LLM Token Counter

How LLM Token Counting and Pricing Works

Large language models (LLMs) like GPT-5, 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 4.6 might cost 40x more than the same call with Gemini 2.5 Flash-Lite or DeepSeek-V3.2. 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. Prices shown reflect official API pricing as of February 2026.