GPT Image 2.5 Sunburst vs Flare: Which to Use

OpenAI shipped two image models on the same day with names that tell you nothing. Sunburst. Flare. Both sound fast. Both sound premium. And when you open the pricing page hoping the numbers will break the tie, you find two identical rows — same input rate, same cached rate, same output rate, line for line.

So the obvious heuristic ("the expensive one is the good one") doesn't work here, and neither does guessing from the names. This piece resolves it using only what OpenAI has published on its own model pages, pricing table, and image generation guide.

The one-line answer

Use Flare. OpenAI marks it Default on its own model page and calls it "the default choice for most applications." Reach for Sunburst only when editing precision on premium creative work is worth accepting longer generation times.

That's the whole decision for most teams. The rest of this article is for the cases where it isn't obvious.

The pain point this article solves

Every comparison you'll find today reprints OpenAI's marketing sentences for both models side by side and stops there. That leaves you with two adjectives ("fast" and "precise") and no way to price the tradeoff. The question you actually need answered is whether Sunburst costs more — and the answer is a genuinely surprising no, not per token, which changes how you should think about the choice entirely.

What OpenAI actually says about each

GPT-Image-2.5 Flare

• Badged Default on the model page, with PERFORMANCE listed as Higher and SPEED as Very fast
• "Our fastest model for high-quality, everyday image generation"
• "Delivering higher-quality images than GPT-Image-2 at 50% lower latency"
• Recommended for: creator and social content, product experiences, visual search, rapid image prototyping, high-volume generation

GPT-Image-2.5 Sunburst

• "Built for premium visual workflows that benefit from tighter control across edits"
• "Offers an extra level of precision for detailed creative work with longer generation times"
• Recommended for: production-ready campaign creative, polished product imagery
• OpenAI's guide phrases the split as: "Choose Sunburst for workflows where editing precision matters most, and Flare for fast, high-quality everyday image generation"

Note what is not claimed anywhere: that Sunburst produces categorically better images from a cold prompt. The stated advantage is control across edits — the multi-turn case, where you keep refining one image and need earlier decisions to survive later instructions.

The pricing trap that trips people up

Here are the published token rates, from OpenAI's pricing page:

Per 1M tokens. The two models are identical to the cent.

And yet your bill will not be identical, because OpenAI is careful to separate rate from consumption. From the image generation guide, verbatim: "Equal token rates don't mean equal cost per image: token consumption can differ by model and quality setting."

So the real decision rule is not "which model is cheaper." It is:

1. Pick the model on speed versus editing precision, not price.
2. Then measure actual token consumption at your chosen size and quality by reading the usage field on your own responses.
3. Never port a per-image cost estimate across models or across quality settings.

There's a second trap stacked on this one. Both 2.5 models support six quality settings — low, medium, high, xhigh, max, auto — while earlier GPT Image models stopped at high. If you're migrating and you set xhigh because it sounds like a modest bump from high, you are moving onto a rung that did not previously exist, with consumption you haven't measured. And OpenAI's Flare model page warns directly: "Token rates match GPT Image 2. The GPT Image 2 calculator does not estimate GPT Image 2.5 token consumption."

The decision table

How to test the tradeoff yourself in ten minutes

Model marketing is written for the average case. Your case is not the average case. The only comparison that settles it is your own prompt, your own reference photo, your own quality setting — run through both, judged on your own screen.

The friction is that a proper API-side comparison means keys, org verification, two code paths, and a way to view results side by side. If you'd rather answer the question before building any of that, Vogoo's GPT Image 2.5 studio lets you run the same brief through both tiers in the browser, picking Flare or Sunburst per render — text to image for generation from a prompt, image to image when you want to hand it a photo and edit it. Judge the outputs first; wire up the API once you know which behavior you want.

What neither model fixes

Fair comparison means naming the ceiling both share. OpenAI's published limitations apply to the GPT Image family as a whole:

• Latency: complex prompts may take up to 2 minutes — that's the ceiling for Flare too, not just Sunburst
• Text rendering: improved, but precise text placement and clarity can still fail
• Consistency: recurring characters and brand elements may still drift across separate generations
• Composition control: precise placement in layout-sensitive compositions is still difficult

If your blocker is any of those four, switching between Sunburst and Flare will not resolve it. That is worth knowing before you spend a week A/B-ing the two.

FAQ

Is Sunburst better than Flare? Not universally. It is more precise across edits and slower. OpenAI recommends Flare for most applications and marks it Default.

Is Sunburst more expensive than Flare? Not per token — the published rates are identical. Cost per image can still differ because token consumption varies by model and quality setting.

What are the exact model IDs? gpt-image-2.5-sunburst and gpt-image-2.5-flare.

Which one does ChatGPT use? OpenAI brands the ChatGPT experience "ChatGPT Images 2.5" and does not publish which API-side model backs it. Sunburst and Flare are named as API models.

Can I switch models mid-conversation in the Responses API? Both models are selectable in the image generation tool's model field with the Responses API, and directly via model in the Image API. OpenAI does not document a constraint against changing it between turns, but consumption and behavior differ, so measure rather than assume.

Do both support transparent backgrounds? Yes. Set background: "transparent" with output_format of png or webp.

Bottom line

The names are noise. The published facts are these: same price per token, different consumption, Flare is faster and is OpenAI's default, Sunburst buys tighter control across edits at the cost of time. Start on Flare, and let a specific failure — an edit that wrecked something it should have kept — be what promotes you to Sunburst.

Before you commit engineering time to either, run your actual brief through a model and look at the output. Open Vogoo's GPT Image 2.5 studio, run the brief on each tier, and compare in the browser first.

Keep reading

• what GPT Image 2.5 changed at launch
• what breaks when you migrate from GPT Image 2

Written by the Vogoo team, September 10, 2026, from OpenAI's published model pages and pricing tables on that date. Model behavior, defaults, and pricing change frequently — re-check the linked sources before a production decision.

Sources

• GPT-Image-2.5 Flare model page — OpenAI API docs — Default badge, speed/performance labels, quality settings, calculator warning
• Image generation guide — OpenAI API docs — model selection guidance, quality settings, consumption caveat, limitations
• OpenAI API pricing — the identical token rate table for both models
• Introducing ChatGPT Images 2.5 — OpenAI — positioning of Flare as default and Sunburst as the precision tier