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FWIW Gemini 2.5 Flash Lite is still very good; I used it in my latest side project to generate entire web sites and it outputs great content and markup every single time.
they are doing the we work approach, gain customers at all costs even if that means losing money.
Aren't all LLMs loosing money at this point?
They likely lose money when you take into account the capital cost of training the model itself, but that cost is at least fixed: once it's trained you can serve traffic from it for as long as you chose to keep the model running in production.
they are doing the we work approach, gain customers at all costs even if that means losing money.
Aren't all LLMs loosing money at this point?
"In a move that at first went unnoticed, Google significantly increased the price of its popular Gemini 2.5 Flash model" It's not quite that simple. Gemini 2.5 Flash previously had two prices, depending on if you enabled "thinking" mode or not. The new 2.5 Flash has just a single price, which is a lot more if you were using the non-thinking mode and may be slightly less for thinking mode. Another way to think about t…
Both great points, but more or less speak to the same root cause - customer usage patterns are becoming more of a driver for pricing than underlying technology improvements. If so, we likely have hit a "soft" floor for now on pricing. Do you not see it this way?
That 80% drop in o3 was only a few weeks ago!
Earlier quoted context omitted.
Both great points, but more or less speak to the same root cause - customer usage patterns are becoming more of a driver for pricing than underlying technology improvements. If so, we likely have hit a "soft" floor for now on pricing. Do you not see it this way?
Even given how much prices have decreased over the past 3 years I think there's still room for them to keep going down. I expect there remain a whole lot of optimizations that have not yet been discovered, in both software and hardware. That 80% drop in o3 was only a few weeks ago!
"In a move that at first went unnoticed, Google significantly increased the price of its popular Gemini 2.5 Flash model" It's not quite that simple. Gemini 2.5 Flash previously had two prices, depending on if you enabled "thinking" mode or not. The new 2.5 Flash has just a single price, which is a lot more if you were using the non-thinking mode and may be slightly less for thinking mode. Another way to think about t…
Both great points, but more or less speak to the same root cause - customer usage patterns are becoming more of a driver for pricing than underlying technology improvements. If so, we likely have hit a "soft" floor for now on pricing. Do you not see it this way?
ChatGPT is simply what Google should've been 5-7 years ago, but Google was more interested in presenting me with ads to click on instead of helping me find what I was looking for. ChatGPT is at least 50% of my searches now. And they're losing revenue because of that.
I think the big thing that really surprised me. Llama 4 maverick is 16x 17b. So 67GB of size. The equivalency is 400billion. Llama 4 behemoth is 128x 17b. 245gb size. The equivalency is 2 trillion. I dont have the resources to be able to test these unfortunately; but they are claiming behemoth is superior to the best SAAS options via internal benchmarking. Comparatively Deepseek r1 671B is 404gb in size; with pretty…
I think the best of both worlds is a sufficiently capable reasoning model with access to external tools and data that can perform CPU-based lookups for information that it doesn't possess.
> If you’re building batch tasks with LLMs and are looking to navigate this new cost landscape, feel free to reach out to see how Sutro can help. I don't have any reason to doubt the reasoning this article is doing or the conclusions it reaches, but it's important to recognize that this article is part of a sales pitch.
Context size is the real killer when you look at running open source alternatives on your own hardware. Has anything even come close to the 100k+ range yet?