AI Token Counter & LLM Cost Calculator
Estimate BPE token counts and calculate API costs for OpenAI (GPT-4o), Anthropic (Claude 3.5), and Gemini 1.5.
How do I use AI Token Counter & LLM Cost Calculator?
Paste or type your system prompt, RAG document chunks, JSON payload, or source code into the Prompt Text Input editor.
Check the real-time Total Estimated Tokens, Character count, Word count, and Line count in the right-hand Control Panel.
Compare exact per-request Input Cost and Output Cost side-by-side across OpenAI GPT-4o, GPT-4o Mini, Anthropic Claude 3.5 Sonnet, Claude 3 Haiku, and Google Gemini 1.5 Pro.
Inspect the Context Window Usage progress bar on each model card to verify your payload fits safely within 128K, 200K, or 2M token limits.
What features does AI Token Counter & LLM Cost Calculator offer?
Zero-latency BPE (Byte-Pair Encoding) token estimation on every keystroke
Side-by-side API cost calculator for OpenAI, Anthropic, and Google Gemini frontier models
Visual Context Window utilization meter (128K, 200K, and 2,000,000 token limits)
Simultaneous token, character, word, and line metrics for prompt optimization
One-click prompt clipboard copy after trimming redundant context
100% Client-Side privacy — confidential prompts, API payloads, and customer data never leave your browser
How are LLM BPE tokens and API input/output costs calculated?
Large Language Models do not read text character-by-character or word-by-word; instead, tokenizers (such as OpenAI's `o200k_base` / `cl100k_base` and Anthropic's BPE tokenizer) segment text into sub-word byte pairs. Common English words map to a single token, whereas rare technical identifiers, JSON punctuation, indentation, and non-Latin Unicode scripts split into multiple tokens.
Estimated Tokens ≈ max(ceil(Words × 1.33), ceil(Characters ÷ 4))In standard English prose and code, 1 token averages ~4 characters or ~0.75 words (100 tokens ≈ 75 words).
Total Call Cost ($) = (Input Tokens ÷ 1,000,000 × Input Rate) + (Output Tokens ÷ 1,000,000 × Output Rate)Output completion tokens are billed at 3× to 5× the price of input prompt tokens due to autoregressive KV-cache memory bandwidth.
Context Usage % = (Estimated Tokens ÷ Max Context Window Tokens) × 100Tracks how much of a model's maximum context window (e.g., 128,000 tokens for GPT-4o) is consumed by your input payload.
| Model Name | Provider | Max Context Window | Input Cost / 1M Tokens | Output Cost / 1M Tokens |
|---|---|---|---|---|
| GPT-4o | OpenAI (o200k_base) | 128,000 tokens | $2.50 / 1M | $10.00 / 1M |
| GPT-4o Mini | OpenAI (o200k_base) | 128,000 tokens | $0.15 / 1M | $0.60 / 1M |
| Claude 3.5 Sonnet | Anthropic | 200,000 tokens | $3.00 / 1M | $15.00 / 1M |
| Claude 3 Haiku | Anthropic | 200,000 tokens | $0.25 / 1M | $1.25 / 1M |
| Gemini 1.5 Pro | Google DeepMind | 2,000,000 tokens | $1.25 / 1M | $5.00 / 1M |
- Minifying JSON payloads (removing pretty-print indentation and newlines before sending to an LLM API) routinely reduces input token counts and API bills by 15%–28%.
How is AI Token Counter & LLM Cost Calculator used in real-world workflows?
Sizing Vector Search Chunks & Preventing Context Overflow
Verify how many retrieved documentation chunks or SQL schema definitions fit inside GPT-4o's 128K or Claude 3.5 Sonnet's 200K context window without triggering HTTP 400 context-length errors.
Forecasting Monthly LLM API Unit Economics
Compare the exact per-call cost difference between routing classification tasks to GPT-4o Mini ($0.15/1M input) or Claude 3 Haiku ($0.25/1M input) versus flagship reasoning models.
Auditing Prompt Length Without Leaking Secrets to Third-Party Webpages
Inspect token counts for proprietary source code, internal database schemas, or PII-redacted logs locally in the browser without sending payloads to external tokenizer APIs.
Frequently Asked Questions about AI Token Counter & LLM Cost Calculator
Why do LLM providers charge 3x to 5x more for Output tokens than Input tokens?
When processing your Input prompt (the prefill phase), GPU tensor cores process all input tokens in parallel in a single forward pass. When generating Output tokens (the decoding phase), the model must generate one token at a time sequentially, loading the entire model weights and KV-cache from high-bandwidth GPU memory for every single token produced.
How does formatting (like JSON indentation or Markdown tables) affect token count?
Every space, tab, newline, and quote character consumes BPE tokens if it cannot be merged into an adjacent token. A pretty-printed JSON array with 4-space indentation can consume 20%–35% more tokens than the exact same JSON minified onto a single line.
Why do non-English languages (like Vietnamese, Japanese, or Arabic) use more tokens per word?
Most BPE tokenizers are trained predominantly on English and code corpora. While common English words map to 1 token, words with diacritics (such as Vietnamese UTF-8 characters) or CJK characters are frequently split into 2 to 4 byte-level sub-tokens, increasing token usage for the same semantic length.
How accurate is this client-side token estimator compared to OpenAI's tiktoken?
Our BPE heuristic engine approximates `o200k_base` and `cl100k_base` segmentation within roughly ±5–8% for standard English prose and source code while executing instantaneously in pure JavaScript without downloading multi-megabyte WASM vocabulary binaries.
Are the prompts I paste into this token counter sent to OpenAI, Anthropic, or any server?
Never. All character, word, BPE token, and cost calculations run 100% locally in your browser's JavaScript runtime. Your system prompts and proprietary context never leave your machine.
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