AI Token Counter
Count tokens and estimate API costs for GPT-4, Claude, Gemini and more
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Tokens (approx.)
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GPT-4.1 — Breakdown
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Free AI Token Counter — Count Tokens & Estimate LLM API Costs
The AI token counter estimates the number of tokens in any text and calculates the API cost for 11 major LLM providers including OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, xAI Grok, DeepSeek, and more.
Tokens are the fundamental unit of measurement for large language models. Understanding token count is critical for controlling API costs, staying within context window limits, and optimizing prompt length for production AI applications.
What is a token in AI / LLMs?
A token is roughly 3–4 characters of English text, or about ¾ of a word. The exact tokenization depends on the model's tokenizer (e.g., tiktoken for OpenAI, SentencePiece for Google). Code, punctuation, and non-Latin scripts may tokenize differently — often using more tokens per character.
- ~1,000 tokens ≈ 750 words ≈ a few paragraphs of text
- ~4,000 tokens ≈ a short article or a typical developer prompt with context
- ~128,000 tokens (GPT-4o context) ≈ ~100,000 words ≈ a full novel
How to reduce LLM API costs
- Use smaller, faster models (GPT-4o mini, Claude 3.5 Haiku, Gemini Flash) for simple tasks
- Trim unnecessary context — only include what the model actually needs
- Use prompt caching (Anthropic, OpenAI) for repeated system prompts
- Batch requests where possible to reduce per-call overhead
- Set
max_tokensto cap output length and prevent runaway generation costs
FAQ
How accurate is the token count?
The estimate is accurate to ±10–15% for typical English text. For exact counts, use the provider's official tokenizer (e.g., tiktoken for OpenAI). Code and non-Latin scripts may have higher variance.
What is a context window?
The context window is the maximum number of tokens a model can process in a single request (input + output combined). Exceeding it truncates your input or causes an error. GPT-4.1 supports up to 1M tokens; Gemini 1.5 Pro supports 2M.
Are input and output tokens priced the same?
No — output tokens are typically 2–5× more expensive than input tokens. This reflects the computational cost of generating text vs. reading it. Always budget for both when estimating API costs.
Which model has the largest context window?
As of 2026: Gemini 1.5 Pro (2M tokens), Llama 4 Scout (10M tokens — experimental), GPT-4.1 and Gemini 2.5 (1M tokens).