For a GPT Image 2.5 API workflow, keep approved words beside each draft and decide who checks them. This guide covers a text acceptance test, the choice of generation or editing route, settings that affect spend, and a small migration test.
Table of Contents
Set a Text Acceptance Test Before You Generate
Start with a copy file instead of improvising the wording in the request body. Store the exact title and time as separate approved strings. In the prompt, quote each string, place it on the poster, and ask for no other words. Keep the source copy available to the reviewer; an image alone cannot tell them whether “7 PM” was the approved time.
Check the rendered pixels at the size people will see. Read every word, look for missing or duplicate letters, and inspect small type after the poster is reduced for a phone. Text in AI images can look plausible at a glance while failing a literal comparison. If the wording is contractual or likely to change, drawing the background with the model and adding final text in a design tool may be easier to maintain.
Where to Call the GPT Image 2.5 API
Flare and Sunburst are the two tiers of OpenAI’s GPT Image 2.5. WaveSpeedAI exposes them through separate text-to-image and edit routes on its GPT Image 2.5 API page. Use text-to-image when the poster has no approved visual source. Use an edit route when a permitted sketch or prior draft must stay recognizable while one part changes. That choice decides whether the request needs reference images and which endpoint’s price to check.
OpenAI’s native Images API is another route to the models, but it uses a different request and response shape. For example, WaveSpeed’s resolution and aspect_ratio controls are its endpoint fields; they are not a copy-ready request for OpenAI’s native API. Keep separate adapters if an application supports both.
If your poster work also involves branding, our guide to the best AI logo makers shows how to compare design tools before you commit to one.
Pick Flare or Sunburst for the Job
Flare is the first candidate when the team needs quick layout options. Sunburst spends longer per image on fine detail. Neither tier guarantees that the poster passes its text test. Send the same approved copy, composition brief, aspect ratio, and quality setting to both candidates before comparing the lettering and visual details. Change one setting at a time so a better-looking result has a traceable cause.
| Trial | Candidate route | Human acceptance check |
|---|---|---|
| First layout | Flare text-to-image | Title, time, reading order, unwanted marks |
| Detail check | Sunburst text-to-image with matched settings | Small lettering and edges against the same checklist |
| Local change | Edit route with one approved draft | Requested change happened; approved copy stayed intact |
The table is a test plan, not a benchmark. A Sunburst output that adds a word or distorts the date still fails, however polished its lighting looks. For a simple social tile, Flare may already pass; for tiny poster lettering, compare the candidate outputs rather than assuming a tier will fix it.
Edit With References, Then Inspect the Details
Suppose the approved workshop poster only needs a darker background. Give an AI image editing API the authorized draft and request that single change. State what must stay: the title, time, layout, and any approved illustration. Compare the result side by side with the source. A prompt asking to “improve the poster” leaves too much room for altered copy.
WaveSpeed’s edit routes accept up to 16 reference images. That is a ceiling, not a reason to attach every asset in the folder. One clear source is enough for this example. If the request omits aspect_ratio, the edit route follows the first input image’s ratio, so set the ratio explicitly when the delivery slot differs. More references may help describe a subject or style, but the reviewer still has to inspect what was preserved.
Choose Quality, Size, and a Cost Ceiling
WaveSpeed lists five quality values for these routes: low, medium, high, xhigh, and max. Its resolution choices are 1k, 2k, and 4k; aspect_ratio has 15 listed choices, including square, 16:9, and 9:16. These values belong to WaveSpeed’s endpoints and may not match OpenAI’s native Images API. The 4k label also does not mean every chosen ratio yields exactly the same width and height.
For the poster, explore layouts at a low-cost setting first. Increase quality or size only when a named acceptance item, such as small lettering at delivery scale, still needs work.
A higher setting can add detail and cost without correcting a wrong date. Check image generation API pricing for the exact operation and settings before submitting a batch; editing and text-to-image are distinct operations. Budget for only as many requests as the team can inspect, and record which outputs were accepted, since not every generated image is usable.
Keep the Request Skeleton Small
The following is a field map for WaveSpeed, not executable code. Authentication, route paths, input-image handling, failures, and result parsing belong in the application and the current endpoint docs.
- selected route: Flare or Sunburst / text-to-image or edit
- prompt: scene instructions + quoted approved copy
- quality: low | medium | high | xhigh | max
- resolution: 1k | 2k | 4k
- aspect_ratio: a listed value for the delivery slot
- output_format: png | jpeg | webp
- images: [authorized reference] (edit route only)
After submission, save the prediction ID. Poll its result until a terminal success or failure, handle timeouts and retries in your own service, then send the actual output to the copy check. A completed API task means an asset was produced. The poster still needs editorial approval. Store the route and settings with each candidate so a rejected image can be traced to its inputs.
Migrate GPT Image 2 With an Output Baseline
Within WaveSpeed, GPT Image 2 and 2.5 use similar request-body fields. The first code change is mostly pointing requests at the new route. Defaults, output behavior, and image quality can still differ. Make a small baseline from real, permitted prompts and references before switching production traffic. Include the fictional poster’s exact wording, one dense-text example, and an edit that must preserve layout.
Keep the prompt, references, dimensions, and output format fixed for the first comparison. Record the old and new results, errors, and the fraction that passed the same human checklist. OpenAI’s image prompting guide recommends representative baselines and complete-result comparisons for migration. That advice applies here too, even though OpenAI’s native Images API and WaveSpeed’s endpoints use different fields.
Teams moving from stills to motion can apply the same baseline habit; see how AI video generators keep getting upgraded and why fixed test prompts matter there too.
Approve the Final Pixels
Before the workshop poster is handed to design or publishing, put the last output beside the approved copy. Verify the two strings character by character, check legibility at the planned crop, and look for unrequested symbols. Record which route, reference image, and settings produced that version. If the exact title or time keeps drifting, use the image as a visual draft and place the approved text as a separate editable layer. The final approval belongs to the person responsible for the published asset.
