Technical Specifications of Doubao-Seed-2-0
| Item | Doubao-Seed-2-0 |
|---|---|
| Provider | ByteDance (Volcengine) |
| Model family | Doubao Seed 2.x series |
| Model type | Multimodal large language model |
| Input types | Text, Image |
| Output types | Text |
| Context window | Up to 256K tokens (long-context variant supported) |
| Max output tokens | Configurable via API (typically 8K–16K default limits depending on deployment) |
| Tool calling | Supported (function calling / structured output) |
| Deployment | API via Volcengine / enterprise private deployment |
| Knowledge cutoff | 2024 (reported in public documentation) |
| Primary positioning | Enterprise-grade multimodal reasoning and Chinese-English performance optimization |
What is Doubao-Seed-2-0?
Doubao-Seed-2-0 is ByteDance’s second-generation flagship multimodal foundation model in the Doubao series. It improves long-context reasoning, Chinese-language fluency, coding performance, and multimodal understanding compared to Doubao 1.x models. The model is designed for enterprise deployment via Volcengine APIs and supports structured outputs and tool invocation.
It targets high-accuracy reasoning, enterprise copilots, document analysis, and multimodal applications.
Main Features of Doubao-Seed-2-0
- Strong Chinese + bilingual optimization: Trained with deep Chinese corpus integration, outperforming many Western models in Chinese reasoning and instruction-following tasks.
- Long-context support (up to 256K tokens): Enables analysis of long policy documents, contracts, research papers, and multi-document workflows.
- Multimodal input capability: Accepts image inputs for chart reading, document parsing, and visual Q&A.
- Structured output & function calling: Designed for enterprise API workflows and tool orchestration.
- Improved coding ability: Enhanced code generation and debugging across mainstream languages.
- Agent capabilities / multi-step reasoning: Pro SKU explicitly targeted at complex, long-chain reasoning and task execution (planning + execution).
- Cost / efficiency optimizations: ByteDance claims a significant cost advantage for large real-world token budgets; targeted engineering to reduce per-token inference costs.
- SKU segmentation: Lite (cost/performance balance), Mini (low latency / high concurrency), Code (programming specialty). This helps operators choose the right trade-off for a product.
Model versions / SKUs
- Doubao-Seed-2.0 Pro — high-capacity SKU for deep inference tasks and long-chain task execution; marketed as comparable to GPT-5.2 / Gemini 3 Pro in capabilities.
- Doubao-Seed-2.0 Lite — mid-tier SKU optimizing cost/performance; described as surpassing Doubao 1.8 in overall capability.
- Doubao-Seed-2.0 Mini — lightweight SKU for low latency, high concurrency, cost-sensitive production endpoints.
- Doubao-Seed-2.0-Code — code/programming specialty model; noted to pair well with TRAE (a code tooling / runtime) in ByteDance reporting.
Use cases & recommended deployment patterns
Primary use cases (immediately practical):
- Agent / task automation: Long-chain planning + execution (Pro) — e.g., enterprise workflow agents that interpret instructions, call services, and synthesize results.
- Conversational assistant / consumer app: Doubao app integration for chat, search, commerce assistance at scale (Lite / Mini for cost/latency tradeoffs).
- Code generation & developer tooling: Doubao-Seed-2.0-Code for code completion, code review, automated test generation and developer assistants.
- Multimodal content generation: Paired with Seedance and Seedream for image/video production workflows, marketing content, short video creation pipelines. (Be mindful of IP/safety.)
Deployment recommendations (practical):
- Use Mini for high-TPS conversational endpoints (caching + quantization).
- Use Lite where cost + quality balance is needed (customer support augmentation, FAQ automation).
- Use Pro for complex agent tasks that require deep reasoning and long context chains (pair with server-side scaling and structured action executors).
- For sensitive workflows (medical/financial/legal), add domain-specific retrieval (RAG) and conservative response filters; treat model outputs as assistive not authoritative until validated. (Best practice; applies to all LLMs.)
How to access and integrate Doubao-Seed-2.0
Step 1: Sign Up for API Key
Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to Doubao-Seed-2.0 pro API
Select the “doubao-seed-2-0-pro-260215” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Chat format.
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Retrieve and Verify Results
Process the API response to get the generated answer. After processing, the API responds with the task status and output data.