Character consistency in GPT Image 2.5 means a subject survives new settings, styles and compositions without losing the features that make it recognisable. The model is clearly better at this than GPT Image 2, and OpenAI stops a long way short of calling it solved.
Getting a reliable result is therefore as much an asset-management problem as a prompting problem, and the difference shows up when you are twenty images into a series.
What OpenAI actually claims about consistency
It is worth reading the announcement closely, because every sentence about fidelity is relative rather than absolute.
- Reference photos: the model is “better at” transforming familiar subjects across new settings and styles
- Subjects look “more recognizable”, and lighting and textures feel “more natural”
- Distinctive features are “more likely to” carry through
- Multi-turn editing: instructions are followed “more reliably across multiple edits”
- Earlier changes are “more likely to” stay consistent, without degrading quality over time
Those hedges are the honest state of the feature. There is no published figure for how often a character survives, and the one number OpenAI does give in this release is about latency, not identity.
Build one canonical reference first
Consistency starts with a single approved image that later rounds point at, rather than a paragraph of description you retype each time. The GPT Image 2.5 overview covers what the model can do before any reference exists.
- Generate neutral front, three-quarter and full-body views under simple lighting
- Approve the face shape, hair, eyes, proportions and any signature prop
- Save that approved image and reuse it as the reference in later rounds
- Do not rebuild the subject from text once a reference has been approved
- Treat the reference as a production asset, not as something you remember
OpenAI’s own guidance points the same way: for recurring characters and brand marks, reuse an approved image as a reference instead of regenerating from a written description, because independent text-only generations tend to drift.
Separate identity, wardrobe and scene state
Most prompt bloat comes from merging three different kinds of information into one growing block.
- Identity is permanent: face shape, eye colour, hair silhouette, proportions, scars
- Wardrobe belongs to a period or an arc, and changes deliberately
- Scene state covers temporary conditions: rain, dirt, injury, expression
- Keep the three on separate lines so an edit touches only one of them
- Promote a variant only when the change is intentional, and name it
Once identity lives in a short fixed block, a new scene becomes a change of environment rather than a rewrite of the character.
Change one variable per round
Consistency is preserved by narrow requests. Each round should have one target and an explicit list of things that must not move.
- For a wardrobe edit, protect identity, pose, framing and background
- For a new scene, keep the subject and change only the environment
- Quote any visible text exactly instead of describing it
- Compare each result against the approved reference, not against the previous attempt
- Return to the reference instead of correcting accumulated drift
The failure mode this avoids is the one OpenAI warns about directly: generative edits do not preserve elements pixel for pixel, so a correction built on top of a bad round carries the bad round forward.
Flare or Sunburst for character work
Both API models are positioned as improved at subject preservation, and the difference between them is a time-and-precision trade.
- OpenAI describes Sunburst as offering an extra level of precision for detailed creative work, with longer generation times
- Flare is the default for most applications and is the faster of the two
- OpenAI does not single out either model as the character-consistency choice
- So compare them on the same reference, prompt and settings before committing
- Prefer the model with reliable acceptance over the one with one flattering frame
For the identifiers and the surrounding parameters, see the GPT Image 2.5 API overview.
What consistency still cannot guarantee
The official position is easier to respect than to fight, and one detail is easy to miss.
- The help centre documents no character consistency feature at all
- Every claim in the announcement is relative, with no accuracy figure published
- Recurring characters and brand marks can still drift across independent generations
- Complex prompts can take up to two minutes, so iteration is not free
- Text placement, repeated characters and structured layouts are still listed as failure cases
That is the reason the workflow above is asset-led rather than prompt-led. When exact preservation matters, save accepted outputs, review every generation, and fall back to compositing for the elements that must not change.
A worked example: the same character in three shots
Consistency is easier to judge on a set than on a single frame. Take one character — a courier in a green canvas jacket — and plan three shots: a portrait, a full-length street shot, and the same figure in a café.
- Generate the portrait first and keep it as the canonical reference, not as the best-looking image of the batch
- Write the identity once and reuse it verbatim: “A courier in their late twenties, short dark hair, a green canvas jacket with a brass zip, a scar over the left eyebrow.”
- For the street shot, repeat the identity sentence and change only the scene: “…walking along a wet pavement at dusk, full length.”
- For the café shot, change only the action and the light and leave the wardrobe sentence untouched
- Compare each result against the canonical reference before you send the next request
The drift to watch for is wardrobe drift, because it is subtle: the jacket stays green but the zip disappears, and by the third image the character reads as someone else. Repeating the sentence verbatim costs nothing and removes the guesswork.
If the identity keeps breaking under scene changes, the GPT Image 2.5 comment editing guide covers a narrower fix that keeps the frame you already approved.
Frequently asked questions
Can I keep one character across a hundred images?
No method guarantees it. You can maximise the odds with a single canonical reference, a short fixed identity block, one change per round and a review step, but OpenAI publishes no guarantee of perfect continuity.
Does the help centre document character consistency?
No. The images help page covers generation, templates, editing and availability, and contains nothing about keeping a subject or character stable across edits.
Which model should I use for a canonical character sheet?
Test both on the same reference and prompt. Sunburst is positioned for precision with longer generation times, while Flare is the faster default, and OpenAI does not label either one as the character model.
Why does my character drift across separate generations?
Because each text-only generation is an independent attempt. Reusing an approved image as a reference, rather than describing the character again from scratch, is what OpenAI itself recommends for recurring subjects.
Character consistency in GPT Image 2.5 is best treated as improved rather than solved. Approve one reference, keep identity separate from wardrobe and scene, change one variable at a time, and let a review step catch what the model leaves behind.