🤖 AI Token Counter & Cost Calculator
Paste any text to estimate its token count and calculate API costs across all major AI models. Enter your monthly request volume for a full cost projection.
* Approximation: ~4 characters per token (actual counts vary by model tokenizer)
Cost per Request & Monthly Estimate
| Model | Input Price/1M | Output Price/1M | Cost/Request | Monthly Cost |
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* Prices are approximate. Always verify current pricing on provider websites.
How AI Tokens Work
Tokens are the units that AI language models use to process text. A token is roughly 4 characters or 0.75 words in English. Tokenization varies by model — GPT uses BPE tokenization, Claude uses its own, and Gemini uses SentencePiece.
Token Counting Tips
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1Use Exact Tokenizers for PrecisionFor exact counts, use tiktoken (OpenAI) or the Anthropic tokenizer. Our 4-char approximation is useful for quick estimates.
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2Count Both DirectionsAPI costs apply to both input (your prompt) and output (model response) tokens. Always estimate both.
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3Include System PromptsSystem prompts count as input tokens on every request. A 500-token system prompt at 10K requests/month = 5B extra input tokens.
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4Consider CachingProviders like Anthropic offer prompt caching at ~10% of the normal input price for repeated content — great for large system prompts.
Frequently Asked Questions
It is a reasonable approximation for English text. Code, non-English languages, and special characters can vary significantly. For production cost planning, use the official tokenizer for your target model.
Most major providers (OpenAI, Anthropic, Google) charge per million tokens with separate rates for input and output. Output tokens are typically 3-5x more expensive than input tokens.
Prompt caching lets you reuse previously processed content (like long system prompts or documents) at a fraction of the cost — typically 10% of normal input pricing — by caching it server-side.
Use a smaller model for simpler tasks, implement prompt caching for repeated content, compress your prompts, set max_tokens limits on outputs, and batch requests where possible.
Context length is the maximum number of tokens a model can process in a single request (input + output combined). GPT-4o supports 128K tokens, Claude supports up to 200K, and Gemini 1.5 Pro up to 2M.
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