LLM Token Counter & API Cost Calculator — GPT, Claude, Gemini

Free LLM Token Counter & API Cost Calculator

This LLM token counter estimates token counts and API costs for the most popular large language models — GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, Llama 3, and more. Paste your prompt, select your model, and get an instant token count with cost breakdown for both input and output tokens. Unlike OpenAI’s tokenizer tool (which only supports their models), this covers models from OpenAI, Anthropic, Google, and Meta in a single interface.

Built for AI engineers, prompt designers, and developers who need to optimize prompt length and estimate API spend before sending requests.

How to Use the Token Counter

Step 1: Paste Your Prompt

Enter or paste the text you plan to send as a prompt. The token count updates in real time as you type.

Step 2: Select Your Model

Choose your target LLM from the model selector. Each model has different token limits and pricing tiers.

Step 3: Review Cost Estimates

The calculator shows estimated cost for your input tokens plus projected output tokens. Adjust the expected output length for more accurate total cost estimates.

What Are Tokens in LLMs?

Tokens are the fundamental units that large language models process. A token roughly corresponds to 3-4 characters or about 0.75 words in English. LLM API providers charge per token for both input and output, making token counting essential for cost optimization.

Supported Models

  • OpenAI — GPT-4o, GPT-4o-mini, GPT-4 Turbo, GPT-3.5 Turbo
  • Anthropic — Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
  • Google — Gemini 1.5 Pro, Gemini 1.5 Flash
  • Meta — Llama 3 70B, Llama 3 8B (via common inference providers)

Frequently Asked Questions

How accurate is the token count?

The tool uses character-to-token ratio approximations calibrated for each model family. For English text, accuracy is typically within 5-10% of actual. For precise counts, use the provider’s official tokenizer library.

Why do different models tokenize differently?

Each LLM provider uses a different tokenizer algorithm (BPE, SentencePiece, etc.) with a different vocabulary. Identical prompts may have different token counts across models.

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