AI Token Cost Calculator

Large Language Models (LLMs) process text in units called tokens. A token is typically 4 characters or about 0.75 words in English. When you send a prompt to an AI API like OpenAI, Anthropic, or Google, you're billed based on the number of input and output tokens processed.

Different models have different pricing tiers. For example, GPT-4o costs significantly less than GPT-4 Turbo for the same task, while open-source models like Llama 3 via providers like Together AI or Groq can be even cheaper. Output tokens typically cost 2-4x more than input tokens.

This calculator uses the same tokenizer (tiktoken) that OpenAI uses internally, giving you accurate token counts. For non-OpenAI models, the count serves as a reliable estimate since most modern LLMs use similar BPE tokenization.

What counts as a token?

Tokens are sub-word units created by a process called Byte Pair Encoding (BPE). Common words like "the", "is", and "hello" are single tokens. Longer or uncommon words get split into multiple tokens. For example, "tokenization" becomes three tokens: "token", "ization" — while "AI" is one token.

How language affects token count

English is the most token-efficient language for most LLMs since their training data is predominantly English. Non-Latin scripts (Hindi, Chinese, Japanese, Arabic) typically use 2-4x more tokens for the same meaning. Code is moderately efficient — Python uses fewer tokens than verbose languages like Java.

Token limits and context windows

Every model has a maximum context window — the total number of tokens (input + output) it can handle in one request. GPT-4o supports 128K tokens, Claude 3.5 Sonnet supports 200K, and Gemini 1.5 Pro supports up to 2M tokens. Exceeding the limit causes the API to reject your request or truncate the input.

Input vs. output tokens

Input tokens are what you send to the model (your prompt, system instructions, conversation history, documents). Output tokens are what the model generates in response. Output tokens are typically 2-4x more expensive because they require sequential generation — each token depends on all previous ones.

Reducing token costs

• Use concise prompts — remove filler words and redundant instructions
• Choose the right model — GPT-4o mini or Claude Haiku for simple tasks, save expensive models for complex reasoning
• Limit output length with max_tokens parameter when you don't need long responses
• Cache repeated prompts — if you send the same system prompt, use prompt caching (available on OpenAI and Anthropic)
• Summarize conversation history instead of sending the full chat log
• Batch similar requests to reduce per-call overhead

1. Paste your prompt — Enter the text you plan to send to the AI model. This can be a system prompt, user message, or a full document you want to process.
2. Select a model — Choose from 200+ models across OpenAI, Anthropic, Google, Meta, Mistral, and other providers. Use the search to filter by name or provider.
3. Review token count — The calculator instantly shows your input token count using the exact tokenizer for that model family. It also estimates expected output tokens based on your prompt type.
4. Click Calculate Cost — See the exact cost breakdown: input cost, output cost, and total in both USD and INR.
5. Compare models — Try the same prompt across different models to find the best price-to-quality ratio for your specific task.