OpenAI’s Introducing Images 2.6 opening stresses faster generation and more precise refinement while “keeping character, product, and brand details consistent” across edits. On the product side, Sunburst is described as delivering higher-quality generation and “even greater image consistency across edits” at quality of medium or high. Customer quotes such as Figma Weave are cited for helping retain details that define a character, product, or brand during generation and editing. Those lines are official marketing and customer quotes—not independent lab consistency scores. This article only organizes mechanisms and advice already written in the docs; it invents no ranking.

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Where the docs place “consistency”

First, model tier: Sunburst is positioned for detailed visuals and precise edits; the prompting guide suggests prioritizing gpt-image-2.6-sunburst for workflows stricter on detail, layout, or edit accuracy. If Sunburst already meets the bar, compare Flare latency under the same conditions. Second, quality tier: the announcement pairs stronger edit consistency with medium/high quality settings; the same quality label does not imply identical visual quality across models—measure on your own load. Third, complexity: for most edit tasks OpenAI recommends low to speed edits and reduce unnecessary changes; omitting defaults to high and may introduce extra variation. For targeted recolors, mark changes, and packaging edits, low complexity usually aligns with “only change X, preserve Y” prompt constraints. Fourth, markup: once annotation editing enters the API, a transparent overlay can precisely indicate change locations, reducing full-image redraw mistakes—directly relevant to SKU recolors and local copy fixes in brand workflows.

General limitations in the image generation guide still say the model may “occasionally fall short” at keeping recurring characters or brand elements visually consistent; structured composition and precise text placement also have boundaries. In other words, OpenAI both emphasizes consistency and acknowledges possible drift. External service commitments should say “sample by workflow,” not “pixel-identical forever.”

Documented advice for multi-turn workflows

The image prompting guide turns multi-turn refinement into executable discipline. Establish a character or product reference: generate (or upload) a canonical likeness first, then use it as input on later edits while repeating appearance constraints (wardrobe, face shape, proportions, palette, logo geometry). Change one item per turn so you can tell which edit actually worked. State both “what to change” and “what must be preserved”—identity, geometry, layout, lighting, labels, saturation, camera position, and surrounding objects can all be written as constraints. When a region must be highly precise, use markup (or mask—the two cannot combine); if a region must stay pixel-identical, docs recommend compositing approved edits back onto the original rather than relying on prompts alone. For transparent assets, explicitly request a transparent background and inspect alpha—a drawn checkerboard is not true transparency.

For brand asset libraries, freeze acceptance items such as: logo glyph and spacing, primary-color delta, product silhouette, character identity features, and forbidden extra logos. When migrating 2.5 workflows, keep prompts and references unchanged on the first comparison, then introduce complexity and markup one step at a time. Track failures, retries, and accepted per-image cost before scaling. ChatGPT multi-turn UX and API batch processing are not the same configuration surface: API production should follow Image API generations/edits and the model pages.

Statement boundaries

Do not rewrite customer quotes as technical specs; do not claim “all brand-consistency problems are solved”; do not name a single default model without business-corpus comparison. Sunburst, higher quality, low complexity (for targeted edits), and markup are a tool combination the official docs allow you to state side by side—results still depend on prompts and acceptance criteria.

For ecommerce and packaging assets, encode “only change the color band,” “only swap the cap,” or “only fix the price tag” as prompts with preserve clauses, and circle the object with markup to lower the chance that backgrounds and neighboring SKUs are rewritten together. For continuous character storyboards, freeze one canonical reference first, then on every edits turn pass both the reference and the prior output while restating key appearance constraints; if drift accumulates, restart a short chain from the reference rather than stacking instructions on an already-drifted result. Docs also remind: the same quality label is not directly equivalent across models; if a final region must be pixel-identical, composite back onto the original. None of this constitutes a third-party consistency benchmark—it turns documented limits into operable production discipline.

Bottom line: Images 2.6 writes “more consistent character/product/brand detail across edits” as a product claim and supplies engineering handles via two tiers, quality, complexity, and markup. Rigorous usage locks identity with references and constraint prompts, shrinks the change surface with markup, and guards the pixel floor with sampling and compositing—while reading official limitation clauses into external commitments.