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Context Window Budget Planner

Large language models enforce strict context window limits that cap the combined size of input + output tokens (e.g., 8K for GPT-3.5, 128K for GPT-4 Turbo, 200K for Claude 3.5). If your prompt plus expected response exceeds this limit, the API truncates input or refuses the request. This planner breaks down your prompt into named sections (system instructions, examples, user query, chat history) and runs a calculation of tokens per section using a 0.75 character-to-token ratio derived from the cl100k_base algorithm. You set your target model's context limit, reserve tokens for output (e.g., 2,000 for a detailed response), and see live budget usage as you type. The planner displays total input tokens, available tokens after the output offset, and a visual progress bar with safe (green, under 70%), warning (yellow, 70-100%), and exceeded (red, over 100%) zones. Use it to plan multi-turn conversations, fit large documents into prompts, or optimize few-shot example counts. Free, client-side planning with no data sent to external servers.

Budget Sections

Budget Overview

Total Input
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Available
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Context Limit
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Budget Usage
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How to Use This Tool

  1. Select your target model from the dropdown to load its context limit (8K to 1M tokens).
  2. Set output reserve in the number input (tokens needed for the model's response, default 2,000).
  3. Add budget sections by clicking the Add Section button for each prompt component (system, examples, query, etc.).
  4. Paste section content into each text area and name each section for easy tracking.
  5. View live token estimates updated per section as you type, with total input and available tokens displayed.
  6. Adjust sections by removing or shortening content if the budget exceeds the available limit.

Why Use This Tool?

Context windows are immutable hardware constraints enforced at the model's attention layer. GPT-4 Turbo's 128K-token window consumes 128 × 512 = 65,536 embedding dimensions per token (assuming 512-dimensional keys), which scales quadratically with attention complexity (O(n^2) memory). This physical limit means exceeding the window causes OOM errors or silent truncation. Common scenarios: a 50-page PDF (roughly 40K tokens) + 5K-token system prompt + 2K-token query + 5K-token output reserve = 52K tokens total, fitting comfortably in 128K but not in 32K. Without budgeting, you risk wasting API calls on truncated prompts that omit critical context.

This tool applies the 70% safe zone rule recommended by Anthropic's prompt engineering guide: reserve 30% headroom for output variability and future edits. The 0.75 character-to-token ratio matches GPT-4's cl100k_base tokenizer (±5% accuracy for English). Common budgeting patterns: allocate 10-20% for system instructions, 30-50% for examples or documents, 5-10% for user queries, and 20-30% for output. For multi-turn conversations, reserve 10-15% per turn for chat history accumulation. Example: a 128K model with 30% output reserve (38K tokens) allows 90K input tokens, enough for 15 few-shot examples (6K tokens each) or a 70-page technical manual.