Alibaba

Wan 2.6

Name: Wan 2.6
Brand: Alibaba
Price: 0.03 USD
Availability: InStock

AI Image & Video Model

Mode:

All Text to Image Image Edit Text to Video Image to Video Video to Video

Image Video Featured #8 Image-to-Image #2 Image-to-Image

Alibaba's multimodal generation model from the Wan AI suite, supporting text-to-video, image-to-video, reference-to-video with audio, and text-to-image, in both Chinese and English

Wan 2.6 generated video of A hyper-realistic close-up of a neon-lit glass signage that reads "LUMINESCEN...

Wan 2.6 generated video of A hyper-realistic close-up of a neon-lit ramen shop window with a glowing sig...

Try Model

Supported Modes

Text to Image Image Edit Text to Video Image to Video Video to Video

Active

Details

Model ID

wan-2.6

Creator

Alibaba

Family

wan

Released

December 2025

Ready to integrate?

Access wan-2.6 via our unified API.

Create Account

Available at 1 provider

Starting from

$0.100 /second via Alibaba Cloud

Popular formats

720p (1280×720)

~$0.100

1080p (1920×1080)

~$0.150

Prices shown are in USD

Full pricing details

Provider Performance

Fastest generation through alibaba at 6,083ms median latency with 98.8% success rate.

Aggregated from real API requests over the last 30 days.

Generation Time

alibaba

p95: 43,475ms

Success Rate

alibaba

98.8%

79 / 80 requests

Time to First Byte

alibaba

9,092ms

p95: 10,051ms

Provider Rankings

#	Provider	p50 Gen Time	p95 Gen Time	Success Rate	TTFB (p50)
1	alibaba	6,083ms	43,475ms	98.8%	9,092ms

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

Providers & Pricing (1)

Wan 2.6 is available exclusively through Alibaba Cloud, starting at $0.03/video.

Alibaba Cloud

Video to Video

alibaba/wan-2.6-r2v

Provider Model ID: wan2.6-r2v

Input

Second 1080p

$0.150

Second 720p

$0.100

Output

Second 1080p

$0.150

Second 720p

$0.100

View official pricing • As of Mar 31, 2026

Wan 2.6 API Async video generation

Integrate Wan 2.6 into your workflow for text-to-image generation and advanced image editing via Lumenfall's unified OpenAI-compatible API. This endpoint supports both direct text prompting and reference image guidance to maintain stylistic consistency across your generated media.

Base URL

https://api.lumenfall.ai/v1

Model

wan-2.6

Code Examples

Video to Video

/v1/videos/generations

# Step 1: Submit video-to-video request

VIDEO_ID=$(curl -s -X POST \

  https://api.lumenfall.ai/v1/videos \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

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

  -d '{

    "model": "wan-2.6",

    "prompt": "Apply cinematic color grading to @Video1",

    "video_url": "https://example.com/source.mp4",

    "keep_audio": true,

    "aspect_ratio": "16:9"

  }' | jq -r '.id')

echo "Video ID: $VIDEO_ID"

# Step 2: Poll for completion

while true; do

  RESULT=$(curl -s \

    https://api.lumenfall.ai/v1/videos/$VIDEO_ID \

    -H "Authorization: Bearer $LUMENFALL_API_KEY")

  STATUS=$(echo $RESULT | jq -r '.status')

  echo "Status: $STATUS"

  if [ "$STATUS" = "completed" ]; then

    echo $RESULT | jq -r '.output.url'

    break

  elif [ "$STATUS" = "failed" ]; then

    echo $RESULT | jq -r '.error.message'

    break

fi

  sleep 5

done

const BASE_URL = 'https://api.lumenfall.ai/v1';

const API_KEY = 'YOUR_API_KEY';

// Step 1: Submit video-to-video request

const submitRes = await fetch(`${BASE_URL}/videos`, {

  method: 'POST',

  headers: {

    'Authorization': `Bearer ${API_KEY}`,

    'Content-Type': 'application/json'

},

  body: JSON.stringify({

    model: 'wan-2.6',

    prompt: 'Apply cinematic color grading to @Video1',

    video_url: 'https://example.com/source.mp4',

    keep_audio: true,

    aspect_ratio: '16:9'

})

});

const { id: videoId } = await submitRes.json();

console.log('Video ID:', videoId);

// Step 2: Poll for completion

while (true) {

  const pollRes = await fetch(`${BASE_URL}/videos/${videoId}`, {

    headers: { 'Authorization': `Bearer ${API_KEY}` }

});

  const result = await pollRes.json();

  if (result.status === 'completed') {

    console.log('Video URL:', result.output.url);

    break;

  } else if (result.status === 'failed') {

    console.error('Error:', result.error.message);

    break;

  await new Promise(r => setTimeout(r, 5000));

import requests

import time

BASE_URL = "https://api.lumenfall.ai/v1"

API_KEY = "YOUR_API_KEY"

HEADERS = {

    "Authorization": f"Bearer {API_KEY}",

    "Content-Type": "application/json"

# Step 1: Submit video-to-video request

response = requests.post(

    f"{BASE_URL}/videos",

    headers=HEADERS,

    json={

        "model": "wan-2.6",

        "prompt": "Apply cinematic color grading to @Video1",

        "video_url": "https://example.com/source.mp4",

        "keep_audio": True,

        "aspect_ratio": "16:9"

video_id = response.json()["id"]

print(f"Video ID: {video_id}")

# Step 2: Poll for completion

while True:

    result = requests.get(

        f"{BASE_URL}/videos/{video_id}",

        headers=HEADERS

    ).json()

    if result["status"] == "completed":

        print(f"Video URL: {result['output']['url']}")

        break

    elif result["status"] == "failed":

        print(f"Error: {result['error']['message']}")

        break

    time.sleep(5)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for video generation	T2I Edit T2V I2V V2V
`negative_prompt`	string	Negative prompt to guide generation away from undesired content	T2I Edit T2V I2V V2V
`seed`	integer	Random seed for reproducibility	T2I Edit T2V I2V V2V
`duration`	number	Video duration in seconds	T2I Edit T2V I2V V2V

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Video dimensions as WxH pixels (e.g. "1920x1080") 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 T2V I2V V2V
`aspect_ratio`	string	Aspect ratio of the output video (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 T2V I2V V2V
`resolution`	string	Output resolution tier (e.g. "1K", "4K") Controls scale independently of shape. Higher tiers produce larger videos 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 T2V I2V V2V

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
`input_reference`	array	Required for I2V. Input image(s) to animate into video	T2I Edit T2V I2V V2V
`input_video`	string	Required. Input video URL to transform	T2I Edit T2V I2V V2V

Multi-Shot Control

Parameter	Type	Description	Modes
`shot_type` alibaba	string	Whether the generated video uses a single continuous shot or multiple switching shots. `multi` `single` Default: `"single"`	T2I Edit T2V I2V V2V

Output & Format

Parameter	Type	Description	Modes
`n`	integer	Number of videos to generate Default: `1` Gateway generates multiple videos in parallel even if provider only supports 1.	T2I Edit T2V I2V V2V

Additional Parameters

Provider-specific passthrough fields, available only when the request is routed to the listed provider.

Parameter	Type	Description	Modes
alibaba
`reference_video_urls`	array	Deprecated reference-video-only field. Use reference_urls for new integrations.	T2I Edit T2V I2V V2V
`watermark`	boolean	Whether to add the provider watermark to the generated media.	T2I Edit T2V I2V V2V

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

Wan 2.6 is best for

See all Use Cases

Alibaba's Wan 2.6 excels in photorealism where it ranks 6th with a 48.8% win rate, though it struggles with text rendering ranking 20th and portrait generation at an 18.2% win rate. It serves as a middle-tier option for anime and commercial branding tasks, ranking 10th and 13th in those respective categories.

Cinematic

Edit · of 2 · 25.0% wins

#7 of 19

Photorealism

Edit · 48.8% win rate

Wan 2.6 FAQ

How much does Wan 2.6 cost?

Wan 2.6 starts at $0.03 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use Wan 2.6 via API?

You can use Wan 2.6 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "wan-2.6". Code examples are available in Python, JavaScript, and cURL.

Which providers offer Wan 2.6?

Wan 2.6 is available through Alibaba Cloud and fal.ai on Lumenfall. Lumenfall automatically routes requests to the best available provider.

Overview

Wan 2.6 is a text-to-image generation model developed by Alibaba as part of the broader Wan AI suite. It is designed for high-fidelity image synthesis from bilingual prompts (English and Chinese) and supports image-to-image workflows through optional reference guidance. The model’s primary distinction lies in its balanced handling of complex prompt adherence and its ability to maintain stylistic consistency when provided with an initial image.

Strengths

Bilingual Prompt Processing: The model demonstrates native-level understanding of both Chinese and English, allowing for nuanced cultural references and idiomatic descriptions without translation artifacts.
Style Reference Integration: Unlike basic text-to-image models, Wan 2.6 can ingest a reference image to guide the aesthetic, lighting, and composition of the generated output while departing from the source content based on text instructions.
Spatial and Compositional Control: It excels at placing subjects accurately within a frame according to descriptive spatial prompts (e.g., “in the bottom-left foreground”).
Texture and Surface Detail: The model is particularly capable of rendering varied surface materials, such as metallic reflections, fabric weaves, and skin textures, with high clarity.

Limitations

Text Rendering: While proficient at photorealistic imagery, the model may struggle with rendering complex, long-form legible text within images compared to models specifically optimized for typography.
Contextual Complexity: In scenes with a high number of distinct interacting subjects (e.g., a crowd where everyone is performing a unique action), the model may occasionally blend attributes between subjects.
Compute Requirements: Due to the complexity of its dual-modality input (text and image), inference times may be slightly higher than simpler, prompt-only diffusion models.

Technical Background

Wan 2.6 is built upon a Diffusion Transformer (DiT) architecture, which scales more effectively with data than traditional U-Net structures. It utilizes a large-scale multimodal pre-training strategy that aligns visual features with a bilingual LLM-based encoder to ensure precise semantic mapping. The model’s reference image capability is implemented via a dedicated vision encoder that injects latent style features into the diffusion process without overwriting the text-driven intent.

Best For

Alibaba’s Wan 2.6 is best suited for cross-cultural creative projects, localized marketing assets for both Western and Asian markets, and iterative design workflows where a “mood board” image is used to set the visual tone. It is particularly effective for concept art where stylistic consistency across a series of images is required.

Wan 2.6 is available for immediate testing and integration through Lumenfall’s unified API and playground, allowing developers to experiment with bilingual prompting and image-guided generation in a single interface.

Try Wan 2.6 in Playground

Generate images with custom prompts — no API key needed.

Open Playground