{ Prompt Token Budget Planner }

// estimate room for instructions, context, examples, and output

Plan and estimate token budgets for AI prompts. Allocate room for system instructions, context, examples, and output within your model token limit.

TOTAL BUDGET 128,000 tokens
SYSTEM INSTRUCTIONS Persona, rules, constraints
%
Allocated: 12,800 tokens
CONTEXT / DOCUMENTS Files, knowledge, background
%
Allocated: 64,000 tokens
EXAMPLES / FEW-SHOT Input-output demonstrations
%
Allocated: 25,600 tokens
OUTPUT RESERVE Max tokens for model response
%

💡 Reserve this for the model's response. Set your API max_tokens to this value. This section has no text input — it's a budget allocation only.

Allocated: 25,600 tokens Suggested max_tokens: 25,600
System 10%
Context 50%
Examples 20%
Output 20%
TOTAL USED 100%

HOW TO USE

  1. 01
    Select Your Model

    Pick your LLM and its context window from the dropdown, or enter a custom token limit.

  2. 02
    Set Percentages

    Use + / − buttons to allocate what percent of the budget goes to each prompt section. Total must equal 100%.

  3. 03
    Paste & Verify

    Optionally paste real text into each section to see how many tokens it uses vs. your allocation.

  4. 04
    Export Your Plan

    Download your budget as JSON or plain text to reference when building your prompt.

FEATURES

4 Section Budget 20+ Model Presets Live Token Estimate Visual Bar Chart JSON Export Custom Limits

USE CASES

  • 🤖 RAG pipeline prompt planning
  • 🔧 API prompt engineering & optimization
  • 📄 Document Q&A context allocation
  • 🧪 Few-shot learning experiment setup
  • 💡 Chatbot system prompt budgeting

WHAT IS THIS?

Every LLM has a context window — a hard limit on how many tokens it can process at once. Exceeding it causes truncation, errors, or degraded responses. This planner helps you divide that budget intelligently across the four main components of a production prompt: system instructions, context/documents, examples, and output reserve.

RELATED TOOLS

🔢 Token Counter 🤖 AI Model Comparator { } JSON Formatter 📝 Word Counter

FREQUENTLY ASKED QUESTIONS

What is a token in the context of LLMs?

A token is roughly 3–4 characters or about ¾ of a word in English. LLMs process text as tokens rather than characters or words. A 1,000-word document is approximately 1,300–1,500 tokens. Images and other modalities have their own token counting rules.

Why does token budgeting matter?

Every model has a fixed context window. If your total prompt + response exceeds this limit, the model will either truncate your input, throw an error, or produce incomplete responses. Planning ahead prevents these issues and ensures predictable API costs.

What percentage should I give to each section?

It depends on your use case. For RAG (retrieval-augmented generation), context may need 60–70%. For few-shot tasks, examples might need 30–40%. A common starting split is: System 10%, Context 50%, Examples 20%, Output 20%.

Is the token count in this tool 100% accurate?

This tool uses a fast heuristic estimator (approx 4 chars per token) for real-time feedback. For production accuracy, use the official tokenizer for your model: tiktoken for OpenAI, or the Anthropic/Google tokenizer APIs. The estimates here are a reliable planning guide.

What is "Output Reserve" and how does it differ?

Output Reserve is the portion of the context window set aside for the model's response. You don't fill it with text — instead, you use this number as your API's max_tokens parameter. If your prompt consumes 80K tokens on a 128K model, you have up to 48K tokens for output.

Can I use this for multi-turn conversations?

Yes. For chat-based models, treat the entire conversation history as your "Context" section. Reserve system instructions separately, and leave enough output budget for the next response. Many teams budget 10–20% per turn for rolling conversation windows.

What happens if my percentages don't add up to 100%?

The tool will show a warning in the summary bar. The percentages must sum to exactly 100% for the plan to be valid. You can adjust any section's percentage using the + / − controls or by typing directly into the percentage field.

How do I export my budget plan?

Use the "Export JSON" button to download a machine-readable plan you can embed in your codebase or CI config. "Export Text" gives a human-readable summary. Both include model name, total budget, per-section allocations in tokens and percentages.

What Is a Prompt Token Budget Planner?

A prompt token budget planner is a tool that helps AI developers and prompt engineers divide their model's context window across the different components of a prompt. Every large language model (LLM) — from OpenAI's GPT-4o to Anthropic's Claude and Google's Gemini — operates within a fixed token limit called the context window. Exceeding this limit causes truncation, API errors, or unpredictable model behavior.

This planner treats the context window as a finite budget and lets you allocate it deliberately across four prompt sections: system instructions, context/documents, few-shot examples, and output reserve. Instead of guessing or discovering limits at runtime, you plan your token distribution before writing a single line of code.

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The Four Components of a Prompt Budget

Structuring a production prompt means dividing your token budget into four logical layers:

Context Windows Across Major LLM Models

Context window sizes vary dramatically across models, which affects how you plan your budget:

Larger context windows don't eliminate the need for budgeting — they just raise the ceiling. Poorly structured prompts with redundant context still degrade quality and inflate costs.

Why Token Budgeting Improves Prompt Quality

Deliberate token budgeting is not just about staying under limits — it actively improves prompt quality. When you plan your budget, you're forced to evaluate the information density of each section. This surfaces wasteful patterns like over-long system prompts, redundant examples, or context that is too broad to be useful.

Production AI systems often have multiple prompt templates running in parallel. Without a documented budget plan, teams discover context window violations at runtime, in production, often when edge-case inputs are longer than expected. Maintaining a budget plan as part of your prompt documentation eliminates this class of bug entirely.

How Token Estimation Works

The industry standard approximation is 1 token ≈ 4 characters for English text, or roughly ¾ of a word. This heuristic is accurate enough for planning purposes. For production systems requiring exact counts, use the official tokenizers: tiktoken for OpenAI models, or the Anthropic and Google tokenizer APIs. Non-English languages, code, and special characters may tokenize differently — code tends to tokenize at roughly 1 token per 3 characters, while CJK languages may tokenize at 1–2 characters per token.

Best Practices for Token Budget Planning

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