When should I use Wan 2.7 instead of Veo 3.1?

Choose Wan 2.7 when you need stronger controllability features such as frame anchoring, reference workflows, or editing-heavy pipelines. Veo-style models may be stronger for cinematic realism in some cases.

Does Wan 2.7 support audio generation and lip sync?

Yes. Wan 2.7 includes audio-aware workflows with voice references, sound generation, and lip-sync-oriented capabilities.

What are the main limitations of Wan 2.7 video generation?

The biggest limitations are short clip durations, 1080P output ceilings, and occasional instability during highly dynamic scenes.

What video resolutions and durations does Wan 2.7 support?

Wan 2.7 commonly supports 720P and 1080P output with clip durations ranging from roughly 2 to 15 seconds depending on workflow configuration.

Can Wan 2.7 API generate videos from both images and text prompts?

Yes. Wan 2.7 supports text-to-video and image-to-video workflows, including start-frame guided generation and continuation tasks.

How does Wan 2.7 API maintain character consistency across scenes?

Wan 2.7 uses reference-driven workflows that allow image or video references to preserve identity, clothing, props, and style between shots.

Can Wan 2.7 API edit existing videos instead of generating new ones?

Yes. The model family includes editing workflows for transforming, extending, or restyling existing clips.

Is Wan 2.7 suitable for production marketing videos and social media content?

Yes. Its combination of consistency controls, editing tools, and multimodal inputs makes it suitable for ads, social clips, and short-form storytelling.

Aliyun

Available

wan2.7

Wan2.7 API

Wan2.7 is a video generation model designed for high-quality visual synthesis and improved motion consistency. It is suitable for cinematic content creation and professional video production workflows.

Starting price

$0.08 / second

Usage-based billing

API health

Available

Production endpoint

Response

Async ready

Observed API response

Released

Jun 1, 2026

Aliyun

Pricing

Use the quick estimate above for a single run, then review the full CometAPI and official-price comparison.

5-second test

$0.4000

Estimated CometAPI usage cost

10-second test

$0.8000

Estimated CometAPI usage cost

15-second test

$1.20

Estimated CometAPI usage cost

Minimum charge transparency This prototype does not assume a hidden per-request minimum. If a production billing floor or minimum top-up applies, it must be displayed before purchase and included in every budget estimate.

Wan Video Generation Pricing

Pricing (Per Second)

Model	720p	1080p
`wan2.6`	$0.08	$0.12
`wan2.7`	$0.08	$0.12

💡 Billed per second. Total cost = price per second × video duration (seconds).

Model brief

A stable model identity for search and evaluation, paired with live catalog data that can change without rewriting the page's core SEO structure.

Stable identity

What is Wan2.7?

Canonical model identity stays stable as pricing and limits change.

Live model data

Current catalog snapshot

Availability

Available

Starting price

$0.08 / second

Context

See capabilities

Catalog date

Jun 1, 2026

Overview

A practical overview of Wan2.7, including important behavior and integration considerations.

Technical Specifications of Wan 2.7

Item	Wan 2.7 (Video Suite)
Provider	Alibaba Tongyi Lab
Model family	Wan 2.7 Video Suite
Architecture	27B parameter Mixture-of-Experts (MoE)
Input types	Text, images, videos, audio references
Output types	Generated / edited video clips with optional audio
Supported modes	Text-to-video (T2V), Image-to-video (I2V), Reference-to-video (R2V), Video Editing
Resolution	720P and 1080P outputs
Video duration	2–15 seconds
Audio support	Native audio generation, voice references, lip-sync workflows
Reference capability	Multi-reference images/videos, identity consistency
Character consistency	Supports up to multiple reference subjects depending on workflow
Release generation	Major successor to Wan 2.6

What is Wan 2.7?

Wan 2.7 is Alibaba’s flagship multimodal video generation suite built for controllable AI filmmaking workflows rather than simple prompt-to-video creation. The model family combines generation, editing, continuation, and reference-driven consistency into one system, enabling creators to build short cinematic clips with stronger subject preservation and scene control.

Unlike earlier video generators that focused mostly on prompt quality, Wan 2.7 emphasizes controllability through frame anchoring, reference inputs, audio synchronization, and structured multi-shot workflows.

Main Features of Wan 2.7

Thinking Mode planning pipeline: The model plans scene composition and motion before rendering, improving prompt adherence and reducing coherence failures.
First-frame and last-frame control: Users can define opening and ending frames so the system interpolates motion between them.
Reference-driven identity consistency: Maintain character appearance, clothing, objects, and style across multiple shots.
Native multimodal workflows: Supports text, image, audio, and video references inside the same workflow.
Integrated audio generation: Background music, environmental sounds, and voice synchronization can be generated alongside visuals.
Editing and continuation support: Existing videos can be extended, transformed, or re-styled without rebuilding from scratch.

Benchmark Performance of Wan 2.7

Public benchmark disclosure for Wan 2.7 remains limited compared with text LLMs, but third-party evaluations and community testing indicate notable improvements in motion stability, prompt adherence, and controllability over Wan 2.6.

Reported ecosystem observations include:

Stronger motion continuity versus earlier Wan releases.
Higher leaderboard placement in third-party text-to-video evaluations.
Improved multi-subject consistency and reference preservation.
Better audio integration than many earlier open video models.

Formal benchmark transparency is still limited, so performance claims should be interpreted cautiously.

Wan 2.7 vs Other Video Models

Feature	Wan 2.7	Veo 3.1	Seedance 2.0
Native audio workflows	Strong	Strong	Moderate
Reference-driven consistency	Strong	Moderate	Moderate
First + Last Frame control	Yes	Partial	Limited
Video editing workflows	Yes	Yes	Limited
Max common resolution	1080P	Higher-end cinematic output	1080P
Multi-reference support	Strong emphasis	Moderate	Moderate

Limitations of Wan 2.7

Short clip duration compared with long-form production tools.
1080P maximum output limits ultra-high-resolution workflows.
Fast motion scenes may still produce instability artifacts.
Multi-reference workflows increase complexity and prompt engineering requirements.
Public benchmark reporting remains relatively sparse.

Representative Use Cases

Character-consistent short films and storyboards.
Marketing clips with audio synchronization.
Social media video generation.
Product visualization and concept trailers.
Video continuation and scene interpolation workflows.
Reference-based avatar and character animation.

How to Use the WAN 2.7 Video API in CometAPI

Step 1: Try the WAN 2.7 Video API in the Kie Al Playground

First, test the WAN 2.7 functionality using the WAN 2.7 Video API in the CometAPI Playground. Upload images, add prompts, or use references to preview the generated WAN video before integrating the WAN 2.7 AI video into your production workflow.

Step 2: Obtain the WAN 2.7 API key and review the API documentation

Obtain the WAN 2.7 API key from the CometAPI console and review the documentation. Understand the WAN 2.7 Video API endpoints, authentication, and parameters to support text-to-video, image-to-video, and WAN video workflows.

Step 3: Generate WAN 2.7 AI videos and integrate them into your workflow

Use the WAN 2.7 Video API to generate WAN 2.7 AI videos with prompts, images, or references. Integrate WAN 2.7 outputs into product workflows, content pipelines, or AI video tools to enable scalable video creation.

FAQ

API

Copy a working endpoint and code example, then open the complete API reference when you need every parameter.

One API, production-ready model access

Authenticate once, call the model endpoint and keep the same billing and observability workflow across providers.

POST/v1/videos

Sample code and API for Wan2.7

Access comprehensive sample code and API resources for Wan2.7 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Wan2.7 in your projects.

POST

/v1/videos

# Create a video with wan2.7
# Step 1: Submit the video generation request
echo "Submitting video generation request..."
response=$(curl -s https://api.cometapi.com/v1/videos \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -F "model=wan2.7" \
  -F "prompt=Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam." \
  -F "seconds=5" \
  -F "size=1280x720")

echo "Response: $response"

# Extract video_id from response (handle JSON with spaces like "id": "xxx")
video_id=$(echo "$response" | tr -d '
' | sed 's/.*"id"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/')
echo "Video ID: $video_id"

# Step 2: Poll for progress until 100%
echo ""
echo "Checking video generation progress..."
while true; do
  status_response=$(curl -s "https://api.cometapi.com/v1/videos/$video_id" \
    -H "Authorization: Bearer $COMETAPI_KEY")

  progress=$(echo "$status_response" | grep -o '"progress"[[:space:]]*:[[:space:]]*"\?[^",}]*"\?' | head -1 | sed 's/.*:[[:space:]]*"\?//;s/"$//')
  status=$(echo "$status_response" | grep -o '"status"[[:space:]]*:[[:space:]]*"[^"]*"' | head -1 | sed 's/.*"status"[[:space:]]*:[[:space:]]*"//;s/"$//')

  echo "Progress: $progress, Status: $status"

  if [ "$status" = "FAILURE" ] || [ "$status" = "failed" ] || [ "$status" = "error" ]; then
    echo "Video generation failed!"
    exit 1
  fi

  if [ "$progress" = "100%" ] || [ "$progress" = "100" ] || [ "$status" = "completed" ] || [ "$status" = "success" ]; then
    echo "Video generation completed!"
    break
  fi

  sleep 10
done

# Step 3: Download the video to output directory
echo ""
echo "Downloading video to ./output/$video_id.mp4..."
mkdir -p ./output
curl -s "https://api.cometapi.com/v1/videos/$video_id/content" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -o "./output/$video_id.mp4"

if [ -f "./output/$video_id.mp4" ]; then
  echo "Video saved to ./output/$video_id.mp4"
  ls -la "./output/$video_id.mp4"
else
  echo "Failed to download video"
  exit 1
fi

cURL Code Example

# Create a video with wan2.7
# Step 1: Submit the video generation request
echo "Submitting video generation request..."
response=$(curl -s https://api.cometapi.com/v1/videos \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -F "model=wan2.7" \
  -F "prompt=Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam." \
  -F "seconds=5" \
  -F "size=1280x720")

echo "Response: $response"

# Extract video_id from response (handle JSON with spaces like "id": "xxx")
video_id=$(echo "$response" | tr -d '\n' | sed 's/.*"id"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/')
echo "Video ID: $video_id"

# Step 2: Poll for progress until 100%
echo ""
echo "Checking video generation progress..."
while true; do
  status_response=$(curl -s "https://api.cometapi.com/v1/videos/$video_id" \
    -H "Authorization: Bearer $COMETAPI_KEY")
  
  progress=$(echo "$status_response" | grep -o '"progress"[[:space:]]*:[[:space:]]*"\?[^",}]*"\?' | head -1 | sed 's/.*:[[:space:]]*"\?//;s/"$//')
  status=$(echo "$status_response" | grep -o '"status"[[:space:]]*:[[:space:]]*"[^"]*"' | head -1 | sed 's/.*"status"[[:space:]]*:[[:space:]]*"//;s/"$//')
  
  echo "Progress: $progress, Status: $status"
  
  if [ "$status" = "FAILURE" ] || [ "$status" = "failed" ] || [ "$status" = "error" ]; then
    echo "Video generation failed!"
    exit 1
  fi
  
  if [ "$progress" = "100%" ] || [ "$progress" = "100" ] || [ "$status" = "completed" ] || [ "$status" = "success" ]; then
    echo "Video generation completed!"
    break
  fi
  
  sleep 10
done

# Step 3: Download the video to output directory
echo ""
echo "Downloading video to ./output/$video_id.mp4..."
mkdir -p ./output
curl -s "https://api.cometapi.com/v1/videos/$video_id/content" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -o "./output/$video_id.mp4"

if [ -f "./output/$video_id.mp4" ]; then
  echo "Video saved to ./output/$video_id.mp4"
  ls -la "./output/$video_id.mp4"
else
  echo "Failed to download video"
  exit 1
fi

Python Code Example

# Create a video with wan2.7 using raw HTTP requests
import os
import time
import requests

api_key = os.environ.get("COMETAPI_KEY")
base_url = "https://api.cometapi.com/v1"
headers = {"Authorization": f"Bearer {api_key}"}

# Step 1: Submit the video generation request
print("Submitting video generation request...")
response = requests.post(
    f"{base_url}/videos",
    headers=headers,
    files={
        "model": (None, "wan2.7"),
        "prompt": (None, "Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam."),
        "seconds": (None, "5"),
        "size": (None, "1280x720"),
    },
)

result = response.json()
print(f"Response: {result}")

video_id = result.get("id") or result.get("task_id")
print(f"Video ID: {video_id}")

# Step 2: Poll for progress until 100%
print("\nChecking video generation progress...")
while True:
    try:
        status_response = requests.get(f"{base_url}/videos/{video_id}", headers=headers)
        status_result = status_response.json()

        data = status_result.get("data") or status_result
        progress = data.get("progress", "0%")
        status = data.get("status", "unknown")

        print(f"Progress: {progress}, Status: {status}")

        if status in ["FAILURE", "failed", "error"]:
            print("Video generation failed!")
            print(status_result)
            exit(1)

        if progress == "100%" or progress == 100 or status in ["completed", "success"]:
            print("Video generation completed!")
            break
    except Exception as e:
        print(f"Temporary error: {e}, retrying...")

    time.sleep(10)

# Step 3: Download the video to output directory
print(f"\nDownloading video to ./output/{video_id}.mp4...")
os.makedirs("./output", exist_ok=True)

video_response = requests.get(f"{base_url}/videos/{video_id}/content", headers=headers)

output_path = f"./output/{video_id}.mp4"
with open(output_path, "wb") as f:
    f.write(video_response.content)

if os.path.exists(output_path):
    file_size = os.path.getsize(output_path)
    print(f"Video saved to {output_path}")
    print(f"File size: {file_size} bytes")
else:
    print("Failed to download video")
    exit(1)

JavaScript Code Example

// Create a video with wan2.7 using raw HTTP requests
import fs from "fs";
import path from "path";

const apiKey = process.env.COMETAPI_KEY;
const baseUrl = "https://api.cometapi.com/v1";
const headers = { Authorization: `Bearer ${apiKey}` };

function sleep(ms) {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

// Step 1: Submit the video generation request
console.log("Submitting video generation request...");
const formData = new FormData();
formData.append("model", "wan2.7");
formData.append("prompt", "Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam.");
formData.append("seconds", "5");
formData.append("size", "1280x720");

const submitResponse = await fetch(`${baseUrl}/videos`, {
  method: "POST",
  headers,
  body: formData,
});

const result = await submitResponse.json();
console.log("Response:", JSON.stringify(result, null, 2));

const videoId = result.id || result.task_id;
console.log("Video ID:", videoId);

// Step 2: Poll for progress until 100%
console.log("\nChecking video generation progress...");
while (true) {
  try {
    const statusResponse = await fetch(`${baseUrl}/videos/${videoId}`, { headers });
    const statusResult = await statusResponse.json();
    const data = statusResult.data || statusResult;
    const progress = data.progress || "0%";
    const status = data.status || "unknown";

    console.log(`Progress: ${progress}, Status: ${status}`);

    if (status === "FAILURE" || status === "failed" || status === "error") {
      console.log("Video generation failed!");
      console.log(JSON.stringify(statusResult, null, 2));
      process.exit(1);
    }

    if (progress === "100%" || progress === 100 || status === "completed" || status === "success") {
      console.log("Video generation completed!");
      break;
    }
  } catch (e) {
    console.log(`Temporary error: ${e.message}, retrying...`);
  }

  await sleep(10000);
}

// Step 3: Download the video to output directory
console.log(`\nDownloading video to ./output/${videoId}.mp4...`);
fs.mkdirSync("./output", { recursive: true });

const videoResponse = await fetch(`${baseUrl}/videos/${videoId}/content`, { headers });
const outputPath = path.join("./output", `${videoId}.mp4`);
fs.writeFileSync(outputPath, Buffer.from(await videoResponse.arrayBuffer()));

if (fs.existsSync(outputPath)) {
  const stats = fs.statSync(outputPath);
  console.log(`Video saved to ${outputPath}`);
  console.log(`File size: ${stats.size} bytes`);
} else {
  console.log("Failed to download video");
  process.exit(1);
}

Capabilities

Scan the model facts that matter before you choose an architecture or estimate production workload.

Supported capabilities

Features returned by the existing model API.

text-to-video

image-to-video

Input and output

Inputs

text

Outputs

video

Production profile

Provider: Aliyun
Context: Model specific
Commercial use: Supported

Use cases

Use Wan2.7 for production workflows that match its video capabilities, then compare alternatives before committing to a long-term integration.

Text-to-video concepts and social content

Product demos and cinematic storyboards

Image-to-video motion and creative iteration

Other models

Compare other models available through CometAPI for different quality, latency, capability and pricing trade-offs.

Open model comparison →

Aliyun

Wan 3.0 Prime

Wan 3.0 Prime is Alibaba Cloud Model Studio's speed-optimized version of the Wan 3.0 video-generation model.

View model

Aliyun

Wan3.0

From fast verification to cinematic output, billing is based on the generated video duration, selecting the appropriate resolution, and turning every generation into a planned creative investment activity rule

View model

Aliyun

Wan2.6

Wan2.6 is a video generation model designed for stable and efficient video synthesis. It provides reliable visual quality and smooth motion generation for general video creation tasks.

View model

Flux

Flux 3

FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence.

View model

Aliyun

Happy Horse 1.1

HappyHorse 1.1 is a multimodal video-generation model designed for professional content creation, advertising, short films, social media production, and storytelling. It extends the capabilities of HappyHorse 1.0—which gained significant attention after ranking highly in independent video-generation evaluations—with stronger scene coherence and improved visual fidelity.

View model

Aliyun

Happy Horse 1.0

Happy Horse 1.0 — A high-quality audio-video generation model that supports text-to-video and image-to-video creation. It can generate synchronized visuals, audio, and lip movements, making it suitable for short films, advertising creatives, and product showcases.

View model

Uptime

Review live heartbeat data, endpoint availability and observed response times before moving into production.

Uptime

Request success rate over the last 30 days, reflecting the reliability of each model provider. CometAPI monitors all connected providers in real time, 24/7.

Respond

269714msAvg. Response

Uptime

85.7%Avg. Uptime

Versions

Review available model identifiers before pinning a production integration.

wan2.7

Updates

Follow meaningful availability, pricing and capability changes for Wan2.7 without losing the stable model page.

Model changelog

One page that gets stronger over time.

Release notes, pricing changes, benchmark updates and migration guidance accumulate here while the canonical URL stays unchanged.

Jun 1, 2026

API availability confirmed

Wan2.7 is available through the CometAPI model catalog and API documentation.

Current

Live data

Pricing and limits synced from the catalog

Price, context, availability and limits are treated as dynamic properties instead of being embedded in the page title or model identity.

Jun 1, 2026

Permanent model entity created

The provider and model slug form a durable canonical URL; future content and data updates remain on this page.

Keep exploring

Compare models and continue your research

Aliyun

Available

wan2.7

Wan2.7 API

Starting price

$0.08 / second

Usage-based billing

API health

Available

Production endpoint

Response

Async ready

Observed API response

Released

Jun 1, 2026

Aliyun

Pricing

Use the quick estimate above for a single run, then review the full CometAPI and official-price comparison.

5-second test

$0.4000

Estimated CometAPI usage cost

10-second test

$0.8000

Estimated CometAPI usage cost

15-second test

$1.20

Estimated CometAPI usage cost

Wan Video Generation Pricing

Pricing (Per Second)

Model	720p	1080p
`wan2.6`	$0.08	$0.12
`wan2.7`	$0.08	$0.12

💡 Billed per second. Total cost = price per second × video duration (seconds).

Model brief

A stable model identity for search and evaluation, paired with live catalog data that can change without rewriting the page's core SEO structure.

Stable identity

What is Wan2.7?

Canonical model identity stays stable as pricing and limits change.

Live model data

Current catalog snapshot

Availability

Available

Starting price

$0.08 / second

Context

See capabilities

Catalog date

Jun 1, 2026

Overview

A practical overview of Wan2.7, including important behavior and integration considerations.

Technical Specifications of Wan 2.7

Item	Wan 2.7 (Video Suite)
Provider	Alibaba Tongyi Lab
Model family	Wan 2.7 Video Suite
Architecture	27B parameter Mixture-of-Experts (MoE)
Input types	Text, images, videos, audio references
Output types	Generated / edited video clips with optional audio
Supported modes	Text-to-video (T2V), Image-to-video (I2V), Reference-to-video (R2V), Video Editing
Resolution	720P and 1080P outputs
Video duration	2–15 seconds
Audio support	Native audio generation, voice references, lip-sync workflows
Reference capability	Multi-reference images/videos, identity consistency
Character consistency	Supports up to multiple reference subjects depending on workflow
Release generation	Major successor to Wan 2.6

What is Wan 2.7?

Main Features of Wan 2.7

Thinking Mode planning pipeline: The model plans scene composition and motion before rendering, improving prompt adherence and reducing coherence failures.
First-frame and last-frame control: Users can define opening and ending frames so the system interpolates motion between them.
Reference-driven identity consistency: Maintain character appearance, clothing, objects, and style across multiple shots.
Native multimodal workflows: Supports text, image, audio, and video references inside the same workflow.
Integrated audio generation: Background music, environmental sounds, and voice synchronization can be generated alongside visuals.
Editing and continuation support: Existing videos can be extended, transformed, or re-styled without rebuilding from scratch.

Benchmark Performance of Wan 2.7

Reported ecosystem observations include:

Stronger motion continuity versus earlier Wan releases.
Higher leaderboard placement in third-party text-to-video evaluations.
Improved multi-subject consistency and reference preservation.
Better audio integration than many earlier open video models.

Formal benchmark transparency is still limited, so performance claims should be interpreted cautiously.

Wan 2.7 vs Other Video Models

Feature	Wan 2.7	Veo 3.1	Seedance 2.0
Native audio workflows	Strong	Strong	Moderate
Reference-driven consistency	Strong	Moderate	Moderate
First + Last Frame control	Yes	Partial	Limited
Video editing workflows	Yes	Yes	Limited
Max common resolution	1080P	Higher-end cinematic output	1080P
Multi-reference support	Strong emphasis	Moderate	Moderate

Limitations of Wan 2.7

Short clip duration compared with long-form production tools.
1080P maximum output limits ultra-high-resolution workflows.
Fast motion scenes may still produce instability artifacts.
Multi-reference workflows increase complexity and prompt engineering requirements.
Public benchmark reporting remains relatively sparse.

Representative Use Cases

Character-consistent short films and storyboards.
Marketing clips with audio synchronization.
Social media video generation.
Product visualization and concept trailers.
Video continuation and scene interpolation workflows.
Reference-based avatar and character animation.

How to Use the WAN 2.7 Video API in CometAPI

Step 1: Try the WAN 2.7 Video API in the Kie Al Playground

Step 2: Obtain the WAN 2.7 API key and review the API documentation

Step 3: Generate WAN 2.7 AI videos and integrate them into your workflow

FAQ

API

Copy a working endpoint and code example, then open the complete API reference when you need every parameter.

One API, production-ready model access

Authenticate once, call the model endpoint and keep the same billing and observability workflow across providers.

POST/v1/videos

Sample code and API for Wan2.7

POST

/v1/videos

# Create a video with wan2.7
# Step 1: Submit the video generation request
echo "Submitting video generation request..."
response=$(curl -s https://api.cometapi.com/v1/videos \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -F "model=wan2.7" \
  -F "prompt=Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam." \
  -F "seconds=5" \
  -F "size=1280x720")

echo "Response: $response"

# Extract video_id from response (handle JSON with spaces like "id": "xxx")
video_id=$(echo "$response" | tr -d '
' | sed 's/.*"id"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/')
echo "Video ID: $video_id"

# Step 2: Poll for progress until 100%
echo ""
echo "Checking video generation progress..."
while true; do
  status_response=$(curl -s "https://api.cometapi.com/v1/videos/$video_id" \
    -H "Authorization: Bearer $COMETAPI_KEY")

  progress=$(echo "$status_response" | grep -o '"progress"[[:space:]]*:[[:space:]]*"\?[^",}]*"\?' | head -1 | sed 's/.*:[[:space:]]*"\?//;s/"$//')
  status=$(echo "$status_response" | grep -o '"status"[[:space:]]*:[[:space:]]*"[^"]*"' | head -1 | sed 's/.*"status"[[:space:]]*:[[:space:]]*"//;s/"$//')

  echo "Progress: $progress, Status: $status"

  if [ "$status" = "FAILURE" ] || [ "$status" = "failed" ] || [ "$status" = "error" ]; then
    echo "Video generation failed!"
    exit 1
  fi

  if [ "$progress" = "100%" ] || [ "$progress" = "100" ] || [ "$status" = "completed" ] || [ "$status" = "success" ]; then
    echo "Video generation completed!"
    break
  fi

  sleep 10
done

# Step 3: Download the video to output directory
echo ""
echo "Downloading video to ./output/$video_id.mp4..."
mkdir -p ./output
curl -s "https://api.cometapi.com/v1/videos/$video_id/content" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -o "./output/$video_id.mp4"

if [ -f "./output/$video_id.mp4" ]; then
  echo "Video saved to ./output/$video_id.mp4"
  ls -la "./output/$video_id.mp4"
else
  echo "Failed to download video"
  exit 1
fi

cURL Code Example

# Create a video with wan2.7
# Step 1: Submit the video generation request
echo "Submitting video generation request..."
response=$(curl -s https://api.cometapi.com/v1/videos \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -F "model=wan2.7" \
  -F "prompt=Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam." \
  -F "seconds=5" \
  -F "size=1280x720")

echo "Response: $response"

# Extract video_id from response (handle JSON with spaces like "id": "xxx")
video_id=$(echo "$response" | tr -d '\n' | sed 's/.*"id"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/')
echo "Video ID: $video_id"

# Step 2: Poll for progress until 100%
echo ""
echo "Checking video generation progress..."
while true; do
  status_response=$(curl -s "https://api.cometapi.com/v1/videos/$video_id" \
    -H "Authorization: Bearer $COMETAPI_KEY")
  
  progress=$(echo "$status_response" | grep -o '"progress"[[:space:]]*:[[:space:]]*"\?[^",}]*"\?' | head -1 | sed 's/.*:[[:space:]]*"\?//;s/"$//')
  status=$(echo "$status_response" | grep -o '"status"[[:space:]]*:[[:space:]]*"[^"]*"' | head -1 | sed 's/.*"status"[[:space:]]*:[[:space:]]*"//;s/"$//')
  
  echo "Progress: $progress, Status: $status"
  
  if [ "$status" = "FAILURE" ] || [ "$status" = "failed" ] || [ "$status" = "error" ]; then
    echo "Video generation failed!"
    exit 1
  fi
  
  if [ "$progress" = "100%" ] || [ "$progress" = "100" ] || [ "$status" = "completed" ] || [ "$status" = "success" ]; then
    echo "Video generation completed!"
    break
  fi
  
  sleep 10
done

# Step 3: Download the video to output directory
echo ""
echo "Downloading video to ./output/$video_id.mp4..."
mkdir -p ./output
curl -s "https://api.cometapi.com/v1/videos/$video_id/content" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -o "./output/$video_id.mp4"

if [ -f "./output/$video_id.mp4" ]; then
  echo "Video saved to ./output/$video_id.mp4"
  ls -la "./output/$video_id.mp4"
else
  echo "Failed to download video"
  exit 1
fi

Python Code Example

# Create a video with wan2.7 using raw HTTP requests
import os
import time
import requests

api_key = os.environ.get("COMETAPI_KEY")
base_url = "https://api.cometapi.com/v1"
headers = {"Authorization": f"Bearer {api_key}"}

# Step 1: Submit the video generation request
print("Submitting video generation request...")
response = requests.post(
    f"{base_url}/videos",
    headers=headers,
    files={
        "model": (None, "wan2.7"),
        "prompt": (None, "Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam."),
        "seconds": (None, "5"),
        "size": (None, "1280x720"),
    },
)

result = response.json()
print(f"Response: {result}")

video_id = result.get("id") or result.get("task_id")
print(f"Video ID: {video_id}")

# Step 2: Poll for progress until 100%
print("\nChecking video generation progress...")
while True:
    try:
        status_response = requests.get(f"{base_url}/videos/{video_id}", headers=headers)
        status_result = status_response.json()

        data = status_result.get("data") or status_result
        progress = data.get("progress", "0%")
        status = data.get("status", "unknown")

        print(f"Progress: {progress}, Status: {status}")

        if status in ["FAILURE", "failed", "error"]:
            print("Video generation failed!")
            print(status_result)
            exit(1)

        if progress == "100%" or progress == 100 or status in ["completed", "success"]:
            print("Video generation completed!")
            break
    except Exception as e:
        print(f"Temporary error: {e}, retrying...")

    time.sleep(10)

# Step 3: Download the video to output directory
print(f"\nDownloading video to ./output/{video_id}.mp4...")
os.makedirs("./output", exist_ok=True)

video_response = requests.get(f"{base_url}/videos/{video_id}/content", headers=headers)

output_path = f"./output/{video_id}.mp4"
with open(output_path, "wb") as f:
    f.write(video_response.content)

if os.path.exists(output_path):
    file_size = os.path.getsize(output_path)
    print(f"Video saved to {output_path}")
    print(f"File size: {file_size} bytes")
else:
    print("Failed to download video")
    exit(1)

JavaScript Code Example

// Create a video with wan2.7 using raw HTTP requests
import fs from "fs";
import path from "path";

const apiKey = process.env.COMETAPI_KEY;
const baseUrl = "https://api.cometapi.com/v1";
const headers = { Authorization: `Bearer ${apiKey}` };

function sleep(ms) {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

// Step 1: Submit the video generation request
console.log("Submitting video generation request...");
const formData = new FormData();
formData.append("model", "wan2.7");
formData.append("prompt", "Create a cinematic detective sequence with clear timed shots. Shot 1 [0-2s]: a rainy city street at night, neon signs reflected in puddles. Shot 2 [2-4s]: a detective in a dark coat enters an old building. Shot 3 [4-5s]: close-up on a clue lit by a narrow flashlight beam.");
formData.append("seconds", "5");
formData.append("size", "1280x720");

const submitResponse = await fetch(`${baseUrl}/videos`, {
  method: "POST",
  headers,
  body: formData,
});

const result = await submitResponse.json();
console.log("Response:", JSON.stringify(result, null, 2));

const videoId = result.id || result.task_id;
console.log("Video ID:", videoId);

// Step 2: Poll for progress until 100%
console.log("\nChecking video generation progress...");
while (true) {
  try {
    const statusResponse = await fetch(`${baseUrl}/videos/${videoId}`, { headers });
    const statusResult = await statusResponse.json();
    const data = statusResult.data || statusResult;
    const progress = data.progress || "0%";
    const status = data.status || "unknown";

    console.log(`Progress: ${progress}, Status: ${status}`);

    if (status === "FAILURE" || status === "failed" || status === "error") {
      console.log("Video generation failed!");
      console.log(JSON.stringify(statusResult, null, 2));
      process.exit(1);
    }

    if (progress === "100%" || progress === 100 || status === "completed" || status === "success") {
      console.log("Video generation completed!");
      break;
    }
  } catch (e) {
    console.log(`Temporary error: ${e.message}, retrying...`);
  }

  await sleep(10000);
}

// Step 3: Download the video to output directory
console.log(`\nDownloading video to ./output/${videoId}.mp4...`);
fs.mkdirSync("./output", { recursive: true });

const videoResponse = await fetch(`${baseUrl}/videos/${videoId}/content`, { headers });
const outputPath = path.join("./output", `${videoId}.mp4`);
fs.writeFileSync(outputPath, Buffer.from(await videoResponse.arrayBuffer()));

if (fs.existsSync(outputPath)) {
  const stats = fs.statSync(outputPath);
  console.log(`Video saved to ${outputPath}`);
  console.log(`File size: ${stats.size} bytes`);
} else {
  console.log("Failed to download video");
  process.exit(1);
}

Capabilities

Scan the model facts that matter before you choose an architecture or estimate production workload.

Supported capabilities

Features returned by the existing model API.

text-to-video

image-to-video

Input and output

Inputs

text

Outputs

video

Production profile

Provider: Aliyun
Context: Model specific
Commercial use: Supported

Use cases

Use Wan2.7 for production workflows that match its video capabilities, then compare alternatives before committing to a long-term integration.

Text-to-video concepts and social content

Product demos and cinematic storyboards

Image-to-video motion and creative iteration

Other models

Compare other models available through CometAPI for different quality, latency, capability and pricing trade-offs.

Open model comparison →

Aliyun

Wan 3.0 Prime

Wan 3.0 Prime is Alibaba Cloud Model Studio's speed-optimized version of the Wan 3.0 video-generation model.

Wan2.6 is a video generation model designed for stable and efficient video synthesis. It provides reliable visual quality and smooth motion generation for general video creation tasks.

View model

Flux

Flux 3

FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence.

Uptime

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Uptime

Request success rate over the last 30 days, reflecting the reliability of each model provider. CometAPI monitors all connected providers in real time, 24/7.

Respond

269714msAvg. Response

Uptime

85.7%Avg. Uptime

Versions

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wan2.7

Updates

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Model changelog

One page that gets stronger over time.

Release notes, pricing changes, benchmark updates and migration guidance accumulate here while the canonical URL stays unchanged.

Jun 1, 2026

API availability confirmed

Wan2.7 is available through the CometAPI model catalog and API documentation.

Current

Live data

Pricing and limits synced from the catalog

Price, context, availability and limits are treated as dynamic properties instead of being embedded in the page title or model identity.

Jun 1, 2026

Permanent model entity created

The provider and model slug form a durable canonical URL; future content and data updates remain on this page.

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