AI Context Windows Reference
Compare context window sizes across AI models — find the right model for your document length needs
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What Are Context Windows?
A context window is the maximum amount of text an AI model can "see" and process at once. Think of it as the model's short-term memory — everything in your conversation, including your prompt, any documents you provide, the conversation history, and the model's response must fit within this limit.
Context windows are measured in tokens, which are roughly 3-4 characters or about 0.75 words. A 128K context window can hold approximately 250 pages of text — enough for a short novel or a large codebase.
This table shows the maximum context window for each model, along with pricing data to help you choose the best option for your document processing needs.
How to Use This Tool
Estimate Your Needs
Calculate how much context your use case requires: document length + conversation history + system prompt + expected response length.
Sort by Context Size
Use the sort toggle to order models by context window size. Find models that meet your minimum requirements.
Compare Pricing
Larger context windows often come with higher costs. The pricing column shows input/output costs per 1M tokens to help you balance capability vs budget.
Copy as Markdown
Export the filtered table for documentation or to share with your team during model selection.
Token Estimation Reference
| Content Type | Approximate Tokens | Fits In |
|---|---|---|
| 1 page of text | ~500 tokens | All models (4K+) |
| 10-page document | ~5,000 tokens | Most models (8K+) |
| 50-page report | ~25,000 tokens | 32K+ models |
| 100-page book | ~50,000 tokens | 128K+ models |
| Entire codebase | 100K-500K tokens | 200K-1M models |
| Novel-length text | ~100K-200K tokens | 200K+ models (Claude, Gemini) |
Why Context Size Matters
Document Analysis
Larger context windows let you analyze longer documents without chunking them into pieces. Process entire contracts, reports, or codebases in a single request.
Conversation Memory
More context means longer conversations before the model "forgets" earlier messages. Essential for complex, multi-turn interactions.
RAG Applications
Larger context allows more retrieved documents to be included for better-informed responses. Reduces the need for sophisticated chunking strategies.
Code Understanding
Large context enables AI to understand entire projects at once. See dependencies, understand architecture, and make coherent cross-file changes.
Pro Tip: Right-Size Your Context
Don't automatically choose the largest context window. You pay for all tokens used, so including unnecessary content wastes money. Use techniques like summarization, chunking with overlap, or retrieval to include only the most relevant information in your context.
Context Window Trade-offs
Benefits of Larger Context
- • Process longer documents at once
- • Better understanding of full context
- • Longer conversation history
- • More examples for few-shot learning
- • Fewer chunking/retrieval complexities
Considerations
- • Higher cost (pay per token)
- • Slower response times
- • May include irrelevant information
- • "Lost in the middle" effect
- • Increased latency for first token
Important: "Lost in the Middle" Effect
Research shows that LLMs pay more attention to the beginning and end of long contexts, potentially missing important information in the middle. For best results, put the most critical information at the beginning or end of your prompt, or use retrieval to surface only relevant portions.
Frequently Asked Questions
What happens if I exceed the context limit?
The API will return an error, typically with a message like "maximum context length exceeded." You'll need to reduce your input by summarizing, chunking, or removing less relevant content. Some frameworks like LangChain handle this automatically by truncating older messages.
Is a bigger context window always better?
Not always. Larger contexts cost more (you pay for all tokens processed) and can suffer from the "lost in the middle" effect where models pay less attention to content in the middle of long prompts. Additionally, response latency increases with context size. Use only as much context as you need.
How do I estimate tokens for my content?
As a rough guide: 1 token ≈ 4 characters in English, or about 0.75 words. A standard page of text is roughly 500 tokens. For precise counts, use OpenAI's tiktoken library, Anthropic's token counting endpoint, or online token counter tools.
What is "context window" vs "max output tokens"?
Context window is the total capacity for input + output combined. Max output tokens is a separate parameter that limits how long the model's response can be. Both must be considered: your input + expected output must fit within the context window.
How does context affect pricing?
You're charged for all tokens used in a request — both input and output. A 100K token input at $3/1M input tokens costs $0.30 just for input. Models with larger context windows often have higher per-token prices. Always calculate expected costs for your use case.
Which models have the largest context windows?
As of 2024, Gemini 1.5 Pro leads with up to 2M tokens. Claude 3 supports 200K tokens. GPT-4 Turbo and GPT-4o support 128K tokens. Context windows continue to grow as providers improve their architectures.
Related Tools
Token Counter
Count exact tokens in your text to plan context usage accurately.
Model Comparison
Compare context windows alongside other model specs and capabilities.
Pricing Table
Compare pricing to understand cost implications of different context sizes.
Capabilities Matrix
Find models with both the context length and features you need.