How Much Text Fits in an LLM's Context Window?
See exactly how many pages, words and documents fit inside GPT-4, Claude and Gemini.
About this tool
Context window size determines how much information an LLM can 'see' at once. A 128K context window can hold an entire novel; a 1M context window can process hours of meeting transcripts. Understanding context limits helps you design better RAG systems, choose the right model for long documents, and avoid costly 'context overflow' errors in production.
Quick Fact
Gemini 1.5 Pro's 1M token context window can hold approximately 2,500 pages of text, 30,000 lines of code, or 11 hours of meeting transcripts.
Common Use Cases
→ Document Q&A
Determine if your PDF or report fits in a single context, or if you need to chunk it for RAG retrieval.
→ Code Analysis
Check if an entire codebase can fit in Claude's 200K context for whole-repository analysis.
→ Long Conversation Bots
Calculate how many conversation turns fit before you need to summarize and compress chat history.
→ Model Selection
Choose between GPT-4o (128K) and Gemini 1.5 Pro (1M) based on your document length requirements.
Frequently Asked Questions
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What is a context window in AI?
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A context window is the maximum amount of text an AI model can process in a single request. It includes your prompt, conversation history, documents you've attached, and the model's response. Context windows are measured in tokens — approximately 4 characters or 0.75 words per token in English.
Which AI has the largest context window?
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As of 2025, Google Gemini 1.5 Pro and Flash offer the largest context window at 1,000,000 tokens (approximately 750,000 words or 2,500 pages). Anthropic's Claude models offer 200,000 tokens, while OpenAI's GPT-4o supports 128,000 tokens.
How many pages can Claude read at once?
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Claude 3.5 Sonnet has a 200,000 token context window, which can hold approximately 150,000 words or 600 pages of English text. For Chinese or Japanese text, the page count is lower due to higher tokens-per-character ratios.
What happens when you exceed the context window limit?
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When you exceed an LLM's context window, one of two things happens: (1) the API returns an error requiring you to shorten your input, or (2) older parts of the conversation are silently truncated. Production systems typically handle this with summarization, sliding window approaches, or RAG (retrieval-augmented generation).
What is RAG and how does it relate to context windows?
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RAG (Retrieval-Augmented Generation) is a technique where only the most relevant chunks of a large document are retrieved and placed into the context window, rather than the entire document. This allows LLMs to effectively 'read' documents much larger than their context limit, while also reducing cost.
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