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Gemini 3.5 Flash

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Re: Gemini 3.5 Flash

#631

Post by flaburgan »

sumedh wrote: post #565
gpm wrote: post #240

Earlier quoted context omitted.

The cost at such they could rent out the TPUs, i.e. the market rate, is the inference cost. Just because you are vertically integrated doesn't mean you get to discount the one business units products to the other. Doing so discounts the opportunity cost you pay and is just bad accounting.

> doesn't mean you get to discount the one business units products to the other That depends, if all developers get used to Claude and Codex it will become harder for Google to attract them in the future. They might lose devs in the long term.

That's actually where AI differs: there is no network effect. So no reason for me to stay with a tool if suddenly another one is better or cheaper. Changing the model I use is literally two clicks in Zed. No retention possible for providers.

Re: Gemini 3.5 Flash

#632

Post by WarmWash »

DCKing wrote: post #583
easygenes wrote: post #466

For those who would like to know the total and active parameter count of this model: even though Google doesn't disclose the model technicals, we can infer them within relatively tight margins based on what we do know. We know they serve the model on TPU 8i, which we have plenty of hard specs for (so we know the key constraints: total memory and bandwidth and compute flops). We can also set a ceiling on the compute c…

If two things hold up - 1) this is actually a 2-300B parameter model and 2) this is actually competitive with frontier OpenAI and Anthropic models (and not just benchmaxing), the implications are pretty big. It would mean you could run "frontier level" performance in one box at home. 300B models at least fit in a single maxed out Mac Studio or a small stack of DGX Sparks or AMD Strix Halo boxes. For comparison, DeepS…

Opus is estimated to be around 4T parameters, and 5.5 around 9T. [1] And while 3.5 at least qualifies to be in the same neighborhood, which is stunning if these numbers are all true, it may be that closing that last ~10% difference needs 50x more parameters.

[1]https://arxiv.org/pdf/2604.24827

Re: Gemini 3.5 Flash

#633

Post by [deleted] »

tskj wrote: post #623
opsnooperfax wrote: post #462

Earlier quoted context omitted.

I think the big 3 are cartelizing and starting to ratchet up costs. GPT5.5 is not easily distinguishable from 5.1. I would it be shocked if we hit the ceiling and everyone is quietly positioning for the exit.

I don't understand why everyone thinks there is a ceiling below human-level intelligence, when we have an existence proof that human-level intelligence is possible.

[deleted]

Re: Gemini 3.5 Flash

#634

Post by irthomasthomas »

tskj wrote: post #622
derefr wrote: post #230

Earlier quoted context omitted.

When you say "improve an svg like this", how are you imagining setting that workflow up? Are you just feeding them the SVG to iterate on; or are you giving them access to a browser to look at the rendering of the SVG? I ask because: Insofar as the original pelican test is zero-shot, it effectively serves as a way to test for the presence of a kind of "visual imagination" component within the layers of the model, that…

This is also my gripe with a lot of this stuff, always evaluating models on what they can literally oneshot is completely pointless; it's not how anything works, neither for humans nor for scaffolded AIs. I guess it's neat if you want to argue that a certain level of intelligence can "never be achieved" in a single forward pass, but like, so what. No one cares about that, except people who have already decided to be…

Asking a model to improve its output is not one-shotting tho? My observation was that asking an llm to iterate and improve a response causes it to add more stuff, rather tha repair the broken stuff. And that model progress in general has the same pattern. This new model adds more details to its responses but continues to make mistakes at about the same rate.

Re: Gemini 3.5 Flash

#635

Post by marknutter »

lmazgon wrote: post #567

Click on "Listen to article", make sure the voice is "Umbriel" and skip to 4:15 - there's a hallucinated part at the end in Russian (I think). On a blog post about the latest and greatest AI model. Oh the irony.

Looks like they removed the option to "listen to article". I wonder why.

Re: Gemini 3.5 Flash

#636

Post by ahknight »

ashirviskas wrote: post #576
__jl__ wrote: post #263

Earlier quoted context omitted.

This understates the cost increase. 3.5 Flash also uses more tokens. artificialanalysis.ai shows these difference to run the whole eval, which I think is more realistic pricing: Gemini 2.5 flash (27 score): $172 (1.0x) Gemini 2.5 pro (35 score): $649 (3.8x) Gemini 3.0 Flash (46 score): $278 (1.6x) Gemini 3.5 Flash (55 score): $1,552 (9.0x or 2.4x compared to 2.5 pro) This is a massive price increase... 5.6x compared…

Gemini 2.0 Flash: $19

... and you get what you pay for. Or less.

Re: Gemini 3.5 Flash

#637

Post by verdverm »

DCKing wrote: post #583
easygenes wrote: post #466

For those who would like to know the total and active parameter count of this model: even though Google doesn't disclose the model technicals, we can infer them within relatively tight margins based on what we do know. We know they serve the model on TPU 8i, which we have plenty of hard specs for (so we know the key constraints: total memory and bandwidth and compute flops). We can also set a ceiling on the compute c…

If two things hold up - 1) this is actually a 2-300B parameter model and 2) this is actually competitive with frontier OpenAI and Anthropic models (and not just benchmaxing), the implications are pretty big. It would mean you could run "frontier level" performance in one box at home. 300B models at least fit in a single maxed out Mac Studio or a small stack of DGX Sparks or AMD Strix Halo boxes. For comparison, DeepS…

Since I started using Qwen-3.6 35B A3B, I believe frontier like capability will be more than enough in these smaller models within a year or two, at least for coding. They don't need to memorize facts into their weights, which likely has very interesting implications that I'm not going speculatively decode

Re: Gemini 3.5 Flash

#638

Post by ahknight »

__jl__ wrote: post #263
GodelNumbering wrote: post #84

Per million input/output tokens: Gemini 2.5 flash: $0.30/$2.50 Gemini 3.0 flash preview: $0.50/$3.00 Gemini 3.5 flash: $1.50/$9.00 Interesting pricing direction. I don't think we have ever seen a 3x price increase for in the immediate next same-sized model (and lol @ 3 only ever getting a preview). 3.5 flash costs similar to Gemini 2.5 pro which was $1.25/$10

This understates the cost increase. 3.5 Flash also uses more tokens. artificialanalysis.ai shows these difference to run the whole eval, which I think is more realistic pricing: Gemini 2.5 flash (27 score): $172 (1.0x) Gemini 2.5 pro (35 score): $649 (3.8x) Gemini 3.0 Flash (46 score): $278 (1.6x) Gemini 3.5 Flash (55 score): $1,552 (9.0x or 2.4x compared to 2.5 pro) This is a massive price increase... 5.6x compared…

Sonnet-level performance at Haiku prices. They know what they have and who the audience is they want.

Re: Gemini 3.5 Flash

#639

Post by xivzgrev »

anyone else see a degradation in performance? it seems like the responses are more generic, especially when asking it to look at google drive files

Re: Gemini 3.5 Flash

#640

Post by verdverm »

mirzap wrote: post #521

The Flash model costs more than the Frontier models. Didn't see that coming.

On a per-token, it's cheaper than Opus, GPT, and Gemini Pro; and while I hear the "it uses more tokens so its more expensive", this discounts a few things (1) improvements over time (2) finding the right way to prompt it (3) finding proper places to use this model.
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