learn.howToCalculate
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An LLM context window calculator shows how much of a model's context limit a given amount of text consumes, and estimates the input cost. Context window is the maximum text a model can process at once.
公式
context_tokens_available = model_context_window - system_prompt_tokens - response_tokens_reserved
- window
- Context window (tokens) — Max tokens the model accepts
- system
- System prompt (tokens) — Tokens used by system instructions
- response
- Max response (tokens) — Tokens reserved for output
- available
- Available for input (tokens) — Tokens left for user input/history
ステップバイステップガイド
- 1Context window measured in tokens (1 token ≈ 0.75 words)
- 2Input tokens include both the prompt and any prior conversation
- 3Exceeding context limit causes earlier content to be forgotten
- 4Cost = (context tokens ÷ 1000) × input price per 1K tokens
解いた例
入力
32K tokens used in 128K model
結果
25% context used, ~$0.08 input cost (GPT-4o)
入力
Full 200K context (Claude)
結果
~150,000 words, ~600 A4 pages
入力
1,000 token conversation
結果
~750 words, minimal cost at most price points
よくある質問
What is a context window?
The maximum number of tokens a model can process in a single request. Longer contexts = more memory and latency. Claude 3.5 Sonnet: 200K tokens.
How do I estimate tokens in my prompt?
Roughly: 1 word ≈ 0.75 tokens; 1 line of code ≈ 5–10 tokens. Use official tokenizer tools for precision.
What happens if I exceed the context window?
The request fails or tokens are truncated. Always verify your total token count (system + input + expected output).
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