The models are meant to preserve subjects from reference photos better and follow editing instructions more reliably across multiple rounds. According to OpenAI, the faster variant cuts image generation latency by up to 50 percent compared to Images 2.0.

Two new models built for different jobs

For developers, OpenAI brings both models to the API. GPT-Image-2.5 Flare is the default pick for most uses, with higher image quality than GPT-Image-2 at 50 percent lower latency. The stronger model, GPT-Image-2.5 Sunburst, targets more demanding visual work with tighter control over edits, and it needs longer generation times to deliver.

Both models use the same token rates. That’s eight dollars per one million image input tokens and 30 dollars per one million output tokens. But since token use varies by model and quality tier, the same rates don’t mean the same cost per image. New in Images 2.5 are the “xhigh” and “max” quality tiers, which go beyond the previous ceiling of “high.”

A 1024×1024 image at the “low” tier costs about 0.006 dollars, same as the predecessor, while “high” runs about 0.053 dollars, and the new “max” tier lands at roughly 0.21 dollars with around 7,024 output tokens. That puts the “max” tier of Images 2.5 at the same price as the “high” tier of GPT-Image-2. Unlike the predecessor, Images 2.5 has no cheaper batch rate so far. In early tests, though, Sunburst usually costs more per image than Flare despite identical token prices, probably because of longer reasoning runs. And unlike last time, OpenAI gives no average price-per-image figure.

It’s also unclear how OpenAI routes ChatGPT users between the two models. Neither the announcement nor the documentation says when ChatGPT reaches for the faster Flare or the more precise Sunburst. In the API you can pick the model explicitly. The ChatGPT interface offers no such control yet.

In our tests, the line currently seems to run mainly between Chat and Work. In Work, our prompts really do change only what’s asked, no matter the reasoning settings. These targeted edits are the focus of the model improvements. In Chat, though, more details keep shifting in the follow-up images, even with reasoning set to high. Only at the “6 Pro” setting does the stronger model sometimes appear to kick in.

Editing that changes only what you ask

As noted, the new model’s focus is editing. It’s meant to change only the requested elements and leave the rest of an image untouched, even with more complex subjects and backgrounds. In longer conversations, earlier changes should stay consistent without image quality dropping over multiple editing steps. OpenAI shows this with a room redesign, for example. Where the older model changed other details with every edit, the new model stays stable even across several iterations.

A quick test through ChatGPT Work with GPT-6 Astra (Max) shows how well this works. We used the following prompt and then iteratively adjusted one small detail (banana color) and one large one (the big cat).

A hyper-realistic DSLR photo. A monkey holding a pink banana is sitting on a tiger in the foreground. In the background, a HORSE is RIDING AN ASTRONAUT. The astronaut is underneath, like a living “spacesuit horse saddle,” and the HORSE is clearly on top, in control, as the rider. Make it 100% unambiguous: the HORSE is the rider and the ASTRONAUT is being ridden, NOT the other way around. High resolution, sharp focus, realistic lighting.

Image: GPT-Images-2.5 / Astra (Max)

On a side note, this is probably the best version of a horse riding an astronaut that an OpenAI image model has produced in our tests. Here’s how it looked with Image 2.0 (Thinking variant).

Tests in ChatGPT’s Chat mode, by contrast, always changed the rest of the image when we adjusted the banana color. Only in “6 Pro” mode did the image stay consistent in one run, but not in another. This may change over the course of the week as the rollout continues.

Images 2.5 is also meant to handle complex visual instructions better, deliver more accurate content for real-world information, and work with transparent backgrounds and more demanding layouts. In our last article, for instance, we had ChatGPT turn the piece into an 80s magazine spread. It looked like this.

GPT-Images-2.5 via Astra (Max) also produced a detailed magazine with a sample image based on our monkey-astronaut prompt, in a weaker and a stronger variant, without losing sight of the original instruction for worse quality.

The model corrected an error we planted (GPT-Images-2.5 instead of 2.0 right above the images on the first page) on command, showing how well it preserves the rest of the content, and it did the same for a translation on the simple request “Create a US English version.”

Image: GPT-Images-2.5 / Astra (Max)

For comparison, the results through plain ChatGPT Chat are also worth a look, but they show that the weaker model changes other details during translation too. In one case, though, it nailed the current Microsoft CEO better.

Image: GPT-Images-2.5 / ChatGPT Chat (Medium and Instant)

Drawing, templates, and shareable prompts

In ChatGPT, OpenAI is adding several new features alongside the new models. The most important one is called “Sketch.” It lets users draw directly in ChatGPT and use the sketch as a visual template for the finished image. You activate it with the “@Sketch” command, and OpenAI says it works well for diagrams, room layouts, or posters.

Templates are ready-made prompts meant to make it easier to get started with formats like posters, logos, infographics, thumbnails, illustrations, or ads. They give you a structure instead of a blank canvas and pin down your requirements through targeted follow-up questions. Users can also place comments directly on images and share the prompts they used, so others can try the same idea with their own photos and details. As an example, OpenAI points to a currently viral prompt that generates portraits in 1980s style.

Both models top the Arena ranking

In Arena’s text-to-image leaderboard, the new models hold the top two spots for now. GPT-Image-2.5 Sunburst leads with a score of 1421, followed by GPT-Image-2.5 Flare at 1399. Both scores carry a “Preliminary” label and rest on still-low vote counts of around 3,100 and 2,900. In third place is the predecessor GPT-Image-2 with 1381 points from about 78,700 votes.

Behind them come Microsoft’s mai-image-2.6 (1331), SpaceXAI’s grok-imagine-image-2.0 (1315), and several models from Reve, Meta, Google, and Bytedance. Since the ratings for the two new OpenAI models are preliminary, their position could still shift as more votes come in.

Watermarking in partnership with Google DeepMind

For provenance labeling, OpenAI still relies on the C2PA industry standard, which embeds metadata for tracking. To make provenance more resistant, OpenAI also adds an invisible watermark via Google DeepMind’s SynthID, in ChatGPT, Codex, and the API. OpenAI says there’s no single solution for provenance labeling, which is why it takes a layered approach.

Images 2.5 is available now worldwide for all ChatGPT, ChatGPT Work, and Codex users across desktop, mobile, and web, including the free version. OpenAI says shorter wait times and higher usage limits apply. Our tests suggest the Chat mode currently uses the weaker model.