GPT Image 2, measured: 24 seconds, $0.019, and mole-level identity retention
Of the three image models we measured, GPT Image 2 was the only one that held a person's features from a reference. It was also the cheapest and emitted the most images per run. Below is only what we confirmed by running it.
Measured
| Item | Measured value |
|---|---|
| Latency | ~24s (22.1 / 25.0 / 25.7s) |
| Cost per image | $0.0174–0.0229 |
| Output | PNG |
| Measured on | 2026-08-28 (via OpenRouter) |
From a single reference it preserved even mole placement across changes of angle, expression, and outfit. For putting a named person into a new scene, it is currently the first choice.
Accepted parameters
| Parameter | Accepted values |
|---|---|
| Aspect ratio | auto / 1:1 / 3:2 / 2:3 / 4:3 / 3:4 / 16:9 / 9:16 / 21:9 |
| Quality | auto / low / medium / high |
| Images per run | 1–10 |
| Reference images | up to 16 |
| Resolution | Not supported — the model has no resolution parameter |
What high quality costs
At quality=high and 16:9 we measured $0.1299 and 72.3 seconds — roughly 6.8× the cost and 3× the latency of auto. There is little reason to use high outside the final image.
Good for
- Putting the same person into a new scene
- Multi-panel layouts and captioned instructions
- Generating several candidates and choosing (up to ten per run)
There is no resolution parameter here. Porting code from another model tends to send one anyway.
Bad for
- Precise camera angles — 30° and 60° drift back toward frontal
- Swimwear, underwear, and similar subjects the provider's safety system rejects
If swimwear is refused, does rewording get through?
It does not. The call fails before generating. Pick a different model for those subjects.
When you need angles
Nano Banana 2 follows angle instructions far more literally — 30° and 60° came out at those angles. It breaks down on multi-panel layouts and embedded text, so a single project may need both models at different stages.
Frequently asked
- How many images can GPT Image 2 return per run?
- One to ten — the highest of the three models we measured, which makes candidate-and-choose workflows practical.
- Can I set the resolution?
- No. The model has no resolution parameter at all. Use aspect ratio and quality instead.
- Why was my generation refused?
- Swimwear, underwear, and similar subjects are rejected by the provider's safety system before generation. Rewording does not get through; you need a different model.
