The Tool Desk
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OpenAI documents two GPT Image 2.5 choices, gpt-image-2.5-flare and gpt-image-2.5-sunburst. They are not interchangeable labels: Flare is positioned for speed, while Sunburst is positioned for demanding quality. The workflow below makes it possible to test those trade-offs against your own production tasks.
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What does reproducible image generation mean?
For a generative image pipeline, reproducibility means being able to identify what went into a run and compare outcomes under controlled conditions. It does not mean a rerun is guaranteed to return the same pixels. OpenAI warns that prompting behavior can change between snapshots and that model outputs are variable; it recommends pinning versions and running evaluations for consistency. See OpenAI’s API compatibility guidance.
Keep the exact model identifier, prompt, reference inputs, request settings, response usage, and output asset. For comparisons, reuse a representative set of tasks and change one variable at a time. OpenAI’s image prompting guide puts the practical test plainly: “Measure response time and quality on your own workload.”
Choose the API that matches the workflow
| Use case | API path | How to select the image model |
|---|---|---|
| One prompt to generate or edit an image | Image API | Set the GPT Image 2.5 model directly in the image request. |
| Conversation, iterative edits, or file-ID image inputs | Responses API with the image-generation tool | Select a supported mainline model at the top level and set the GPT Image 2.5 model in the image-generation tool. |
These are the documented fits in OpenAI’s image generation guide. GPT Image access may require organization verification, so check eligibility in your developer console rather than assuming every account has access.
Choose Flare or Sunburst with a task-specific test
- Start with Flare if speed is your priority or your existing workflow already clears its quality bar and you want to test a latency-oriented option.
- Start with Sunburst if the work has demanding quality or editing-precision requirements.
Those are OpenAI’s model-selection recommendations, not independent benchmark results. To compare them, run the same representative prompts and reference images, and keep dimensions, format, and quality fixed where both models support the setting. Judge outputs against a written acceptance bar, then measure latency and token use on the workload you intend to ship. Results depend on prompts, references, dimensions, and quality settings; the documentation does not establish a universal speed advantage or quality score. See OpenAI’s image prompting guide.
Build a run manifest for every image
Store one record alongside each generated or edited asset. The following fields are a practical workflow recommendation based on the controls and response data documented by OpenAI, not an OpenAI-mandated schema.
- Workflow or application version and run date.
- API path, endpoint, and exact model ID; include the dated snapshot if you use one.
- Original prompt and, when present, the Responses API tool’s revised prompt.
- Reference-image identifiers or immutable copies, with checksums so you can detect changed files.
- Request settings:
quality,size,background,output_format, compression settings, moderation setting, and requested image count where applicable. - Request and response identifiers, response usage data, and any errors.
- Output file, format, dimensions, and review or evaluation result.
For the Responses API image-generation tool, OpenAI says the mainline model automatically revises prompts for improved performance; the revised prompt is available in the revised_prompt field. Preserve both versions when that field is returned: the original is what your workflow supplied, while the revised version helps explain what the tool processed. The Image API reference and image generation guide document relevant request controls and response information; compatibility guidance covers version pinning.
Freeze a baseline and evaluate changes
- Choose representative tasks. Include ordinary production cases and the hard cases that matter to your product—for example, exact text, faces, product geometry, transparent assets, or difficult edits.
- Save the baseline. Retain prompts, reference inputs, model ID, request settings, output images, and review results for each case.
- Set pass criteria before testing. Define what acceptable quality means for the task, such as legible required text, preserved product shape, or a usable transparent edge.
- Change one variable to diagnose a result. Keep the remaining inputs and settings fixed when investigating whether a prompt, quality setting, or other change caused an outcome.
- Compare models under matched conditions. Reuse prompts and references, and hold dimensions, format, and quality constant where supported. Record quality-bar results, latency, editing precision, and usage for the same workload.
- Rerun the set after updates. Use the same saved cases before adopting a new model snapshot or workflow version, and record failures as well as successes.
This controlled approach follows OpenAI’s advice to save a baseline, compare models on representative prompts and inputs, and measure quality and response time for your workload. A fixed evaluation set makes changes easier to diagnose; it does not make image generation deterministic. See the image prompting guide and API compatibility guidance.
Make output settings explicit and validate them
Quality and size
The GPT Image 2.5 prompting guide lists low, medium, high, xhigh, max, and auto as quality settings. For a controlled comparison, choose a quality value explicitly rather than relying on auto, and set size explicitly. Common sizes include 1024×1024, 1536×1024, and 1024×1536; the guide also gives larger 2K and 4K examples.
For custom resolutions, the guide states that each edge must be no more than 3,840 pixels, both edges must be multiples of 16, the longer edge must be no more than three times the shorter edge, and the total pixel count must be between 655,360 and 8,294,400. Outputs above 3,686,400 pixels—the area of 2560×1440—are labeled experimental. Validate custom sizes against the current image prompting guide before relying on them, since API limits can change.
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Transparent assets
For transparency, request 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 background alone. OpenAI’s image generation API reference lists opaque and transparent background support for both GPT Image 2.5 variants and their documented 2026-09-08 snapshots, with PNG or WebP required for transparent output.
Pin versions without treating them as a guarantee
OpenAI’s model page lists the Sunburst alias gpt-image-2.5-sunburst and the dated snapshot gpt-image-2.5-sunburst-2026-09-08. Record the exact identifier you use and run your evaluation set before changing it. A dated snapshot can help keep a workflow on a selected version, but it does not guarantee pixel-identical output, and the documentation does not establish that a snapshot will remain available indefinitely. Check the Sunburst model page and API compatibility guidance when choosing or updating a version.
Track usage and estimate cost from actual requests
As listed in OpenAI’s image generation guide checked on 2026-10-05, standard GPT Image 2.5 token rates are $8 per million image input tokens, $2 per million cached image input tokens, $30 per million image output tokens, $5 per million text input tokens, and $1.25 per million cached text input tokens. These are token rates, not fixed prices per image: usage varies with model, quality, size, and inputs. Cached input pricing applies only through the Responses API image-generation tool, not direct Image API requests; the guide also says usage output does not expose cached token counts for verification.
The Sunburst model page lists image output tokens at $15 per million under Batch processing. Because the actual amount depends on token use and billing conditions, capture response usage and estimate with the exact model, quality, size, and API path you plan to use. Verify current rates and conditions in the image generation guide and Sunburst model page before budgeting or deployment.
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