ToolPilot

Context Budget Planner

Calculate how your system prompt, user input, examples, and output fit within any AI model's context window. Get token estimates, cost breakdown, and fit verdict.

1. Select model
2. Enter your content
~0 tokens
~0 tokens
4.1K
256100.0K
Budget Breakdown

Fits comfortably

195.9K tokens remaining (98% free)

4.1K used200.0K total
System User Examples Output
System prompt~0
User input / context~0
Examples (0 x 500)~0
Reserved output4.1K
Total~4.1K / 200.0K

Estimated cost: $0.061 per call

Input: <$0.001 + Output: $0.061 · Claude Sonnet 4 pricing

Token estimates use ~4 chars/token (BPE approximation). Actual counts vary by model tokenizer. For exact counts, use your model provider's tokenizer API.

How to Plan Your AI Context Budget Before Running Out of Tokens

Every AI model has a context window — a fixed limit on how much text it can process at once. Feed it too much and your request fails. Feed it too little and the AI lacks context for a good answer. Planning your token budget is essential for reliable AI applications.

The context window must fit everything: your system prompt, user input, any documents or examples you include, AND the space reserved for the AI's response. Most developers don't realize that output tokens count against the same window — reserving too little means truncated responses.

Our Context Budget Planner lets you paste your actual system prompt and user input, set your example count, and choose your reserved output size. It calculates the token breakdown for any model (Claude, GPT-4, Gemini, Llama, DeepSeek, Mistral), shows whether it fits, estimates the API cost per call, and suggests optimizations if you're over budget.

Token estimation uses the ~4 characters per token heuristic common to BPE tokenizers. For exact counts, use your model provider's tokenizer. But for planning and budgeting, this approximation is within 10% for English text — more than enough to avoid context overflows.

Frequently Asked Questions

How accurate are the token estimates?▾
We use ~4 characters per token, which is the standard BPE approximation for English text. This is typically within 10% of the actual token count. For code, JSON, or non-English text, accuracy may vary. For exact counts, use your provider's tokenizer API.
Do output tokens count against the context window?▾
Yes. The context window must fit input + output. If you have a 128K window and use 120K for input, you only have 8K left for the response. This is the most common context budget mistake.
What happens if I exceed the context window?▾
The API will return an error and your request will fail. Some providers truncate silently. Either way, you lose money on the failed request and need to retry with less input.
Which model has the largest context window?▾
As of 2026, GPT-4.1, Gemini 2.5 Pro, Gemini 2.5 Flash, and Llama 4 Maverick all support 1M tokens. Claude Opus/Sonnet support 200K.
How do I reduce my token usage?▾
Common strategies: condense system prompts (remove redundancy), use fewer few-shot examples, summarize long documents before including them, and only reserve as much output as you actually need.

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