Qwen Image 3 Pro vs GPT Image 2: Same-Prompt Test and Price

Both are on OmniaKey: Qwen Image 3 Pro and GPT Image 2. The Qwen figure is Alibaba's official 2K rate in five regions, and the GPT figure comes from the usage our own request returned.

GPT Image 2 is not OpenAI's newest image model: gpt-image-2.5-sunburst and gpt-image-2.5-flare shipped on September 8, 2026, per OpenAI's changelog (checked November 3, 2026). Alibaba pitches the Pro model on dense layouts such as menus and exam papers, with text down to 10 px (Model Studio release notes, checked November 3, 2026).

How we tested

• Models and IDs: Qwen Image 3 Pro (qwen-image-3-pro on OmniaKey, qwen-image-3.0-pro at Alibaba) and GPT Image 2 (gpt-image-2).
• November 3, 2026. Five prompts, one run each, so ten pictures plus one shared edit source.
• Qwen settings: size=2048*2048, prompt_extend=false, watermark=false. GPT settings: size=2048x2048, quality=medium.
• The edit source was a 1024x1024 picture of a teapot made by GPT Image 2.
• Time runs from submit to succeeded, so it includes queue and network.
• A separate vision model read every picture at native resolution and transcribed every word. For the poster, the sign and the fine print it also re-read a native-resolution crop.
• OmniaKey sells API access to both models, so we ran the tests through it. Every price is Alibaba's or OpenAI's own, not ours.
• Limits and prices come from Alibaba's Qwen image API reference and Model Studio pricing, plus OpenAI's image generation guide and pricing page, all checked November 3, 2026.

This is one run per prompt, so read the results as examples rather than a benchmark.

Five tests, same prompts

Test 1: English bakery poster (1:1)

This test checks whether both models set three lines of English type exactly as written.

Prompt:

A printed poster for a neighbourhood bakery's weekend baking class, photographed flat on a wooden table. Top line in large mixed-case letters: "THE FLOUR HOUR". Under it, two smaller lines: "Sourdough from scratch · Saturday 21 March" and "9:00 to 13:00 · 12 seats". A simple line drawing of a loaf in the middle, cream paper, dark brown ink, generous white margins, everything spelled exactly as written here.

• Qwen Image 3 Pro (29.0 s): all 80 characters right in the native-resolution crop. The poster lies flat on a wooden table with wood grain around it, and the headline is set in a handwritten face over a fine-line loaf.
• GPT Image 2 (57.6 s): the same 80 characters right, on cream paper with an engraved-print loaf and extra hairlines and wheat ears above and below the type.

Verdict: Tie on text. GPT's file is more decorated and took 57.6 s, Qwen's is plainer and took 29.0 s.

Test 2: Chinese noodle-shop sign (1:1)

This test checks five lines of Chinese menu text, so we wrote the prompt in Chinese instead of translating it.

Prompt:

一家面馆门口的木质菜单牌，竖排手写中文。最上面一行大字“老陈牛肉面”，下面四行小字分别是“红烧牛肉面 18元”“番茄牛腩面 20元”“酸菜肉丝面 16元”“加蛋 2元”。招牌下方挂着一串红辣椒，背景是虚化的街道，白天自然光，写实摄影。

In English it asks for a wooden menu board outside a noodle shop: five handwritten lines, a string of red chillies, a blurred street and daylight.

• Qwen Image 3 Pro (28.4 s): all five lines right, one dish per line with its price after the name, black handwritten type on a wooden board against a blurred street with red chillies at the edge. A native-resolution crop confirmed all five lines character by character.
• GPT Image 2 (32.8 s): all five lines right, split into two columns with the prices right-aligned under a cream rule, which reads like a printed menu. The crop check found the same clean result.

Verdict: Tie. Both got every character right, and the two-column menu is a layout preference.

Test 3: Blacksmith portrait (1:1)

This test checks skin detail, the horseshoe in his hand, and whether each model builds the workshop the prompt describes.

Prompt:

Candid portrait of a blacksmith in his fifties holding a finished horseshoe, standing in a dim workshop with a glowing forge behind him. Soot on his forearms, sweat on his forehead, leather apron worn smooth, deep wrinkles around his eyes. Shot on an 85mm lens at f/2, warm forge light from behind, cool window light on his face.

• Qwen Image 3 Pro (28.7 s): forehead lines, crow's feet, pores and stubble all read as a photograph. The iron in his hand breaks into two pieces with no connecting curve, so it does not read as a horseshoe, and the fingers overlap and bend.
• GPT Image 2 (24.0 s): the same photographic skin and stubble, with one complete horseshoe in his grip and some fingers hidden behind it. The forge sits on the right and the window on the left, the mirror image of Qwen's framing.

Verdict: GPT Image 2, the only complete horseshoe. That was one run, and everything else in the portrait matched.

Test 4: Teapot edit (1:1)

This test checks whether the teapot survives a new background, and how long an edit takes. Both models edited the same 1024x1024 source picture.

Prompt:

Put this exact teapot on a pale linen tablecloth beside a small bowl of loose tea and a wooden scoop, with soft morning light from the left and a simple plaster wall behind. Keep the teapot, its lid, its handle, its glaze and every mark on it exactly as they are.

• Qwen Image 3 Pro (56.6 s): kept the teapot, its lid, spout, handle and glaze, then added the linen cloth, the bowl of loose tea, the wooden scoop and morning light from the left.
• GPT Image 2 (31.0 s): the same props and the same light direction, with the teapot's shape and glaze intact. It finished in 31.0 s, 25.6 s before Qwen.

Verdict: Tie on keeping the pot. GPT Image 2 is the faster edit by 25.6 s.

Test 5: Fine-print spec card (1:1)

This test checks a title, four rows in two columns and a footnote set in small grey type.

Prompt:

A clean product comparison card, portrait orientation, white background. Title at the top in bold: "TRAIL RUNNER X2". Below it a table with four rows and two columns. Left column: "Weight", "Drop", "Price", "Sizes". Right column: "268 g", "6 mm", "$149", "EU 39-47". Under the table a single line in small grey type: "Prices include VAT. Weights are for a EU 42." All text in a modern sans-serif, left aligned, nothing else in the frame.

• Qwen Image 3 Pro (24.9 s): every word and figure right, including 268 g, 6 mm, $149, EU 39-47 and the footnote's "a EU 42". Four rows, thin light-grey rules, labels left-aligned.
• GPT Image 2 (26.3 s): every word and figure right, also including the footnote. The label column carries a light grey fill and the price column is wider.

Verdict: Tie. Both handled the fine print, and the table styling is the only difference.

Results by test

Price and speed

Alibaba prices Qwen Image 3 Pro per image at $0.068761 for 2K output in China, Hong Kong, Frankfurt, Virginia and Tokyo, and $0.075 in Singapore (Model Studio pricing, checked November 3, 2026). An edit adds an input-image charge: $0.00275 per input image in those five regions, $0.003 in Singapore. Our teapot edit therefore lands at $0.068761 + $0.00275 = $0.071511 in the five regions and $0.075 + $0.003 = $0.078 in Singapore.

OpenAI bills GPT Image 2 by token instead of by picture: $30 per million image output tokens, $8 per million image input tokens and $5 per million text input tokens (OpenAI pricing, checked November 3, 2026). A generated picture returned 2,058 image output tokens and 97 text input tokens, so 2,058 × $30 ÷ 1,000,000 = $0.0617 and 97 × $5 ÷ 1,000,000 = $0.0005, about $0.0622 all in. The edit returned 2,058 image output, 194 image input and 60 text input tokens: 2,058 × $30 + 194 × $8 + 60 × $5 per million = $0.0636.

Treat those two numbers with one caveat: Qwen's $0.068761 buys a 2048×2048 file, while GPT Image 2's $0.0622 bought a 1254×1254 file in four of five runs, under half the pixels. Per pixel the two are not close. The OpenAI guide's per-image table prices only 1024x1024, 1024x1536 and 1536x1024, so the 2048 figure has to come from the usage a request returns.

Speed split the two by job. Qwen's median was 28.55 s for a new image and 56.6 s for the edit. GPT's was 29.55 s for a new image and 31.0 s for the edit, so the edit is where the gap opens.

Size is the practical difference. GPT Image 2 returned 1254×1254 on four of five requests for size=2048x2048 (the poster, the sign, the edit and the card), and 2048×2048 only on the portrait. Qwen returned 2048×2048 every time, so a print pipeline that needs a fixed 2048 px file is simpler on Qwen.

Pros and cons

Qwen Image 3 Pro

• Pros: 2048×2048 on every request, every character right in the three text tests, and a 28.55 s median for new images.
• Cons: 56.6 s for the edit, and the broken horseshoe in the portrait.

GPT Image 2

• Pros: the only complete horseshoe, a 31.0 s edit, and about $0.0622 per image at medium quality.
• Cons: four of five files came back at 1254×1254, and the poster test took 57.6 s.

Which one for which job

Run both from one key

OmniaKey serves both models on one media-task endpoint, so only model and parameters change. This creates one 2K poster with Qwen Image 3 Pro:

curl https://api.omniakey.com/v1/media/tasks \
  -H "Authorization: Bearer $OMNIAKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: flour-hour-qwen-001" \
  -d '{
    "model": "qwen-image-3-pro",
    "type": "image",
    "operation": "generate",
    "input": {"prompt": "A printed poster for a neighbourhood bakery. Top line: THE FLOUR HOUR."},
    "parameters": {"size": "2048*2048", "prompt_extend": false, "watermark": false}
  }'

The same request on GPT Image 2, with its own size and quality fields:

curl https://api.omniakey.com/v1/media/tasks \
  -H "Authorization: Bearer $OMNIAKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: flour-hour-gpt-001" \
  -d '{
    "model": "gpt-image-2",
    "type": "image",
    "operation": "generate",
    "input": {"prompt": "A printed poster for a neighbourhood bakery. Top line: THE FLOUR HOUR."},
    "parameters": {"size": "2048x2048", "quality": "medium"}
  }'

1. The reply is 202 with a task id.
2. Poll https://api.omniakey.com/v1/media/tasks/{id} every few seconds until status is succeeded.
3. Download the picture from assets[0].url and check its pixel size before you ship it. GPT Image 2 answered 1254×1254 for a 2048 request in four of our five runs.

For an edit, set operation to edit and send the source picture in input.image, with the same prompt_extend and watermark fields on Qwen. The Qwen Image 3 Pro and GPT Image 2 model pages list the current per-call prices. To run a prompt without code, open the AI Image Generator.

Common questions

Which model writes text more accurately, Qwen Image 3 Pro or GPT Image 2?

Neither pulled ahead in our five tests. Both spelled all 80 characters of the English poster, all five lines of the Chinese menu and the whole fine-print card correctly.

Is Qwen Image 3 Pro cheaper than GPT Image 2?

Per file, no. At OpenAI's token rates a GPT Image 2 picture cost about $0.0622 and an edit $0.0636, while Qwen costs Alibaba's 2K list price of $0.068761 a picture and $0.0715 an edit once the $0.00275 input image is added. But in four of five runs GPT returned 1254×1254 where Qwen returned 2048×2048, so Qwen gave more pixels for the money.

Why did GPT Image 2 return 1254×1254 when I asked for 2048×2048?

Ours did the same on four of five requests. Read the width and height from the downloaded file before you promise a size, and send the request again if the file has to be 2048 px.

Can I call both models with one key?

Yes. Both run on OmniaKey's media-task endpoint, so you change model and parameters and keep the same polling steps. The Qwen Image 3 Pro and GPT Image 2 pages list the current prices.

Sources checked

• Qwen Image generation and editing API reference, Alibaba Model Studio, checked November 3, 2026.
• Model Studio model pricing, Alibaba Model Studio, checked November 3, 2026.
• Newly released models, Alibaba Model Studio, checked November 3, 2026.
• Image generation guide, OpenAI, checked November 3, 2026.
• Pricing, OpenAI, checked November 3, 2026.
• Changelog, OpenAI, checked November 3, 2026.

Update log

• 2026-11-03: First version, with five same-prompt tests, times and per-image costs.