Advanced text-to-image system with LoRA adapters, style control, and photoreal accuracy for design professionals.
GPT Image 2 is a text-to-image generation model from OpenAI that takes a written prompt and returns a high-quality image. On RunComfy, it accepts a text prompt and supports fixed output sizes, making it suitable for product mockups, marketing visuals, concept art, and design exploration.
Output format: Resolution: 1024×1024, 1024×1536, 1536×1024 / fps: n/a / duration: n/a / aspect ratio: 1:1, 2:3, 3:2 / audio: n/a
| Parameter | Required | Type | Default | Range / Options | Description |
|---|---|---|---|---|---|
| prompt* | Yes (*) | string | — | — | The positive prompt for the generation. |
| size | No | string | 1024*1024 | 10241024, 10241536, 1536*1024 | The size of the generated media in pixels (width*height). |
On RunComfy, GPT Image 2 is billed per image.
| Billing Unit | Price | Notes |
|---|---|---|
| Per image | $0.10 | Sizes: 1024×1024, 1024×1536, 1536×1024 |
In short, GPT Image 2 on RunComfy offers a balanced mix of quality, control, and dependable text rendering for production workflows.
Advanced text-to-image system with LoRA adapters, style control, and photoreal accuracy for design professionals.
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GPT Image 2 introduces enhanced instruction following, support for up to 4K resolution, and significantly better text rendering within images. This text-to-image model also supports multilingual prompts, offering creators more flexibility across languages and visual detail than earlier GPT Image versions.
GPT Image 2 supports up to ~8.3 million total pixels (approximately 4K resolution) and a minimum limit of around 655,360 pixels per image. Aspect ratios are flexible, but extremely wide or tall frames are auto-resized. Prompt token limits follow standard OpenAI API constraints—typically a few thousand tokens for text-to-image tasks.
At present, GPT Image 2 allows a single reference image input for inpainting or editing, but does not officially support multiple concurrent image inputs like a full ControlNet stack would. However, advanced wrappers or layer-based approaches may simulate dual input reference for text-to-image consistency.
You can start with the RunComfy Playground at https://www.runcomfy.com/playground to experiment with GPT Image 2 using free trial credits. For production, switch to the RunComfy API layer, which uses similar endpoints to the playground. Authentication and model selection parameters remain consistent—simply set the model parameter to 'gpt-image-2-2026-04-21' for consistent text-to-image results.
Yes. GPT Image 2 is competitive in photorealism, particularly in product, studio, and branding use cases. While some rivals like Nano Banana Pro remain slightly ahead in hyperrealistic portraits, GPT Image 2 excels in layout accuracy, multilingual text inclusion, and faithful reproduction of logos—all key for high-end text-to-image workflows.
GPT Image 2’s architecture is optimized for accurate layout and sharpness when generating embedded text or logos. This means that signage, captions, or brand marks appear more naturally integrated—a major step forward for text-to-image generation consistency.
Yes. GPT Image 2 supports multilingual understanding and rendering, including Japanese, Korean, Chinese, Hindi, and Bengali, enabling native-language captions or labels to appear inside generated imagery without manual post-processing.
The intelligent routing layer in GPT Image 2 automatically chooses optimal generation settings—resolution, composition ratio, and resource allocation—based on your text-to-image prompt. This reduces trial-and-error and ensures consistent quality for both prototyping and high-throughput production.
GPT Image 2 performs best when instructions, structure, and clarity are vital—such as product photography, advertising, UI mockups, or scientific illustrations. While artistic models like Flux 2 may excel in stylized imagery, GPT Image 2 leads in precise, directive text-to-image generation and consistent visual logic.
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