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AI Token Calculator
Count tokens instantly

Paste any text and instantly see token counts, context window usage, and cost estimates for 30+ AI models including GPT-5, Claude 4, Gemini 2.5, DeepSeek, and more.

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Context Window Usage & Cost per Model
Model Tokens Context Used Input Cost

Token counts use a ~4 chars/token approximation (accurate for English). Actual tokenization may vary by model.
Prices as of May 2026 — always verify at OpenAI, Anthropic, Google.

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Frequently Asked Questions

What is a token in AI? +
Tokens are the basic units that AI models use to process text. A token is roughly 4 characters in English — so "hello world" is about 2–3 tokens. For code, numbers, and special characters, tokenization can differ significantly. Most major models (GPT, Claude, Gemini) use variations of Byte-Pair Encoding (BPE) tokenisation.
Why does token count matter? +
Every AI API charges based on input and output tokens. Understanding token counts helps you estimate costs, avoid hitting context window limits, and optimize prompts to save money. A prompt that uses 500 tokens instead of 5,000 can cost 10× less on models like GPT-4o.
What is a context window? +
The context window is the maximum number of tokens a model can "see" at once — including your prompt, chat history, and the model's response. GPT-4o has a 128K context window, meaning it can handle about 96,000 words in one session. Gemini 1.5 Pro supports up to 2 million tokens.
Which AI model has the largest context window? +
As of 2026, Llama 4 Scout leads with a 10 million token context window, followed by Gemini 3.1 Pro (10M), Gemini 1.5 Pro (2M), and Claude models (200K–1M). For most use cases, 128K–200K is more than sufficient.
How can I reduce token usage? +
Use concise, structured prompts. Remove unnecessary context. Use system prompts for role definitions. Use prompt caching (supported by OpenAI and Anthropic) to reuse common context at 90% discount. Tools like PromptChief help you build and reuse optimised prompts that reduce per-request token usage.