LLM Token Counter

Estimate token counts for popular LLM models. Compare GPT-4o, Claude, Llama, Gemini, and more — instantly in your browser.

How it works: Paste your text or prompt below. The tool estimates token counts for each LLM model using character-based heuristics. Results are approximate — within 5-10% of actual tokenizer output.

380
Characters
68
Words
4
Sentences
6
Lines
GPT-4o / GPT-4o-mini
112tokens
0.09% of 128.0K
GPT-4 / GPT-3.5 Turbo
106tokens
0.08% of 128.0K
Claude 3.5 / 4
116tokens
0.06% of 200.0K
Llama 3 / Mistral
110tokens
0.09% of 128.0K
Gemini 2.5 Pro
114tokens
0.01% of 1.0M
DeepSeek V3
116tokens
0.09% of 128.0K

Token counts are estimates based on average characters-per-token ratios. For exact counts, use the official tokenizer for each model (tiktoken for OpenAI, etc.).

What Are LLM Tokens?

Tokens are the basic units that large language models (LLMs) use to process text. A token can be a word, part of a word, or even a single character depending on the tokenizer. For example, the word 'tokenization' might be split into 'token' and 'ization' — two tokens. Understanding tokens is essential because LLM pricing, rate limits, and context windows are all measured in tokens, not words or characters.

How Token Counting Works

Each LLM family uses a different tokenizer. OpenAI models use tiktoken (BPE-based), Claude uses a custom tokenizer, and Llama/Mistral use SentencePiece. This tool provides estimates based on average characters-per-token ratios for each model family. While not exact, these estimates are within 5-10% of actual tokenizer output — accurate enough for cost estimation, prompt engineering, and context window planning.

Context Windows Explained

A context window is the maximum number of tokens an LLM can process in a single conversation. This includes both the input (your prompt and system instructions) and the output (the model's response). GPT-4o supports 128K tokens (~96K words), Claude 3.5 supports 200K tokens (~150K words), and Gemini 2.5 Pro supports up to 1M tokens (~750K words). Staying within the context window is critical — exceeding it causes truncation or errors.

Tips for Token Optimization

  • Be concise — remove filler words and redundant instructions to save tokens
  • Use system prompts wisely — they count toward your context window
  • Code uses more tokens per character than natural language
  • Structured formats (JSON, XML) use more tokens than plain text
  • Monitor usage — token costs add up quickly with large prompts
  • Consider model size — smaller models are cheaper but may need more detailed prompts