GPT Image 2 –
Top-scoring image model

GPT Image 2 is OpenAI's latest AI image generation model running at its medium-quality tier. It generates images from text prompts or edits existing images, and it is the first model we have tested where the output looks — and reads — like it could pass for human-shot photography on the first try.

It posted the highest total score in our image benchmarks (8.8/10), with a perfect 10/10 on text rendering, 9.5/10 on human realism, prompt accuracy, product shots, and visual quality, and 9.0/10 on composition. Independent users on arena.ai agree: it sits at #1 with a 1512 Elo across 15,000+ votes — well clear of every other model we evaluated.

The trade-off is middling speed and cost. At 7.0/10 on both generation speed and cost efficiency, it is not the fastest or cheapest — Nano Banana 2 beats it on throughput and Seedream v4.5 beats it on price per image. But when the ad image has to be right, and right quickly, this is the model that gets to a usable result in the fewest generations.

GPT Image 2's advantage shows up most clearly in scenarios where text accuracy, human realism, and prompt precision separate a usable image from a throwaway one. Here is where it leads — and where a cheaper or faster model is a better fit.

Ads with on-image text: This is the standout use case. Headlines, pricing, sale banners, app-install CTAs, packaging labels, and promotional graphics all come out clean and legible on the first generation. The 10/10 text rendering score means you can stop adding copy in post — the model handles it natively in the right typeface and position.

People-focused creatives: With a 9.5/10 human realism score, faces, hands, skin textures, and clothing hold up under close inspection. UGC-style creator ads, talking-head stills, lifestyle portraits, and people-in-product scenes all land convincingly without the usual AI tells.

Prompt-heavy briefs: The 9.5/10 prompt accuracy score means detailed, multi-clause prompts — specific poses, props, materials, camera angles, lighting — get respected. If you already know exactly what the image should look like, GPT Image 2 turns that spec into the image more reliably than anything else we tested.

Where to use cheaper or faster models: High-volume A/B testing (use Nano Banana 2 for faster generation at the same cost), lowest-cost bulk variations (use Seedream v4.5 at ~50% the credits), and workflows where sub-5-second generation is the binding constraint.

1. Put on-image text in quotes. Write the exact words you want rendered inside quotation marks: “headline reading 'SUMMER SALE' in bold white sans-serif.” GPT Image 2's 10/10 text rendering means you get the text right the first time — use it.
2. Describe materials and surfaces explicitly. “Matte ceramic,” “brushed gold,” “frosted glass with condensation,” “soft leather with visible grain.” Generic descriptions waste the quality headroom you are paying for.
3. Specify lighting like a photographer. “Soft studio lighting from the upper left with a warm fill from below” produces dramatically better results than “good lighting.” The model responds well to photographic lighting setups: key light, fill, rim light, and light modifiers.
4. Lean into people-focused scenes. With 9.5/10 human realism, GPT Image 2 handles faces, hands, and multi-person compositions better than the competition. If your ad concept calls for a person, do not default to the cheaper models — you will spend more fixing faces than you save on credits.
5. Use reference images for brand consistency. If you have existing product photos or brand assets, use image-to-image mode rather than text-only. This keeps packaging, colors, and logos consistent rather than relying on the model to imagine your product from description alone.
6. Match aspect ratio to the target placement. 1:1 for Instagram Feed and Facebook Feed. 4:5 for Instagram portrait. 9:16 for Stories and Reels. 1.91:1 for Facebook link ads. 16:9 for YouTube thumbnails. Generating in the wrong ratio and cropping wastes composition.
7. Edit instead of regenerating. If 80% of the image is right, use the editing capability to fix the specific area. Faster, cheaper, and preserves the parts that already work.