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-flare

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-flare

Input

Output

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

Pricing

gpt-image-2.5-flare per image

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

Overview

gpt-image-2.5-flare is the version in OpenAI's gpt-image-2.5 image series that emphasizes a balance between speed and quality. It is called on HiAPI.ai through a unified interface and supports both text-to-image generation and image editing based on reference images. It is suited for commercial visual production that requires stable image output, batch content generation, and image retouching workflows, and can output images at up to 3840px resolution, with transparent backgrounds, and in a variety of aspect ratios and formats.

Key Highlights

  • Speed-first without compromising quality — compared with the previous generation GPT-Image-2, it significantly reduces latency while improving quality, making it suitable for batch tasks that have requirements on image generation throughput.
  • Text-to-image and editing on the same pipeline — the same model can generate from scratch based on a prompt, or accept reference images for editing, compositing, and subject/style preservation.
  • Broad aspect ratio and resolution coverage — supports a variety of ratios from 1:1 to 16:9 and 9:16, as well as direct output at pixel dimensions up to 3840x2160.
  • Transparent backgrounds and controllable formats — you can choose transparent backgrounds and png, jpeg, or webp output formats, reducing post-processing after assets are finalized.

Capabilities

Text-to-image generation: generate an image from a descriptive prompt; supports longer structured prompts and complex layout requirements, making it suitable for producing usable images directly from text.

Reference image editing and preservation: you can pass in up to 16 reference images for editing, compositing, or preserving the subject and style during modifications, reducing repeated parameter tuning.

Quality level selection: provides quality levels from low to max plus auto, letting developers trade off image generation speed against detail based on the scenario.

Resolution and aspect ratio control: you can specify a ratio or specific pixel dimensions, including levels such as 1024, 1536, 2048, and 3840; auto lets the model decide.

Output format and background: supports png, jpeg, and webp formats with automatic, transparent, or opaque background settings, directly producing usable asset files.

Use Cases

  • Commercial imagery for brand and marketing teams — input brand visual descriptions or reference images, produce images for presentations, landing pages, and ads, and deliver them to asset libraries in batches through the unified interface.
  • Social media imagery for content creators — submit prompts in batches based on scripts or topics, obtain images in multiple aspect ratios, and use them for social media publishing and content scheduling.
  • E-commerce and product page asset creation — use product reference images plus modification instructions as input to generate scenario-based display images, directly usable for product detail pages and visual search assets.
  • Iterative refinement in design workflows — perform multiple rounds of editing based on existing images while keeping the subject and layout consistent, for prototype validation and design iteration.

Limitations and Selection Advice

As a closed-source model, it cannot be deployed locally or fine-tuned on your own, and output depends on API calls. High-resolution image generation incurs higher compute and time costs; dense small-font text and complex legends remain error-prone scenarios and require manual review. If a task demands extremely high detail fidelity and can tolerate longer waits, gpt-image-2.5-sunburst is more suitable; if you want low-latency daily batch image generation, this pipeline is the default choice. Content compliance must follow the applicable usage policies.

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