Row-Bot 上 Flare vs Sunburst：放大鏡硬對比

提示詞

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."

中文翻譯

GPT-Image-2.5 - Flare 對比 Sunburst

想在 Row-Bot 裏測試一下 GPT Image 2.5。我注意到它開放了 2 個變體——Sunburst 和 Flare。於是就變成了一次對比。

我給 Row-Bot 的提示詞是：“gpt image 2.5 的 sunburst 和 flare 有什麼區別？我想讓你用這兩個模型依次生成同一張圖，然後對比結果。試一個真正能發揮圖像生成模型實力的複雜提示詞。”

原始輸出：

“完成了，先生。兩張圖都已生成，我也仔細查看過了。以下是坦誠的評判。

這兩個模型究竟是什麼
GPT-Image-2.5 — Flare 對比 Sunburst - Flare：速度優化的變體——延遲比 GPT-Image-2 低約 50%（實測約 19.7 秒 vs 約 37.3 秒），專爲高吞吐、快速迭代的工作而設計。質量高於更早的 2.0，但爲了吞吐量犧牲了一部分保真度。 - 

Sunburst：精度變體——OpenAI“能力最強”的圖像模型。細節渲染更銳利、光照更自然、質感更豐富，對複雜/精細生成的控制也最嚴密，代價是生成時間更長。

兩者都支持透明背景和最高 4K 的輸出，並且都已在你的環境中可用。

對比結果（說實話——它們比我預期的更接近）

簡單的部分兩者都做對了：燭光氛圍、溫暖的琥珀/青色調色、木質與黃銅的質感，以及平滑可信的景深衰減。乍看之下，兩者都像是高端渲染作品。

它們在難點上的分歧：

放大鏡（真正的區分點）：Flare 確實渲染出了透過鏡片的放大、折射視圖——這在物理上更合理——不過被放大的蠟燭與真實的蠟燭位置對不上，看起來有點“貼上去”的感覺。Sunburst 則把鏡片渲染成了一面平面鏡子，沒有鏡頭畸變，也沒有放大效果。就這一個具體元素而言，Flare 更接近概念本意。

齒輪微觀機械結構：兩者以相同的方式失敗——齒牙粘連/不均勻、齒輪組無法齧合，以及看起來像通用紋理塗抹而非真正細密加工的加工痕跡。兩者都沒有實現提示詞所要求的“極其銳利的擒縱機構”。

不合理的部分：兩者都生成了懸浮的滾花錶冠和被扭曲的雕刻文字。Flare 還加了一段奇怪的“香蕉形”黃銅碎片，上面有隨機的寶石孔；Sunburst 則出現了一顆懸浮脫離的螺絲。

結論
在這個特定的複雜提示詞上，差異是細微的，而非戲劇性的——這本身就是一個有用的發現：兩個 2.5 變體在氛圍、光照和構圖上都遠遠領先於更早的 2.0，並且共享同一個核心弱點（真實的微觀機械與鏡頭光學精度）。如果非要在這張圖上做選擇：

Flare 給出的放大鏡在物理上略微更自洽，我會因爲迭代速度和大多數日常生成而選擇它。

如果你最看重細節一致性和編輯控制，那就選 Sunburst——預計這種差距在編輯/多輪工作中會比像這樣的一次性生成中拉得更大。”

溯源

原作者

sydsachar

發佈日期

2026-09-16

原始出處

查看原帖 ↗

收錄來源

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