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AI Token Calculator
Count, Estimate & Compare

Instantly calculate tokens for any text or image, estimate API costs across 20+ models, and compare pricing for OpenAI, Claude, Gemini, Llama & more.

Calculations Done
20+
AI Models Covered
100%
Free to Use
<1ms
Calculation Speed
✍️ Enter Your Prompt / Text
Characters: 0 | Words: 0
0
Tokens (Est.)
0
Words
0
Characters
0
Sentences
Token Usage0%
🕐 Recent Calculations
No history yet — start typing above!
⚙️ Model Configuration
Pricing updated:
💲 Cost Estimate
🔢 Input Tokens 0
📤 Output Tokens 500
💵 Input Cost $0.00000
💵 Output Cost $0.00000
🔁 Requests 1
⚡ Total Estimated Cost $0.00000
📏 Context Window Usage
Used: 0 tokens Max: 128,000
0.00% of context used
Live Pricing Reference

AI Model Pricing Comparison

Compare token pricing across leading AI models. Input & output costs per 1M tokens.

Model Provider Input (per 1M) Output (per 1M) Context Window Type Use 🔗

⚠️ Pricing is approximate and may vary. Always verify at the official provider pricing page before billing. Last verified:

What You Get

Powerful Features

Everything you need to optimise your AI API usage and control costs.

🧮
Accurate Token Counting
Uses tiktoken-style BPE estimation for accurate token counts matching OpenAI, Claude, and Gemini tokenizers — not just a word-count guess.
🖼️
Image Token Estimator
Upload any image and get token estimates for GPT-4 Vision (tile-based), Claude 3 (pixel-based), and Gemini Vision models instantly.
💲
Real-Time Cost Calculator
Calculate exact API costs for any model, adjust input/output token ratio, set request volumes, and get a full cost breakdown in milliseconds.
📊
Multi-Model Comparison
Compare 20+ models side-by-side with filterable and sortable pricing tables. Find the cheapest model for your specific use case.
🔬
Token Density Visualizer
See which parts of your text are most token-intensive with our colour-coded density map. Optimize prompts to reduce token usage.
💰
Budget Mode
Enter your budget and instantly see how many tokens / requests you can afford with any model — great for project cost planning.
📦
Batch Calculator
Paste multiple prompts at once and calculate aggregate token counts and costs — ideal for dataset processing and bulk API operations.
📏
Context Window Meter
Visualise exactly how much of a model's context window your prompt consumes — never get cut-off or waste context again.
⬇️
Export & Share
Copy a detailed cost report to clipboard or download a CSV with all token metrics — perfect for project proposals and billing reports.
Getting Started

How It Works

Get accurate token counts and cost estimates in seconds.

1
Enter Your Text
Paste your prompt, system message, or any text into the calculator. Supports plain text, code, JSON, and markdown.
2
Select Your Model
Choose your AI provider and specific model. The pricing and context window data update automatically based on your selection.
3
Set Output & Volume
Estimate how many output tokens you expect and how many requests you'll make. Adjust to model your actual usage patterns.
4
Get Your Cost
Instantly see your total token count, per-request cost, and total batch cost. Export the report or share it with your team.

What is an AI Token Calculator? A Complete Guide

An AI Token Calculator is an essential tool for developers, content creators, and businesses using large language model (LLM) APIs. At its core, it answers one critical question: how much will this AI interaction cost? Understanding tokens is the first step to controlling your AI budget.

A token is the basic unit of text that AI models like GPT-4, Claude 3, and Gemini process. Roughly speaking, 1 token equals about 4 characters or 0.75 words in English — but tokenisation is nuanced. Punctuation, spaces, uncommon words, and code can all consume tokens differently. For example, "ChatGPT" might be 1–2 tokens, while a complex technical term might be split into 3–4 tokens.

The OpenAI Prompt Cost Calculator and similar tools work by multiplying token count by the model's per-token price. OpenAI charges separately for input tokens (your prompt) and output tokens (the model's response). A typical GPT-4o API call might use 500 input tokens and generate 300 output tokens — knowing this helps you budget projects accurately.

Best practices for minimizing token usage: Be concise in system prompts. Remove redundant instructions. Use shorter variable names in code tasks. Prefer bullet points over verbose paragraphs. For image inputs, use lower resolutions when detail is not critical — a 512×512 image uses far fewer tokens than a 2048×2048 one in tile-based models like GPT-4 Vision.

When comparing AI models, don't look at price alone — weigh tokens-per-dollar against quality. Claude Haiku and GPT-4o mini are excellent for high-volume tasks where cost matters most. GPT-4o and Claude 3.5 Sonnet offer the best balance of capability and price. For the most complex reasoning tasks, GPT-4 and Claude 3 Opus remain top choices despite higher per-token costs.

Use this free AI token calculator to plan budgets, compare models, optimise prompts, and avoid bill surprises — whether you're building a chatbot, automating workflows, or analysing large document sets.

Common Questions

Frequently Asked Questions

A token is the smallest unit of text that AI language models process. In English, one token ≈ 4 characters or 0.75 words. Common words like "the", "a", "is" are typically 1 token. Longer or rare words may split into 2–3 tokens. Numbers, punctuation, and code characters each consume tokens too. Different AI providers use slightly different tokenisers (e.g., tiktoken for OpenAI), which is why token counts can vary slightly across models.
Our calculator uses a BPE (Byte-Pair Encoding) estimation algorithm that closely mirrors the official tiktoken library used by OpenAI, achieving ~97–99% accuracy for standard English text. For other languages, code, or highly specialised vocabulary, there may be slight variance. For production billing-critical applications, always use the official tokeniser library of your chosen provider.
OpenAI's GPT-4 Vision uses a tile-based model: images are divided into 512×512 tiles, each costing 170 tokens, plus 85 base tokens. A 1024×1024 image uses approximately 765 tokens. Anthropic's Claude uses pixel-count estimation: tokens ≈ (width × height) / 750, capped at model limits. Google Gemini calculates image tokens based on resolution tiers. Our image tab estimates all three so you can compare costs across providers.
As of 2026, the most cost-effective models for high-volume tasks are Claude 3 Haiku (~$0.25/1M input tokens), GPT-4o mini (~$0.15/1M input), and Gemini 1.5 Flash (~$0.075/1M input). For ultra-cheap tasks, Gemini 1.5 Flash 8B can be even cheaper. However, cheaper models may sacrifice quality for complex reasoning. Always benchmark your specific use case before scaling.
Generating (output) tokens is computationally more expensive than processing (input) tokens. Output requires the model to run its full forward pass for each token it generates, using significant GPU compute. Reading input tokens is comparatively cheaper since the model processes them in parallel. This is why output prices are typically 3–5× higher than input prices across most AI providers.
Key strategies: (1) Use system prompt caching where available (Claude, OpenAI) — repeat prompts are billed at a fraction of the cost. (2) Be concise — remove filler, examples, or repeated context. (3) Use structured output formats (JSON) instead of verbose text. (4) Choose the smallest capable model for your task. (5) Implement response caching for identical queries. (6) Use the Batch API (OpenAI) for non-time-sensitive tasks at 50% discount.

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