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What does reproducibility mean for AI image generation?
For a build pipeline, reproducibility often means capturing the inputs and configuration needed to understand or repeat a build. With generative images, use the same idea for traceability and controlled comparison—not as a promise that rerunning a request will reproduce the same pixels.
Record what went into a generation or edit, retain the resulting asset, and compare workflow changes against a fixed evaluation set. A dated model snapshot can improve consistency across runs, but it does not remove the variability inherent in model output.
Should you use the Image API or Responses API?
Choose the API around how the image task proceeds. OpenAI documents the Image API for a standalone generation or edit and the Responses API for conversational or multi-step image work.
- Image API: A fit for a single prompt-driven generation or edit. Set the image model directly.
- Responses API: A fit for iterative, multi-turn editing and workflows that use file-ID image inputs. Select a supported mainline model at the top level and configure GPT Image 2.5 in the image-generation tool.
GPT Image access may require organization verification. Eligibility can depend on the account; check the developer console rather than assuming access is universal. See OpenAI’s image generation guide.
How do you choose between GPT Image 2.5 Flare and Sunburst?
The documented GPT Image 2.5 choices are separate models, not interchangeable labels: gpt-image-2.5-flare and gpt-image-2.5-sunburst. OpenAI characterizes Flare as speed-oriented and Sunburst as quality-oriented, but does not publish a universal performance score that establishes which is faster or better for every task.
- Start with Flare when latency is a priority or an existing workflow already meets its quality bar and you want to assess a speed-oriented alternative.
- Start with Sunburst when the task has demanding quality or editing-precision requirements.
Test both against the same real workload. Keep prompts, reference images, dimensions, and format constant; keep quality fixed where both models support the chosen setting. Define an acceptance bar, then compare output quality, latency, editing precision or subject preservation where relevant, and token use. OpenAI advises: “Measure response time and quality on your own workload.” These are vendor recommendations, not independent benchmark results. See the image prompting guide.
What should a GPT Image run manifest contain?
Keep a manifest for every generation or edit so you can identify what changed and associate each output with its inputs. The fields below are a practical workflow recommendation, not an OpenAI-mandated schema.
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- Workflow or pipeline version and run date.
- API path and exact model ID, including a dated snapshot if selected.
- Original prompt and, for Responses API work, any revised prompt returned by the image-generation tool.
- Reference image identifiers or immutable copies, plus checksums to detect changed files.
- Request settings such as
quality,size,background,output_format, compression, moderation setting, and requested image count when applicable. - Request and response identifiers, plus response usage data.
- Output file, format, dimensions, and review or evaluation result.
OpenAI says the mainline model in a Responses API image-generation request automatically revises the prompt for improved performance; the revised version is available in the revised_prompt field. Save it alongside—not instead of—the original user-authored prompt. The Image API and Responses API expose different workflow details, so record fields that actually apply to each request. Consult the image generation guide, Create image API reference, and API compatibility guidance.
How do you build a baseline and evaluate changes?
- Select representative tasks. Include ordinary production requests and difficult cases that matter to your product, such as exact text, faces, product geometry, transparent assets, or challenging edits.
- Save the baseline. Retain the model ID, prompts, reference inputs, request settings, generated outputs, and evaluation results.
- Define pass criteria before testing. Specify what counts as acceptable for the task, including visual requirements and any latency or cost constraints you need to enforce.
- Change one variable at a time when diagnosing. For a model comparison, hold the prompt, references, dimensions, and format constant, and keep quality fixed when supported by both choices.
- Run the same cases after a change. Reuse the exact prompts and assets after updating a model, snapshot, or workflow. Record failures as well as passes.
This setup makes it easier to distinguish a prompt or settings change from a model change. It also gives a practical basis for deciding whether a new version meets your own bar; OpenAI recommends saved baselines, controlled comparisons, workload-specific measurement, pinned versions, and evaluations in its image prompting guide and API compatibility guidance.
Which quality and size settings should you record?
Set output parameters explicitly when comparing results. The GPT Image 2.5 prompting guide lists low, medium, high, xhigh, max, and auto as quality choices. Common sizes include 1024×1024, 1536×1024, and 1024×1536; the guide also includes larger 2K and 4K examples.
For custom resolutions, the guide documents these constraints: each edge must be no more than 3,840 pixels; both edges must be multiples of 16; the longer-to-shorter edge ratio must not exceed 3:1; and the total pixel count must be from 655,360 to 8,294,400. Outputs above 3,686,400 pixels (2560×1440) are labeled experimental. Validate against the current image prompting guide before relying on a specific resolution.
For transparent assets
Set background="transparent" and use PNG or WebP. Inspect the decoded alpha channel, including edges and semi-transparent details, rather than judging transparency from a preview alone. The Create image API reference lists opaque and transparent backgrounds for both 2.5 models and their 2026-09-08 snapshots, with PNG or WebP required for transparent output.
How do you track API usage and cost?
Use actual response usage with the selected model and request settings to estimate cost; a token rate is not a fixed price per image. At the time of the cited OpenAI documentation, standard GPT Image 2.5 rates are:
| Token category | Documented rate | Billing qualification |
|---|---|---|
| Image input | $8 per million tokens | Standard rate in OpenAI’s image generation guide; actual use varies. |
| Cached image input | $2 per million tokens | Applies only through the Responses API image-generation tool, not direct Image API requests. |
| Image output | $30 per million tokens | Standard rate in the image generation guide; actual use varies. |
| Text input | $5 per million tokens | Standard rate in the image generation guide. |
| Cached text input | $1.25 per million tokens | Cached input rate; the guide says usage output does not expose cached token counts for verification. |
| Sunburst image output with Batch processing | $15 per million tokens | Rate listed on the Sunburst model page for Batch processing. |
Rates and billing conditions can change. Check the image generation guide and Sunburst model page before budgeting or deployment. Responses API requests also include usage from the mainline model, so account for that alongside image generation.
When should you pin a dated model snapshot?
At the time documented, the Sunburst model page listed the alias gpt-image-2.5-sunburst and dated snapshot gpt-image-2.5-sunburst-2026-09-08. Use a dated snapshot when you need a more controlled reference point, store its exact ID in the manifest, and run your evaluation set before switching to a newer version. OpenAI recommends pinned versions and evals because behavior can change between snapshots. The documentation does not establish that a snapshot will remain available indefinitely. See the Sunburst model page and API compatibility guidance.
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