AI Token Calculator
Estimate LLM token weights from text and calculate prompt costs across major model providers in real-time.
Real-time Metrics
Token Pricing Comparison Table
| Provider | Model | Context Window | Input Cost / M | Output Cost / M | Estimated Cost / M (Blended) | User Token Cost (Prompt) |
|---|---|---|---|---|---|---|
| OpenAI | GPT-4o | 128,000 | $2.50 | $10.00 | $4.00 | $0.00 |
| OpenAI | GPT-4o mini | 128,000 | $0.15 | $0.60 | $0.24 | $0.00 |
| Anthropic | Claude 3.5 Sonnet | 200,000 | $3.00 | $15.00 | $5.40 | $0.00 |
| Anthropic | Claude 3.5 Haiku | 200,000 | $0.80 | $4.00 | $1.44 | $0.00 |
| Gemini 1.5 Pro | 2,000,000 | $1.25 | $5.00 | $2.00 | $0.00 | |
| Gemini 1.5 Flash | 1,000,000 | $0.07 | $0.30 | $0.12 | $0.00 | |
| Meta | Llama 3.3 70B | 128,000 | $0.35 | $0.40 | $0.36 | $0.00 |
| Mistral | Mistral Large 2 | 128,000 | $2.00 | $6.00 | $2.80 | $0.00 |
| Mistral | Mistral Codestral | 32,000 | $0.20 | $0.60 | $0.28 | $0.00 |
* Blended cost assumes an 80% input (prompt) and 20% output (completion) split. User token cost represents the cost of executing the current text input as a prompt.
Frequently Asked Questions
What is an LLM token?
Tokens are the basic units of data processed by Large Language Models. Instead of reading word-by-word, LLMs break down text into sub-word segments (e.g., 'learning' might become 'learn' and 'ing').
Why do different text types have different token counts?
Tokenizers are trained on specific corpus distributions. Plain English is highly compressed (about 4 characters per token), while code, JSON, and technical terms are less common and require more tokens (often 2 to 2.5 characters per token) to represent the same length.
How accurate is this token estimator?
Since different providers use different tokenization algorithms (like Tiktoken for OpenAI, LlamaTokenizer for Meta, etc.), this tool uses statistical heuristics. It is an estimation, usually accurate within 5-10% of the actual API token counts.
What Are AI Tokens?
In natural language processing, a token is the fundamental unit of text that a language model reads or generates. LLMs do not comprehend text as strings of characters or entire words; instead, they split text into semantic sub-words. For instance, common words like "the" or "and" are typically represented as a single token, whereas rare words or code syntaxes are split into multiple tokens.
How Tokenization Works
Tokenizers use algorithms like Byte-Pair Encoding (BPE) or WordPiece to recursively merge characters that frequently appear together. When you input text, it is converted into a list of token IDs. In English, a general rule of thumb is that 1 token is equal to approximately 4 characters or 0.75 words.
How Token Costs Affect AI Applications
LLM APIs charge developers based on the number of tokens processed. Crucially, input tokens (prompts) are priced cheaper than output tokens (completions), often by a factor of 3x to 5x. When designing agentic systems that run continuously or RAG pipelines that pull massive document segments into the context window, token efficiency becomes a key operational metric. Over-allocating tokens directly degrades gross margins.
Tokens vs Words: A Reference Scale
- 100 Words: ~135 Tokens (English)
- 1 Page of Text: ~500 Words / ~675 Tokens
- Short Code Snippet (JSON): ~50 Words / ~110 Tokens (JSON notation consumes substantial tokens due to punctuation brackets).