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Pricing per million input/output tokens: 2.5 Flash: $0.3 / $2.5 3.0 Flash: $0.5 / $3 3.5 Flash: $1.5 / $9 3.6 Flash: $1.5 / $7.5 --- 2.5 Flash-Lite: $0.1 / $0.4 3.1 Flash-Lite: $0.25 / $1.5 3.5 Flash-Lite: $0.3 / $2.5
Am I off, or does Google have the pricing that varies the most between model generation releases?
I keep saying this and people dont believe me, but I have b2b saas systems with actual agents running around the clock, and the performance/stability of the flash model is higher than most other models. Meaning, its predictable with tool calls, wont spin off a million tools/do weird behavior, its reasonable. Even sonnet in a real world decision making scenario is not reliable, or will reason so long its incredibly ex…
I wonder if there is something with their TPU cycles that makes them want to postpone training a new model. My guess is that they have been on the same base model for 6 months and they may have waited for the next gen TPUs to train Gemini 4, which greatly limits how much intelligence they can increase and forces them to do cost efficiency increases.
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…
It's a bit disheartening to see no comparison to other models here - and I'm not sure this pushes the curve anywhere. 3.6 flash is more expensive than GLM 5.2 - but seemingly worse, although this post is really light (lite?) on details. It seemed for a time that Google had finally gotten the ball rolling, but I'm doubting that more and more as time passes. We'll see what happens with 3.5 pro I suppose.
> really light (lite?) on Light. Lite is product marketing seepage.
I use 3.1 Flash Lite regularly to classify listings on eCommerce websites. It's great for this task - fast, cheap and accurate.
In fact, it was the single best model we tried in terms of the speed vs accuracy vs price tradeoffs - including the Chinese models.
Of course, 3.5 Flash was more accurate but the 5x cost increase couldn't be justified.
3.5 Flash Lite sounds like it could be a strict upgrade for our use case, without a significant increase in costs or drop in speed.
It's not GPT-6 but it's not trying to be. It's a completely different tool and great at what it does.
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.
Pricing per million input/output tokens: 2.5 Flash: $0.3 / $2.5 3.0 Flash: $0.5 / $3 3.5 Flash: $1.5 / $9 3.6 Flash: $1.5 / $7.5 --- 2.5 Flash-Lite: $0.1 / $0.4 3.1 Flash-Lite: $0.25 / $1.5 3.5 Flash-Lite: $0.3 / $2.5
As is, they are thoroughly outclassed for most usecases. I will say the one area where i do see Gemini punching above its weight class is in tasks that are effectively "Google this for me" / knowledge stuff. So it does have a role, and I do use it. So while I think Google is still in a strong position overall, they are really stuck as a tier 2 AI player right now with text models. They are tier 1 in bio, images, and video.