GPT Image 2 vs GPT Image 1.5: Is It More Reliable for Marketing Assets?

The missing evidence is a same-input GPT Image 2 vs GPT Image 1.5 test with a published pass/fail rubric, first-pass acceptance rate, and retry-rate reporting. OpenAI’s image-evals cookbook is relevant because it covers evaluation for image generation and editing use cases, but the available source does not provide a marketing-specific head-to-head result for these two models.

What OpenAI’s materials actually establish

The key distinction is simple: launch language and model documentation can justify evaluation, but they cannot replace task-level evidence.

Why newer is not the same as marketing-ready

A marketing-ready variation has to satisfy constraints that generic image quality claims often miss. A useful review should ask whether the output:

• Follows the creative brief without adding unwanted elements.
• Keeps required copy readable, correctly spelled, and placed as requested.
• Preserves product shape, color, packaging, and other key details.
• Stays consistent with the intended brand style across variations.
• Changes only the requested parts when editing an existing asset.
• Produces variations that are meaningfully different without drifting off-brand.
• Passes human review on the first attempt.
• Requires fewer retries before a usable asset is produced.

OpenAI’s GPT Image 1.5 prompting guide illustrates how constraint-heavy these workflows can be: example prompts include requirements such as original design only, no trademarks, no watermarks, no logos, and packaging text included verbatim. Those constraints are relevant to marketing QA, but they are prompt-design guidance, not proof that either model will pass brand review more often.

What would actually prove GPT Image 2 is more reliable

A credible comparison would need more than model names or examples. It should include:

1. Identical inputs: the same prompts, source images, copy blocks, aspect ratios, brand constraints, and editing tasks for both models.
2. A clear pass/fail rubric: measurable criteria for copy accuracy, product fidelity, style consistency, edit precision, and review readiness.
3. Blind review: designers, marketers, or brand reviewers should score outputs without knowing which model produced them.
4. Retry tracking: the test should measure whether each model reaches an acceptable result on the first attempt, second attempt, or after multiple retries.
5. Task-level reporting: results should be separated by use case, because text-heavy ads, product mockups, social variants, and image edits can fail in different ways.

The sources reviewed here document the models and point to evaluation concepts, but they do not publish this marketing-specific comparison.

How teams should pilot GPT Image 2

Treat GPT Image 2 as a candidate for evaluation, not an automatic replacement. A practical pilot should use work your team already understands:

• Build a representative test set covering social, display, email, product, and landing-page assets.
• Include both prompt-only generations and edits to existing images, since OpenAI’s image documentation covers both workflows.
• Reuse the same product references, copy blocks, visual constraints, and brand rules across both models.
• Score outputs blind with the people who normally approve assets.
• Track first-pass acceptance, total retries, and the reason each failed output was rejected.
• Decide by task type rather than by model hype. If one model wins for copy-heavy variants but not product edits, route only that work to the stronger model.

Bottom line

GPT Image 2 may turn out to be better for some marketing workflows, but the current public evidence does not prove that claim. The source-backed position is narrower: GPT Image 2 and GPT Image 1.5 are both documented, OpenAI’s image documentation covers generation and editing, and OpenAI provides image-evaluation guidance. Until a same-prompt, marketing-specific benchmark exists, the responsible answer is to test before switching.