9 image prompts worth saving: we ran each one over the API

Nine image prompts collected from GitHub prompt repositories and Threads posts, each one run once over the API on 2026-10-26. Every prompt is plain text-to-image: no photo upload needed.

• Every image below is the first result we got from the exact prompt shown, through the QuickSilver Pro API. Nothing was retried or cherry-picked. Prompts are credited to their authors.
• Short prompts ran on GPT Image 2.5 Flare ($0.018 per image, 1024x1024). The three prompts longer than 1,000 characters ran on GPT Image 2 ($0.01 per image), because Flare on QuickSilver Pro accepts prompts up to 1,000 characters and 1024x1024 only.
• All ten images together cost $0.16.
• Replace anything in [brackets] with your own content.
• The last section shows how to run these prompts yourself.

1. City-name letter poster

A giant city name where every letter is filled with a local street scene, with a panorama strip on top. The original Threads post has 23,000 likes, and the replies are full of "do my city".

GPT Image 2, requested 1536x1024, returned 1672x941, 78 seconds, $0.01.

The prompt is written in Traditional Chinese, and the English lettering still came out spelled correctly. To change the city, swap three things: the city name, the scene in each letter, and the panorama strip.

製作一張精緻的香港城市旅遊插畫海報，16:9 橫式構圖，暖米白色紙張背景，四周保留乾淨留白。整體結合復古旅行海報、現代平面設計與細緻建築插畫，使用清晰色塊、柔和漸層、細微印刷顆粒與霧面紙張質感。
【核心文字構圖】
畫面中央以極大型英文大寫「HONG KONG」作為主體。文字必須拼寫正確，八個字母完整入鏡，排列為同一行，兩個單字之間保留清楚間距。使用筆畫厚實、窄長比例、幾何感強烈的無襯線字體，所有字母頂部與底部整齊對齊，佔畫面寬度約96%、高度約50%。
使用精準的字母遮罩填景效果：Typography Masking、Image-filled Typography、Clipping Mask Lettering。香港城市風景只出現在字母的實心筆畫內，字母輪廓清楚銳利；字母外部、字母間隙與字母內部孔洞均維持乾淨米白色。每個字母是一幅精緻的小型城市風景插畫，所有場景共享一致畫風與配色。
【八個字母的場景】
第一個 H：香港街道，紅色雙層電車沿軌道行駛，兩旁高樓、直立中文招牌與行人。
第一個 O：青藍色維港海面、紅色中式帆船，背景為高樓與層疊山巒。
第一個 N：茂密綠色山坡，一列紅色登山纜車沿斜坡軌道行駛，遠處可見城市。
第一個 G：中式廟宇入口，綠色琉璃瓦、紅柱、紅燈籠、石階與石獅。
K：繁忙商業街，密集垂直招牌、商店立面、行人與黃色道路標線。
第二個 O：寧靜海灣、淺色沙灘、碧綠海水、沿岸山坡與前景粉紅花叢。
第二個 N：珊瑚粉與蜜桃色夕陽下的香港天際線，海面呈現柔和倒影，一艘紅帆船航行其中。
最後一個 G：淡粉與奶油白色的歷史風格鐘樓建築，拱形窗、柱廊、階梯與棕櫚樹。
【上方全景】
在巨型文字上方安排一條細長、橫跨畫面的香港城市全景帶。由左至右自然串連中式廟宇、樹木、紅色的士、紅色雙層巴士、渡輪、跨海橋樑、維港高樓、紅帆船與沿岸歷史建築。遠方是青綠山巒，天空留白充足，點綴幾隻飛鳥、少量淡粉雲朵與右上方一輪珊瑚粉色太陽。全景帶與中央字母之間保留米白色間隔。
【色彩與質感】
以湖水藍、青綠、珊瑚粉、蜜桃色、奶油白為主，少量朱紅作為電車、帆船與燈籠的視覺亮點，深藍灰用於局部陰影。配色柔和清爽，帶復古印刷感。建築透視合理，線條俐落，細節豐富但不凌亂。
【底部排版】
中央放置一行較小的深藍灰色英文「HONG KONG」，使用寬字距無襯線字體，左右各搭配一條纖細水平線。
下方以更小字級、寬字距排入：
「A CITY OF VIBES, A PLACE LIKE NO OTHER」
全部文字清晰、準確、水平置中。
【避免】
不要寫實照片、3D立體字、厚重陰影、金屬字體、霓虹招牌風格、油畫筆觸、過度飽和、雜亂拼貼。不要讓風景溢出中央字母邊界，不要填滿字母孔洞，不要變形字母、錯字、缺字、額外字母、裁切標題、水印或額外Logo。

Source: @bunny_digital on Threads; the prompt was shared in the replies by @ailuna4482.

2. Pixar-style 3D avatars

A consistent avatar set: keep the sentence structure and change the person. Replace MODEL with a role (software engineer, barista...) and adjust hair, glasses, expression and clothes.

MODEL = software engineer	MODEL = data analyst

GPT Image 2.5 Flare, 1024x1024, $0.018 each.

Create a head-and-shoulders Pixar-style 3D avatar of a cute, introverted MODEL with dark hair in space buns and oversized round glasses. She features a wide-eyed expression of wonder with a small "O" shaped mouth, wearing a chunky cream-colored knit sweater. Set against a seamless white background, the render showcases flawless 3D skin and soft cinematic studio lighting for a clean, minimalist aesthetic.

Create a head-and-shoulders Pixar-style 3D avatar of a sharp-witted MODEL with curly dark hair and thick-rimmed modern glasses. He features a skeptical expression with one raised eyebrow, a calculating smirk, and eyes looking sideways against a seamless white background. Wearing a dark charcoal hoodie, the render showcases flawless 3D skin and soft cinematic studio lighting for a clean, minimalist aesthetic.

Source: @designbysaqi_ on Threads. The prompts are printed under the original images; these two are transcribed from them.

3. Exploded food infographic

A dish split into floating layers, stacked vertically, with a label line for each layer. Change the food and the layer list for a burger, a ramen bowl or bubble tea.

GPT Image 2.5 Flare, 1024x1024, $0.018. All five labels are spelled as given.

Create a hyper-realistic exploded vertical infographic composition of tacos.
Top → Bottom structure:
Fresh Lettuce (crisp green texture with natural folds)
→ Tomato & Salsa Layer (juicy diced tomatoes and salsa mix)
→ Melted Cheese (smooth cheddar texture)
→ Grilled Meat Filling (juicy seasoned meat detail)
→ Taco Shell Base (crispy golden shell texture)
Perfect vertical alignment, rustic background, soft studio lighting, realistic shadows beneath each floating element.
Add clean infographic text labels with thin pointer lines using these exact labels:
"Lettuce" "Salsa" "Cheese" "Meat" "Shell"
Ultra-detailed food textures, premium commercial aesthetic, 8K.

Source: awesome-gpt-image-2, case 443, by @Strength04_X.

4. A country as a felt miniature world

One cohesive diorama of a country: daily life in front, streets and rivers in the middle, one landmark at the back. Fill in the country on the first line; we used Portugal.

GPT Image 2, 1024x1536, 78 seconds, $0.01. The layout follows the prompt closely (café life in front, a tram street in the middle, a castle and the bridge at the back), but the result looks more like a painted illustration than wool felt. If the felt texture matters, push it harder in the prompt or try another model.

Country: [INSERT COUNTRY NAME]

A miniature felt diorama made of fluffy yarn, wool, and needlework, designed as a single cohesive small world (not a collage), reflecting the real landscape, culture, and daily life of the country.

Structure:
- Select 4–7 naturally integrated elements (landscape, architecture, transport, street life, culture)
- Everything must flow together as one environment

Foreground:
Everyday life — small shops, cafes, markets, people interacting, local clothing, street details, subtle movement, warm human presence

Midground:
Connecting flow — streets, bridges, rivers, paths, transport, cultural spaces that guide the eye naturally from front to back

Background:
One strong identity anchor — landmark, skyline, mountain, or symbolic landscape clearly representing the country (keep it clean, not overcrowded)

Style:
Handmade wool felt, yarn, needle-felt textures, visible fibers, soft edges, miniature craftsmanship, premium diorama realism

Lighting:
Warm golden hour or bright noon, soft shadows, clear visibility, gentle glow enhancing depth

Color:
Soft but saturated palette reflecting the country’s natural tones (greens, sky blues, architectural hues, cultural accents), warm and balanced

Composition:
Vertical frame, balanced or centered perspective, slight top-down or immersive angle, clear foreground–midground–background depth

Mood:
Warm, emotional, calm, refined — like a handcrafted fairy-tale version of real life (not childish)

Rules:
- No logos or text overlays
- No collage-style composition
- No random symbolic clutter
- Keep it realistic but stylized as a handmade miniature world

Quality:
16K, ultra-detailed, hyper-realistic miniature textures, cinematic depth, sharp focus

output_goal:
A single, cohesive felt diorama world that instantly conveys the identity and atmosphere of the chosen country through integrated landscape, culture, and daily life

Source: awesome-gpt-image-2, case 390, by @volkan_iras.

5. A seed packet growing into its garden

A surreal product scene: the illustration on a seed packet turns into the real plant. Fill in the plant, two details and an insect. We used sunflowers, "A sunflower head", "A tight green sunflower bud" and honeybees.

GPT Image 2, requested 1024x1024, returned 1536x1024, 78 seconds, $0.01. The back of the packet even turned "sow in spring" and "full sun" into printed instructions.

Epic 3D scene: a weathered seed packet lying open on a potting bench, its promise erupting into the garden it describes. The illustration on the front becomes real. [PLANT / FLOWER] growing at full scale from the paper, roots visible through the packet's base pushing into soil below.
[DETAIL 1] in full bloom at one corner. [DETAIL 2] mid-growth at the other, not yet what it will be.
Tiny insects that belong to this plant, [BEE / BUTTERFLY / BEETLE], hovering at correct scale.
The written instructions on the back become garden calendar, "sow in spring" manifests as actual spring light. "full sun" manifests as a single shaft of it, hitting the tallest bloom perfectly.
Scattered seeds between packet and soil each showing their germination stage, split coat, first root, first shoot, first leaf.
The packet's torn top edge becomes a treeline.
Potting bench surface with soil scatter and water droplets.
Tilt-shift depth of field, greenhouse morning light, the packet as the garden it always intended.

Source: awesome-gpt-image-2-API-Prompts, case 143 (CC0), by @AllaAisling.

6. A miniature city hidden in hair

A macro photo with a tiny city between the hair strands. The run of "no glow", "no CGI", "no perfect surfaces" at the end is what makes it look like a real photo, so keep it.

GPT Image 2.5 Flare, 1024x1024, $0.018.

Macro photograph of a miniature city hidden in human hair, clearly on a real human head, with part of the forehead and hairline visible, realistic skin texture with pores, tiny people walking through the streets between the hair strands, extremely small but realistic proportions, macro photography, 85mm lens, shallow depth of field, natural lighting, neutral colors, no warm tones, ultra realistic hair with visible roots, natural imperfections, slightly messy strands, realistic materials, slightly dirty buildings, no perfect surfaces, photorealistic, looks like a real photo, no illustration, no CGI, no glow

Source: awesome-gpt-image-2, case 392, by @krafterlab.

7. Tiny text on a grain of rice

One sentence that tests how well a model handles very small text. Replace "wOw" with your own word.

GPT Image 2.5 Flare, 1024x1024, 23 seconds, $0.018. The word is on one grain just above the centre.

A massive pile of rice, and on one single grain of rice there is tiny text that reads "wOw"

Source: awesome-gpt-image, by @adonis_singh.

8. One-liners that fill in the layout themselves

Short prompts where the model invents the whole interface or document.

A YouTube video by a time traveller:

GPT Image 2.5 Flare, 1024x1024, $0.018. It invented the title, channel, view count and comments.

Screenshot of a YouTube video showing someone who time-traveled to the Middle Ages

A Chinese national exam paper (replace the subject; we used mathematics):

GPT Image 2.5 Flare, 1024x1024, $0.018. Header, instructions, numbered questions and a geometry figure, all in Chinese.

Generate an image of a 2026 Gaokao exam paper for [subject name]

Sources: YouTube screenshot by @flowersslop and exam paper by @MrLarus, both via awesome-gpt-image.

9. A quick test for text rendering

To see which model spells text correctly, this prompt is the fastest check: three lines on a coffee bag, so a wrong letter is obvious. These four images come from the same prompt with the same settings (size="1024x1024", n=1).

GPT Image 2.5 Flare	Qwen Image 3.0	FLUX.2 Klein	SDXL Turbo
All three lines correct	All three lines correct	All three lines correct	Garbled

Product photo of a matte black coffee bag standing on a wooden table. The front label reads "NORTHWIND ROASTERS" in large white capital letters, and below it, in smaller text, "Ethiopia Yirgacheffe" and "250 g". Soft studio lighting, shallow depth of field.

Source: our own test in Image generation API compared: 11 models, the same 3 prompts, which has the full results.

Tips for editing these prompts

1. Fill in every placeholder. [brackets], MODEL and similar markers get drawn literally if you leave them in.
2. Keep the structure of long prompts. In sections 1 and 4 each block controls one thing (layout, colour, what to avoid). Change the content, not the blocks.
3. Quote any text that must appear, for example "HONG KONG", so the model does not rephrase it.
4. Check the model's limits before you pick a shape. On QuickSilver Pro, GPT Image 2.5 Flare takes 1024x1024 and prompts up to 1,000 characters only. GPT Image 2 takes longer prompts and other sizes, but it does not always return the size you ask for: in our runs 1536x1024 came back as 1672x941 and 1024x1024 as 1536x1024.
5. Keep the "avoid" list. Listing what you do not want (watermarks, 3D text, oversaturation) is more reliable than describing only what you want.

Running them over an API

ChatGPT and Gemini work for one image at a time. To generate a batch (a poster for every city on a list, a full avatar set) or compare models on the same prompt, an API is easier. The examples below use QuickSilver Pro: one key for a dozen image models behind the OpenAI-compatible /v1/images/generations endpoint, billed per image.

Prices per image on 2026-10-26:

Model	Model ID	Per image
GPT Image 2	gpt-image-2	$0.01
GPT Image 2.5 Flare	gpt-image-2.5-flare	$0.018
Nano Banana 2.1	gemini-nano-banana-2.1	$0.036
Seedream 5.0 Pro	seedream-5.0-pro	$0.07

curl:

curl https://api.quicksilverpro.io/v1/images/generations \
  -H "Authorization: Bearer $QSP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2.5-flare",
    "prompt": "A massive pile of rice, and on one single grain of rice there is tiny text that reads \"wOw\"",
    "size": "1024x1024",
    "n": 1
  }' | jq -r '.data[0].b64_json' | base64 --decode > rice.png

Python, with the official OpenAI SDK and a different base_url:

import base64
import urllib.request

from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key="YOUR_QSP_KEY",
)

result = client.images.generate(
    model="gpt-image-2.5-flare",
    prompt=open("prompt.txt", encoding="utf-8").read(),
    size="1024x1024",
    n=1,
)

image = result.data[0]
if image.b64_json:
    data = base64.b64decode(image.b64_json)
else:  # Seedream and Bria return a download link instead
    data = urllib.request.urlopen(image.url).read()

with open("out.png", "wb") as f:
    f.write(data)

For a batch, loop over a list of prompts and call client.images.generate for each.

Which model to use

From the comparison in section 9 and today's runs:

• Images with text (posters, packaging, labels): GPT Image 2.5 Flare got the text and object counts right in every run of the comparison. In today's runs it took 23 seconds for a single request and 37 to 60 seconds with five requests in parallel.
• Long prompts or non-square images: GPT Image 2 at $0.01 per image. It took about 78 seconds per image today and did not keep the requested size.
• Seedream and Bria return a URL instead of the image. Download it straight away; we did not test how long the links stay valid.

Model list, prices and docs: quicksilverpro.io.