A context window calculator helps developers and AI engineers understand how much of a model's context is consumed by their text, documents, or code. Knowing your token usage relative to the context limit prevents silent truncation errors, helps you design RAG retrieval budgets, and enables accurate cost forecasting before you make API calls.
Model & Context Window
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Context Window Analysis
Based on your selected model and document type
Capacity Breakdown
Cost Estimates
Note: Token counts use the ~4 chars/token approximation for text and ~3.5 for code. Exact counts require running the model's tokenizer (e.g., tiktoken). Pricing reflects approximate 2026 rates — verify with your provider before budgeting.
How to Use the Context Window Calculator
Understanding your LLM's context window is fundamental to building reliable AI applications. Exceed the limit and your content gets silently truncated; waste most of the window and you pay more than necessary. This context window calculator gives you instant token estimates and cost projections without running any API calls.
Step 1: Select Your Model
Choose the model you plan to use. Each model has a different context window: GPT-4o supports 128K tokens, Claude 3.5 Sonnet offers 200K, Gemini 1.5 Pro provides 1 million tokens, Llama 3 70B handles 128K, and Mistral Large is limited to 32K. Smaller context windows mean you must be more careful about what content you include in each call — especially for long document Q&A, RAG pipelines, or multi-turn conversations.
Step 2: Choose Your Document Type
Token density varies by content type. Plain text (prose, emails, articles) averages roughly 4 characters per token. Code is more token-dense at approximately 3.5 chars/token because of special characters, indentation, and identifiers. JSON and YAML are similar to code. Markdown is close to plain text but has slightly higher density due to formatting syntax. Selecting the right document type ensures your estimates are as accurate as possible.
Step 3: Paste Your Text or Enter a Manual Count
Paste the actual text you plan to send to the model — your system prompt, user message, retrieved document chunks, conversation history, or any combination. The calculator instantly estimates the token count. If you do not have the exact text yet, use the manual word count or character count fields to estimate based on your document size. A 500-page PDF is roughly 250,000 words or 1.25 million characters — about 312,500 tokens.
Step 4: Interpret the Context Usage Bar
The context window usage bar shows what percentage of the model's total context you are consuming. As a best practice, avoid filling more than 80% of the context window with input content — leave at least 20% for the model's response. If the bar turns red, your content exceeds the context limit and you will need to chunk it, summarize earlier sections, or upgrade to a model with a larger context window.
Understanding the Cost Estimates
The cost section shows input cost for your content, the additional cost for a typical 500-token output, and projected daily and monthly costs assuming 100 and 3,000 calls respectively. These projections help you decide whether to optimize your prompt, use a cheaper model, or cache common context sections. For example, if your system prompt is 5,000 tokens and you make 10,000 calls per day, that is 50M input tokens daily — potentially $125/day with GPT-4o. Prompt caching (available on Claude and GPT-4o) can cut this cost by 50–90% for repeated context.
Planning Your Context Budget
A well-designed context budget allocates space to each component: system prompt (typically 500–2,000 tokens), conversation history (variable), retrieved RAG chunks (2,000–20,000 tokens), user query (100–500 tokens), and response buffer (500–4,000 tokens). Use this context window calculator to verify each component fits within your chosen model's limits before building your pipeline.
FAQ
Is this context window calculator really free?
Yes, completely free with no signup required. All token estimation runs entirely in your browser using JavaScript — no text is ever sent to any server. Use it as many times as you need.
Is my pasted text private?
Absolutely. Your text never leaves your device. All token counting and calculations happen locally in your browser with no network requests made. Your data stays completely private.
How accurate is the token count estimate?
The calculator uses the widely accepted approximation of ~4 characters per token for plain text and ~3.5 characters per token for code. This is accurate to within 5–10% for most content. Exact token counts require running the model's actual tokenizer (e.g., tiktoken for OpenAI models).
What does context window size mean for LLMs?
A context window is the maximum amount of text (measured in tokens) an LLM can process in one call. This includes your system prompt, conversation history, retrieved documents, and the model's response. Once the limit is exceeded, the model cannot see earlier content, which can degrade quality.
Why do different document types have different token rates?
Code and JSON are more token-dense than plain text because they have fewer common words and more special characters that each consume tokens. Code averages ~3.5 chars/token vs ~4 chars/token for prose. Markdown is similar to plain text with slightly higher density due to formatting characters.
How do I calculate how many pages fit in a context window?
A standard document page is approximately 500 words or 2,500 characters. At 4 characters per token, one page is roughly 625 tokens. For a 128K context window, that's about 200 pages — enough for a short novel. However, you should reserve 20–30% of the context for the system prompt and model response.
What is the largest context window available?
As of 2026, Google Gemini 1.5 Pro offers a 1 million token context window, and Gemini 1.5 Flash offers 1M tokens as well. Claude models offer 200K tokens, GPT-4o offers 128K, and Llama 3 and Mistral offer 128K and 32K respectively. Larger windows allow processing entire codebases or books in one call.
How much does it cost to fill an entire context window?
At current 2026 rates, filling a 128K GPT-4o context costs approximately $0.32 (128K × $2.50/1M tokens). Filling Claude's 200K context costs about $0.60. Gemini 1.5 Pro's 1M context costs roughly $1.25. Output tokens are typically 2–4× more expensive than input tokens per million.