TokenCalc - AI Token Cost Calculator

AI Token Calculator & Cost Estimator

Count tokens and compare costs across 300+ AI models. 6 powerful tools. Completely free.

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How Token Counting Works

AI language models like GPT-5, Claude, and Gemini do not process raw text. Instead, they break text into smaller units called tokens using a process called tokenization. Most modern LLMs use Byte Pair Encoding (BPE), which splits text into subword units based on frequency patterns learned during training.

How BPE tokenization works: The algorithm starts with individual characters, then iteratively merges the most frequent adjacent pairs to build a vocabulary of common subword units. Common words like "the" become a single token, while rare words get split into multiple tokens. For example, "tokenization" might become ["token", "ization"] -- two tokens instead of one.

Why token counts matter for costs: AI providers charge per token for both input (your prompt) and output (the model's response). Output tokens typically cost 3-4x more than input tokens. Understanding your token usage helps you choose the right model and optimize prompts to reduce API costs. TokenCalc uses the official tiktoken library to provide 100% accurate counts for OpenAI models and highly accurate estimates for Claude and Gemini.

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Token Optimizer

Analyze your code and get smart optimization suggestions

Python
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Conversation Trajectory

Simulate multi-turn conversation costs over time

Static tokens sent each turn
10 turns
200 tokens
500 tokens

Pipeline Cost Modeler

Model multi-stage AI pipelines and find cost hotspots

1

"Good Enough" Model Finder

Find the most cost-effective model for your task

Quality Requirement

Optimization Pattern Library

Proven patterns to reduce token costs while maintaining quality

The most accurate AI token calculator and cost estimator for developers. Count tokens and compare costs across GPT-5, Claude Opus 4.5, Gemini 3, DeepSeek V3, and 300+ AI models. Go beyond simple counting with 6 powerful tools: Python Code Analyzer for component-level token breakdown, Token Optimizer for smart cost reduction, Conversation Trajectory Simulator for multi-turn cost forecasting, Pipeline Cost Modeler for multi-stage AI workflows, Model Finder to match your task with the most cost-effective model, and an Optimization Pattern Library with proven strategies.

Quick Reference: 100 words = ~133 tokens | 1000 words = ~1,333 tokens | 1 page = ~500 tokens

Frequently Asked Questions

Everything you need to know about AI tokens, pricing, and optimization

What is a token in AI/LLM?

A token is the basic unit of text that AI language models process. Tokens can be words, parts of words, or punctuation. In English, 1 token is approximately 4 characters or 0.75 words. For example, "Hello world!" is 3 tokens. Different models use different tokenization methods, with most modern models (GPT-5, Claude, Gemini) using Byte Pair Encoding (BPE).

How do I count tokens in Python code?

TokenCalc offers a specialized Python Code Analyzer that breaks down your code into components (functions, classes, docstrings, imports, comments) and counts tokens for each. Simply paste your Python code or upload a .py file, and get an accurate token count using the official tiktoken library with cl100k_base encoding, the same tokenizer used by GPT-4 and GPT-5.

How many tokens is 1000 words?

On average, 1000 words equals approximately 1,333 tokens in English (using the standard ratio of 0.75 words per token). However, this varies by language and content type. Technical content with code often has more tokens per word. Use TokenCalc to get an exact count for your specific text.

Quick reference: 100 words = ~133 tokens | 500 words = ~667 tokens | 5000 words = ~6,667 tokens

What is the cheapest AI model in 2026?

As of February 2026, the cheapest AI models for most use cases are:

  • DeepSeek V3: $0.07/1M input tokens
  • Gemini 3 Flash: $0.075/1M input tokens
  • GPT-4o-mini: $0.15/1M input tokens
  • Claude Haiku 4.5: $0.25/1M input tokens (best quality at low cost)
GPT-5 vs Claude Opus 4.5, which is cheaper?

Claude Opus 4.5 is slightly cheaper than GPT-5 for input tokens ($12/1M vs $15/1M), but GPT-5 is cheaper for output tokens ($45/1M vs $60/1M). For most conversational use cases where output exceeds input, GPT-5 may be more cost-effective. Use TokenCalc to compare costs for your specific use case.

How to reduce AI API costs?

Key strategies to reduce AI API costs:

  1. Use smaller models (GPT-4o-mini, Claude Haiku) for simple tasks
  2. Leverage prompt caching for repeated system prompts
  3. Remove unnecessary whitespace and formatting
  4. Use efficient system prompts, they're sent with every message
  5. Batch similar requests together when possible
  6. Consider cached token discounts offered by providers
What are cached tokens?

Cached tokens are input tokens that have been previously processed and stored by the AI provider. When you send the same prompt prefix repeatedly, providers like Anthropic and OpenAI offer discounted rates for cached tokens (typically 50-90% cheaper). This is especially useful for applications with consistent system prompts.

How does BPE tokenization work?

Byte Pair Encoding (BPE) is a tokenization algorithm used by most modern LLMs:

  1. Start with individual characters as tokens
  2. Iteratively merge the most frequent adjacent pairs
  3. Build a vocabulary of common subwords

This allows efficient handling of any text, including rare words and code. TokenCalc uses the official tiktoken library with cl100k_base encoding for accurate BPE token counts.

How accurate is this token calculator?

TokenCalc uses the official tiktoken library with cl100k_base encoding, providing 100% accurate token counts for GPT-4, GPT-4o, and GPT-5 models. For Claude and Gemini models, counts are within 1-3% accuracy as they use similar BPE-based tokenizers. Our pricing data is updated monthly from official provider documentation.

Is my data private when using this calculator?

Yes, 100% private. TokenCalc processes your text securely without storing or logging your prompts. We never share your data with third parties. No account or API keys required. Your data is never used for training or any other purpose.

What AI models does TokenCalc support?

TokenCalc supports 300+ AI models including:

  • OpenAI: GPT-5, GPT-5.2, GPT-4.5, GPT-4o, GPT-4o-mini
  • Anthropic: Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5
  • Google: Gemini 3 Pro, Gemini 3 Flash, Gemini 2.0
  • DeepSeek: V3, R1

Pricing is updated monthly from official provider documentation.

How do I convert words to tokens?

A quick conversion guide for English text:

  • 100 words = ~133 tokens
  • 500 words = ~667 tokens
  • 1000 words = ~1,333 tokens
  • 5000 words = ~6,667 tokens

For precise counts, paste your text into TokenCalc. Note: code, special characters, and non-English languages may have different token ratios.

How much does ChatGPT API cost per 1000 tokens?

ChatGPT API costs vary by model:

  • GPT-4o: $2.50 per 1M input tokens ($0.0025 per 1K), $10 per 1M output
  • GPT-4o-mini: $0.15 per 1M input tokens ($0.00015 per 1K)
  • GPT-5: $15 per 1M input tokens

For simple tasks, GPT-4o-mini offers the best value.

How many tokens can GPT-5 process?

GPT-5 has a context window of 256,000 tokens, approximately 192,000 words or 400+ pages of text in a single request. This is a significant upgrade from GPT-4's 128K context window, making GPT-5 ideal for analyzing long documents, codebases, and complex conversations.

What's the difference between input and output tokens?

Input tokens are the tokens in your prompt (what you send to the API), while output tokens are in the model's response (what the API returns). AI providers charge different rates for each, output tokens are typically 3-4x more expensive than input tokens because generating text requires more computation than reading it.

How do I count tokens in Python?

Use the official tiktoken library:

import tiktoken
enc = tiktoken.encoding_for_model("gpt-4o")
tokens = enc.encode("Your text here")
print(f"Token count: {len(tokens)}")

TokenCalc uses this same library for 100% accurate counts. For GPT-5 and GPT-4o, use cl100k_base encoding.

Which is cheaper: Claude or GPT?

It depends on the tier and usage pattern:

  • Budget tier: GPT-4o-mini ($0.15/1M) is cheaper than Claude Haiku ($0.25/1M)
  • Flagship tier: Claude Opus ($12/1M input) is cheaper than GPT-5 ($15/1M) for inputs
  • Output-heavy: GPT-5 ($45/1M) is cheaper than Claude Opus ($60/1M)
  • With caching: Claude offers 90% discount vs OpenAI's 50%
What is prompt caching and how does it work?

Prompt caching stores frequently used prompt prefixes so you pay less on subsequent requests:

  • Anthropic (Claude): Up to 90% discount on cached tokens
  • OpenAI: 50% discount on cached tokens

It's perfect for applications with consistent system prompts. Keep your system prompt the same across requests to automatically benefit from caching.

How do I reduce my LLM API costs?

Key strategies to reduce costs:

  1. Right-size your model: Use GPT-4o-mini or Claude Haiku for simple tasks
  2. Enable prompt caching: 50-90% savings on repeated prompts
  3. Optimize prompts: Remove unnecessary whitespace and text
  4. Use batch APIs: 50% discount for non-urgent work
  5. Monitor usage: Use TokenCalc to estimate costs before sending