Side-by-side comparison of speed, quality, prompt fidelity, cost, and best use cases. Pick the right model for your project — or try both in the studio.
The headline differences in one table.
| Feature | GPT Image 1.5 | GPT Image 2 |
|---|---|---|
| Render speed | Very fast (~1–1.5s) | Fast (~2s) |
| Photorealism | Good | Excellent |
| Prompt fidelity | Good | Strong |
| Text in images | Limited (a few chars) | Strong (headlines, labels) |
| Multi-subject scenes | Inconsistent | Reliable |
| Style range | Wide (illustrative bias) | Wide (photoreal bias) |
| Aspect ratios | Standard set | Standard set |
| Per-image cost | Lower | Higher |
| Best for | Drafts, batches, stylized | Final assets, photoreal |
GPT Image 1.5 renders most prompts in around a second. The smaller architecture means less compute per image, which translates directly to wall-clock latency.
GPT Image 2 sits at roughly two seconds — still inside the "stay in flow" envelope but slower than 1.5. The extra time buys meaningfully better output, especially for photoreal work.
For most subject matter, GPT Image 2 produces noticeably more polished images. Skin texture, fabric, glass reflections, foliage, and complex lighting all hold up better. Compositions are more balanced. Faces are more anatomically consistent.
GPT Image 1.5 still produces excellent illustrative work — flat vector, isometric, low-poly, hand- drawn — where photoreal fidelity isn't the goal.
GPT Image 2 does a noticeably better job of holding onto the specifics of a prompt — subject pose, camera angle, lens choice, color palette, secondary props. Re-runs land in the same neighborhood instead of drifting.
Text inside the image (headlines, signs, labels, logos) is the clearest gap. GPT Image 1.5 can handle a few characters; GPT Image 2 can render short headlines accurately. If your image has to include readable text, GPT Image 2 is the only practical choice.
GPT Image 1.5 has a lower per-image inference cost than GPT Image 2. On GPTimage.com both models are free to use through the studio — the cost difference matters mainly if you're using these models programmatically at scale through a provider.
Open the studio and run the same prompt through each. The differences become obvious.
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