Row-Bot 上 Flare vs Sunburst：放大镜硬对比

提示词

GPT-Image-2.5 - Flare vs Sunburst

Wanted to test GPT Image 2.5 in Row-Bot. Noticed that it exposes 2 variants - Sunburst & Flare. So it became a comparison.

I gave Row-Bot this prompt: "what is the difference between gpt image 2.5 sunburst and flare? i want you to generate the same image using both models one by one and then compare the results. try a complex prompt that really works the image gen model."

Raw Output:

"Done, sir. Both images are generated and I've examined them closely. Here's the honest verdict.

What the two models actually are
GPT-Image-2.5 — Flare vs Sunburst - Flare: the speed-optimised variant — ~50% lower latency than GPT-Image-2 (measured ~19.7s vs ~37.3s), designed for high-volume, rapid-iteration work. Higher quality than the older 2.0, but trades off some fidelity for throughput. - 

Sunburst: the precision variant — OpenAI's "most capable" image model. Sharper detail rendering, more natural lighting, richer textures, and the tightest control for complex/precise generations, at the cost of longer generation time.

Both support transparent backgrounds and up to 4K output, and both are available in your setup.

The comparison (honestly — they're closer than I expected)

Both nailed the easy stuff: candlelight mood, warm amber/teal grading, the wood and brass texture, and a smooth, believable depth-of-field falloff. At a glance both read as a high-end render.

Where they diverge on the hard stuff:

The loupe (the real discriminator): Flare actually rendered a magnified, refracted view through the glass — the more physically sensible choice — though the magnified candle didn't line up with the real one and it reads slightly "pasted in." Sunburst rendered the glass as a flat mirror with no lens distortion or magnification. On this specific element, Flare got the concept closer.

Gear micro-mechanics: Both failed in the same way — fused/uneven teeth, non-meshing gear trains, and machining marks that look like a generic texture wash rather than true hairline finishing. Neither delivered the "tack-sharp escapement" the prompt asked for.

Nonsensical parts: Both produced a floating knurled crown and mangled engraved text. Flare added a strange "banana-shaped" brass fragment with random jewel holes; Sunburst had a floating detached screw.

Bottom line
On this particular complex prompt, the difference was subtle, not dramatic — which is itself a useful finding: both 2.5 variants are far ahead of the older 2.0 on mood, lighting, and composition, and both share the same core weakness (true micro-mechanical and lens-optics accuracy). If I had to pick on this image:

Flare gave a marginally more physically-coherent loupe and I'd take it for iteration speed and most everyday generations.

Sunburst is where you'd go if detail consistency and editing control matter most — the gap is expected to widen more on editing/multi-turn work than on single-shot generation like this."

中文译文

GPT-Image-2.5 - Flare 对比 Sunburst

想在 Row-Bot 里测试一下 GPT Image 2.5。我注意到它开放了 2 个变体——Sunburst 和 Flare。于是就变成了一次对比。

我给 Row-Bot 的提示词是：“gpt image 2.5 的 sunburst 和 flare 有什么区别？我想让你用这两个模型依次生成同一张图，然后对比结果。试一个真正能发挥图像生成模型实力的复杂提示词。”

原始输出：

“完成了，先生。两张图都已生成，我也仔细查看过了。以下是坦诚的评判。

这两个模型究竟是什么
GPT-Image-2.5 — Flare 对比 Sunburst - Flare：速度优化的变体——延迟比 GPT-Image-2 低约 50%（实测约 19.7 秒 vs 约 37.3 秒），专为高吞吐、快速迭代的工作而设计。质量高于更早的 2.0，但为了吞吐量牺牲了一部分保真度。 - 

Sunburst：精度变体——OpenAI“能力最强”的图像模型。细节渲染更锐利、光照更自然、质感更丰富，对复杂/精细生成的控制也最严密，代价是生成时间更长。

两者都支持透明背景和最高 4K 的输出，并且都已在你的环境中可用。

对比结果（说实话——它们比我预期的更接近）

简单的部分两者都做对了：烛光氛围、温暖的琥珀/青色调色、木质与黄铜的质感，以及平滑可信的景深衰减。乍看之下，两者都像是高端渲染作品。

它们在难点上的分歧：

放大镜（真正的区分点）：Flare 确实渲染出了透过镜片的放大、折射视图——这在物理上更合理——不过被放大的蜡烛与真实的蜡烛位置对不上，看起来有点“贴上去”的感觉。Sunburst 则把镜片渲染成了一面平面镜子，没有镜头畸变，也没有放大效果。就这一个具体元素而言，Flare 更接近概念本意。

齿轮微观机械结构：两者以相同的方式失败——齿牙粘连/不均匀、齿轮组无法啮合，以及看起来像通用纹理涂抹而非真正细密加工的加工痕迹。两者都没有实现提示词所要求的“极其锐利的擒纵机构”。

不合理的部分：两者都生成了悬浮的滚花表冠和被扭曲的雕刻文字。Flare 还加了一段奇怪的“香蕉形”黄铜碎片，上面有随机的宝石孔；Sunburst 则出现了一颗悬浮脱离的螺丝。

结论
在这个特定的复杂提示词上，差异是细微的，而非戏剧性的——这本身就是一个有用的发现：两个 2.5 变体在氛围、光照和构图上都远远领先于更早的 2.0，并且共享同一个核心弱点（真实的微观机械与镜头光学精度）。如果非要在这张图上做选择：

Flare 给出的放大镜在物理上略微更自洽，我会因为迭代速度和大多数日常生成而选择它。

如果你最看重细节一致性和编辑控制，那就选 Sunburst——预计这种差距在编辑/多轮工作中会比像这样的一次性生成中拉得更大。”

溯源

原作者

sydsachar

发布日期

2026-09-16

原始出处

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收录来源

xianyu110/awesome-gpt-image2.5 · 2100216986538029207 · MIT