Context Window Details

What is Context Window

Context window refers to the maximum number of tokens a model can process in a single request, including both input (Prompt) and output (Response).

Context Window Diagram

seed Series Model Context Windows

Model ID	Capabilities	Token Limit	Rate Limit
deepseek-v4-pro-ga-260813	Deep Thinking, Text Generation, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 384k Max Chain of Thought: 128k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
deepseek-v4-flash-ga-260731	Deep Thinking, Text Generation, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 384k Max Chain of Thought: 128k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
deepseek-v4-1-flash-260910	Deep Thinking, Text Generation, Multimodal Understanding, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 384k Max Chain of Thought: 384k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
glm-5-2-260617	Deep Thinking, Text Generation, Tool Calling	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-character-251128	Text Generation, Tool Calling	Context Window: 128k Max Input: 96k Max Output (default 4k): 32k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-evolving	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 256k Max Chain of Thought: 256k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-1-pro-260628	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output (json_schema mode recommended)	Context Window: 256k Max Input: 256k Max Output (default 4k): 256k Max Chain of Thought: 256k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-1-lite-260915	Deep Thinking, Text Generation, Multimodal Understanding, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 256k Max Chain of Thought: 256k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-1-pro-260915	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 256k Max Chain of Thought: 256k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
glm-5-3-flash-260826	Deep Thinking, Text Generation, Multimodal Understanding, Tool Calling	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
glm-5-3-260814	Deep Thinking (forced), Text Generation, Tool Calling, Structured Output, Streaming Tool Parameters	Context Window: 1024k (1M) Max Input: 1024k Max Output: 128k Max Chain of Thought: 128k (built into the context)	Max RPM: 500 (standard) Max TPM: 1,000,000 Note: Deep thinking mode is always on and cannot be disabled; supports tool stream=true for streaming tool parameter returns.
kimi-k3-260716	Deep Thinking, Text Generation, Tool Calling, Native Multimodal Visual Understanding, Streaming Output (stream), JSON Structured Output	Context Window: 1024k Max Input: 1024k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 500 Max TPM: 1000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-0-pro-260215	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output	Context Window: 256k Max Input: 224k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 30000 Max TPM: 5000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-0-lite-260215	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output	Context Window: 256k Max Input: 224k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 30000 Max TPM: 5000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
doubao-seed-2-0-mini-260215	Deep Thinking, Text Generation, Multimodal Understanding, GUI Task Processing, Tool Calling, Structured Output	Context Window: 256k Max Input: 224k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 30000 Max TPM: 5000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details
seed-2-0-code-preview-260215	Deep Thinking, Text Generation, Multimodal Understanding, Tool Calling	Context Window: 256k Max Input: 256k Max Output (default 4k): 128k Max Chain of Thought: 128k	Max RPM: 30000 Max TPM: 5000000 Non-rigid guarantee, affected by platform load/calling method, see documentation for details

Impact of Context Window on Model

1. Input Length Affects Available Output Space

Context window: 4096 tokens

Scenario A: Simple task
├── Input: 100 tokens
└── Available output: 3996 tokens ✅ Ample

Scenario B: Medium task
├── Input: 3500 tokens
└── Available output: 596 tokens ⚠️ Limited

Scenario C: Long text task
├── Input: 4000 tokens
└── Available output: 96 tokens ❌ Almost no output

2. Attention Decay Phenomenon

As sequence grows, model's attention to early content gradually decreases:

Input: [User Q] [Background 1] [Background 2] ... [Latest]
       ↑
     Strong                               Weak
    Attention                            Attention

Potential Issues:

Ignoring important instructions at the beginning
Forgetting background information in the middle
Incorrectly citing content sources

3. Theoretical Challenges of Long Context

Challenge	Description
Computational Complexity	Self-attention computation is proportional to sequence length squared
Memory Usage	KV Cache grows linearly with sequence length
Communication Overhead	Cross-node data transfer increases in distributed inference

Application Scenario Recommendations

Short Context Scenarios (≤ 32K)

Scenario	Recommended Reason
Customer Service	Single-turn interaction, concise response
Simple Q&A	Single task, no long background needed
Text Completion	Short text generation
Code Generation	Function-level code, snippet output

Medium Context Scenarios (64K - 128K)

Scenario	Recommended Reason
Document Analysis	Can analyze 10-20 page PDFs
Multi-turn Chat	Retain multi-turn conversation history
Code Debugging	Include complete file context
Content Creation	Medium-long article writing

Long Context Scenarios (> 128K)

Scenario	Recommended Reason
Long Novel	Can process entire chapter content
Codebase Understanding	Analysis of entire files
Book Summary	Summarize entire books
Knowledge Base Q&A	Large document retrieval augmentation

Parameter Configuration Recommendations

max_tokens Parameter

Control the maximum number of output tokens:

python

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Explain quantum computing in detail"}
    ],
    max_tokens=2000
)

Scenario-based Configuration

Scenario	Recommended max_tokens	Description
Brief Q&A	300-500	Concise answer
Standard Q&A	1000-2000	Complete answer
Detailed Explanation	3000-4000	In-depth analysis
Long Writing	5000-8000	Article output

Configuration Guidelines

Recommended formula:
max_tokens = Context window × 0.4 ~ 0.5

Example (128K window):
max_tokens ≈ 50000 ~ 64000

Note: Reserve space for input, recommended not to exceed 50% of window

Strategies for Exceeding Limits

1. Chunking

Applicable for long document analysis, book summarization:

python

def process_long_document(text, chunk_size=4000, overlap=200):
    chunks = []
    tokens = text.split()

    for i in range(0, len(tokens), chunk_size - overlap):
        chunk = ' '.join(tokens[i:i + chunk_size])
        chunks.append(chunk)

    return chunks

2. Summarization Compression

Applicable when multi-turn conversation history is too long:

python

def summarize_conversation(messages, max_tokens=4000):
    summary_prompt = f"""Summarize the following conversation into key points,
controlled within {max_tokens} tokens:

{messages}

Summary:"""

    summary = call_llm(summary_prompt)
    return [
        {"role": "system", "content": "Previous conversation summary: " + summary},
        messages[-1]
    ]

3. Retrieval Augmented (RAG)

Applicable for scenarios requiring processing massive knowledge:

User Question ──→ Retrieve ──→ Relevant Docs ──→ Build Prompt ──→ LLM Inference
                                ↑
                          Only most relevant excerpts

Best Practices

1. Important Information Placement

Place key instructions at the beginning or end
Use structured format to highlight key points

2. Choose Model Based on Task

Task Type	Recommended Model	Reason
Short Chat	GPT-4o-mini	Fast and low cost
Document Analysis	Claude 3.5	Long context support
Ultra-long Tasks	Gemini 3 Pro	Million-level context

3. Monitoring and Optimization

python

def estimate_tokens(text):
    return len(text.split()) * 1.3

def check_context_usage(prompt, max_tokens, context_window):
    estimated = estimate_tokens(prompt)
    available = context_window - max_tokens
    usage_ratio = estimated / available

    if usage_ratio > 0.9:
        return "warning"
    return "ok"

FAQ

Q: Why is output truncated?

Possible reasons:

max_tokens set too small
Context window is full

Q: How to avoid "forgetting" issues?

Place important information at the beginning or end of input
Use structured format to highlight key points
Use chunking for ultra-long tasks

Q: Is larger context window always better?

Not necessarily. Larger context brings higher latency and cost, and too long context may reduce attention to middle sections. Recommended to choose the appropriate model based on actual tasks.

Doubao Speech Synthesis Large Model

Doubao ASR Large Model

seedance –2.0 Series

seedance –2.5

Real/Virtual Asset Library

Context Window Details

What is Context Window

seed Series Model Context Windows

Impact of Context Window on Model

1. Input Length Affects Available Output Space

2. Attention Decay Phenomenon

3. Theoretical Challenges of Long Context

Application Scenario Recommendations

Short Context Scenarios (≤ 32K)

Medium Context Scenarios (64K - 128K)

Long Context Scenarios (> 128K)

Parameter Configuration Recommendations

max_tokens Parameter

Scenario-based Configuration

Configuration Guidelines

Strategies for Exceeding Limits

1. Chunking

2. Summarization Compression

3. Retrieval Augmented (RAG)

Best Practices

1. Important Information Placement

2. Choose Model Based on Task

3. Monitoring and Optimization

FAQ

Q: Why is output truncated?

Q: How to avoid "forgetting" issues?

Q: Is larger context window always better?

Context Window Details ​

What is Context Window ​

seed Series Model Context Windows ​

Impact of Context Window on Model ​

1. Input Length Affects Available Output Space ​

2. Attention Decay Phenomenon ​

3. Theoretical Challenges of Long Context ​

Application Scenario Recommendations ​

Short Context Scenarios (≤ 32K) ​

Medium Context Scenarios (64K - 128K) ​

Long Context Scenarios (> 128K) ​

Parameter Configuration Recommendations ​

max_tokens Parameter ​

Scenario-based Configuration ​

Configuration Guidelines ​

Strategies for Exceeding Limits ​

1. Chunking ​

2. Summarization Compression ​

3. Retrieval Augmented (RAG) ​

Best Practices ​

1. Important Information Placement ​

2. Choose Model Based on Task ​

3. Monitoring and Optimization ​

FAQ ​

Q: Why is output truncated? ​

Q: How to avoid "forgetting" issues? ​

Q: Is larger context window always better? ​

Context Window Details

What is Context Window

seed Series Model Context Windows

Impact of Context Window on Model

1. Input Length Affects Available Output Space

2. Attention Decay Phenomenon

3. Theoretical Challenges of Long Context

Application Scenario Recommendations

Short Context Scenarios (≤ 32K)

Medium Context Scenarios (64K - 128K)

Long Context Scenarios (> 128K)

Parameter Configuration Recommendations

max_tokens Parameter

Scenario-based Configuration

Configuration Guidelines

Strategies for Exceeding Limits

1. Chunking

2. Summarization Compression

3. Retrieval Augmented (RAG)

Best Practices

1. Important Information Placement

2. Choose Model Based on Task

3. Monitoring and Optimization

FAQ

Q: Why is output truncated?

Q: How to avoid "forgetting" issues?

Q: Is larger context window always better?