Ideogram sells 4.5 on holding an image steady through repeated edits

The test I would run needs no human judge. It starts from a real product image and the chain of edits a designer would actually request. Each instruction gets a mask over the region it is allowed to change. After every round, the pixels outside the mask are diffed against the original. Ideogram says 4.5 reduces pixel shifts, color changes and texture artifacts across repeated edits ^[2]. Each of those shows up in that diff as a number per round.

The same chain then goes through the model already in production. For the launch thread's verdict on GPT Image and Nano Banana ^[4] to transfer, Ideogram's chosen images and edits would have to resemble a team's own. That means the same kind of change and a chain of similar length. A vendor side-by-side contains the cases the vendor chose to post. Runtimewire notes that the thread does not specify an independent evaluator, a benchmark protocol or a measured failure rate ^[5].

I like the shape of the API. The precise-edit endpoint returns an image at the input's dimensions, and a second endpoint combines generation with editing ^[11]. Same-size output drops back into the layout slot the original came from. It also lets the diff line up pixel for pixel with no rescaling. The diff should run at production size. Ideogram's model page edits a 4,016-by-6,016-pixel source ^[12], about 24.2 megapixels ^[17], while the launch post advertises native 2K output ^[3]. If untouched pixels get resampled on the way between those sizes, the diff will show it.

The quality tier has to stay fixed for the whole run. The four modes span a 27.5x price range ^[15]. A stability result at one tier is not evidence for another.

I would also put at least one text edit in the chain. Ideogram built its earlier reputation on text rendering and graphic design ^[14]. It lists translating stylized lettering while keeping the design as a 4.5 use case ^[8].