GPT Image, the Yardstick With a Fingerprint

GPT Image's outputs also turn up as training material. One person who uses it to make synthetic images for training another model sees "nothing wrong with using hybrid workflows of closed and open source models". That is borrowing on purpose and in the open, defended as ordinary practice. Someone who thinks directly training on another model's outputs was "really stupid" adds "at least do it with a good model, not gpt image". Those views differ on whether GPT Image is a good source to train from.

Where the model is disliked, the complaint is about texture, not about any one picture. One person says GPT Image 2 and 2.5 have "awful artifacts and a certain slop style that makes them unusable", and in the same post names Nano Banana Pro as the best for edits. That is a verdict on the whole output, hedged by a comparison. Someone else keeps it shorter: "The gpt image noise is so bad." Nothing in that post compares it with another model.

Different explanations for the look are on offer this week. One person writes "It is distilled from gpt image, it's gpt image watermark". On that account the look is a record of lineage: it shows where a model's training came from. A different account holds that every GPT Image output carries a SynthID watermark, and that the noise in regions where there shouldn't be any is part of it. On that account the look is a stamp from the maker, applied to each picture.

The case for the model is obedience. One person credits gpt-image-2.5 with the ability to "follow instructions precisely" but says it "doesn't come close to Midjourney's ability to capture your own personal style and taste". That is obedience from one place and taste from another. Someone describing a different art model calls it amazing "For art" but adds that "it won't accurately follow your prompt like GPT image". Both treat GPT Image as the measure of how closely a model does what it is told.

People who want control over GPT Image find it outside the chat window. One person who returned to image generation after a long break says they moved to GPT Image because it is "cheaper and can be API driven". The decision rests on how the model can be run, not on the picture it makes. A detailed test of the API opens by saying the chat app hides some choices that the API exposes, so the controls over which model runs, quality, size and references sit outside the chat app, and the writer ran each one and wrote down what happened.

People who bring GPT Image work that has to hold together across pictures report it slipping. Someone making product images from a furniture photo found that a line locking the product mattered more than the style words, because otherwise "the chair would quietly turn into a different chair". A lock like that holds one object against its reference photo, in the prompt itself. A person making a comedy series frame by frame says the generator "forgets the previous shot", and wrote a checker to review each frame against their character sheet. That is a different problem, a character and a scene carried from one frame to the next, and the writer's answer is a checker, not a better prompt.

One person advising on how to compare image models suggests a small frozen set of tests, with character consistency among them, notes that a GPT Image 2.5 collection could seed part of it, and says to version the prompts and the input images too, "otherwise the 'test' moves every release". On that person's account, a comparison against GPT Image is only as steady as the person who fixes the test in place.