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Seedance 2.0
- Text Generation
Seedance 2.0 is ByteDance’s next-generation multimodal AI video generation model that natively combines audio and video to create highly realistic clips from simple prompts. It is notable for its quad-modal inputs (text, image, audio, video) and strong consistency across complex, multi-shot scenes.
About the model
What is Seedance 2.0?
Seedance 2.0 is a unified multimodal audio-video generation model developed by ByteDance for high-fidelity, realistic video creation. It is mainly used for text-to-video and story-driven clip generation where creators script cinematic sequences with detailed control over motion, camera, and scene transitions. It is also used in consumer and professional tools like CapCut and Dreamina-style services to turn scripts, reference images, or rough edits into polished short-form content and trailers. Seedance 2.0 follows earlier Seedance 1.0/1.5 video models within ByteDance’s broader SEED family that also includes the Doubao language models and Seedream image generators.
Model capabilities
5 Core Capabilities
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Text-to-video
Generates coherent, cinematic video clips directly from text prompts, supporting multi-shot narratives with consistent characters, scenes, and camera movements.
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Multimodal Inputs
Accepts combined text, image, audio, and video inputs in a unified model to guide structure, style, and motion of generated videos.
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Native Audio Generation
Jointly generates synchronized soundtracks, effects, and dialogue with videos, enabling frame-accurate lip-sync and environment-aware sound design.
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Interactive Editing
Supports shot-by-shot control and reference-based editing, allowing users to refine pacing, composition, and continuity without manual post-production.
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Cross-language Prompts
Handles prompts and control inputs in multiple languages, enabling creators worldwide to direct and customize video generation workflows.
Use cases
6 Most Valuable Use Cases
- Text-to-video ads
- Storyboarding clips
- Social media snippets
- Video-based case studies
- Content performance monitoring
- Multimodal video generation
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Seedance 2.0–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~150ms | ~120 tps | 99.99% | $0.10 | $0.30 | 128K tokens |
| ByteDance | Asia Pacific | ~220ms | ~80 tps | ~99.9% | ~$0.18 | ~$0.50 | ~64K tokens |
| OpenAI-compatible Gateway | Global | ~260ms | ~70 tps | ~99.9% | ~$0.22 | ~$0.60 | ~64K tokens |
| Cloud Aggregator X | US East | ~240ms | ~65 tps | ~99.5% | ~$0.25 | ~$0.70 | ~32K tokens |
Performance benchmarks
Technical Specifications
| Metric | Seedance 2.0 | GPT-4o | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~220ms | ~280ms | ~180ms |
| Context Window | 128K | 200K | 128K |
| Input Price ($/1M) | $0.40 | $1.00 | $0.15 |
| Output Price ($/1M) | $0.60 | $2.00 | $0.60 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 40 tps | 30 tps | 60 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 7.8B
- Completion tokens generated (last 30 days)
- 29.6M
- API requests served (last 30 days)
- 99.8%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent AI Routing
Automatically route each request to the optimal model across providers based on latency, quality, and cost, without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Orchestration
Optimize spend with dynamic provider selection, granular usage controls, and model-tier policies so you can keep quality high while staying within strict budgets.
Maximum value per token -
Resilient Fallback Flows
Define automatic failover chains across providers and models, ensuring your production workloads keep running even during outages, rate limits, or degraded service.
No more single points -
End-to-End Observability
Get full visibility into latency, errors, token usage, and provider performance with request-level traces and metrics that plug into your existing monitoring stack.
Debug across providers -
Task-Level Abstractions
Call high-level tasks like chat, tools, or RAG through a unified schema, while LLM.API handles provider quirks, formats, and evolving capabilities under the hood.
Code to tasks, not APIs -
High-Throughput Batch Runs
Process large workloads with efficient batching, concurrency controls, and job-level status APIs, letting you scale evaluations, backfills, and bulk inference safely.
Scale jobs, not stress
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose model from a major Chinese provider for broad tasks.
- You need decent chat, Q&A, and summarization for consumer-style applications.
- Your use case involves casual content generation, like marketing blurbs or social posts.
- Your use case involves integrating with the ByteDance ecosystem or China-focused products.
- You need a model likely optimized for mobile-scale deployment and consumer-facing latency.
- You need an additional model to ensemble with others for diversity of outputs.
- Your use case involves experimentation across multiple vendors, including major Chinese LLM providers.
Avoid if...
- You need strong guarantees about data residency and compliance in US or EU jurisdictions.
- Your workload requires state-of-the-art reasoning, coding, or tool-use comparable to top-tier frontier models.
- You need highly transparent documentation, benchmarks, and long-term stability suitable for regulated industries.
- You need best-in-class support, SLAs, and English-language developer ecosystem and examples.
- Your workload requires advanced safety controls, fine-grained policy tuning, or enterprise governance features.
- You need strict assurances that training or inference will not involve cross-border data transfer.
- Your workload requires proven performance on niche domains like biomedical, legal, or financial reasoning.
FAQ
Frequently Asked Questions
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What is Seedance 2.0?
Seedance 2.0 is a large language model by ByteDance focused on fast, low-cost text generation for general-purpose applications.
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What is Seedance 2.0 best suited for?
Seedance 2.0 is best for high-volume chatbots, content generation, and lightweight reasoning where throughput and cost efficiency matter more than frontier-level capabilities.
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What context window does Seedance 2.0 support?
Seedance 2.0 supports a 16K token context window, suitable for long conversations, multi-step tools workflows, and moderately long documents.
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What modalities does Seedance 2.0 support via LLM.API?
Through LLM.API, Seedance 2.0 currently supports text input and text output only, without native image, audio, or video support.
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How fast is Seedance 2.0 on LLM.API?
Typical end-to-end latency ranges from 300ms to a few seconds per request on LLM.API, depending on prompt length and concurrency.
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How is Seedance 2.0 priced on LLM.API?
LLM.API exposes Seedance 2.0 with per-token pricing, charging separately for input tokens and output tokens, plus any provider-specific minimums.
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How do I call Seedance 2.0 through LLM.API?
Use the LLM.API chat or completion endpoint with the provider set to "bytedance" and the model name set to "Seedance 2.0".
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How does Seedance 2.0 compare to similar general-purpose models?
Seedance 2.0 typically trades off peak reasoning quality for higher throughput and lower cost than many flagship frontier models.
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Does Seedance 2.0 support tools or function calling via LLM.API?
Yes, Seedance 2.0 can be integrated with tools on LLM.API using the standard function-calling or tool-calling schema supported by the gateway.
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What are the main limitations of Seedance 2.0?
Seedance 2.0 may struggle with complex long-horizon reasoning, precise mathematical proofs, and tasks requiring up-to-date proprietary or domain-specific knowledge.