GPT Image 2.6 Flare vs Sunburst: Which Should You Try?

Quick verdict: choose the first experiment by task

Start with Flare when turnaround matters most; start with Sunburst when detail, layout or edit accuracy determines acceptance. Those are OpenAI's documented positions, not guarantees for every request.

For a fictional café, Flare is a reasonable first experiment for broad scene directions. Sunburst is a reasonable first experiment when revising an approved cup image and preserving small details matters. If your existing workflow already passes review, compare before replacing it.

• OpenAI's model-selection guidance

Documented comparison at a glance

Both are image-generation and editing models. Shared API features do not establish identical output quality or identical per-image cost. Availability inside another application depends on that application's integration; Ciyo's Image Generator lists both.

• Flare model documentation
• Sunburst model documentation

Flare: a starting point for fast direction finding

Flare's appeal is short iteration cycles when you need to choose a scene or visual direction. A solo creator can use that first pass to reject poor compositions before investing in detailed revisions.

Its limitation is the same acceptance problem as any image model: a quick output can still miss a handle, headline area or brand cue. Review the original brief, keep the acceptable reference and change one variable at a time. Do not infer precise latency from the model name.

Sunburst: a starting point when details decide acceptance

Sunburst is positioned for more demanding detail and editing accuracy. It suits a first test where the cup's rim, material or layout relationships determine whether the image is usable. An agency can use the same criteria to make a review decision explicit.

That positioning is not a promise of perfect preservation. Compare fine edges, accidental lettering and changes outside the requested area. More planning or higher quality can affect waiting time and token use, so record the actual result rather than assuming a better return on spend.

Pricing and access: equal token rates are not equal task costs

OpenAI lists both variants at the same standard API image-output rate: $30 per million tokens. The total also depends on applicable inputs, output token consumption and other work in the request. Do not turn this into a fixed price per picture.

ChatGPT subscription access is a different route from selecting an API model ID. Ciyo's generation quote is another price system, with separate Billable LLM usage when the Design agent reasons. In Ciyo, both variants have the same quote for the same settings, for example 6 credits for a 1K square Still image at Medium quality. Effort does not change it.

• Current OpenAI API rates

Use a matched-task check before switching

Prepare one original fictional cup reference and one instruction, such as warming only the backdrop. Use the same access path, prompt, reference order, requested dimensions, quality and complexity wherever both models support them; in Ciyo, keep the aspect ratio, resolution, quality and Effort fixed and change only the model. Record any setting that cannot be matched.

For each attempt, record elapsed time, actual usage or settled charge, required revisions and whether it passes the same checklist: cup silhouette, handle opening, rim, copy space and requested backdrop. Review exports at the same scale. Repeat enough examples from your own workload to avoid selecting a model from one lucky output.

Task and acceptance criteria:
Original reference and exact prompt:
Access path and model ID:
Requested size / quality / complexity:
Returned dimensions:
Elapsed task time and revisions:
Actual usage or settled charge:
Required change achieved:
Unwanted changes:
Accepted / rejected, with reason:

• Model comparison using one original reference

Make the choice fit your team

Solo creators can start with the model aligned to their main bottleneck, then stop when an output passes review. Small teams should agree on the product details that cannot change. Agencies should retain the prompt, reference, settings and rejection reasons so another reviewer can reproduce the decision.

Use the launch guide for availability and new output controls, and the prompt guide to build the first brief. None of these choices establishes campaign performance; measure the actual creative task before attributing a business result.

• GPT Image 2.6 launch and access guide
• Three practical campaign prompts

Create with 2.6

Choose your model and format, describe the result, and take the request into Ciyo.

• Create with 2.6
• Capabilities
• GPT Image 2.6