Question 1

What is an AI token?

Accepted Answer

A token is the basic unit of text that AI language models process. Tokens can be whole words, parts of words (subwords), punctuation marks, or special characters. For example, the word 'tokenization' might be split into 'token' and 'ization' — two tokens. Most English words are 1-2 tokens, while complex or uncommon words may be split into more pieces.

Question 2

How accurate is this token counter?

Accepted Answer

For OpenAI models (GPT-5.x, GPT-4o), we use js-tiktoken which provides exact token counts using the same tokenizer (o200k_base) that OpenAI uses. For other providers like Claude, Gemini, Llama, DeepSeek, and Mistral, we use carefully calibrated character-ratio estimates, clearly labeled as 'Estimated' in the results.

Question 3

Is my text sent to a server for tokenization?

Accepted Answer

No. All tokenization happens locally in your browser. The js-tiktoken library runs entirely client-side — the token vocabulary is loaded once from a bundled file and cached in memory. No API calls, no server processing, no data collection. Your prompts never leave your device.

Question 4

Why do different models produce different token counts?

Accepted Answer

Each AI model family uses a different tokenizer with a different vocabulary. OpenAI uses BPE (Byte Pair Encoding) tokenizers like o200k_base, while Anthropic, Google, and others use proprietary tokenizers. A word that's one token in GPT-5 might be two tokens in Claude, or vice versa. This affects both cost and context window usage.

Question 5

What is o200k_base encoding?

Accepted Answer

o200k_base is OpenAI's latest tokenizer encoding, introduced with GPT-4o and used by all GPT-5.x models. It has a vocabulary of approximately 200,000 tokens and is more efficient than the older cl100k_base encoding (100K vocabulary) used by GPT-4 and GPT-3.5. It handles multilingual text and code more efficiently.

Question 6

What is a context window?

Accepted Answer

A context window is the maximum number of tokens a model can process in a single request, including both the input prompt and the generated output. GPT-5.6 has a 1.05M token context window, meaning it can process over a million tokens at once. If your prompt exceeds the context window, the API will return an error.

Question 7

How is the API cost calculated?

Accepted Answer

AI API costs are based on token usage, typically priced per million tokens ($/M). Input tokens (your prompt) and output tokens (the model's response) are priced separately, with output tokens usually costing more. Our calculator multiplies your input tokens by the input price and your expected output tokens by the output price.

Question 8

What does 'Estimated' mean for non-OpenAI models?

Accepted Answer

For models like Claude, Gemini, Llama, and others, no open-source client-side tokenizer exists. We estimate token counts using calibrated character-to-token ratios (e.g., ~3.5 characters per token for Claude). These estimates are typically within 5-10% of actual counts, but exact counts require the provider's official API.

Question 9

Why are output tokens more expensive than input tokens?

Accepted Answer

Generating output tokens requires more computation than processing input tokens. The model must run inference to predict each output token one at a time, while input tokens are processed in parallel. This makes generation more resource-intensive and therefore more expensive.

Question 10

How can I reduce token usage?

Accepted Answer

Remove unnecessary whitespace, duplicate instructions, and verbose formatting. Use concise language and avoid repeating context. For code reviews, include only relevant code sections rather than entire files. Move stable context to system messages (which may be cached). Structure prompts clearly to minimize misunderstandings that require follow-up.

Question 11

What counts as a token in code?

Accepted Answer

Code is tokenized differently from natural language. Common keywords (function, const, return) are usually single tokens, but variable names, string literals, and less common syntax may be split into multiple tokens. Indentation and whitespace in code also consume tokens. Minified code uses fewer tokens but is harder to read.

Question 12

How do URLs affect token count?

Accepted Answer

URLs are tokenized very inefficiently because they contain unusual character combinations that don't match common subword patterns. A single URL like 'https://www.example.com/path/to/resource?param=value' might use 15-25 tokens. If URLs are reference-only, summarizing their content saves tokens.

Question 13

What is the difference between tokens and words?

Accepted Answer

A word is a human-readable unit of text separated by spaces. A token is a subword unit determined by the model's tokenizer. Simple English words are often one token, but longer words, technical terms, and non-English text may be split into multiple tokens. On average, one English word is approximately 1.3-1.5 tokens.

Question 14

Are emojis expensive in tokens?

Accepted Answer

Yes. Emojis are encoded as multi-byte Unicode characters and typically require 2-4 tokens each in most tokenizers. If your prompt uses many emojis for decoration, removing them can save a meaningful number of tokens without affecting the semantic content.

Question 15

How does multilingual text affect tokenization?

Accepted Answer

Non-English text, especially languages with non-Latin scripts (Chinese, Japanese, Arabic, Hindi), typically requires more tokens per word than English because tokenizer vocabularies are primarily trained on English text. A single Chinese character might be 2-3 tokens, making multilingual prompts significantly more expensive.

Question 16

What is prompt engineering?

Accepted Answer

Prompt engineering is the practice of designing and optimizing prompts to get better results from AI models. It involves structuring instructions clearly, providing relevant context, using examples (few-shot learning), and specifying the desired output format. Good prompt engineering can improve response quality while keeping token usage efficient.

Question 17

Can I compare my original and optimized prompts?

Accepted Answer

Yes. Use the Compare mode to paste your original prompt on one side and your optimized version on the other. The tool shows the difference in token count, characters, words, and calculates the percentage of token savings — helping you measure the impact of your optimization efforts.

Question 18

What is the Token Composition breakdown?

Accepted Answer

The Token Composition panel analyzes what types of content make up your prompt: words, numbers, symbols, URLs, emails, emojis, punctuation, whitespace, and code. This helps you identify which content types consume the most tokens and where optimization efforts would be most effective.

Question 19

Does the tool work offline?

Accepted Answer

Yes. Once the page is loaded (including the js-tiktoken vocabulary data), the tool works entirely offline. All tokenization, analysis, and calculations happen in your browser's JavaScript engine. You can disconnect from the internet and continue using the tool.

Question 20

How are the model prices kept current?

Accepted Answer

The model registry includes pricing as of July 2026. AI model pricing changes frequently as providers release new models and adjust costs. The model data is stored in a single file (models.ts) designed for easy updates. Check the provider's official pricing page for the most current rates.

Question 21

What is the Prompt Optimization Insights feature?

Accepted Answer

This feature analyzes your prompt for common inefficiencies: excessive whitespace, repeated instructions, very long prompts, high URL density, code-heavy content, and formatting overhead. Each insight includes a severity level and specific recommendation for reducing token usage.

Question 22

How large a prompt can this tool handle?

Accepted Answer

The tool handles very large prompts efficiently. For OpenAI tokenization, the js-tiktoken library processes text at near-native speed. The analysis is debounced (300ms delay) to avoid tokenizing on every keystroke. In practice, prompts up to 1 million characters are processed smoothly.

Question 23

Why is Llama 4 Scout shown as free?

Accepted Answer

Llama 4 Scout is an open-source model from Meta released under the Llama license. As an open-source model, there is no API cost from Meta — you can run it on your own hardware. However, if you use it through a third-party hosting provider, they may charge their own fees.

Question 24

What export formats are available?

Accepted Answer

You can export your analysis in four formats: TXT (human-readable report), CSV (spreadsheet-compatible model comparison data), JSON (machine-readable complete analysis with breakdown), and Markdown (formatted report). You can also copy summary statistics to your clipboard.

Question 25

What is the difference between exact and estimated tokenization?

Accepted Answer

Exact tokenization uses the actual tokenizer algorithm and vocabulary that the model uses, producing the same token count you'd get from the official API. Estimated tokenization uses a character-ratio approximation. Exact counts are available for OpenAI models; all other providers show estimates.

Question 26

How does context window usage percentage work?

Accepted Answer

Context usage shows what percentage of a model's maximum context window your input tokens consume. For example, if your prompt is 10,000 tokens and the model has a 1,000,000 token context window, usage is 1%. This helps you understand how much room remains for output generation and conversation history.

Question 27

Should I optimize for fewer tokens?

Accepted Answer

Not always. While reducing tokens saves money, overly compressed prompts can reduce response quality. The goal is to be concise without losing clarity. Remove genuine waste (duplicates, excessive formatting, irrelevant content) but keep context that helps the model understand your intent.

Question 28

How do special characters affect token count?

Accepted Answer

Special characters (brackets, mathematical symbols, currency signs) are typically individual tokens. Markdown formatting characters (#, *, -, |) also consume tokens. In prompts that use extensive formatting or structured notation, these characters can add up significantly.

Question 29

What is the best model for cost efficiency?

Accepted Answer

It depends on your use case. For simple tasks, smaller models like GPT-5.6 Luna ($0.20/M input) or DeepSeek V4 Flash ($0.14/M input) offer excellent cost efficiency. For complex reasoning, larger models justify their higher price. Use our cost calculator to compare models at your expected usage volume.

Question 30

Can I use this tool for non-English text?

Accepted Answer

Yes. The tool tokenizes any text including non-English languages. However, note that non-English text, especially with non-Latin scripts, typically produces more tokens per word. The exact OpenAI tokenization handles this correctly; estimated counts for other models may be less accurate for non-English text.

Question 31

What is prompt caching and how does it save money?

Accepted Answer

Prompt caching stores recently-used prompt prefixes on the provider's servers so repeated requests with the same prefix are cheaper. OpenAI charges 10% of normal input price for cached tokens (e.g., $0.50 becomes $0.05). Anthropic offers 5-minute and 1-hour cache windows. This is especially valuable for system prompts, few-shot examples, and any prefix that stays constant across requests.

Question 32

What is the Batch API and when should I use it?

Accepted Answer

The Batch API (offered by OpenAI, Anthropic, and Google) processes requests asynchronously at a 50% discount. Responses arrive within 24 hours instead of in real-time. Use it for non-time-sensitive workloads: data processing, content generation at scale, bulk analysis, and evaluation runs. Our calculator shows batch pricing for models that support it.

Question 33

What are reasoning models (o3, Fable) and why are they more expensive?

Accepted Answer

Reasoning models like OpenAI o3, o4 Mini, GPT-5.5 Pro, and Claude Fable 5 use 'chain-of-thought' reasoning internally before producing output. They generate internal 'thinking tokens' that count toward output costs, making them significantly more expensive per request. They excel at math, logic, coding, and multi-step problems where regular models struggle.

Question 34

How do I choose between models by cost?

Accepted Answer

Start with the cheapest model that meets your quality requirements. For simple classification/extraction: GPT-5.6 Luna ($0.20/M) or Mistral Small ($0.10/M). For general tasks: GPT-5.6 Terra ($2/M) or Claude Sonnet 5 ($2/M). For complex reasoning: GPT-5.6 Sol ($5/M) or Claude Opus 5 ($5/M). Always test quality at each tier before committing.

Question 35

What is the difference between context window and max output tokens?

Accepted Answer

The context window is the total token budget for input + output combined. Max output tokens is the maximum length of the model's response. For example, GPT-5.6 Sol has a 1.05M context window but 100K max output tokens. Your 'safe prompt size' is context window minus max output — the amount you can input while still allowing the model to respond fully.

Question 36

What are long-context pricing surcharges?

Accepted Answer

Some providers charge more when prompts exceed certain lengths. OpenAI charges 2x input and 1.5x output for prompts exceeding 272K tokens on GPT-5.6 models. Google Gemini 2.5 Pro doubles pricing above 200K tokens. Our context analyzer warns you when approaching these thresholds. Gemini 2.5 Flash notably has flat pricing at all context lengths.

Question 37

What is the difference between cached and uncached pricing?

Accepted Answer

Uncached (standard) pricing applies to the first time a prompt prefix is sent. Cached pricing applies to subsequent requests that share the same prefix. The savings are dramatic: OpenAI charges 10% for cache hits ($5 becomes $0.50), Anthropic charges 10% on cache hits. To maximize caching, keep your system prompt and examples as a stable prefix and vary only the final user message.

Question 38

How do Gemini Flash-Lite, Flash, and Pro compare?

Accepted Answer

Gemini 2.5 Flash-Lite ($0.10/$0.40 per M) is the budget option for high-volume, simpler tasks. Gemini 2.5 Flash ($0.30/$2.50 per M) is the best price-performance for most workloads with 1M context and no long-context surcharge. Gemini 2.5 Pro ($1.25/$10 per M) offers the strongest reasoning with 2M context but charges 2x above 200K tokens. Choose based on task complexity and budget.

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