GPT-5 Image Mini vs Nano Banana (Gemini 2.5 Flash Image)

GPT-5 Image Mini's $2/M output pricing reflects OpenAI's scale advantages and mature infrastructure, while Google's $2.5/M for Nano Banana suggests either higher compute costs or strategic pricing for a lower-performing model. The 100/100 vs 50/100 score gap indicates GPT-5 Image Mini delivers significantly better image quality while maintaining lower operational costs, making Nano Banana's 25% premium particularly hard to justify.

Surprisingly, you'd save money - GPT-5 Image Mini's output costs are $0.5/M tokens cheaper despite adding critical function calling capabilities that Nano Banana lacks entirely. The real cost increase comes from input tokens where GPT-5 Image Mini charges $2.5/M vs Nano Banana's $0.3/M, but for typical image generation workflows with small prompts and large outputs, the 8.3x input price difference is offset by the 20% output savings and massive capability gains.

Nano Banana only makes sense for extremely input-heavy workflows where the $0.3/M vs $2.5/M input pricing overcomes the quality deficit - think bulk image captioning or analysis tasks with minimal generation. With its 33K token limits on both input and output, Nano Banana can't handle the complex multi-image projects that GPT-5 Image Mini's 400K input and 128K output windows enable, making it suitable only for simple, high-volume processing where a 50/100 quality score is acceptable.

GPT-5 Image Mini's 400K token window enables processing entire image datasets or multi-page documents in a single call, while Nano Banana's 33K limit forces chunking and state management overhead. Combined with GPT-5 Image Mini's exclusive reasoning and web search capabilities, this allows for autonomous workflows that Nano Banana simply cannot replicate - you're comparing a full multimodal platform against a basic image generator with a 50-point quality disadvantage.