Powered by ByteDance Seed
Seed-2.0-Lite
- Text Generation
Seed-2.0-Lite is a mid-tier large language model from ByteDance Seed that offers long-context, multimodal capabilities with a focus on cost efficiency. It is positioned for agentic workloads and retrieval-augmented generation where extended context and tool use matter.
About the model
What is Seed-2.0-Lite?
Seed-2.0-Lite is a ByteDance Seed large language model designed as a cost-effective, long-context and multimodal option for general-purpose AI applications. It is commonly used for text generation and chat-style assistants, including retrieval-augmented generation scenarios that benefit from its extended context window. It is also applied in agentic workflows, tools integration, and some vision or video understanding tasks where balance between price and performance is important. It belongs to the Doubao/Seed 2.0 family of models, sitting below the Pro variants as a lighter, more efficient configuration.
Model capabilities
5 Core Capabilities
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Multimodal Reasoning
Understands and reasons over text, images, audio, and video jointly, enabling complex cross-modal analysis and decision-making tasks.
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Conversational Chat
Provides coherent, context-aware dialogue for assistants and chatbots, optimized for low-latency enterprise and high-frequency interactions.
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Image Understanding
Performs detailed visual comprehension, supporting tasks like object recognition, visual reasoning, and fine-grained perception in images.
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Tool and Agent Use
Supports function calling and agentic workflows, invoking tools and APIs to accomplish multi-step tasks in real environments.
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Cross-Lingual Tasks
Handles multilingual text, enabling instructions, responses, and content generation across languages for global applications and workflows.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice And Contract Review
- Legal Case Research Assistant
- Compliance Case Monitoring
- E-commerce Product Recommendations
- Tool-Using AI Agents
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and fastest Seed-2.0-Lite-compatible inference across providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.03 | $0.06 | 128K tokens |
| ByteDance Seed | Global | ~220ms | ~40 tps | ~99.9% | ~$0.06 | ~$0.12 | ~64K tokens |
| OpenAI (closest: GPT-4.1-mini) | Global | ~250ms | ~35 tps | 99.9% | ~$0.15 | ~$0.60 | 128K tokens |
| Anthropic (closest: Claude 3 Haiku) | US/EU | ~260ms | ~30 tps | 99.9% | ~$0.12 | ~$0.48 | 200K tokens |
| Google (closest: Gemini 1.5 Flash) | Global | ~240ms | ~32 tps | 99.9% | ~$0.10 | ~$0.40 | 1M tokens |
Performance benchmarks
Technical Specifications
| Metric | Seed-2.0-Lite (ByteDance Seed) | GPT-4.1-mini (OpenAI) | Claude 3 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.10 | $0.15 | $0.25 |
| Output Price ($/1M) | $0.40 | $0.60 | $0.80 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 60 tps | 40 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 5.8B
- Prompt tokens processed (last 30 days)
- 42M
- Completion tokens generated (last 30 days)
- 9.3M
- API requests served (last 30 days)
- 99.8%
- Avg uptime (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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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Orchestration
Set price and performance constraints, then let LLM.API choose cheaper equivalents, downshift for bulk work, or upshift for critical paths—no manual tuning required.
Max performance, minimal spend -
Resilient Fallback Logic
Define smart failover chains so requests automatically retry on alternative models or providers when timeouts, rate limits, or outages hit—without extra error-handling glue.
Built-in reliability layer -
Deep Observability
Get unified logs, traces, and metrics for every provider in one place—latency, cost, tokens, and errors—so you can debug faster and optimize your AI stack.
See every token, everywhere -
Task-Level Abstractions
Describe tasks like chat, tools, RAG, or agents at a high level; LLM.API handles prompt shaping, model quirks, and upgrades behind a stable interface.
Code to tasks, not models -
High-Throughput Batch
Submit large batches across providers with automatic chunking, concurrency control, and retry policies—maximizing throughput while keeping queues healthy and costs predictable.
Scale jobs, not ops
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a lightweight, general-purpose model for everyday chat and virtual assistant tasks.
- You need reasonably capable text generation for short posts, product descriptions, or marketing blurbs.
- You need a compact model suitable for cost-sensitive, high-traffic consumer applications.
- Your use case involves prototyping AI features where low latency matters more than perfect accuracy.
- Your use case involves moderate reasoning, like FAQs, simple decision trees, or form-filling helpers.
- You need a general LLM for classification, tagging, and summarizing short to medium documents.
- Your use case involves multilingual but simple interactions, such as support triage or intent routing.
Avoid if...
- You need frontier-level reasoning performance for complex planning, coding, or mathematical problem solving.
- Your workload requires handling very long context windows with reliable recall of earlier details.
- You need highly specialized domain expertise, such as legal analysis or advanced medical reasoning.
- Your workload requires state-of-the-art code generation, refactoring, or large multi-file repository understanding.
- You need strongest possible safety, robustness, and alignment guarantees for high-risk decision-making workflows.
- Your workload requires top-tier performance on complex multimodal tasks beyond simple text-centric interactions.
- You need rigorous tool-use orchestration, multi-agent reasoning, or sophisticated function-calling reliability.
FAQ
Frequently Asked Questions
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What is Seed-2.0-Lite?
Seed-2.0-Lite is a lightweight text generation model from ByteDance Seed, designed for fast, cost-efficient general-purpose language tasks via LLM.API.
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What is Seed-2.0-Lite best suited for?
Seed-2.0-Lite is best for high-volume chatbots, lightweight agents, and general text processing where low latency and low cost are important.
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What context window does Seed-2.0-Lite support on LLM.API?
Seed-2.0-Lite supports up to an 8K token context window on LLM.API, suitable for typical conversations and moderately long documents.
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How fast is Seed-2.0-Lite in terms of latency and throughput?
Seed-2.0-Lite is optimized for low latency responses and high throughput, making it suitable for interactive applications and large-scale parallel requests.
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What input and output modalities does Seed-2.0-Lite support?
Seed-2.0-Lite supports text-only input and text-only output on LLM.API; it does not handle images, audio, or video.
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How is Seed-2.0-Lite priced on LLM.API?
Seed-2.0-Lite is priced as a budget-friendly model on LLM.API, with significantly lower per-token costs than larger frontier models.
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How do I call Seed-2.0-Lite through LLM.API?
You call Seed-2.0-Lite by specifying the model name "Seed-2.0-Lite" in your LLM.API chat or completions endpoint requests.
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How does Seed-2.0-Lite compare to larger Seed models?
Seed-2.0-Lite is smaller and cheaper but generally less capable at complex reasoning and long-context tasks than larger Seed family models.
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What are the main limitations of Seed-2.0-Lite?
Seed-2.0-Lite may struggle with very long documents, advanced reasoning, niche domains, and tasks requiring multimodal understanding.
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Can Seed-2.0-Lite be used for code generation?
Seed-2.0-Lite can generate and edit code for common languages, but its coding abilities are weaker than specialized or larger code-focused models.