How to Choose an Image Generation AI: ChatGPT Image 1.5 vs. NanoBanana—What’s the Difference?

Hello, I’m an AI Research representative at GDX Co., Ltd.

Introduction

When it comes to image-generation AI, satisfaction is determined less by “which one is the best” and more by “which one fits your use case.” In this article, we organize OpenAI’s image-generation capability (commonly referred to as “ChatGPT image 1.5”) based on information that can be verified in official documentation. We also compare it with NanoBanana—whose identity can be ambiguous if you only go by the name—focusing on functionality and safety (security/guardrails). By the end, you should find it easier to choose the tool that best suits your needs.

1. Functional comparison points: generation, editing, and multi-turn workflows

In OpenAI’s official guides, GPT Image is described as having strengths such as strong instruction-following, text rendering, detailed editing, and real-world knowledge. Both the Image API and the Responses API are designed to allow output adjustments such as quality, size, and format. (OpenAI Platform)

Gemini (Nano Banana), in its official guides, lists capabilities such as iterative refinement, high-quality text rendering, compositing with multiple images, and style transfer. The guidance emphasizes use cases where you refine results through conversation rather than relying on a single-shot generation—an approach that contrasts with OpenAI’s Responses API philosophy as well. (Google AI for Developers)

2. Watermarking, safety, and rights considerations (where differences can be surprising)

OpenAI states that prompts and generated images are filtered based on its content policies, and that GPT Image provides a moderation parameter (auto / low) to adjust strictness. In real-world operations, it is important to decide who can generate images and at what moderation level. OpenAI also notes that using GPT Image may require API Organization Verification. For team or commercial adoption, it’s not enough that the technology “can be called”—approval and access permissions also become key evaluation points. (OpenAI Platform)

Gemini (Nano Banana) explicitly states that generated images include the SynthID watermark. In addition, its image-editing sections include cautions regarding rights to uploaded images and prohibited-use policies, which directly affects how outputs should be handled and shared internally or externally. (Google AI for Developers)

3. Pricing: OpenAI makes “per-image cost by quality × resolution” easy to see / Gemini presents “per-image output pricing”

For OpenAI’s gpt-image-1.5, the official model page lists per-image pricing by quality level (Low/Medium/High) and resolution, in addition to token-based costs. In a comparison article, it’s helpful to clarify that even for the same “one image,” costs can vary depending on the selected quality tier. (OpenAI Platform)

On the Gemini Developer API pricing page, image output for gemini-2.5-flash-image is shown as a per-image equivalent, and Batch pricing further reduces the unit cost. For products that require large-scale generation or batch processing, this “batch-oriented unit pricing” can be a meaningful differentiator. (Google AI for Developers)

Gemini also states officially that the handling of generated content can differ between Free and Paid plans (e.g., whether it may be used for product improvement), meaning that data-handling requirements—not just price—can become a deciding factor (this often matters in enterprise use).

Conclusion

ChatGPT image 1.5 (based on the Images API and related models that can be verified in OpenAI’s official documentation) is well-suited for teams that want to proceed with image generation and editing within clearly defined specifications and support boundaries. By contrast, NanoBanana may refer to different providers or versions depending on context, so it is important to confirm the model code, API entry point, pricing, and constraints before comparing. Ultimately, the practical way to choose is to look beyond generation quality and decide based on your usage scenario—workflow fit, cost, compliance requirements, and sharing needs.

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Note: This article was generated by ChatGPT. The content does not represent GDX’s official views, nor does it imply responsibility on the part of the company. Thank you for your understanding.