
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