Turns out AI alignment just means "align to the customer current subscription plan", and not protecting the world. Classic.

What's a good use case for a defense contractor to generate AI images besides to include in presentations?

That tier is also available for text generation, not just images.

It's "tier 5", I've had an account since the 3.0 days so I can't be positive I'm not grandfathered in, but, my understanding is as long as you have a non-trivial amount of spend for a few months you'll have that access.

(fwiw for anyone curious how to implement it, it's the 'moderation' parameter in the JSON request you'll send, I missed it for a few hours because it wasn't in Dalle-3)

Do you work with OpenAI models via FedRAMP GGC High Azure? If so I would love to hear more about your experience.

> 4 different defense contractors in the last day

Now I'm just wondering what the hell defense contractors need image generation for that isn't obviously horrifying...

This is on purpose so OpenAI can then litigate against them. This API isn't about a new feature, it's about control. OpenAI is the biggest bully in the space of generative AI and their disinformation and intimidation tactics are working.

Crazy even photos have the OpenAI yellow color grade

> the same prompt for each of the quality types. ‘Auto’, ‘low’, ‘medium’, ‘high’.

“Auto” is just whatever the best quality is for a model. So in this case it’s the same as “high”.

> a cute dog hugs a cute cat

This prompt is best served by Midjourney, Flux, Stable Diffusion. It'll be far cheaper, and chances are it'll also look a lot better.

The place where gpt-image-1 shines if if you want to do a prompt like:

"a cute dog hugs a cute cat, they're both standing on top of an algebra equation (y=\(2x^{2}-3x-2\)). Use the first reference image I uploaded as a source for the style of the dog. Same breed, same markings. The cat can contrast in fur color. Use the second reference image I uploaded as a guide for the background, but change the lighting to sunset. Also, solve the equation for x."

gpt-image-1 doesn't make the best images, and it isn't cheap, and it isn't fast, but it's incredibly -- almost insanely -- powerful. It feels like ComfyUI got packed up into an LLM and provided as a natural language service.

Not bad. Photo forums will be soon full of them. Slightly edited to remove metadata and make them look like human made.

> but too expensive to be used in a customer facing product.

Enhance headshots for putting on Linkedin.

> where people need to generate hundreds or thousands of these images

Anyone using image gen for real work not just for fun.

Although you're way better off finding your own workflows with local models at that scale.

As usual for AI startups nowadays, using this API you can create a downstream wrapper for image generation with bespoke prompts.

A pro/con of the multimodal image generation approach (with an actually good text encoder) is that it rewards intense prompt engineering moreso than others, and if there is a use case that can generate more than $0.17/image in revenue, that's positive marginal profit.

An obvious one is for video games, interactive fiction, that sort of thing. AI dungeon with visuals could be pretty interesting.

I use the api because i don’t use chatgpt enough to justify the cost of their UI offering.

AI-assisted education is promising.

Interior design, fashion, and advertising all come to mind

Imagine an AI recipe building app that helps you create a recipe with certain ingredients, then generates an image of what the final product might look like.

Imagine a news feed that never ends full of AI slop to sell ads on

ChatGPT's prompt adherence is light years ahead of all the others. I won't even call Flux/Midjoueny its competitors. ChatGPT image gen is practically a one-of-its-kind unique product on the market: the only usable AI image editor for people without image editing experience.

I think in terms of image generation, ChatGPT is the biggest leap since Stable Diffusion's release. LoRA/ControlNet/Flux are forgettable in comparison.

So, I've long dreamed of building an AI-powered https://iconfinder.com.

I started Accomplice v1 back in 2021 with this goal in mind and raised some VC money but it was too early.

Now, with these latest imagen-3.0-generate-002 (Gemini) and gpt-image-1 (OpenAI) models – especially this API release from OpenAI – I've been able to resurrect Accomplice as a little side project.

Accomplice v2 (https://accomplice.ai) is just getting started back up again – I honestly decided to rebuild it only a couple weeks ago in preparation for today once I saw ChatGPT's new image model – but so far 1,000s of free to download PNGs (and any SVGs that have already been vectorized are free too (costs a credit to vectorize)).

I generate new icons every few minutes from a huge list of "useful icons" I've built. Will be 100% pay-as-you-go. And for a credit, paid users can vectorize any PNGs they like, tweak them using AI, upload their own images to vectorize and download, or create their own icons (with my prompt injections baked in to get you good icon results)

Do multi-modal models make something like this obsolete? I honestly am not sure. In my experience with Accomplice v1, a lot of users didn't know what to do with a blank textarea, so the thinking here is there's value in doing some of the work for them upfront with a large searchable archive. Would love to hear others' thoughts.

But I'm having fun again either way.

It seems to me like this is a new hybrid product for -vibe coders- beacuse otherwise the -wrapping- of prompting/improving a prompt with an LLM before hitting the text2image model can certainly be done as you say cheaper if you just run it yourself.

maybe OpenAI thinks model business is over and they need to start sherlocking all the way from the top to final apps (Thus their interest on buying out cursor, finally ending up with windsurf)

Idk this feels like a new offering between a full raw API and a final product where you abstract some of it for a few cents, and they're basically bundling their SOTA llm models with their image models for extra margin

I find prompting the model substantially easier than traditional models, is it really more difficult or are you just used to traditional models?

I suspect what I'll do with the API is iterate at medium quality and then generate a high quality image when I'm done.

> Prompting the model is also substantially more different and difficult than traditional models

Can you elaborate? This was not my experience - retesting the prompts that I used for my GenAI image shootout against gpt-image-1 API proved largely similar.

https://genai-showdown.specr.net

It may lose against other models on prompt-to-image, but I'd be very excited to see another model that's as good at this one as image+prompt-to-image. Editing photos with ChatGPT over the past few weeks has been SO much fun.

Here's my dog in a pelican costume: https://bsky.app/profile/simonwillison.net/post/3lneuquczzs2...

Similarly to how 90% of my LLM needs are met by Mistral 3.1, there's no reason to use 4o for most t2i or i2i, however there's a definite set of tasks that are not possible with diffusion models, or if they are they require a giant ball of node spaghetti in comfyui to achieve. The price is high but the likelyhood of getting the right answer on the first try is absolutely worth the cost imo.

pretty amazing that in ~two years a 15 second latency AI image generation API that cost 4 cents lags behind competitors.

> A generated `medium` 1024x1024 is $0.04/image

It's actually more than that. It's about 16.7 cents per image.

$0.04/image is the pricing for DALL-E 3.

It's far and away the most powerful image model right now. $0.04/image is a decent price!

Huh? For me the quality of the API seems to be identical to what I'm getting in ChatGPT.

It's a mix of both it feels to me as I've been testing it. For example, you can't get it to make a clock showing custom time like 3:30, or someone writing with their left hand.. And it can't do follow many instructions or do them very precisely. But it shows that this kind of architecture will be be capable of that if scaled up most likely.

Source? It's much more likely that the LLM generates the latent vector which serves as an input to the diffusion model.

Isn't there also a cost per image? The pricing page shows $0.25 for a high quality 1536x1024 image. 25 cents per image is ... steep lol

Recraft also has an svg model: https://replicate.com/recraft-ai/recraft-v3-svg

One note with these is most of the production ones are actually diffusion models that get ran through an image->svg model after. The issue with this is that the layers aren't set up semantically like you'd expect if you were crafting these by hand, or if you were directly generating svgs. The results work, but they aren't perfect.

I was impressed with recraft.ai for SVGs - https://simonwillison.net/2024/Nov/15/recraft-v3/ - though as far as I can tell they generate raster images and then SVG-ize them before returning the result.

SVGFusion https://arxiv.org/abs/2412.10437 which is a new paper from SVGRender group https://huggingface.co/SVGRender

OmniSVG https://arxiv.org/abs/2504.06263v1

Try neoSVG or Recraft, it is awesome!

"Image from a reference" is a bit of a rabbit hole. For traditional image generation models, in order for it to learn a reference, you have to fine-tune it (LoRA) and/or use a conditioning model to constrain the output (InstantID/ControlNet)

The interesting part of this GPT-4o API is that it doesn't need to learn them. But given the cost of `high` quality image generation, it's much cheaper to train a LoRA for Flux 1.1 Pro and generate from that.

maybe it's a kind of watermark?

Not being glib, but this is like the famous comment when dropbox was first announced: "you can already build such a system yourself quite trivially by getting an FTP account, mounting it locally with curlftpfs, and then using SVN or CVS on the mounted filesystem". [1]

You might say, "but chatGPT is already as dead simple an interface as you can imagine". And the answer to that is, for specific tasks, no general interface is ever specific enough. So imagine you want to use this to create "headshots" or "linkedin bio photos" from random pictures of yourself. A bespoke interface, with options you haven't even considered already thought through for you, and some quality control/revisions baked into the process, is something someone might pay for.

[1] https://news.ycombinator.com/item?id=9224

different personas require different UXs. not everyone is going to understand and enjoy the chat interface; many will require a different UX.

Seems like exactly one of their examples, or am I missing something? "Create a new image using image references" https://platform.openai.com/docs/guides/image-generation#cre...

I think this is technically "image variations" and I think image variations are still only dall-e 3 for now (best I could tell earlier today from the API)

just based on how long it takes to produce these images, and how much text responses cost, I wouldn't be surprised at all if it was close to cost

Likely because they've seen a lot of the potential abuse capabilities. i.e. the "generate a drivers license with this face".

So the options are: 1) nerf the model so it can't produce images like that, or 2) use some type of KYC verification.

It says "Organization verification" but I got my personal account (with Personal as organization) verified with just a passport.

I'm embarassed that you find revolutionary tech uninteresting.

This news is relevant for developers though.

This is the new model that's available in ChatGPT, which most notably can do transfer generation. i.e. "take this image and restyle it to look like X". Or "take this sneaker and give me a billboard ad for it"

This is their presumably auto regressive image model. It has outstanding prompt adherence and great detail in addition to strong style transfer abilities.

The new image generation model is miles ahead of DALL-E 3, especially when generating text.

Basically they are charging for the ability to make accurate text generation.