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Gemini 3.5 Flash

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Re: Gemini 3.5 Flash

#241

Post by verdverm »

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

At the same time, it is supposedly Gemini 3.1 Pro level at 3/4 the price

and far cheaper than comparable models, Gemini Pro is cheaper than Claude Sonnet (Anthropic still gets to charge a brand premium)

Re: Gemini 3.5 Flash

#243

Post by ls612 »

ai_fry_ur_brain wrote: post #220
ls612 wrote: post #205

Earlier quoted context omitted.

Anyone can host Deepseek V4 on rented GPUs and sell inference on it. Price will very quickly converge to the marginal cost of inference. This is as close to a pure commodity as it gets in the AI space so competitive market economics will put in work. Same is true for any open-weights model.

You dont understand the costs involved to run inference at scale Please go run some numbers.The hardware needed to Run Deepseek v4 flash at 20 tps for a single session is nowhere close to what is required to run it at 50tps for 5,000 concurrent sessions. Imagine what it takes to be profitible when running at 150 tps for 30cents per 1mm. You make less than 1k per month and the hardware required to run that cost 10k a…

Yes it is more efficient in $/tok to run at scale than to run just for yourself. Everyone selling Deepseek V4 inference is selling an undifferentiated good. They have run the numbers on how much it costs and are competing against a dozen other outfits also selling undifferentiated open weights tokens. Whatever the dollar cost they face to rent those GPUs will be what they are able to charge in the competitive market. That is great for you and me because we can buy tokens at pretty much exactly what it costs to produce them.

Re: Gemini 3.5 Flash

#244

Post by goatlover »

OhMeadhbh wrote: post #177

Am I really so old that when someone says "Flash" my immediate response is... "consider HTML5 instead" ??

The Flash designer was really nice. One thing the web kind of set back was all the RAD tools from the 90s and 2000s.

Re: Gemini 3.5 Flash

#245

Post by throwa356262 »

aesthesia wrote: post #127
throwa356262 wrote: post #86

Earlier quoted context omitted.

According to people who have access to Mythos, it is slightly worse than GPT-5.5-xhigh. At least for security tasks. Hold on, I think this claim needs some hard data. Here you go gentlemen: https://www.aisi.gov.uk/blog/our-evaluation-of-openais-gpt-5...

See the later post testing a newer Mythos checkpoint, though: https://www.aisi.gov.uk/blog/how-fast-is-autonomous-ai-cyber...

Fair enough

Re: Gemini 3.5 Flash

#247

Post by irthomasthomas »

derefr wrote: post #230
irthomasthomas wrote: post #161

Earlier quoted context omitted.

This is a perfect illustration of something I noticed with llm progress. Ask them to improve an svg like this, and it never fixes the missing crossbar or disconnected limbs, it just adds more stuff. In this example they have obviously improved greatly, and it contains a ridiculous amount of detail, but they still to get the basic shape of the frame wrong. It's weird. And the pattern shows up everywhere, try it with a…

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…

I'm talking about two type of improvement, model improving, and prompt based improving. I am noticing that the baseline output has a lot more going on, the model has improved, yet it still makes those obvious looking mistakes with the shape of the frame or disconnected limbs etc.

And I am saying that if you take one of these SVGs and ask an LLM to look for flaws, it rarely spots those obvious flaws and instead suggests adding a sunset and fish in the birds mouth.

Re: Gemini 3.5 Flash

#248

Post by pcwelder »

owentbrown wrote: post #228

Has anyone switched from Claude 4.7 Opus or ChatGPT 5.5 to this? How does it feel? Dumber? Worth it for the speed? I'd love someone's subjective take on it, after doing a long session of coding. Reiner Pope gave a talk on Dwarkesh Patel about token economics. I guess faster is a lot more expensive, generally. Someone should make a harness that uses a fast model to keep you in-flow and speed run, and then uses a slow,…

Opus is not the correct tier to compare this flash model with.

On my tasks it has not been as good as even Sonnet 4.6 so far.

Instruction following over long context feels worse.

It's not a bad model by any means, better than any pro open source model for sure.

Re: Gemini 3.5 Flash

#250

Post by SwellJoe »

hei-lima wrote: post #138
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

We need another "Deepseek moment" or else it will become impossible for the regular dude to use AI. It will become something that only big companies can afford.

We're having DeepSeek moments every couple of weeks.

Qwen 3.6 hit hard in the self-hosting space. It's incredibly capable for its size, really shaking up what's possible in 64GB or even 32GB of VRAM.

The Prism Bonsai ternary model crams a tremendous amount of capability into 1.75GB.

And, DeepSeek V4 is crazy good for the price. They're charging flash model prices for their top-tier Pro model, which is competitive with the frontier of a few months ago.

The winners in the AI war will be the companies that figure out how to run them efficiently, not the ones that eke out a couple percent better performance on a benchmark while spending ten times as much on inference (though the capability has to be there, I think we're seeing that capability alone isn't a strong moat...there's enough competent competition to insure there's always at least a few options even at the very frontier of capability).

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