# Flare and Sunburst need different creative acceptance checks

Flare and Sunburst target different image workflows; compare exploratory variation and precise corrections under separate artifact acceptance criteria.

Author: Retrivium editorial team
Language: en
Analysis published: 2026-11-03
Event date: 2026-09-08
Original announcement: https://openai.com/index/introducing-chatgpt-images-2-5/
Canonical URL: https://retrivium.ai/retrospectives/images25-flare-sunburst-acceptance/

Two image variants create a selection problem only when the application knows what it is accepting. Images 2.5 introduced Flare and Sunburst with different positioning, but a creative workflow still needs to distinguish rapid exploration from a precise edit whose protected details must survive.

## The variants followed earlier image workflows

[4o image generation](https://openai.com/index/introducing-4o-image-generation/) launched on March 25, 2025, and [Images 2.0](https://openai.com/index/introducing-chatgpt-images-2-0/) on April 21, 2026. The [September 8, 2026 Images 2.5 announcement](https://openai.com/index/introducing-chatgpt-images-2-5/) introduced Flare and Sunburst, positioning Sunburst for greater editing precision and Flare for faster everyday generation. These are release claims, not local measurements or a universal ranking.

## Exploration and correction have different losses

Imagine exploring several poster compositions. Many outputs can be useful because the objective is discovering a direction. An imperfect candidate may still contribute an idea.

Now consider correcting a single word in an approved poster while preserving its layout. Most visible elements are no longer negotiable. An attractive redesign can be a failure because it discards accepted work. The cost of that failure differs from rejecting an exploratory candidate.

## Define the comparison before generating

For exploration, review useful diversity, adherence to the broad brief and the work needed to reach a selected direction. For correction, review the requested change and protected properties separately.

Keep the source reference, instructions and output requirements identical when comparing variants on the same task. If one receives a clearer brief or a different image, the result does not isolate the model choice. Human review criteria belong in the comparison record, not only in a final preference vote.

## A mixed creative fixture

Prepare illustrative tasks containing an open composition brief, an exact text correction and a product-background replacement. For each, define which properties can vary and which must remain fixed.

Inspect rejected outputs as well as selected ones. A candidate that looks impressive but changes the product label provides different evidence from a dull candidate that follows the constraints. Record the reason for acceptance so a later reviewer can distinguish creative preference from instruction fidelity.

## The useful deployment choice

A workflow can use different variants for different stages, provided the handoff preserves the chosen reference and constraints. It can also retain one variant if local evidence supports that simpler arrangement.

The retrospective lesson is that model positioning becomes useful through task-specific acceptance. Flare and Sunburst expanded the available choices; they did not decide how much variation a particular artifact could tolerate or which kind of error mattered most to its recipient.

## Related reading

- [Reproducible image-generation comparisons](/journal/stable-diffusion-gpu-benchmark/)
- [Fixing the evaluation contract](/journal/inference-benchmark-method/)