WAN 2.7 Pro variants & parameters
| Parameter | WAN 2.7 Pro |
|---|---|
| Duration | — |
| Aspect ratios | 1:1 · 16:9 · 9:16 · 4:3 · 3:4 |
| Resolution | — |
| Native audio | — |
| Image-to-video | — |
| Reference-to-video | — |
| Credits per second | — |
| 5-second clip cost | 5 cr/image |
What is WAN 2.7 Pro?
WAN 2.7 Pro is DeepSeek's professional-grade image generation model — 4K output, Thinking Mode for prompt analysis, watermark control, and up to 9 reference images for image-to-image work. Pricing is 5 credits per image, matching Nano Banana Pro and well below Midjourney's high-quality tiers when you factor in the 4K resolution.
The headline feature is Thinking Mode. WAN spends additional compute parsing your prompt before generating — identifying entities, resolving ambiguities, planning composition — which materially improves output quality on complex or unusual prompts. For straightforward prompts the improvement is small; for prompts that mix multiple subjects, specific spatial arrangements or fine constraints, the gap is large.
Five aspect ratios are supported (1:1, 16:9, 9:16, 4:3, 3:4) with three resolution tiers in text-to-image (1K / 2K / 4K, default 2K). In image-to-image (edit) mode the resolution options are 1K and 2K — 4K is not a valid upstream value for the edit endpoint, so the picker is automatically restricted in i2i mode.
Up to 9 reference images can be supplied — the highest reference budget of any image model on FlyAIgh, matching HappyHorse and Seedance on the video side. This makes WAN particularly strong for brand-asset composition, product photography variants and any context where you need consistency across a batch.
WAN is weaker than GPT Image 2 for in-image text rendering and weaker than Midjourney v7 for distinct artistic style. Pick WAN when 4K resolution, prompt-handling depth and multi-reference fidelity matter most.
WAN 2.7 Pro vs Nano Banana vs Midjourney v7
| Capability | WAN 2.7 Pro | Nano Banana | Midjourney v7 |
|---|---|---|---|
| Native audio | |||
| Image-to-video | |||
| Reference-to-video | |||
| First/last frame |