LLM Token Counter

How LLM Token Counting and Pricing Works

Large language models (LLMs) like GPT-4, Claude, and Gemini process text as "tokens" rather than words or characters. A token is roughly 4 characters or about 0.75 words in English. Understanding tokenization is critical for managing API costs, because you are billed per token for both the text you send (input/prompt tokens) and the text the model generates (output/completion tokens).

Why Token Counts Matter for Your Budget

LLM API pricing is expressed as cost per million tokens. Different models have vastly different price points: a single API call with Claude Opus might cost 100x more than the same call with Gemini 1.5 Flash. Choosing the right model for each task is one of the biggest levers for controlling costs. Many production applications use a tiered approach — a cheap, fast model for simple tasks and a premium model only when quality demands it.

Tips to Reduce LLM Token Costs

• Trim your prompts: Remove unnecessary instructions, examples, and whitespace. Every character in your system prompt is billed on every single request.
• Use caching: If your system prompt is static, providers like Anthropic offer prompt caching that can reduce input costs by up to 90%.
• Limit output length: Set a max_tokens parameter to prevent the model from generating more text than you need.
• Route by complexity: Use a small, cheap model for classification, extraction, and simple Q&A. Reserve expensive models for creative writing, complex reasoning, or coding tasks.
• Batch requests: Many providers offer batch APIs at 50% discounts for non-time-sensitive workloads.

Input vs. Output Token Pricing

Most providers charge significantly more for output tokens than input tokens — often 3-5x more. This is because generating text requires more compute than reading it. When estimating costs, pay close attention to how much output you expect. A summarization task (long input, short output) will be cheaper than a generation task (short input, long output) even at the same total token count.

About This Estimator

This tool uses the standard approximation of 1 token per 4 characters. Actual token counts vary by model and tokenizer — some models use byte-pair encoding (BPE), others use SentencePiece. For precise counts, use the official tokenizer libraries (like OpenAI's tiktoken). However, the characters-divided-by-4 rule is accurate enough for cost estimation and budget planning.