Context Window Calculator

A context window calculator is a tool that helps you visualize how tokens are distributed across the different components of an LLM prompt and how much of the model's maximum context window they consume. Every large language model has a fixed context window — the maximum number of tokens it can process in a single request, including both the input prompt and the generated output. When your combined input exceeds this limit, the model will either truncate your prompt silently or return an error. Our context window calculator online lets you break your prompt into named slots, set token counts for each, and immediately see the total utilization, remaining capacity, and estimated input cost — all computed privately in your browser.

1. Select a Model:Choose from 16 popular LLMs including GPT-4o, Claude Opus 4, Gemini 2.5 Pro, DeepSeek-V4-Flash, Grok 3, and Mistral Large. The calculator loads the model's context window size and current per-token pricing automatically.
2. Configure Your Slots:Adjust the pre-loaded slots (system prompt, conversation history, RAG documents, user message, output reserve) or add your own custom slots. Each slot has a color-coded label and a token count you can edit directly. Use the "Add Slot" panel to model any additional prompt component.
3. Read the Visualization: The segmented context bar shows each slot as a proportional colored segment of the total window. The breakdown table shows the token count, window percentage, and input cost per slot. The cross-model table shows whether your payload fits in each supported model and flags overflows.

• System Prompt: Standing instructions, persona definitions, and tool schemas. These are sent with every request and typically consume 200–2,000 tokens depending on complexity.
• Conversation History: Prior turns in a multi-turn conversation. This grows with each exchange and is the most common cause of context overflow in chatbot applications.
• Retrieved Context (RAG): Documents or chunks retrieved from a vector store and injected into the prompt. RAG context can easily consume 2,000–20,000 tokens depending on the number and size of retrieved chunks.
• Output Reserve:The number of tokens you're expecting the model to generate in its response. Always reserve space equal to your expected max output length — the context window is shared between input and output.

The most effective way to reduce context consumption is to compress your system prompt — remove redundant instructions, use concise phrasing, and move static reference material to a RAG store instead of embedding it inline. For conversation history, implement a sliding window or summary compression strategy: keep the last N turns verbatim and replace earlier turns with a short summary. For RAG pipelines, tune your retrieval step to return fewer, higher-relevance chunks rather than broad topical sets. Finally, always set an explicit max_tokens parameter in your API call so the output reserve is accounted for and you avoid unexpected truncation of the generated response.