Context Window Calculator
Calculate how much of an LLM context window your prompts use. Visual breakdown for GPT-4o, Claude, Llama, and Gemini.
Breakdown
Features
- ✓Calculate context window usage for 14 LLM models across 5 providers
- ✓Separate inputs for system prompt, few-shot examples, and user message
- ✓Visual progress bar with color-coded usage levels
- ✓Real-time token estimation as you type
- ✓Breakdown of tokens per section with total and remaining counts
- ✓Copy full analysis summary to clipboard
How to Use
- 1Select your target model from the dropdown — context window size updates automatically
- 2Enter your system prompt in the first textarea
- 3Add any few-shot examples in the second textarea (optional)
- 4Type the user message in the third textarea
- 5Watch the usage bar and breakdown update in real time as you type
- 6Click "Copy Summary" to export the full analysis as plain text
Examples
Input
Model: GPT-5.4 | System: "You are a helpful assistant." | Message: "What is TypeScript?"
Output
System: ~10 tokens | Message: ~5 tokens | Total: ~15 / 272K (0.01%)
Input
Model: Claude Opus 4.6 | System: 150 chars | Examples: 800 chars | Message: 200 chars
Output
System: ~43 tokens | Examples: ~229 tokens | Message: ~57 tokens | Total: ~329 / 1.0M (0.03%)
Why Use a Context Window Calculator?
Every LLM has a context window — a maximum number of tokens it can process in a single request. This includes your system prompt, conversation history, few-shot examples, the user message, and the model's response. If your input exceeds the context window, the API will reject the request or truncate your content.
This calculator helps you design prompts that fit within your target model's limits. By separating inputs into system prompt, few-shot examples, and user message, you can see exactly where your tokens are going and optimize each section independently. The visual bar gives instant feedback on how close you are to the limit.
Context window sizes vary dramatically across models. DeepSeek V3 offers 128K tokens, GPT-5.4 provides 272K, Claude Opus 4.6 and Sonnet 4.6 support 1M, and Llama 4 Scout leads with 10M tokens. Choosing the right model for your use case often depends on how much context you need. This tool lets you compare models and find the most efficient fit.
For production applications, you need to reserve tokens for the model's response. If your prompt uses 90% of the context window, the model only has 10% left for its answer. A good rule of thumb is to keep prompt usage under 50-60% for conversational applications and under 80% for single-turn tasks. The color-coded bar helps you stay in safe territory.
All calculations happen in your browser. Your system prompts, examples, and messages are never sent to any server. This makes the tool safe for confidential prompts, proprietary few-shot examples, and internal documentation that you plan to include in your LLM context.