LLM Token Counter: Count Tokens & Calculate API Costs

Calculate exactly how many tokens your prompts use and how much they'll cost across different AI models. Tokens are the building blocks that LLMs (like GPT-4o, Claude, and Gemini) use to process text—each word or piece of punctuation is converted into tokens. Understanding your token usage helps you optimize prompts, stay within context limits, and predict API costs accurately.

Enter your text to see instant token counts for 38+ major AI models. Compare costs across OpenAI, Anthropic, Google, Meta, and DeepSeek. Visualize your context window usage. All calculations happen in your browser—no data is sent to any server.

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Cost Breakdown

Pricing updated: January 2026
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⚠️ Note:

What Are Tokens?

Tokens are the basic units that Large Language Models (LLMs) use to process text. When you send a prompt to an AI like GPT-4 or Claude, the model doesn't read it word-by-word—it breaks your text into tokens first. A token can be a whole word, part of a word, or even just punctuation. On average, one token equals about 4 characters of English text, or roughly 3/4 of a word.

Why Token Counting Matters

  • API Costs: You're charged based on token usage, not characters. Knowing your exact token count helps predict costs.
  • Context Limits: Each model has a maximum context window. Exceed it and your request fails.
  • Optimization: Shorter prompts = lower costs and faster responses. Count tokens to optimize.
  • Planning: Estimate costs before running expensive operations on large datasets.

How Different Models Tokenize

Different AI providers use different tokenization methods:

  • OpenAI (GPT-4o, GPT-4.1, o1, o3): Uses tiktoken with cl100k_base encoding. Most accurate tokenization available.
  • Anthropic (Claude): Uses a proprietary tokenizer similar to GPT-4. We estimate using tiktoken (±10% accuracy).
  • Google (Gemini): Proprietary tokenizer. We estimate at ~4 characters per token (±10% accuracy).
  • Meta (Llama): Uses sentencepiece tokenization. We estimate at ~4 characters per token (±10% accuracy).
  • DeepSeek: Proprietary tokenizer. We estimate at ~4 characters per token (±10% accuracy).

Supported Models

OpenAI

  • GPT-4o (128K context, $2.50/$10.00 per 1M tokens) - Most popular
  • GPT-4o mini (128K context, $0.15/$0.60 per 1M tokens)
  • GPT-4.1 (1M context, $2.00/$8.00 per 1M tokens)
  • GPT-4.1 mini (1M context, $0.40/$1.60 per 1M tokens)
  • GPT-4.1 nano (1M context, $0.10/$0.40 per 1M tokens)
  • o3 (200K context, $2.00/$8.00 per 1M tokens)
  • o3-mini (200K context, $1.10/$4.40 per 1M tokens)
  • o1 (200K context, $15.00/$60.00 per 1M tokens)
  • o1-mini (128K context, $3.00/$12.00 per 1M tokens)

Anthropic

  • Claude Opus 4.5 (200K context, $5.00/$25.00 per 1M tokens)
  • Claude Sonnet 4.5 (200K context, $3.00/$15.00 per 1M tokens)
  • Claude Haiku 4.5 (200K context, $1.00/$5.00 per 1M tokens)
  • Claude Sonnet 4 (200K context, $3.00/$15.00 per 1M tokens)
  • Claude Opus 4.1 (200K context, $15.00/$75.00 per 1M tokens)
  • Claude 3.5 Sonnet (200K context, $3.00/$15.00 per 1M tokens)
  • Claude 3.5 Haiku (200K context, $0.80/$4.00 per 1M tokens)

Google

  • Gemini 2.5 Pro (1M context, $1.25/$10.00 per 1M tokens)
  • Gemini 2.5 Flash (1M context, $0.30/$2.50 per 1M tokens)
  • Gemini 2.5 Flash-Lite (1M context, $0.10/$0.40 per 1M tokens)
  • Gemini 2.0 Flash (1M context, $0.10/$0.40 per 1M tokens)
  • Gemini 1.5 Pro (2M context, $1.25/$5.00 per 1M tokens)
  • Gemini 1.5 Flash (1M context, $0.075/$0.30 per 1M tokens)

Meta (Llama)

  • Llama 4 Maverick (1M context, $0.20/$0.60 per 1M tokens)
  • Llama 4 Scout (10M context, $0.10/$0.30 per 1M tokens) - Largest context window
  • Llama 3.3 70B (128K context, $0.59/$0.79 per 1M tokens)
  • Llama 3.1 405B (128K context, $3.00/$3.00 per 1M tokens)
  • Llama 3.1 70B (128K context, $0.59/$0.79 per 1M tokens)
  • Llama 3.1 8B (128K context, $0.05/$0.08 per 1M tokens)

DeepSeek

  • DeepSeek V3 (128K context, $0.14/$0.28 per 1M tokens) - Best value
  • DeepSeek R1 (128K context, $0.55/$2.19 per 1M tokens)

Note: Prices are current as of January 2026 and may change. Always verify current pricing with the provider.

Features

  • Accurate token counting using official OpenAI tokenizer (tiktoken)
  • Cost estimation for 38+ popular AI models across 5 providers
  • Real-time character and token counting with debounced updates
  • Context window visualization with color-coded warnings
  • Support for input and output token calculation
  • 100% client-side - your text never leaves your browser
  • Compare costs across OpenAI, Anthropic, Google, Meta, and DeepSeek

Frequently Asked Questions

What are tokens in AI language models? +

Tokens are the basic units that AI models process. A token can be a word, part of a word, or punctuation. For example, 'hello' is 1 token, but 'tokenization' might be split into 'token' + 'ization' (2 tokens). English text averages about 4 characters per token, but this varies by language and content.

Why do different AI models have different token counts? +

Each AI model uses its own tokenizer—the algorithm that splits text into tokens. GPT-4 uses cl100k_base, Claude uses its own tokenizer, and other models have their own systems. The same text can have different token counts across models, affecting both context limits and pricing.

How are API costs calculated? +

AI API pricing is based on tokens processed, typically quoted per 1 million tokens. Input (prompt) and output (completion) tokens often have different prices. Total cost = (input tokens × input price) + (output tokens × output price). Our calculator shows both per-request and estimated monthly costs.

What's the difference between input and output tokens? +

Input tokens are what you send to the model (your prompt, system message, conversation history). Output tokens are what the model generates in response. Most APIs charge different rates for each—output tokens are often 2-4x more expensive because they require more computation.

How can I reduce my token usage and costs? +

Write concise prompts, remove unnecessary context, use shorter system messages, and limit conversation history. Consider using smaller models for simple tasks. Batch similar requests when possible. Cache frequent responses. Use streaming to stop generation early when you have enough output.