201–210 of 616 posts
Earlier quoted context omitted.
You and a few other people, but enough people still appreciate the bit that I'm going to keep doing it. They're easy enough to skip - click the little "-" icon and you'll collapse the entire sub-thread.
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It's scary relying on Google's models. I have a very price sensitive workload that used to run on flash 2.5 lite - it's deprecated now. The replacement 3.1 flash lite is a lot more expensive, but now also has a sunset date. 3.5 flash lite is even more expensive. So the price is rising and you have no choice but to keep paying more and more.
gemini-2.5-flash-lite: $0.10 input / $0.40 output
gemini-3.1-flash-lite: $0.25 input / $1.50 output
gemini-3.5-flash-lite: $0.30 input / $2.50 output (a 6.25x increase over 2.5!)
Now watch them deprecate Gemini 2.5 Flash-Lite in the coming months...
No word about updating Jules, which is still stuck on 3.1 Pro. I get that it's probably niche but I've really appreciated basically being able to give directions to Jules on my phone, then reviewing and merging a GitHub PR fifteen minutes later. It's been great for getting some progress in on a few personal projects during my commute when I can't exactly pull out my laptop. Anyone have any good alternatives?
Deepseek Pro: 0.435/m 0.87/m
That's wildly ambitious pricing by Google. You can maybe get away with spicy pricing at the SOTA edge but at the lower tiers everything is a lot more price sensitive.
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones. Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public. edit: looks like benchmarks are up on…
I wonder if the broad use of AI overviews on Google search results is having an impact. Maybe the numbers make it more profitable to use their compute on several billion searches a day rather than selling API access.
https://huggingface.co/microsoft/bitnet-embedding-0.6b
It’s a small multilingual embedding model designed for things like search, RAG, and semantic similarity. It supports a fairly large context window and is designed to run efficiently on a CPU in a GPU starved world.
The interesting part is that it builds on BitNet, using ternary weights of -1, 0, and 1 instead of the usual floating-point weights. That should make indexing and searching large amounts of text much cheaper without giving up too much accuracy.
It's scary relying on Google's models. I have a very price sensitive workload that used to run on flash 2.5 lite - it's deprecated now. The replacement 3.1 flash lite is a lot more expensive, but now also has a sunset date. 3.5 flash lite is even more expensive. So the price is rising and you have no choice but to keep paying more and more.
You can also just write code like you did a year or two ago.
It is both less intelligent and more expensive than GLM-5.2, while being closed weight.