Prompt Tokenizer
Estimate token count and API cost for any prompt across GPT-4.1, Claude 3.7, Gemini 2.5 and more. Adjust expected output tokens to calculate total cost.
Token estimate uses the standard ~4 chars/token approximation (actual tokenization varies by model). Pricing is indicative and may change. Verify current rates on provider pricing pages.
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Prompt Token Counter — Estimate LLM Token Count & API Cost for GPT-4, Claude, Gemini
Paste any prompt and instantly see how many tokens it contains, along with the estimated API cost for the model of your choice. This prompt tokenizer supports GPT-4.1, GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Pro, and other leading LLMs.
Knowing your token count before calling an API prevents unexpected costs and helps you optimize prompts for budget efficiency. A well-trimmed prompt can cut your API spend by 30–60% with no loss in quality.
What you can do with this tool
- Count tokens in any text for GPT-4.1, GPT-4o mini, Claude 3.7 Sonnet, Gemini 2.5 Pro/Flash, and more
- See the exact cost per prompt based on the model's input price per million tokens
- Add expected output token count to calculate total request cost
- Compare cost across models side-by-side to find the most cost-efficient option
- Identify bloated prompts and cut unnecessary tokens to reduce API spend
How to count tokens and estimate cost
- Paste your prompt text into the input area
- Select the target model (GPT-4o, Claude 3.7, Gemini 2.5, etc.)
- Enter the expected output token count (or use the default estimate)
- See total token count and cost breakdown instantly
FAQ
What is a token in LLM terms?
A token is roughly 4 characters or 0.75 words in English text. The word “tokenization” is 2 tokens; “hello” is 1 token. Different models use different tokenizers, so counts vary slightly between providers.
Is the token count exact?
For OpenAI models, this tool uses a cl100k_base-based estimator which closely matches the real tokenizer. For Claude and Gemini, the count is an approximation, as those providers use proprietary tokenizers. Expect ±5% variance.