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ChatGPT Images 2.0

openai.com

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Re: ChatGPT Images 2.0

#411

Post by altcognito »

strangescript wrote: post #409
schneehertz wrote: post #407

Generating a 4096x4096 image with gemini-3.1-flash-image-preview consumes 2,520 tokens, which is equivalent to $0.151 per image. Generating a 3840x2160 image with gpt-image-2 consumes 13,342 tokens, which is equivalent to $0.4 per image. This model is more than twice as expensive as Gemini.

this is apples to oranges, the flash version version a full version this thing is like 5x better than flash at fine grain detail

.40 cents for high quality output is insanely cheap

it is only going to get cheaper

Re: ChatGPT Images 2.0

#412

Post by davebren »

rafael-lua wrote: post #404
davebren wrote: post #381

Earlier quoted context omitted.

The point of a diagram is that you have something in your head to turn into the diagram. There's no point if you can't do it yourself and the image generator is coming up with it for you.

I disagree. Diagrams are a type of visual communication, and not everyone is good at translating things to visual. I open an excalidraw with clear concepts in my head, but nothing comes out of it. I try C4 or flow diagrams, and I spend an excessive amount of time refactoring them to end up mediocre anyway. Not just me, I know MANY developers that are amazing at explaining things but are mind-blocked when drawing simp…

Learn how to draw simple circles and arrows, this is the epitome of learned helplessness.

Re: ChatGPT Images 2.0

#413

Post by eclipticplane »

altcognito wrote: post #411
strangescript wrote: post #409

Earlier quoted context omitted.

this is apples to oranges, the flash version version a full version this thing is like 5x better than flash at fine grain detail

.40 cents for high quality output is insanely cheap it is only going to get cheaper

> .40 cents

Warning: Verizon math ahead.

Re: ChatGPT Images 2.0

#414

Post by lucaslazarus »

runarberg wrote: post #304
bulletsvshumans wrote: post #228

Earlier quoted context omitted.

This is also a stylistic choice that the New Yorker magazine uses for words with double vowels where you pronounce each one separately, like coöperate, reëlect, preëminent, and naïve. So possibly intentional.

That makes sense[1] but it prompts the obvious question: does this style write it as typeö then? 1: Though personally I hate it, I just cannot not read those as completely different vowels (in particular ï → [i:] or the ee in need ; ë → [je:] or the first e here; and ö → [ø] or the e in her )

No. Firstly because it is spelled “typo.” Secondly you typically use the diaeresis to tell the reader to not confuse it with a similarly spelled sound or diphthong. So it tells a reader that “reëlect” is not pronounced REEL-ect, “coöperate” is not COOP-uh-ray-t, and “naïve” is not NAY-v.

Re: ChatGPT Images 2.0

#415

Post by ac29 »

strangescript wrote: post #409
schneehertz wrote: post #407

Generating a 4096x4096 image with gemini-3.1-flash-image-preview consumes 2,520 tokens, which is equivalent to $0.151 per image. Generating a 3840x2160 image with gpt-image-2 consumes 13,342 tokens, which is equivalent to $0.4 per image. This model is more than twice as expensive as Gemini.

this is apples to oranges, the flash version version a full version this thing is like 5x better than flash at fine grain detail

Google's naming might be misleading, currently 3.1 flash image outperforms the available pro version (3.0 pro) on most benchmarks: https://deepmind.google/models/model-cards/gemini-3-1-flash-...

Re: ChatGPT Images 2.0

#416

Post by jetbalsa »

Aurornis wrote: post #47
jackpirate wrote: post #34

Earlier quoted context omitted.

Also, the racoon it circled isn't in the original.

I love how perfectly this captures the difficulties of using generative AI for detection tasks.

Oh god yes, I've been trying to make a LLM Assisted Magic the Gathering card scanner... its been a hell of a time trying to get it to just OCR card names well....

Re: ChatGPT Images 2.0

#417

Post by [deleted] »

strangescript wrote: post #409
schneehertz wrote: post #407

Generating a 4096x4096 image with gemini-3.1-flash-image-preview consumes 2,520 tokens, which is equivalent to $0.151 per image. Generating a 3840x2160 image with gpt-image-2 consumes 13,342 tokens, which is equivalent to $0.4 per image. This model is more than twice as expensive as Gemini.

this is apples to oranges, the flash version version a full version this thing is like 5x better than flash at fine grain detail

[deleted]

Re: ChatGPT Images 2.0

#418

Post by davebren »

Filligree wrote: post #397
davebren wrote: post #52

Earlier quoted context omitted.

The faces...that's nice that it turned a kid's book into an abomination

By image generation standards this is a ridiculously good result. No surprise that people instantly find the new limits, but they are new limits.

It could already copy the art styles from its training data, what is the advancement here?

Re: ChatGPT Images 2.0

#419

Post by dvt »

minimaxir wrote: post #234

So during my Nano Banana Pro experiments I wrote a very fun prompt that tests the ability for these image generation models to follow heuristics, but still requires domain knowledge and/or use of the search tool: Create a 8x8 contiguous grid of the Pokémon whose National Pokédex numbers correspond to the first 64 prime numbers. Include a black border between the subimages. You MUST obey ALL the FOLLOWING rules for th…

This is an amazing test and it's kinda' funny how terrible gpt-2-image is. I'd take "plagiarized" images (e.g. Google search & copy-paste) any day over how awful the OpenAI result is. Doesn't even seem like they have a sanity checker/post-processing "did I follow the instructions correctly?" step, because the digit-style constraint violation should be easily caught. It's also expensive as shit to just get an image that's essentially unusable.

Re: ChatGPT Images 2.0

#420

Post by ToValueFunfetti »

runarberg wrote: post #283
dilDDoS wrote: post #264

Earlier quoted context omitted.

Cheaper/faster tech increases overall consumption though. Without the friction of commissioning a graphics artist to design something, a user can generate thousands of images (and iterate on those images multiple times to achieve what they want), resulting in way more images overall. I'm not really well versed on the environmental cost, more just (neutrally) pointing out that comparing a single 10s image to a 5-6 hou…

Also, ignoring training when talking about the environmental costs is bad faith. Without training this image would not exist, and if nobody generating images like these, the training would not happen. So we should really ask, the 10 seconds it took for inference, plus the weeks or months of high intensity compute it took to train the model.

You'd want to compare against the fraction of training attributable to the image
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