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B

Seedance-2-5

Input:$60/M
Output:$240/M
coming soon
New
Commercial Use

Technical Specifications of Seedance 2.5

ItemSeedance 2.5
ProviderByteDance
Model FamilySeedance
TypeMultimodal video generation model
Input ModalitiesText, Image, Video, Audio
OutputAI-generated video
Video LengthUp to 30 seconds (reported for 2.5 launch)
ResolutionUp to 1080p officially; 4K support reported in community discussions
Reference AssetsUp to 50 multimodal assets (reported for 2.5)
Supported WorkflowsText-to-video, image-to-video, multimodal reference generation, video editing

What is Seedance 2.5?

Seedance 2.5 is the next-generation video generation model from ByteDance's Seed team. It builds on Seedance 2.0's unified multimodal architecture, which combines text, images, audio, and video references in a single generation pipeline. The model is designed for cinematic video creation, advertising, storytelling, character consistency, and advanced editing workflows.

Unlike many video models that rely primarily on text prompts, Seedance emphasizes multimodal control, allowing creators to combine visual references, motion references, audio guidance, and detailed instructions within one generation process.

Main Features of Seedance 2.5

  • 30-second native generation: Significantly longer continuous shots compared with the 4–15 second generation range of Seedance 2.0.
  • Massive reference support: Reported support for up to 50 multimodal reference assets, improving character and scene consistency.
  • Advanced multimodal conditioning: Combines text, image, audio, and video references in a unified architecture.
  • Enhanced editing controls: Improved local editing and controllable scene modifications while preserving global consistency.
  • Cinematic motion quality: Built for smooth camera movement, narrative sequencing, and realistic motion dynamics.
  • Professional content creation: Suitable for advertising, film previsualization, e-commerce, social media, and creative production.

Seedance 2.5 vs Competitors

FeatureSeedance 2.5Google Veo 3Runway Gen-4
Multimodal InputsText, image, video, audioText, image, audioText, image
Native Audio GenerationYesYesLimited
Long Video GenerationUp to 30s reportedStrongModerate
Reference Asset CapacityUp to 50 reportedNot publicly emphasizedLower
Editing ControlStrong focusStrongStrong
Narrative ConsistencyMajor focusStrongStrong

Representative Use Cases

AI Advertising Production: Generate marketing videos, product showcases, and promotional campaigns.

Social Media Content Creation: Create short-form vertical videos for social platforms.

Storyboarding and Previsualization: Develop cinematic concepts before full production.

E-commerce Product Videos: Generate visual product demonstrations from images and descriptions.

AI-Assisted Filmmaking: Prototype scenes, transitions, and camera movements.

Educational and Training Media: Produce instructional videos with multimodal references.

How to Build with Seedance 2.5 API on CometAPI

Seedance 2.5 can be accessed through CometAPI once the model is enabled within the platform's supported catalog. Developers can use their CometAPI credentials and model routing infrastructure to submit video generation requests using a unified API experience.

Step 1: Get Your API Key

  1. Create or sign in to your CometAPI account.
  2. Generate an API key from the developer dashboard.
  3. Verify the latest model identifier for Seedance 2.5.
  4. Review supported parameters including video duration, reference assets, and output formats.

Step 2: Test the Model

Start with realistic video-generation tasks:

  • Text-to-video marketing content
  • Product demonstrations
  • Image-to-video animation
  • Storyboard generation
  • Multimodal reference-driven video creation

Testing with representative production prompts helps establish generation quality before deployment.

Step 3: Integrate into Production

For production systems:

  • Use OpenAI-compatible SDKs where supported.
  • Enable asynchronous processing for long-running video jobs.
  • Implement webhook callbacks for generation completion.
  • Store prompts and metadata for auditability.
  • Add retry logic for transient failures.
  • Use human review for customer-facing content.
  • Monitor generation latency and output quality across workflows.

Video generation workloads typically benefit from queue-based architectures rather than synchronous request handling.

FAQ