Token Count Estimator & Cost Calculator
Estimate token counts for your text and calculate costs for various LLM models like GPT-4, Claude 3, and Llama 2.
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Characters: 0
Pricing prices per 1M tokens
Token Estimation Method
This tool uses the standard rule of thumb: 1 token ≈ 4 characters
- Estimation: Length / 4 (rounded up)
- Actual token count varies by model and tokenizer
- GPT models use BPE encoding (Byte Pair Encoding)
- Claude uses SentencePiece tokenizer
- Use
tiktokenlibrary for exact counts
Pricing Information
Current pricing per 1 million tokens (subject to change):
ℹ️ Prices are approximations. Check official API documentation for current rates.
Use Cases
- Budget planning for API usage
- Compare costs between different LLM providers
- Estimate total project costs
- Optimize prompts for token efficiency
- Plan resource allocation
- Monitor API spending trends
What is Token Counting?
What is Token Counting?
Token counting determines how many tokens—the fundamental units LLMs process—are in a piece of text. Since LLM pricing, context limits, and performance are all token-based, accurate token counting is essential. Tokens don't map 1:1 to words or characters; understanding tokenization helps optimize prompts and manage costs.
- Count exact tokens for any text or prompt
- Support multiple tokenizer models (GPT-3.5, GPT-4, Claude)
- Calculate estimated API costs based on token count
- Check if text exceeds model context limits
- Analyze token-to-word and token-to-character ratios
- Visualize tokenization (how text is split)
- Compare token counts across different models
- Optimize prompts for token efficiency
- 100% Client-side counting: Text/Prompts never sent to server
Why Token Counting Matters
LLM APIs charge by tokens, not characters or words. Context windows have strict token limits (e.g., 8K, 32K, 128K). Exceeding limits truncates inputs or fails requests. Accurate token counting enables cost estimation, prevents limit errors, and guides optimization. Even small prompt rewordings can significantly impact token usage and costs at scale.
How to Use
- Paste text, prompt, or data into input area
- Select target LLM model (different models tokenize differently)
- View exact token count and cost estimates
- Review token-to-word ratio (typically 1 token ≈ 0.75 words)
- Check against model's context window limit
- Visualize tokenization to understand splitting
- Optimize: shorten prompts, use abbreviations, compress JSON
- Recount after optimizations to verify savings
Token Counting & LLM Tokenization Glossary
- Token
- The fundamental unit of text that LLMs process; roughly equivalent to 0.75 words in English, but varies by language and content.
- Tokenization
- The process of breaking text into tokens using specific algorithms (e.g., Byte Pair Encoding) that LLMs understand.
- Context Window
- The maximum number of tokens an LLM can process in a single request, including both input (prompt) and output (completion).
- BPE (Byte Pair Encoding)
- A tokenization algorithm that splits text into common subword units, used by GPT models and others.
- Token Limit
- The maximum token count allowed by a model's context window; exceeding this causes truncation or errors.
- Token Price
- The cost per token charged by LLM APIs, typically measured per 1,000 or 1 million tokens.
- Prompt Engineering
- Crafting effective prompts for LLMs, often involving token optimization to fit within limits and reduce costs.
- Tiktoken
- OpenAI's tokenization library used by GPT models, providing exact token counting for GPT-3.5/4 and other models.
What is Token Counting?
What is Token Counting?
Token counting determines how many tokens—the fundamental units LLMs process—are in a piece of text. Since LLM pricing, context limits, and performance are all token-based, accurate token counting is essential. Tokens don't map 1:1 to words or characters; understanding tokenization helps optimize prompts and manage costs.
- Count exact tokens for any text or prompt
- Support multiple tokenizer models (GPT-3.5, GPT-4, Claude)
- Calculate estimated API costs based on token count
- Check if text exceeds model context limits
- Analyze token-to-word and token-to-character ratios
- Visualize tokenization (how text is split)
- Compare token counts across different models
- Optimize prompts for token efficiency
- 100% Client-side counting: Text/Prompts never sent to server
Why Token Counting Matters
LLM APIs charge by tokens, not characters or words. Context windows have strict token limits (e.g., 8K, 32K, 128K). Exceeding limits truncates inputs or fails requests. Accurate token counting enables cost estimation, prevents limit errors, and guides optimization. Even small prompt rewordings can significantly impact token usage and costs at scale.
How to Use
- Paste text, prompt, or data into input area
- Select target LLM model (different models tokenize differently)
- View exact token count and cost estimates
- Review token-to-word ratio (typically 1 token ≈ 0.75 words)
- Check against model's context window limit
- Visualize tokenization to understand splitting
- Optimize: shorten prompts, use abbreviations, compress JSON
- Recount after optimizations to verify savings
Token Counting & LLM Tokenization Glossary
- Token
- The fundamental unit of text that LLMs process; roughly equivalent to 0.75 words in English, but varies by language and content.
- Tokenization
- The process of breaking text into tokens using specific algorithms (e.g., Byte Pair Encoding) that LLMs understand.
- Context Window
- The maximum number of tokens an LLM can process in a single request, including both input (prompt) and output (completion).
- BPE (Byte Pair Encoding)
- A tokenization algorithm that splits text into common subword units, used by GPT models and others.
- Token Limit
- The maximum token count allowed by a model's context window; exceeding this causes truncation or errors.
- Token Price
- The cost per token charged by LLM APIs, typically measured per 1,000 or 1 million tokens.
- Prompt Engineering
- Crafting effective prompts for LLMs, often involving token optimization to fit within limits and reduce costs.
- Tiktoken
- OpenAI's tokenization library used by GPT models, providing exact token counting for GPT-3.5/4 and other models.