Alibaba

Wan 2.7 Pro

Name: Wan 2.7 Pro
Brand: Alibaba
Price: 0.075 USD
Availability: InStock

AI Image Editing Model

Image

Alibaba's Wan 2.7 Pro image generation and editing model with higher-quality outputs and support for 4K image generation

Try Model

4096 x 4096

Max Resolution

Max Images per Request

Supported Modes

Text to Image Image Edit

Active

Details

Model ID

wan-2.7-pro

Also known as: wan2.7-image-pro

Creator

Alibaba

Family

wan

Released

April 2026

Max Input Images

Ready to integrate?

Access wan-2.7-pro via our unified API.

Create Account

Available at 1 provider

Starting from

$0.075 /image via Alibaba Cloud

Prices shown are in USD

Full pricing details

Provider Performance

Fastest generation through alibaba at 11,444ms median latency with 57.7% success rate.

Aggregated from real API requests over the last 30 days.

Generation Time

alibaba

p95: 36,440ms

Success Rate

alibaba

57.7%

15 / 26 requests

Time to First Byte

alibaba

10,853ms

p95: 35,708ms

Provider Rankings

#	Provider	p50 Gen Time	p95 Gen Time	Success Rate	TTFB (p50)
1	alibaba	11,444ms	36,440ms	57.7%	10,853ms

Data updated every 15 minutes. Based on all API requests through Lumenfall over the last 30 days.

Providers & Pricing (1)

Wan 2.7 Pro is available exclusively through Alibaba Cloud, starting at $0.075/image.

Alibaba Cloud

Text to Image Image Edit

alibaba/wan-2.7-pro-image

Provider Model ID: wan2.7-image-pro

$0.075 /image

wan2.7-image-pro API OpenAI-compatible

Lumenfall provides an OpenAI-compatible API for Wan 2.7 Pro, enabling programmatic text-to-image generation and advanced image editing operations.

Base URL

https://api.lumenfall.ai/openai/v1

Model

wan-2.7-pro

Code Examples

Text to Image

/v1/images/generations

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/generations \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -H "Content-Type: application/json" \

  -d '{

    "model": "wan-2.7-pro",

    "prompt": "",

    "size": "1024x1024"

}'

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.generate({

  model: 'wan-2.7-pro',

  prompt: '',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.generate(

    model="wan-2.7-pro",

    prompt="",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Image Edit

/v1/images/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=wan-2.7-pro" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'wan-2.7-pro',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.edit(

    model="wan-2.7-pro",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for image generation	T2I Edit

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9") WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.	T2I Edit
`aspect_ratio`	string	Aspect ratio of the output image (e.g. "16:9", "1:1") Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.	T2I Edit
`resolution`	string	Output resolution tier (e.g. "1K", "4K") Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.	T2I Edit

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

Media Inputs

Parameter	Type	Description	Modes
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP.	T2I Edit

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	T2I Edit
`output_format`	string	Output image format `png` `jpeg` `gif` `webp` `avif` Gateway converts to requested format if provider doesn't support it natively.	T2I Edit
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	T2I Edit
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit

Parameter Normalization

How we handle parameters across different providers

Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:

Behavior	What happens	Example
`passthrough`	Sent as-is to the provider	style, quality
`renamed`	Same value, mapped to the field name the provider expects	prompt
`converted`	Transformed to the provider's native format	size
`emulated`	Works even if the provider has no concept of it	n, response_format

Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.

Full API Reference Authentication, endpoints, and more

Gallery

View all 4 images

Wan 2.7 Pro FAQ

How much does Wan 2.7 Pro cost?

Wan 2.7 Pro starts at $0.075 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use Wan 2.7 Pro via API?

You can use Wan 2.7 Pro through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "wan-2.7-pro". Code examples are available in Python, JavaScript, and cURL.

Which providers offer Wan 2.7 Pro?

Wan 2.7 Pro is available through Alibaba Cloud on Lumenfall. Lumenfall automatically routes requests to the best available provider.

What is the maximum resolution for Wan 2.7 Pro?

Wan 2.7 Pro supports images up to 4096x4096 resolution.

Overview

Wan 2.7 Pro is a high-resolution diffusion model developed by Alibaba designed for advanced image synthesis and sophisticated image-to-image editing. It represents a significant iteration in the Wan model family, distinguished by its native support for 4K resolution output and enhanced spatial coherence. The model allows users to generate visual content from natural language descriptions or modify existing images through precise editing workflows.

Strengths

High-Resolution Fidelity: Supports native 4K image generation, maintaining sharp textures and fine details that often blur or artifact in lower-resolution models.
Multi-Image Contextual Awareness: Excels at tasks requiring the synthesis of information across multiple input images, making it effective for consistent character rendering or style transfer.
Precise Image Editing: The model provides high control during image-to-image tasks, allowing for structural modifications while preserving the overall composition and lighting of the source material.
Complex Prompt Adherence: Demonstrates improved understanding of lengthy, descriptive prompts, accurately mapping nested attributes and spatial relationships to the final output.

Limitations

Hardware and Latency Requirements: Due to the complexity of 4K synthesis and the model’s architecture, generation times are typically longer compared to “Turbo” or distilled small-scale models.
Specific Aesthetic Bias: Like many models in the Wan family, it may lean toward a specific digital art style or photorealistic polish that might require prompt engineering to override for more stylized or abstract requests.

Technical Background

Wan 2.7 Pro is built on an evolution of the DiT (Diffusion Transformer) architecture, optimized for handling massive spatial dimensions without losing global consistency. The training process involved a multi-stage approach, utilizing a curated dataset of high-resolution imagery and detailed captioning to improve the alignment between text tokens and visual patches. This version introduces refined attention mechanisms to manage the computational overhead of 4K processing while maintaining high signal-to-noise ratios.

Best For

This model is best suited for professional workflows where output resolution and detail are non-negotiable, such as digital marketing assets, background plates for VFX, and high-end conceptual art. Its image-editing capabilities make it a strong choice for iterative design cycles where an artist needs to transform a sketch or a low-fidelity reference into a production-ready asset. Wan 2.7 Pro is available for testing and integration through Lumenfall’s unified API and interactive playground.

Try Wan 2.7 Pro in Playground

Generate images with custom prompts — no API key needed.

Open Playground