AI Token Counter & Cost Estimator
Estimate how many tokens your prompt costs, and what it'd run across Claude, GPT, and Gemini
count_tokens endpoint.1.00× your input length — drag to model a shorter reply or a long generated response.
About this tool
The ToolNinja AI Token Counter estimates how many tokens a piece of text would cost against a language model, and what that translates to in dollars across several current Claude, GPT, and Gemini models side by side. It uses a character/word-based approximation rather than shipping any provider's actual BPE tokenizer client-side — real tokenizers (tiktoken, Claude's tokenizer) are trained vocabularies of tens of thousands of sub-word pieces, too large to bundle for an instant, no-dependency browser tool. The approximation mirrors how those tokenizers actually behave in shape — short common words cost about one token, longer or unusual words cost roughly a token per four characters, punctuation gets its own tokens — and lands within roughly 10–15% of the real count for ordinary English prose. For an exact count, use the provider's own token-counting endpoint (Anthropic's Messages API has a dedicated count_tokens call). Everything runs 100% in your browser. Your prompt is never sent anywhere — not even to count its own tokens.
When to use it
- →Getting a quick ballpark on how much a prompt or document would cost before sending it to an LLM API
- →Comparing relative cost across Claude, GPT, and Gemini models for the same piece of text
- →Estimating total spend for a batch job by multiplying a per-call estimate by call count
- →Checking whether a document is likely to fit inside a model's context window before chunking it
Tips
- ◆This is an approximation, not an exact count — for billing-critical accuracy, use the provider's own tokenizer or token-counting API endpoint.
- ◆The 'assumed output length' slider lets you model cost scenarios beyond just the input — most real usage cost comes from output tokens, which are typically priced several times higher than input.
- ◆Code and non-English text tokenize less predictably than English prose — treat the estimate as looser for those inputs.
Frequently asked questions
Why isn't this using the real GPT or Claude tokenizer?
Real tokenizers are trained vocabularies with tens of thousands of entries — too large to bundle into a lightweight, instant-load browser tool without adding a heavy dependency. The approximation here is tuned to track real tokenizer behavior closely for typical English text, but isn't a substitute for an exact count when precision actually matters (e.g. right before a batch job you're billed for).
Why do output tokens cost so much more than input tokens?
Generating a token requires a full forward pass through the model for every single token produced, run sequentially — it can't be parallelized the way processing a big input prompt can. That computational asymmetry is why every major provider prices output tokens at roughly 4-6x the input rate.
Are the prices shown here accurate?
They're approximate list rates captured as of October 2026 — AI API pricing changes frequently as providers release new models and adjust tiers. Treat the numbers here as useful for relative comparison between models, and always check the provider's own current pricing page before making a budgeting decision.