GPT Image 2.5 Guide for Choosing Flare or Sunburst

Both models use the same token rates. The model pages list text input at $5 per million tokens, image input at $8 per million, and image output at $30 per million. Equal rates do not guarantee equal cost per accepted image because token use can vary by model, dimensions, and quality. Record the full cost of outputs that pass review instead of comparing only one request.

Write GPT Image 2.5 prompts like production briefs

An effective prompt starts with the deliverable. State whether you need a product hero image, vertical poster, teaching diagram, or interface preview. Then describe the subject, composition, lighting, texture, and constraints. For required copy, provide the exact text and the number of times it should appear. For edits, separate what should change from what must remain stable.

This prompt can be adapted for a product image.

Create a 1536x1024 landscape hero image for a coffee machine product page.
Preserve the machine shape, control placement, brushed-metal material, logo proportions, and camera angle from the reference.
Change the setting to a light kitchen in early morning with window light from the left. Place only one white coffee cup on the counter.
Leave roughly one third of the right side clean for later typography.
Do not add text, change the machine color, or invent controls that are absent from the reference.

The result is easier to inspect than an unstructured list of style words. After the first output, change one variable at a time. Lock composition first, adjust light next, and handle background objects in a later pass. Repeat the critical preservation rules in every round instead of relying on a vague request to keep everything else unchanged.

Apply the model to common jobs

For social posters, specify the aspect ratio, exact copy, reading order, and reserved space. Ask for required text to appear exactly once and proofread every character after generation. Flare is useful for exploring directions quickly. Decide whether Sunburst is needed for final output by measuring text errors and unwanted changes on your own examples.

For product imagery, assign each reference image a role. Identify which input supplies the subject, style, or background. Preserve geometry, color, labels, and camera angle before changing the scene. For transparent assets, set a transparent background in the API and use PNG or WebP, then inspect the alpha channel around hair, glass, shadows, and object edges.

For portrait edits, describe the local change first and list the facial features, pose, clothing, framing, and lighting that must remain. Image comments in ChatGPT Images 2.5 can point to a target region, and the API also supports masks. OpenAI notes that masks guide the model but do not guarantee an exact boundary, so pixel-precise edits still need a conventional editor at the end.

Build an acceptance-driven workflow

Save a representative baseline that includes difficult faces, exact copy, product geometry, transparent assets, and multi-turn edits. Keep prompts, references, dimensions, and quality fixed while comparing Flare and Sunburst. Inspect the complete edit sequence, not just the first image.

After quality passes, optimize speed and cost. Change one setting at a time, then record typical and slow response times, failures, retries, and cost per accepted image. The API supports low, medium, high, xhigh, max, and auto quality. A higher setting does not guarantee a better result for every prompt, so find the lowest setting that consistently clears your acceptance bar.

The Responses API is the better fit for a conversational editing product because it keeps context across steps. The Image API fits a single generation or single edit. This distinction is explicit in OpenAI's image generation documentation and helps keep the implementation proportional to the job.

Keep a human review gate

GPT Image 2.5 is stronger at maintaining visual intent, but every image is still fallible. Check data relationships in infographics, period details in historical scenes, and labels in educational diagrams. Add logos, portraits, and product geometry to a written acceptance checklist instead of approving a result on overall appearance alone.

OpenAI continues to attach C2PA metadata and invisible watermarking, with safety checks at input and output. Its system card also says greater realism increases the risk of convincing fabrications involving real people and events. Teams working with portraits, news, or paid campaigns should preserve source, permission, and version records from generation through publication.

The useful gain in this release is the reduction in repeated work between an almost-right image and a deliverable asset. Use Flare for high-frequency production, move to Sunburst for demanding edits, write prompts as production briefs, change one variable per pass, and review every result against a fixed acceptance standard.