HiAPI
21:9 电影宽幅 Hero:左侧柔光留白,右侧悬浮多层虹彩玻璃图像板与陶瓷光环,gpt-image-2.5 为发光立体字,冷蓝粉金多色渐变。作为 gpt-image-2.5-flare 详情页顶部横幅。

OpenAI

GPT Image 2.5

gpt-image-2.5-sunburst

OpenAI's two-tier image generation and editing models: Flare focuses on speed, Sunburst pursues precision, covering both text-to-image and multi-turn image refinement

Route

Try gpt-image-2.5-sunburst

Input

Output

image
Fill in the parameters and run — results show up here.

Pricing

gpt-image-2.5-sunburst per image

Specifications Unit Price
low $0.0172
medium $0.0672
high $0.1829
xhigh $0.3572
max $0.7143
auto $0.3572

Positioning Overview

gpt-image-2.5-sunburst is the precision-oriented variant in OpenAI's gpt-image-2.5 image family. It supports both text-to-image generation and image editing and can be called through the unified HiAPI.ai interface. It improves on lighting and material rendering, reference subject consistency, and adherence to complex visual instructions, and it supports output at sizes up to 3840px as well as transparent backgrounds. It is suited to creative design work that demands high detail fidelity, brand asset production, and image workflows that require repeated refinement.

Key Highlights

  • Precision-focused detail reproduction — trades longer single-generation times for finer detail and material rendering, making it suitable for tasks with high final-image quality requirements.
  • Subject consistency across multi-round editing — when repeatedly modifying based on a reference image, the reference subject and layout are easier to keep consistent, reducing rework on each round.
  • High resolution and transparent background — size options go up to 3840px, and transparent background output is available, reducing post-processing matting and compositing costs.

Capability Description

Text-to-image and image editing on the same variant: the same variant can generate images directly from prompts or accept reference images for editing, compositing, or preserving style and subject, with no need to switch models.

Multi-image reference input: up to 16 reference images can be passed in at once for multi-asset compositing or for preserving subject and style across images, suitable for scenarios that need to combine multiple visual elements.

Selectable aspect ratio and pixel dimensions: supports ratios such as 1:1 and 16:9 as well as pixel dimensions at the 2048 and 3840 levels, or the model can decide automatically, making it easy to align with landing pages and asset slots.

Quality and output format control: quality can be set from low to max across multiple levels, affecting detail, speed, and billing tier; output supports png, jpeg, and webp, with automatic or transparent background options.

Complex layouts and long prompts: prompts support up to 20000 characters, allowing longer visual instructions and layout descriptions, suitable for more structurally complex images and infographic needs.

Applicable Scenarios

  • Brand and marketing design teams: fine creative drafts — input design requirements and reference images to produce high-resolution brand visual assets for official websites, presentation decks, and advertising materials.
  • Design workflows: repeated refinement based on existing images — pass in original images and change instructions, iterating round by round while keeping subject and layout consistent, and produce deliverable final drafts for design review.
  • E-commerce and product pages: multi-element composite imagery — use multiple product images and scene references as input to composite hero images and produce product display images ready to list.
  • Asset production: transparent-background elements — generate graphic elements with transparent backgrounds from prompts, producing assets that can be overlaid directly onto pages or posters.

Limitations and Selection Advice

sunburst targets precision, so a single request takes longer and is not suitable for low-latency or high-concurrency batch image generation; high-resolution output further increases computation and waiting time. Scenes containing large amounts of small text or complex legends remain high-difficulty scenarios and should be manually reviewed. If you need a balance of speed and quality or large-batch image generation, consider the flare variant in the same family. Use of all generated content is subject to OpenAI's usage policy constraints; the model is closed-source and does not support local deployment or self-tuning.

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