Qwen Image 2 Edit vs WAN 2.7 Image Edit
Generated on PixelDojo. Produced by PixelDojo's generation pipeline.
Qwen Image 2 Edit and WAN 2.7 Image Edit both cost 1 credit per image at their default tiers. We sent both the same reference photo and the same instruction to place her on a bicycle in a rainy crosswalk on August 26, 2026. Qwen Image 2 Edit finished in about 22 seconds at 1024x1024, matching the input image's own size. WAN 2.7 Image Edit finished in about 15 seconds at 2048x2048, its default output tier, four times the pixel count at the same price. Both kept her face, glasses and outfit intact.
The same reference photo and scene instruction on both
Every example below was produced on PixelDojo. Hover to see the prompt.
Place this exact woman on a bicycle crossing a rainy city crosswalk at dusk, neon shop signs reflecting in puddles, cinematic wide shot, keep her face, glasses, red curly hair, denim jacket and yellow shirt identical
Qwen Image 2 Edit
Place this exact woman on a bicycle crossing a rainy city crosswalk at dusk, neon shop signs reflecting in puddles, cinematic wide shot, keep her face, glasses, red curly hair, denim jacket and yellow shirt identical
WAN 2.7 Image Edit
Same reference photo, same scene instruction, same 1 credit price. Qwen Image 2 Edit: 1024x1024 in about 22 seconds. WAN 2.7 Image Edit: 2048x2048 in about 15 seconds.
Why Choose Pixel Dojo for Qwen Image 2 Edit vs WAN 2.7 Image Edit
Professional-quality results with cutting-edge AI technology
Both preserved identity cleanly
Face, glasses, red curly hair and the denim jacket over the yellow shirt all carried over correctly on the first try on both models.
WAN 2.7 Image Edit defaults to a bigger file
2048x2048 at its default size setting against 1024x1024 for Qwen Image 2 Edit, at the identical 1 credit price.
How It Works
How we ran the comparison, so you can repeat it:
Start from one reference photo
The same character photo went to both tools with default settings on each.
Send the identical scene instruction
Both got the same request: place her on a bicycle in a rainy city crosswalk at dusk, keep the face, glasses, hair and outfit identical.
Bracket each request and check the file size
We timed both runs and read the actual pixel dimensions of each output rather than assuming they matched.
The Pixel Dojo Advantage
Same reference photo, same instruction, same 1 credit price. Resolution and the reference image ceiling are where they split.
| Others | Pixel Dojo |
|---|---|
| Qwen Image 2 Edit: 1 credit at the default qwen-image-2.0 tier, about 22 seconds on our run | WAN 2.7 Image Edit: 1 credit at the default wan-2.7-standard tier, about 15 seconds on the identical prompt and photo |
| Qwen Image 2 Edit: output matched the input image's own size, 1024x1024 on our run | WAN 2.7 Image Edit: defaults to a 2K size tier, 2048x2048 on our run, four times the pixels at the same price |
| Qwen Image 2 Edit: up to 3 reference images per request | WAN 2.7 Image Edit: up to 9 reference images per request |
| Qwen Image 2 Edit: a Pro model tier at 2 credits per image for higher fidelity | WAN 2.7 Image Edit: a Pro model tier at 2 credits per image, same pattern |
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Common Questions
Everything you need to know about Qwen Image 2 Edit vs WAN 2.7 Image Edit
Which is cheaper, Qwen Image 2 Edit or WAN 2.7 Image Edit?
Neither, they tie at 1 credit per image at their default tiers. Both also offer a Pro variant at 2 credits for higher fidelity.
Which one is faster?
WAN 2.7 Image Edit, in our test: about 15 seconds against about 22 seconds for Qwen Image 2 Edit on the identical reference photo and instruction.
Which one gives a bigger image by default?
WAN 2.7 Image Edit. Its default size setting produced 2048x2048 on our run, while Qwen Image 2 Edit matched the input photo's own size of 1024x1024, at the same 1 credit price.
How many reference images can each one take?
Qwen Image 2 Edit accepts 1 to 3 images per request. WAN 2.7 Image Edit accepts up to 9, useful if you are combining more than a few source images in one call.
Did both keep the person's identity intact?
Yes. Face, glasses, hair color and outfit all matched the reference photo on both outputs, with no retries needed.
How do I call either from the API?
POST to /api/v1/models/qwen-image-2-edit/run with an image array, or to /api/v1/models/wan-2.7-image-edit/run with image_urls. Same API key for both.