Count LLM Tokens and Estimate API Cost
Check prompt size, context usage and estimated model cost
Input tokens
36
Context used
0.0%
Input cost
$0.000090
Total cost
$0.002090
| Tokens | Rate (per 1M) | Cost | |
|---|---|---|---|
| Input | 36 | $2.50 | $0.000090 |
| Output | 200 | $10.00 | $0.002000 |
| Total | 236 | $0.002090 |
Token counts are approximations using a BPE heuristic. For exact counts use the official tokenizer for each model. Prices as of Q2 2025 — verify at provider pricing pages.
Use this LLM Token Counter to estimate how many tokens your prompt, document, code snippet or conversation may use before sending it to an AI model. It helps developers, AI product teams, prompt engineers, students, writers and automation builders understand prompt size, context-window usage and estimated API cost. Token estimates are useful when preparing long prompts, testing RAG inputs, summarizing documents, batching requests or controlling AI spend. Because tokenizers vary by model, results should be treated as planning estimates, not exact billing records.
How to Count Tokens and Estimate Cost
Paste your text, pick your model, and read the estimate instantly.
Paste your text or prompt
Paste any text — a system prompt, a document, a conversation, or a code snippet — into the input area. The character count updates immediately.
Select a model
Choose the target model from the dropdown. Each model uses a slightly different tokeniser and has different pricing — the estimate updates for every selection.
Read the token estimate and cost
The panel shows estimated token count, context window usage as a percentage, and the estimated input cost in USD. Use this to optimise prompts before making expensive API calls.
Features
Estimates token count for prompts, documents, code and conversations
Calculates approximate context-window usage for selected model limits
Estimates input cost using model pricing values configured in the tool
Helps plan long prompts before sending API requests
Highlights when content may be too large for a model context window
Supports prompt budgeting for AI apps, agents and automation workflows
Helps compare prompt sizes across model families and use cases
Reduces surprise costs during testing, batching and document processing
Guides RAG chunk planning by showing approximate token volume
Clarifies that token counts are estimates and may differ from official billing
What This Tool Helps You Do
Estimate how many tokens your prompt, document or conversation may use before sending it to an AI model. This helps you understand whether the input fits the selected context window and what the approximate request cost may be.
It is useful for prompt testing, AI app development, RAG workflows, document summarization, batch processing and cost planning.
Why Token Counting Matters
LLM pricing and limits are usually based on tokens, not words or characters. A short-looking prompt can become expensive if it includes repeated instructions, large documents, chat history or structured data.
The important detail is that token counts are model-dependent. The same text may be counted differently by different tokenizers, so estimates are useful for planning but should not be treated as exact invoices.
Practical Ways to Use This Tool
- Estimate prompt size before sending an API request
- Check whether a document fits inside a model context window
- Plan RAG chunks before adding them to a retrieval pipeline
- Compare cost impact of short and long prompt versions
- Review repeated instructions in system prompts or agent workflows
- Clean structured prompt data with a JSON Formatter
- Compare prompt versions with a Text Diff Checker
- Convert Markdown notes with a Markdown to HTML Converter before preparing content workflows
What to Check Before Trusting the Estimate
Check the selected model, current pricing, input and output token rates, and whether your application adds hidden context such as system messages, memory, tool schemas or chat history. Output tokens also matter; a cheap input can still produce a costly long response.
If billing accuracy is critical, confirm final numbers with the provider's official tokenizer or API usage reporting.
Expert Tips
Shorten repeated instructions first. Remove duplicate context before optimizing small wording. For document workflows, chunk by meaning rather than fixed character count when possible. Reserve enough context space for the model's answer, not just the input.
For AI products, track average and worst-case token usage separately. The expensive requests are often the long-tail cases, not the normal examples.
Common Mistakes to Avoid
- Treating estimated tokens as exact billing tokens
- Forgetting output tokens when estimating total request cost
- Sending full documents when only relevant sections are needed
- Ignoring hidden system prompts, tool definitions or chat history
- Comparing model costs without checking context limits
- Assuming English prose and code have the same token density
- Leaving repeated instructions in every message of a workflow
- Building batch jobs without estimating worst-case input size
Related Search Keywords
llm token counter, gpt token counter, ai token counter, token estimator, prompt token count, api cost calculator, llm cost estimator, context window calculator, prompt cost calculator, openai token estimator, claude token estimator, gemini token estimator, chatgpt token counter, model token calculator, prompt length checker, token usage calculator, count tokens online, estimate prompt tokens, rag token counter, ai model cost planner
Long Tail Keywords
estimate llm tokens before api request, count tokens in prompt online, calculate ai api cost from prompt length, estimate context window usage for long document, token counter for rag chunks, prompt token estimator for developers, estimate gpt tokens from text, calculate llm prompt cost online, compare prompt versions by token count, check if document fits model context window
Search Intent Queries
how to count llm tokens, gpt token counter online, estimate api cost for prompt, what is context window usage, how many tokens is my prompt, token counter for ai models, calculate prompt cost, llm cost estimator, how to reduce prompt tokens, estimate tokens before api call
Related Tools
Frequently Asked Questions
What does an LLM token counter do?
An LLM token counter estimates how many tokens a piece of text may use when sent to an AI model. Tokens are the units many language models use for processing and billing.
How accurate is the token estimate?
The result is an estimate, not a guaranteed billing number. Exact counts depend on the model tokenizer, language, formatting, whitespace, code and special tokens used by the API.
Can this estimate API cost?
Yes. It can estimate cost by combining token count with configured model pricing. Always verify final pricing against the provider's official pricing page before making business or billing decisions.
Does every AI model count tokens the same way?
No. Different model families may use different tokenizers, so the same text can produce different token counts. Code, non-English text and structured data can also change token density.
Is character count the same as token count?
No. A token may be a word, part of a word, punctuation or a symbol depending on the tokenizer. A rough English estimate is often a few characters per token, but exact results vary.
Why should I check context-window usage?
The context window limits how much input and output a model can handle in one request. If the prompt uses too much context, the request may fail, truncate older content or leave too little space for the response.
When should I use a token counter?
Use it before sending long prompts, documents, chat histories, RAG chunks, code files or batch requests. It helps you plan size, cost and model fit before running the call.
What should I do if my prompt is too large?
Reduce repeated instructions, summarize earlier context, split documents into chunks or choose a model with a larger context window. For RAG workflows, send only the most relevant chunks.
Can token estimates help reduce AI costs?
Yes. Estimating tokens helps identify overly long prompts, repeated context and expensive batches. Smaller, clearer prompts often reduce cost and improve response quality.
What should I check before relying on the estimate?
Check the target model, current provider pricing, input and output token rates, context limit and whether your application adds hidden system prompts or chat history.
Rate this tool
How was your experience? Your feedback helps us build better tools.