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Seed-2.0-Mini

  • Instruction Following

Seed-2.0-Mini is a compact multimodal large language model from ByteDance Seed optimized for latency-sensitive, high-concurrency, and cost-sensitive applications, offering long context and flexible reasoning modes.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is Seed-2.0-Mini?

Seed-2.0-Mini is a proprietary ByteDance Seed model that accepts text, image, and video inputs and produces text outputs with a context window of roughly 256k tokens. It is mainly used for fast, cost-efficient text generation, chat, and lightweight reasoning workloads where rapid response and high throughput are important. It is also used for multimodal understanding tasks—such as interpreting images or video alongside text—and for tool use, structured outputs, and extended reasoning within long documents. Seed-2.0-Mini belongs to the Doubao/Seed 2.0 model family and offers performance comparable to ByteDance Seed 1.6 while emphasizing lower latency and cost.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Supports multi-turn dialogue, following instructions and maintaining basic context for everyday assistance and information-seeking conversations.

  • Multilingual Translation

    Translates text between major languages, enabling cross-lingual understanding for short messages, simple documents, and online content.

  • Document OCR

    Extracts machine-readable text from images or scanned documents, enabling search, editing, and downstream text processing of visual materials.

  • Image Captioning

    Generates concise descriptions of images, identifying key objects and scenes to support accessibility and content understanding.

  • Content Moderation

    Analyzes text or media for policy-violating, harmful, or unsafe content to support automated screening and compliance workflows.

Use cases

6 Most Valuable Use Cases

  • High-volume Chatbots
  • Customer Support Triage
  • Knowledge Base Search
  • Alert & Log Monitoring
  • Cost-efficient Workflows
  • Lightweight Code Assistance

Transparent pricing

Cost Comparison

LLM API offers the lowest Seed-2.0-Mini equivalent prices with better latency and uptime than major providers.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 80ms 70 tps 99.99% $0.03 $0.06 128K
ByteDance Seed Global ~180ms ~40 tps ~99.9% ~$0.05 ~$0.10 ~64K
OpenAI Global ~220ms ~35 tps 99.9% ~$0.10 ~$0.20 ~128K
Anthropic US East ~210ms ~30 tps 99.9% ~$0.09 ~$0.18 ~200K
Google Cloud AI Global ~250ms ~25 tps 99.9% ~$0.08 ~$0.16 ~128K

Performance benchmarks

Technical Specifications

Metric Seed-2.0-Mini (ByteDance Seed) GPT-4o-mini (OpenAI) Gemini 1.5 Flash (Google)
Avg Latency ~180ms ~200ms ~220ms
Context Window 128K 128K 1M
Input Price ($/1M tokens) ~$0.05 ~$0.15 ~$0.10
Output Price ($/1M tokens) ~$0.15 ~$0.60 ~$0.30
Max Output Tokens 4K 4K 8K
Throughput ~120 tps ~100 tps ~90 tps
Uptime 99.9% 99.9% 99.9%

30-day usage via LLM API

7.8B
Prompt tokens processed (last 30 days)
5.1B
Completion tokens generated (last 30 days)
32.4M
API requests served (last 30 days)
99.8%
Avg uptime (last 30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the optimal model across providers based on latency, cost, and capability—without changing your integration or redeploying.

    One endpoint, best model.

  • Cost-Aware Orchestration

    Dynamically pick cheaper models for non-critical calls and reserve premium models for high-value tasks, cutting spend without sacrificing quality or reliability.

    Optimize every token.

  • Resilient Fallback Flows

    Define provider-agnostic fallback chains so requests transparently retry on alternate models when failures, rate limits, or timeouts occur—no custom glue code required.

    Stay up, even when they don’t.

  • Full-Stack Observability

    Get unified logs, traces, metrics, and cost breakdowns across all LLM providers in one place to debug issues faster and tune your workloads with confidence.

    See every token hop.

  • Task-Level Abstractions

    Describe tasks like chat, tools, RAG, or scoring once and let LLM.API translate them into provider-specific calls, simplifying logic and reducing integration drift.

    Code to tasks, not APIs.

  • High-Throughput Batch Jobs

    Run massive batches of prompts or evaluations through multiple models with automatic chunking, retries, and aggregation so you can ship experiments and pipelines faster.

    Scale experiments effortlessly.

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a lightweight general-purpose model for everyday chat and simple Q&A.
  • You need fast, low-cost inference for high-volume, low-complexity API requests.
  • Your use case involves simple classification, tagging, or short-text extraction tasks.
  • Your use case involves basic code help, such as small edits or snippets.
  • You need a small model to embed inside latency-sensitive consumer or mobile apps.
  • Your use case involves drafting short marketing copy, titles, or social media blurbs.

Avoid if...

  • You need top-tier reasoning quality for complex, multi-step analytical or scientific tasks.
  • You need reliable handling of very long documents, conversations, or context windows.
  • Your workload requires strong coding assistance across large codebases or complex refactors.
  • You need state-of-the-art performance on math, logic puzzles, or formal proofs.
  • Your workload requires highly nuanced instruction following with strict business or safety constraints.
  • You need specialized domain expertise, such as legal, medical, or financial advisory tasks.

FAQ

Frequently Asked Questions

  • What is Seed-2.0-Mini?

    Seed-2.0-Mini is a compact text generation model from ByteDance Seed designed for fast, low-cost inference via the LLM.API platform.

  • What is Seed-2.0-Mini best suited for?

    Seed-2.0-Mini is best for lightweight chatbots, short-form content generation, and utility functions where low latency and cost matter more than maximal reasoning depth.

  • How is Seed-2.0-Mini priced on LLM.API?

    Seed-2.0-Mini uses LLM.API’s unified per-token or per-request pricing; check your LLM.API dashboard for the latest specific rates.

  • What is the context window of Seed-2.0-Mini?

    Seed-2.0-Mini supports a mid-sized context window suitable for short to medium conversations and prompts; consult LLM.API docs for the exact token limit.

  • How fast is Seed-2.0-Mini in terms of latency?

    Seed-2.0-Mini is optimized for low latency and high throughput, making it appropriate for real-time or interactive applications.

  • What modalities does Seed-2.0-Mini support?

    Seed-2.0-Mini is a text-only model, accepting text prompts and returning text completions through LLM.API.

  • How do I access Seed-2.0-Mini through LLM.API?

    Call the LLM.API completion or chat endpoint and specify the model name "Seed-2.0-Mini" in your request payload.

  • How does Seed-2.0-Mini compare to larger Seed models?

    Seed-2.0-Mini is cheaper and faster but generally less capable at complex reasoning and long-context tasks than larger Seed series models.

  • Does Seed-2.0-Mini support function calling or tool integration via LLM.API?

    If LLM.API exposes function-calling metadata, Seed-2.0-Mini can be used with it; check the LLM.API capabilities matrix for this model.

  • What are the main limitations of Seed-2.0-Mini?

    Seed-2.0-Mini may struggle with very long documents, nuanced multi-step reasoning, or highly specialized domain knowledge compared to larger frontier models.

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