GPT Image 2.6 Review: Which Model and Output Should You Use?

GPT Image 2.6 gives you two model choices: Sunburst for detailed visual work and Flare for fast iteration. Its more interesting additions are animated GIFs, and a separate complexity control for how much planning goes into a composition or edit.

The practical question is which combination fits your job. Here is the shorter route through the model choice, output settings, official access, and cost.

How to choose between Sunburst and Flare

The Sunburst and Flare descriptions establish their intended roles. A quality winner still needs a matched test; the model names alone do not prove which will handle your prompt better.

How to choose quality: our six-image comparison

We used the same desk-lamp poster prompt with Flare and Sunburst at low, medium, and high quality. Prompt enhancement was off, and all six requests selected 1K output. Each combination was generated once. These settings vary quality, not complexity.

Our visual-quality stars are editorial judgments of this poster task: text readability, requested object placement, visible details, and overall finish. Five stars means fewer visible compromises. Cost stars rank the recorded platform charges within these six still-image requests: more stars means more expensive. They are not official API dollar prices.

What changed? Lamp silhouettes, spacing, and reflections vary more visibly than overall polish. All six place the lamp, notebook, and plant in the requested order, with the lamp taller than the plant and the two text lines readable at this viewing size. Flare medium makes the brass control harder to verify. All six favor a natural wood tabletop over the requested pale-cream surface.

Our choice for this brief: start with low quality. Both low-quality results are usable starting points, and the high-quality outputs do not show a decisive gain for this simple poster. This is one sample per setting, not a general success-rate or speed benchmark.

How to choose your output

Choose the model first, then the task, then the output. Sunburst is the detailed-work model; Flare is the fast-iteration model. These are two model variants, and either can generate a new image or edit an existing one. PNG, JPEG, WebP, and GIF describe the deliverable, rather than another model choice.

Choose the output for the job

For a GIF, describe the motion and loop explicitly: “A paper-cut mascot waves once, then returns to its starting pose; fixed camera and unchanged background.” Request frame_count: 9 and frame_duration_ms: 200 for a nominal 1.8-second animation. Use the edit endpoint to supply still references. Frame size and count must fit the model's size and total pixel limits; reduce them if the request is too large.

Our animated GIF result

This is the original animated file, rather than a still-image preview. Its relative cost was ★★★★ on the same observed charge scale, above each still-image request. Its output size and task differ, so that price difference is not a like-for-like model comparison.

How to choose complexity

The six images above do not test complexity: the saved requests contain no explicit complexity field. The following is official parameter guidance, rather than a measured comparison.

Complexity changes planning effort; quality controls rendering fidelity. Keep quality fixed when comparing complexity so the result answers one question at a time.

How to access GPT Image 2.6

from openai import OpenAI
import base64

client = OpenAI()

result = client.images.generate(
    model="gpt-image-2.6-sunburst",
    prompt=(
        "A clean editorial product photograph of a modern desk lamp, "
        "warm window light, walnut desk, realistic materials, no text"
    ),
    size="1536x1024",
    quality="medium",
)

image_bytes = base64.b64decode(result.data[0].b64_json)
with open("desk-lamp.png", "wb") as image_file:
    image_file.write(image_bytes)

curl https://api.openai.com/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-image-2.6-flare",
    "prompt": "A bold editorial poster of a coastal city at sunset, geometric architecture, orange and teal palette, no text",
    "size": "1024x1024",
    "quality": "medium"
  }'

GPT Image 2.6 pricing

OpenAI lists GPT Image 2.6 pricing by token unit. The official image-generation pricing table is summarized below. Both official model pages list these same USD rates. Cached rates are separate rate-card entries, not a guaranteed discount on every request; do not assume every cache hit is exposed in response usage.

There is no separate fee for the complexity setting. Higher complexity can take longer, but it does not add a second complexity surcharge to the rate card. Equal token rates do not guarantee equal cost per result.

Our take

Flare is the sensible starting point for exploring directions; Sunburst is the detailed-work option to evaluate for your final composition. GIF is useful when a small loop communicates more than a still. Choose the output for the deliverable, then tune complexity for the task rather than assuming the highest value will always look best.