Images 2.6

From what I can tell after some preliminary testing, you have to pass an additional large language model to the OpenAI API when generating SVGs.

I couldn't find this requirement in the documentation, but if you don't pass one in, you get this error:

  400 {"message": "SVG generation requires 'svg.model'.",
     "param": "svg.model", "code": "missing_required_parameter"}

I tried it with Sunburst with both gpt-5.5 and gpt-5.6-sol, and the results were nothing spectacular. It’s something you could already do in the ChatGPT interface by asking it to convert an existing image to an equivalent SVG.

https://imgpb.com/bkUrc

Recraft is probably the only group I know of that had a native text-to-SVG generator pretty early on, but since it’s a closed model, I’m not familiar with what they were doing under the hood technically.

https://fal.ai/models/fal-ai/recraft/v4/pro/text-to-vector

Re SVG: and it would likely strip any SynthID watermarks.

It could be viable to encode SynthID-like variables in subtle variations of positioning of elements, path lengths, etc. I think I saw an article on here a couple weeks ago which used such techniques to hide 'messages' in SVGs. It might even be possible to do it in a way which survives basic manipulations, as with rasters.

> It's possible OpenAI has pipelined a raster-to-vector tool that does the conversion automatically. (I developed a similar pipeline and it works extremely well; I'll open source it at some point)

If I could somehow subscribe to ensure I see this when you do, I would.

Please do so and share on HN!

Image 2.6 costs more tokens for generating low and medium quality images, so it ends up being a bit more expensive. Could refer to https://developers.openai.com/api/docs/guides/image-generati...

Yeh using sprites to make frames consistent (kinda tilting latent space towards a character consistency, zero-shot ~LoRA hack?) is old-school. I did this 3 years ago. https://j11y.io/dance/

There have been a lot of strides in open-weight models, well beyond SDXL. Flux, back in August of 2024 was a game changer, finally allowing us to move past SDXL’s nuclear pile of comma-delimited keywords for prompting and allowing more natural language.

And this summer we got Ideogram V4 and Krea2, both of which are highly capable. You can see how the open/closed models have advanced since SDXL in the Progress tab.

https://genai-showdown.specr.net

MJ is pretty out-of-date by this point though it can still be fun from an image space exploration perspective. It scored a rather pitiful 2 out of 15 on my bench back in the summer of 2025.

Progress peaked in 2022?

Otherwise you’re implying the quality plateaued 4 years ago which is ludicrous.

> Feels like ai image generation has plateaud. > Coding related have had massive step-up improvements over time. > The same cannot be said for image generation. If anything, I swear it's gone backwards.

I would argue both are exactly the same. The images are getting nicer, the AI can alter pictures better. It's the same as coding.

The difference is our perception. Looking at image takes 2s, and we can spot what we don't like. Looking at a code change takes hours to understand. It's very difficult to tell how much code generation is improving because almost nobody is reading it.

Image and code generation don't correlate exactly, but I would be surprised to see a big jump of capabilities in either without the other quickly following.

I disagree entirely. Imagine generation in the last 6-months has been an entirely different animal. Generating large artistic concepts has plateaued for sure, but nitty gritty image work is massively better in 2026.

Your feeling is generally right. There have been no seminal moments in image generation anymore in the last couple of months. Most attention from the big labs has gone to video since that‘s where the money is. We have a page to track the history of it all here https://lumenfall.ai/arena/history

(Right now missing some models which I‘ll add today.)

You could argue that progress has been relatively flat since Nano Banana Pro, but that's still less than a full year old (just barely), and that was the first model that could really fully maintain visual consistency across complex edits. The GPT ones are a little stylistically disappointing in ways, but they've still been making steady progress too. I don't think we can claim a peak just yet, even if it's been slower.

I think with software engineering the biggest jumps have been agentic platforms or work flows. You can create a ticket and this triggers na agent to read the relevant docs and submit an MR, another to validate multiple concerns (correctness, security, performance, costs), another to uodate docs, etc. That's far more impressive than "dev sits down and asks LLM to write code".

With images, I don't see such potential. A builder and a reviewer iterating on improvements? Maybe the reviewer noticing it just needs programmable improvements, eg contrast, color balance etc and triggering a script on top of the LLM generation?

There’s less money to be made with images, than code gen.

Investment in new models follows the money.

> Feels like ai image generation has plateaud.

I'd bet the future is in combined AI-driven 3D modelling + rendering + AI post-processing. This pipeline solves quite a bit of typical image generation problems.

Try running Krea 2 locally (I'm using ComfyUI). If you think stable diffusion peaked in 2022, I think you may be a bit surprised at what it's capable of. It leaves modern competitors like Flux, Moss, etc in the dust.

Krea has kept improving editing

Seedream 4 continues to be the best model by far at character consistency. I havnt done my tests on this model yet, however.

Coding flattened out like 9 months ago. In reality, people are getting dumber and lazier from over using them, so they feel like they're improving when they aren't really, outside some areas like RE.

In reality RE has only gotten better because they RLd it into the models because they NEED data, so now every kid is decomping proprietary softwate and sending them back great training data.

"It takes 90% of the time to do 90% of the work. The other 10% takes the other 90% of the time"

Turns out you can't brute force art, to the surprise of no one except tech bros.

Visual creativity requires experimentation - trying things that shouldn't work, and teasing out some measure of emotional or intellectual interest. Systems that are increasingly only capable of outputting marketing visuals are caught in a blandness spiral. They're just going to get worse, so long as they filter out all the weird things artists might actually want to generate (six-fingered hands, off-center subjects, nude bodies). Imagine training an artist solely on magazine ads, or only allowing them to produce that kind of work. No figure drawing, no sketching from life where a pen slip produces a unique texture that becomes a signature element.

I say this as someone who's generating smut-adjacent, art-adjacent images almost every day. It's amazing what image models can produce, but (as with my traditional skills) they'll always be held back by a refusal to embrace holistic study of the world. Sometimes you gotta draw a lamp or weird sculptures or a vacuum cleaner or a dick. Sometimes you have to paint an empty sky or a nipple and do your damnedest to find an epic novel in them.

The splotchiness on that simple chess animation makes this GIF feature a nonstarter.

I’d say the main thing I miss (in the proprietary image gen world) is access to to `gen_id` in DALL-E 3, which functioned almost like the seed value you’d use in ComfyUI workflows.

It was fun to be able to use it to make small iterations on an existing image.

Do you mean the transparency mask? They still support that on all endpoints, although not exposed in the Codex native tool.

Similar results for my workflow. And the Round art comes out significantly less round without prompt changes (more input tokens)

  Setting (1024x1024)   Tokens   Cost/image   Time
  2.5 medium              439     $0.014     12.0 s
  2.6 low                 824     $0.025      6.7 s
  2.6 medium            1,449     $0.044      7.9 s
  2.6 high              1,758     $0.053      8.9 s

same in my testing; super deceptive marketing here. it’s 3-4x the true cost of 2.5.

Output image tokens per image (2.5 Flare → 2.6 Flare / Sunburst)

low, 1024x1024: 196 → 824 (4.2x) medium, 1024x1024: 439 → 1,449 (3.3x) low, 1024x1536: 158 → 664 (4.2x) medium, 1024x1536: 343 → 1,132 (3.3x)

Same $30/1M output tokens on all three, so per image: low: $0.006 → $0.025 medium: $0.013 → $0.043

Same results here, seems like a hidden price increase, hopefully they will still keep 2.5 running for some time.

from 800 to 1500 is barely 2.

from 200 to 450 is just a little over 2.

Nothing near "quadrupling"

I would caution against using the webapp to test the quality of this new model because OpenAI does not indicate which image model and effort is being used to generate the image, to my continued frustration.

That said, I gave it one of my more difficult image prompts and the model now surprisingly passes (without plagiaristic cheating): https://chatgpt.com/share/6aebcbcd-6b8c-83e8-b52b-dbedf20ce4...

I think that particular one would be a prompting problem. Instagrammers and anime artists do left-right two-tone dyes all the time, so left-right two-tone rather than meshed gray blonde would be a reasonable interpretation of "half blond and grey hair" based on representations on Internet collected data.

It precisely matches your prompt, more accurately I think than your expectation which it meets if described accurately:

https://chatgpt.com/share/6aec2ba7-6edc-83ea-9a48-b41b53874a...

That prompt seems almost deliberately obtuse. The natural phrasing would be “greying blonde hair”. If you described someone as having half-X, half-Y hair, I’d expect to see something like that image, too.

Update: based on info from design arena, it looks like gpt-image-2.6 was post-trained to output less text: https://x.com/DesignArena/status/2118124381155303918

This may be why text elements in particular appear more "zoomed in", and why it's providing less detail for UI design.

I preferred 2.6 in all examples supplied!

Whatever site you are hosting images seems to trap my mobile browser and require 3-4 back buttons to get out of.

tweezers not holding the gear, and THOSE FINGERS, yick. What else?

I wonder if there's some weird accounting going on, or if this is real. It would mean around 24M images an hour and that's in the area of "more images than anyone will see". It means there's lots of wasted automated effort in this process somewhere, even if we assume some creators will request 8x variants of their ideas.

As a 20$ subscription user, I get unlimited imagegen on chat. I could go up to 150 a day.

How depressing.

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It has to be Google, since OpenAI always compares to other companies when it feels its winning at some benchmarks. They didn't even compare on cost, speed, or any benchmarks at all.

Depends on the use. For pics, nano banana still feels like the best. For designs/posters, gpt-image is king by a very large margin.

Generally depends on use case and style. Midjourney, Seedream, GPT Image are commonly regarded contenders IMO.

I've been running a comparative image benchmark site for years now (see my profile for the link), ever since SDXL back in 2023, and the answer really comes down to it depends. My benchmark focuses primarily on prompt adherence, but that's just one metric you can use. (fidelity, censorship, explorative, speed, features, etc are others)

In terms of overall state of the art, the two major contenders are probably Nano Banana Pro (Google) and the GPT Image 2 series (OpenAI).

For pure prompt adherency, OpenAI has consistently lead the pack since the original gpt-image-1 released back in March of 2025.... but its OpenAI's fidelity has always left something to be desired. Version 2 of the series has definitely closed the gap a bit.

But then Meta/Apple/Google gets your behavioral surplus for selling Ads instead of OpenAI. Do you want higher subscription costs?

Generative AI is created by human worth.

It's not a stupid question. It is multi-modal but it's about efficiency. A model that is trained purely on generating images is smaller and faster and has its own set of optimisation techniques, compared to a "generalist" multi modal LLM.

Same applies for speech models.

GPT-6 has multimodal input, not output. It can call GPT images by itself so it's seamless, but it still isn't generating the images.

I don't personally see the issue. It's a highly stylised image so there's not any reflections or anything that immediately jump out to me as obviously wrong

Yeah. I think that the pelican SVG benchmark is actually a much better reflection of the state of AI art than the visual quality of the images.

Based on current trends I would not be at all surprised if LLMs working with tools, SVGs and looking at their own work get there before diffusion models. I've stopped keeping up with the image models so maybe I'm underestimating the progress but I dropped out because they were so frustratingly bad at composition and integrating details together.

That whale image makes me think that the situation hasn't improved as much as I'd expected. It is a really cool idea and a pretty picture, but the whale looks a lot like it is flying over the top of some sort of huge glass dome on the ground.

There's a moment in Cory Doctorow's The Reverse Centaur's Guide To Life After AI where he quotes Mark Fisher's definition of eeriness (from The Weird and the Eerie) as "when there is something present where there should be nothing, or is [sic] there is nothing present when there should be something." It's a great description of why so much AI art in particular just feels wrong, even if you can't put your finger on it.

These type of AI can't make art, art is about the human intention, the way the artist looks at the world, the experiences they have had, the feelings they want the viewer to have and how that viewer's perception can be affected by the techniques the artist developed.

These type of AI imitate existing art but have no intention or experience behind the image which is why they feel like slop because they are soulless.

We've gone quickly from "I can't believe AI can generate photo realistic images like that" to "Looking at this image makes me grumpy because it doesn't feature ray tracing accuracy of the light sources".

Open an issue of something like Communication Arts magazine from 30 years ago and you'll see countless examples of stock photographs composited with Photoshop to get fake glowing light effects that are not ray traced perfect.

Some fuddy duddy was probably complaining back then that "it's not art."

You must not be a fan of Rembrandt.

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Yeah, I've been mystified for years why they can't just train the "AI look" out of images. Is it, like, something undetectable by machine? But then how come we can often detect it so easily?

I doubt it, same with people that use ChatGPT for everything and don’t see the slip anymore and are confused when you point it out. LLM blindness is a real thing.

Steve's quote about Microsoft

> They absolutely have not taste. And I don't mean that in a small way. I mean that in a big way.

applies here.

That stained glass sky-whale, at the bottom of the page, is pretty nice.

whats incredible is when in their video they're talking about complexity, the cat's images have extra limbs in low and medium complexity. They could've easily generated more versions to get the clean versions. But it just doesn't seem like they care. Maybe this is what someone who is so used to using AI to move fast becomes: someone who drops the quality bar to 80%?

I created some great designs and illustrations with imagen 2.5. I'm sure 2.6 will be even better.

It does look terrible. But keep in mind these decisions aren't made to please you or me on HN. The numbers clearly show the average user does not care about the AI look because they can't really tell the difference between human-gen and AI-gen. Whatever saves them money is what they will go with.

I'm deeply interested in this subject. In conversation, most people I talk to say they hate the look of AI generated "art," or find it uncanny or uncomfortable. However, whenever I'm on the bus or train, if I peek over the shoulder of someone, it's 50% chance they're endlessly scrolling AI generated short form video, either as unrelated clips or Chinese dramas (this being in Taiwan).

I suspect people are somewhat hypnotized by the content, in the same way a toddler is hypnotized by Cocomelon. Something about it is enthralling and keeps the attention. I wonder if it's related to the phone game icon / YouTube thumbnail phenomenon of "person's face making an extreme expression with the mouth open." AI generated videos for example still tend to have very exaggerated facial expressions and the mouth is always unusually wide when talking. I also wonder if something about the uncanny smoothness of movement (I assume caused by any form of transition defaulting to "linear") has a hypnotic effect.

I'm not sure how to look into this further. Elsagate is an interesting past example, and what I've learned so far I threw in a blog post a couple months back https://blog.calebjay.com/posts/psychic-attack/

Right now I'm looking into "Lula's army" and the AI generated content they used. I feel like there's something significant and perhaps even dangerous that could be developed bere.

If you define pixel art as just a vague overall look, then yes, it can do pixel art. If you define pixel art as being limited to a fixed color palette, like 16 or 256 colors and aligned to a square grid, then no, not without some manual post-processing.

It's not helped by the fact that a lot of people who are not artistically inclined will generate pixel art on these image generators at 1MP resolutions (far higher than traditional pixel art), such that even the casual observer can typically see the bad smearing and fringing in the final result.

That depends on your definition of pixel art. (people are exceptionally pedantic about this)

With the right prompt engineering, it (and similar quality models) can indeed do relevant pixel art, but due to the existence of blotchiness/SynthID watermarks you'll need to do some sort of postprocessing regardless.

There are SDXL pixel art models out there that do actual pixel art. They generate images that are 8x too large, you need to downscale them and reduce colors externally.

Video generation models work much differently than what this GIF pipeline is doing here.

Documentation has two: https://developers.openai.com/api/docs/guides/image-generati...

The only animation format it supports right now is GIF so you can only have a transparency channel that's either on or off. It doesn't support animated WebP.

See the second image of the bird flying.

https://imgpb.com/OzSMKJ

“Is the topic and concept from the original clear?“

Follow by

“Make it”

Seems to resolve the refusals

...I tried a similar prompt today and it refused