Alibaba

Wan 2.6

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

AI Image & Video Model

Image Video Featured #9 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

Try Model Benchmark examples

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

Ready to integrate?

Access wan-2.6 via our unified API.

Create Account

Available at 2 providers

Image Generation

Starting from

$0.030 /image via Alibaba Cloud, fal.ai

Prices shown are in USD

Available at 1 provider

Video Generation

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

Provider Performance

Fastest generation through alibaba at 1,126ms median latency with 93.8% success rate.

Aggregated from real API requests over the last 30 days.

Generation Time

alibaba

p95: 35,304ms

Success Rate

alibaba

93.8%

136 / 145 requests

Provider Rankings

#	Provider	p50 Gen Time	p95 Gen Time	Success Rate	TTFB (p50)
1	alibaba	1,126ms	35,304ms	93.8%	—

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

Providers & Pricing (6)

Wan 2.6 is available from 6 providers, with per-video pricing starting at $0.03 through Alibaba Cloud.

Alibaba Cloud

Text to Image

alibaba/wan-2.6-image

Provider Model ID: wan2.6-t2i

$0.030 /image

fal.ai

Image Edit

fal/wan-2.6-edit

Provider Model ID: wan/v2.6/image-to-image

$0.030 /image

View pricing details

fal.ai

Text to Image

fal/wan-2.6-image

Provider Model ID: wan/v2.6/text-to-image

$0.030 /image

View pricing details

Alibaba Cloud

Text to Video

alibaba/wan-2.6

Provider Model ID: wan2.6-t2v

Output

Second 1080p

$0.150

Second 720p

$0.100

View official pricing • As of Mar 31, 2026

Alibaba Cloud

Image to Video

alibaba/wan-2.6-i2v

Provider Model ID: wan2.6-i2v

Output

Second 1080p

$0.150

Second 720p

$0.100

View official pricing • As of Mar 31, 2026

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 Image & 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/openai/v1 Image

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

Model

wan-2.6

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-2.6",

    "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-2.6',

  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-2.6",

    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-2.6" \

  -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-2.6',

  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-2.6",

    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)

Text to Video

/v1/videos/generations

# Step 1: Submit video generation 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": "",

    "size": "1024x1024"

  }' | 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 generation 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: '',

    size: '1024x1024'

})

});

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 generation request

response = requests.post(

    f"{BASE_URL}/videos",

    headers=HEADERS,

    json={

        "model": "wan-2.6",

        "prompt": "",

        "size": "1024x1024"

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)

Image to Video

/v1/videos/generations

# Step 1: Submit image-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": "",

    "image_url": "https://example.com/start-frame.jpg",

    "duration": "10",

    "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 image-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: '',

    image_url: 'https://example.com/start-frame.jpg',

    duration: '10',

    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 image-to-video request

response = requests.post(

    f"{BASE_URL}/videos",

    headers=HEADERS,

    json={

        "model": "wan-2.6",

        "prompt": "",

        "image_url": "https://example.com/start-frame.jpg",

        "duration": "10",

        "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)

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 image 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

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 T2V I2V V2V
`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 T2V I2V V2V
`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 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
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP.	T2I Edit T2V I2V V2V

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	T2I Edit T2V I2V V2V
`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 T2V I2V V2V
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	T2I Edit T2V I2V V2V
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit T2V I2V V2V

Additional Parameters

Parameter	Type	Description	Modes
`input_reference`	array	Input image(s) to animate into video	T2I Edit T2V I2V V2V
`input_video`	string	Input video URL to transform	T2I Edit T2V I2V V2V
`enable_prompt_expansion` fal	boolean	Enable LLM prompt optimization. Significantly improves results for simple prompts but adds 3-4 seconds processing time.	T2I Edit T2V I2V V2V
`enable_safety_checker` fal	boolean	Enable content moderation for input and output.	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 Benchmarks

Wan 2.6 ranks 23rd in global text-to-image performance with a 1214 Elo, while achieving a significantly higher 7th place ranking in image editing with a 1220 Elo. The model demonstrates competitive stability across the Wan AI suite for both English and Chinese prompt instructions.

Lumenfall Arena

#25

Text-to-Image

1224 Elo

Lumenfall Arena

Image Editing

1206 Elo

Text-to-Image Landscape

Elo vs Cost

Elo vs Speed

8 without speed data omitted.

Image Editing Landscape

Elo vs Cost

Elo vs Speed

1 without speed data omitted.

Competition Results

Image Editing

Photorealism

View leaderboard

Man and Car in California

13 models

Image Editing

Source

Edit instruction

“Make a photo of the man driving the car down the California coastline”

Golden Hour Stroll

13 models

Image Editing

Source

Edit instruction

“Add dynamic motion to this photo: make hair blow in the wind, add leaves flying, energetic and lively feel.”

Wan 2.6 edited result for Neutral Expression to Genuine Smile

Before After

#10

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

Wan 2.6 edited result for Night Sky Transformation

Before After

#14

Night Sky Transformation

16 models

Image Editing

Edit instruction

“Change the scene to night: a deep, dark sky with subtle, glistening stars visible behind the mountain.”

Wan 2.6 edited result for Bald man challenge

Before After

#13

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Text-to-Image

Text Rendering

View leaderboard

#17

Modern Clean Menu

19 models

Text-to-Image

Prompt

“Modern minimalist restaurant menu design, white background with colorful food photos in grid, sections for appetizers/pizza/mains, bold sans-serif fonts, vibrant accents, clean professional layout for casual dining.”

#19

Vintage Cafe Logo

21 models

Text-to-Image

Prompt

“Vintage minimalist restaurant logo for "Caffè Florian", retro cloche dome with steam and "Est. 1720" banner, classic typography, warm brown and cream tones, subtle texture on light background, vector emblem style.”

#19

Apollo 11: Journey to Tranquility

19 models

Text-to-Image

Prompt

“Create a clean, modern vector infographic poster about the Apollo 11 mission. NASA-inspired palette (navy, white, muted red, light gray). Flat-vector style, crisp lines, consistent iconography, subtle gradients only. Steps (stop at landing): 1. Launch (Saturn Vicon) 2. Earth Orbit (Earth + orbit ring icon) 3. Translunar (trajectory arc icon) 4. Lunar Orbit (Moon + orbit ring icon) 5. Descent (lunar module descending icon) 6. Landing (lunar module on the surface icon) Small supporting elements (minimal text): • Crew strip: three silhouette icons with only last names: Armstrong, Aldrin, Collins. • Landing site marker: Moon pin labeled "Tranquility" only. Layout constraints: generous margins, large readable labels, clean background with subtle stars. Vector-only, print-poster look, high resolution.”

Image Editing

Anime

View leaderboard

#13

Studio Ghibli Anime Style

14 models

Image Editing

Source

Edit instruction

“Transform this photo into a Studio Ghibli–inspired illustration. Use soft pastel colors, hand-painted textures, gentle lighting, dreamy backgrounds, and a warm, nostalgic mood”

Image Editing

Portrait

View leaderboard

Before After

#10

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

Before After

#13

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Text-to-Image

Product, Branding & Commercial

View leaderboard

#19

Vintage Cafe Logo

21 models

Text-to-Image

Prompt

Uncategorized

Isometric Miniature Diorama Scenes

21 models

Text-to-Image

Prompt

“Create a clear, 45° top-down isometric miniature 3D cartoon scene of Japan's signature dish: sushi, with soft refined textures, realistic PBR materials, gentle lighting, on a small raised diorama base with minimal garnish and plate. Solid light blue background. At top-center: 'JAPAN' in large bold text, 'SUSHI' below it, small flag icon. Perfectly centered, ultra-clean, high-clarity, square format.”

Victorian Greenhouse Oasis

17 models

Text-to-Image

Prompt

“Hyper-photorealistic interior of a lush Victorian glass greenhouse filled with exotic tropical plants, vibrant blooming orchids, tall ferns, colorful butterflies in flight, sunlight filtering through ornate glass roof creating realistic caustics and dew on leaves, intricate iron framework visible, misty atmosphere, 8K masterpiece.”

Intricate Floral Mandala

15 models

Text-to-Image

Prompt

“Perfectly symmetrical mandala made entirely of real flowers, petals, leaves, fruits, and seeds in vibrant natural colors, intricate layered patterns with radial symmetry, top-down view on a soft neutral background, hyper-detailed organic textures and subtle shadows, photorealistic, 8K masterpiece.”

#12

Over-the-top cartoon caricature

13 models

Image Editing

Source

Edit instruction

“Create a caricature of me and my job. Make it exaggerated and humorous, incorporating my profession as a tv show anchor and my love for dogs and hockey.”

Top Matchups

See how Wan 2.6 performs head-to-head against other AI models, ranked by community votes in blind comparisons.

vs Nano Banana

Challenge: Neutral Expression to Genuine Smile

33% W · 67% L

vs Stable Diffusion 3.5 Large

Challenge: Apollo 11: Journey to Tranquility

0% W · 100% L

vs Grok Imagine Image

Challenge: Modern Clean Menu

0% W · 67% L · 33% T

vs FLUX.2 [dev] Turbo

Challenge: Intricate Floral Mandala

0% W · 100% L

vs Nano Banana 2

Challenge: Studio Ghibli Anime Style

50% W · 50% L

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.

#7 of 16

Photorealism

Edit · 47.4% win rate

Gallery

View all 17 images

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 fal.ai and Alibaba Cloud 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