AI Token Calculator & API Cost Estimator

What is Token Counting?

Token counting determines how many tokens a piece of text will consume when sent to a large language model API. Tokens are the fundamental units that LLMs process — roughly 1 token per 4 characters in English, though the exact mapping varies by language and content type. LLM APIs charge per token, making accurate token counting essential for cost estimation and budget management.

Each major LLM provider uses different tokenization algorithms. OpenAI's GPT models use the cl100k_base (GPT-4, GPT-3.5) and o200k_base (GPT-4o) encodings via the tiktoken library. Anthropic's Claude uses a similar but distinct tokenizer. Google's Gemini uses yet another. While exact token counts differ between models, tiktoken provides highly accurate estimates for comparison purposes.

When Should You Count Tokens?

Token counting is essential for estimating API costs before sending requests. Developers optimizing prompts need to understand how text length affects cost. Teams managing API budgets track token usage across projects. Researchers comparing model costs need accurate token counts for different models.

Startups projecting API costs for product features count tokens in representative prompts. Enterprise teams negotiating API contracts need usage estimates. Individual developers managing personal API budgets track token consumption. Any scenario where LLM costs matter benefits from accurate token counting.

How It Works

The tool loads the tiktoken algorithm via WebAssembly, which provides native-speed tokenization in the browser. Your text is processed entirely locally — no data is transmitted to any server. The token count is calculated using the cl100k_base encoding for GPT models and o200k_base for GPT-4o. The tool then multiplies the token count by each model's pricing tier to display estimated costs for input and output tokens.

Tips for Best Results

Remember that token counts are estimates when comparing across different model families (e.g., OpenAI vs Claude). Count both input and output tokens separately, as output tokens are typically more expensive. For batch operations, count tokens in representative samples and extrapolate. Monitor your actual API usage against estimates to calibrate your budget projections.