gpt-image-1.5-2025-12-16

imagetext image

1 provider
5 test samples

What we tested, and what came back

Each capability below is a fixed experiment: the same prompt and the same input images for every model we test. That's what makes these outputs comparable — and it's why the same bicycle shows up on every image model's page.

Each of these is a fixed experiment — the same prompt and the same input images for every model we test, so the outputs are directly comparable.

Renders a data-faithful infographic ✓ passed
We asked

“Create a clean infographic: a vertical bar chart titled 'HIFIBOTS USERS' with exactly three labeled bars: Jan = 10, Feb = 25, Mar = 40. Show each label and value as legible text.”

We gave it

Nothing — prompt only

It returned
a vision model was asked a yes/no question about the output 28s
Renders legible text in image ✓ passed
We asked

“Generate an image of a wooden sign with the word HIFIBOTS painted clearly on it.”

We gave it

Nothing — prompt only

It returned
a vision model was asked a yes/no question about the output 32s
Text to image ✓ passed
We asked

“A red bicycle leaning against a green wall, photorealistic”

We gave it

Nothing — prompt only

It returned
passed if the model returned an image 36s
Outputs transparent background (alpha) ✓ passed
We asked

“Generate a single red circle on a fully transparent background. Output a PNG with alpha.”

We gave it

Nothing — prompt only

It returned
the returned pixels were inspected for a real alpha channel 30s
Honors requested aspect ratio ✗ did not pass
We asked

“Generate a wide 16:9 landscape photo of a mountain range.”

We gave it

Nothing — prompt only

It returned
the output’s dimensions were measured 35s
Specs
latency s
34.52671790122986
speed tier
medium

Where to run it

The same model, priced by each provider that serves it. Cheapest first — the card's headline price is the best available anywhere, which is the wrong number for choosing where to actually send your traffic.

ProviderPriceContext
laozhang $5 / $32 per 1M
See all openai models we've tested →

How we test

Every claim is tagged by how we know it. Tested means we ran the model against a fixed prompt and checked the output — the images above are those runs. Researched means it's documented by the provider and cited. Nothing here is inferred.