GPT Image 2 vs GPT Image 1.5: The Photorealism Upgrade Isn’t Proven

Verdict: not proven

There is not enough evidence in the reviewed sources to say GPT Image 2 is more photorealistic than GPT Image 1.5. That is a not proven rating, not a finding that GPT Image 2 is worse.

The missing piece is a direct, reproducible comparison: the same prompts, comparable settings, multiple outputs, blind review, and a scoring method focused specifically on photographic realism. The reviewed OpenAI sources document GPT Image 1.5 and API workflows, but they do not provide an official GPT Image 2 photorealism comparison.^{[1][11][12][15][16][17]}

What OpenAI’s sources confirm

OpenAI’s GPT Image 1.5 model page describes GPT Image 1.5 as a “state-of-the-art image generation model” and says it is OpenAI’s latest image generation model, with better instruction following and prompt adherence.^[1]

OpenAI’s image generation guide documents two core workflows: generating images from text prompts and editing existing images.^[12] The API reference also describes image-edit inputs and parameters such as the model, number of images, quality, and output format for GPT image model workflows.^[11] OpenAI cookbook and prompting materials cover practical GPT image workflows, including masked edits and GPT Image 1.5 prompting examples.^[15][16][17]

Those sources establish useful product capabilities. They do not establish that GPT Image 2 is more photorealistic than GPT Image 1.5, because they do not show a GPT Image 2 model page, an official GPT Image 2 benchmark, or a head-to-head realism study.^{[1][11][12][15][16][17]}

Why the GPT Image 2 claims fall short

The GPT Image 2 pages in the reviewed set do not provide the kind of evidence needed for a verified realism claim.

One page frames GPT Image 2 around a leak and workflow guidance.^[19] Another discusses upgrades to “expect,” including text-rendering and camera-control improvements.^[20] A MindStudio article says GPT Image 2’s text-rendering improvements appear to put it ahead on that dimension, but that is a narrower claim than photorealism.^[21] A JXP article makes stronger claims, including “99%+” text accuracy, expected native 4K output, and a significant leap in photorealism, but the supplied snippet does not include a reproducible evaluation method or direct GPT Image 1.5 comparison.^[22] Higgsfield’s page markets GPT Image 2 around commercial image generation and “perfect text.”^[24]

That matters because text rendering, prompt adherence, resolution, and commercial usability are not the same as photographic realism. A model can be better at typography or instruction following without being measurably more photorealistic. The reviewed GPT Image 2 sources do not provide prompt sets, sample counts, generation settings, blind reviewer methods, or photorealism-specific scores against GPT Image 1.5.^{[19][20][21][22][24]}

The strongest benchmark-style signal points to caution

The most useful leaderboard-style source in the record is Artificial Analysis. Its Text to Image Arena snippet says GPT Image 1.5 (high) currently leads with an Elo score of 1274, and that rankings are based on blind user votes where higher Elo means a model is preferred more often.^[33]

That is meaningful evidence about user preference within that arena. But it still does not prove the GPT Image 2 photorealism claim. The supplied Artificial Analysis snippet is an overall text-to-image preference leaderboard, not a GPT Image 2 vs GPT Image 1.5 photorealism-only benchmark.^[33]

Evidence check

What would count as real proof?

A decision-grade GPT Image 2 vs GPT Image 1.5 test should separate photorealism from other strengths. At minimum, it should include:

• The same prompt set for both models. Prompts should cover people, products, interiors, outdoor scenes, low light, reflections, materials, and edge cases.
• Comparable generation settings. If testing through an API, document the model, quality, output format, and other relevant parameters exposed in the workflow.^[11]
• Multiple outputs per prompt. One unusually strong or weak sample should not decide the comparison.
• Blind review. Reviewers should not know which model produced each image. Artificial Analysis uses blind user votes for its arena, which is a useful pattern even though its supplied leaderboard does not answer this exact photorealism question.^[33]
• A photorealism-specific rubric. Score lighting, shadows, lens behavior, skin and material texture, physical consistency, reflections, and visible artifacts separately from typography or prompt obedience.
• Public examples and failure cases. Cherry-picked galleries are not enough to prove a broad model advantage.

Practical takeaway for teams

If you are evaluating image models for production, treat GPT Image 2 photorealism claims as hypotheses until you can verify them. OpenAI’s sources give you a documented baseline for GPT Image 1.5 and describe generation and editing workflows, but they do not prove a GPT Image 2 realism upgrade.^[1][11][12]

For internal testing, run the two models on your own prompts if both are available in the same workflow. For production migration, avoid switching solely because a third-party page claims a realism leap. For marketing copy, avoid saying GPT Image 2 is more photorealistic unless you have a transparent benchmark that supports that claim.^{[19][20][21][22][24]}

Bottom line

GPT Image 2 may eventually prove stronger, but the reviewed evidence does not verify that it is more photorealistic than GPT Image 1.5. The defensible conclusion is narrower: GPT Image 1.5 is officially documented by OpenAI, Artificial Analysis lists GPT Image 1.5 (high) first in its blind-vote Text to Image Arena with Elo 1274, and no reviewed source proves a GPT Image 2 photorealism advantage over GPT Image 1.5.^[1][33]