AI Token Counter & Cost Calculator
Estimate token counts and API costs for GPT-4o, Claude, Gemini, and Llama models. Paste your prompt or completion text below — all processing happens locally in your browser.
| Model | Est. Tokens | Input $/1M | Output $/1M |
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Cost Calculator
Understanding Tokens and Why They Matter
In large language models, text is not processed character-by-character or word-by-word. Instead, it's broken into tokens — subword units that the model recognizes from its vocabulary. Most modern LLMs use a variant of Byte-Pair Encoding (BPE), a compression algorithm that iteratively merges the most frequent pairs of bytes or characters into single tokens. Common English words like "the" or "hello" are typically a single token, while less common words or technical jargon may be split into two or more tokens. A rough rule of thumb is that one token is approximately four characters or about 0.75 words in English.
Token counts directly determine the cost of API calls. Providers like OpenAI, Anthropic, and Google charge per token for both input (your prompt) and output (the model's response). Knowing how many tokens your prompt consumes helps you budget API costs, stay within rate limits, and optimize your prompts. For instance, GPT-4o charges $2.50 per million input tokens and $10.00 per million output tokens — seemingly small numbers that add up quickly at scale. A single 2,000-token prompt with a 1,000-token response costs about $0.015, but run that across 100,000 API calls and you're looking at $1,500.
Every model also has a context window — the maximum number of tokens it can process in a single request (prompt + response combined). GPT-4o supports 128K tokens, Claude 3.5 Sonnet supports 200K, and Gemini 1.5 Pro supports up to 2M tokens. Exceeding the context window causes the API to reject the request or truncate your input, so estimating token counts before sending long documents or multi-turn conversations is essential for reliable application behavior.
Tips for reducing token usage: Remove unnecessary whitespace and formatting from prompts. Use concise instructions — "Summarize in 3 bullets" instead of lengthy preambles. For code, strip comments and blank lines before including them in prompts. Consider shorter model responses by setting a max_tokens limit. Use caching features (like Anthropic's prompt caching or OpenAI's assistants) to avoid re-sending identical system prompts. Finally, for very long documents, consider chunking and summarization strategies rather than feeding the entire text into the context window at once.