Ready to calculate
Paste your prompts and click Calculate// paste your prompt → see exactly how many tokens remain
Calculate LLM token usage instantly. Paste your system prompt, conversation history, and user message to see token breakdown and remaining context window space.
Ready to calculate
Paste your prompts and click CalculatePick the LLM model you're working with from the pill buttons. Choose "Custom" to set any token limit.
Fill in the system prompt, conversation history, and user message — only fill the parts you have.
Click Calculate to see token usage per section, remaining space, and an output estimate.
LLMs have a fixed "context window" — the maximum number of tokens they can process in one call. This tool estimates how many tokens your input uses across system prompt, history, and user message, so you can plan accordingly.
Token estimation uses the ~4 chars per token heuristic (close to GPT-4 / Claude tokenizers for English text).
This tool uses the ~4 characters per token heuristic, which is accurate for typical English text within 10-15%. For production use, always verify with the official tokenizer (tiktoken for OpenAI, Claude's counter for Anthropic). Non-English text, code, and special characters may differ.
A context window is the maximum number of tokens an LLM can process in a single call, including both your input (system prompt + history + user message) and its output. Once exceeded, the model either truncates input or refuses to respond.
Context limits depend on model architecture and training. GPT-3.5 has 16K tokens, GPT-4o and Claude have 128K–200K, and Gemini 1.5 Pro supports up to 2 million tokens. Larger windows cost more per API call.
No. All calculation happens entirely in your browser using JavaScript. Nothing is sent to any server. Your prompts remain completely private.
The remaining tokens are what's left for the model's output (completion). Most models reserve a portion of the context window for output — typically 4K–8K tokens. If remaining is very low, the model may produce a truncated response.
Common strategies: summarize old conversation history, use retrieval-augmented generation (RAG) to fetch only relevant chunks, split tasks into shorter sessions, or switch to a higher-limit model like Claude or Gemini 1.5.
A context window calculator helps you understand how much of an LLM's available token budget your input is consuming — and how much space remains for the model's output. When building applications on top of GPT-4, Claude, Gemini, or any other large language model, token limits are one of the most important constraints to manage. This free browser-based tool breaks your input into three parts — system prompt, conversation history, and user message — so you can see exactly where your tokens are going.
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Each major LLM has a defined context window size measured in tokens:
Tokens are not the same as words or characters. In OpenAI's tokenizer, a token is roughly 4 characters of English text on average. Common words like "the", "is", "in" are often a single token. Longer or rarer words may be split into two or more tokens. Special characters, code syntax, and non-English text can have very different tokenization ratios. This tool uses the 4 chars/token heuristic as a fast approximation — for exact counts, use the official tokenizers (tiktoken for OpenAI, or the Claude tokenizer API for Anthropic).
System prompts are sent with every single API call. A verbose system prompt of 2,000 tokens costs those tokens on every request, even if the conversation itself is short. This is why lean, precise system prompts matter at scale — not just for readability, but for cost and context efficiency. This calculator helps you measure exactly how large your system prompt is and whether it's crowding out room for conversation history and output.
In multi-turn applications, conversation history grows with every exchange. After 10–20 turns, even a 128K context model can feel the pressure. Common mitigation strategies include:
The remaining token count shown by this calculator represents what's available for the model's completion. Models like GPT-4o can produce up to 4,096 tokens of output by default (configurable up to 16,384). Claude models can produce much longer responses. If your input consumes 127,000 of a 128K context window, the model only has 1,000 tokens left to respond — roughly 700–800 words. For tasks requiring long-form output (code generation, reports, analysis), keeping input lean is critical.
Reducing token usage without losing quality is a core prompt engineering skill. Some practical approaches:
This context window calculator is most useful during the development and debugging phase of LLM-powered applications. Paste your system prompt once and note its baseline token count. Then as you adjust conversation history and user messages, recalculate to ensure you're staying within budget. If you're hitting context limits in production, this tool can help you understand exactly which part of your input is responsible — system prompt bloat, long history, or large user messages — so you can optimize accordingly.