AI Token Counter
AI Token Counter estimates how many tokens your text will use in GPT-5, Claude 4.6, Gemini 3, Llama 4, and other language models. Tokens determine API costs and context limits. Select a provider and model below, or use "Generic" for a universal estimate.
This counter estimates raw token counts and does not account for cached (prompt caching) tokens. Some providers offer reduced pricing when the same prompt prefix is reused across API requests -- check your provider's caching documentation for details.
Cost Comparison Across Providers
Compare how much your text would cost as input to popular models. Select a model above to see specific pricing, or compare all providers below:
| Model | Input / 1M | Output / 1M | Context | Your Cost |
|---|---|---|---|---|
| GPT-5.1 | $1.25 | $10.00 | 400K | $0.0000 |
| GPT-4o | $2.50 | $10.00 | 128K | $0.0000 |
| Claude 4.5 Sonnet | $3.00 | $15.00 | 200K | $0.0000 |
| Claude Opus 4.6 | $5.00 | $25.00 | 200K | $0.0000 |
| Gemini 2.5 Pro | $1.25 | $10.00 | 2M | $0.0000 |
| Llama 3.3 70B (Groq) | $0.59 | $0.79 | 128K | $0.0000 |
Token Counters by Provider
For model-specific counting with detailed pricing, tokenizer info, and FAQs:
How AI Tokenization Works
Tokens are not words. AI language models split text into subword units using algorithms like Byte Pair Encoding (BPE). This means:
- Common words like "the" or "is" = 1 token
- Less common words get split: "tokenization" = "token" + "ization" (2 tokens)
- Rare words split further: "pneumonoultramicroscopicsilicovolcanoconiosis" = 9+ tokens
- Spaces often attach to words: " hello" (with leading space) = 1 token
- Numbers, punctuation, and code symbols = separate tokens
Token Estimation by Content Type
| Text Type | Chars/Token | Example |
|---|---|---|
| English prose | ~4.0 | 1,000 chars = ~250 tokens |
| Code (Python/JS) | ~3.0-3.5 | More symbols = more tokens |
| CJK text | ~1.5-2.0 | Each character may be 2-3 tokens |
| Mixed content | ~3.5 | Prose + code + formatting |
Tokenizer Comparison
| Provider | Tokenizer | Vocab Size | Chars/Token |
|---|---|---|---|
| OpenAI | tiktoken (o200k_base) | ~200K | ~4.0 |
| Anthropic | Proprietary BPE | Undisclosed | ~3.9 |
| SentencePiece Unigram | ~256K | ~4.2 | |
| Meta (Llama 3+) | SentencePiece BPE | ~128K | ~4.0 |
| Meta (Llama 2) | SentencePiece BPE | ~32K | ~3.5 |
Frequently Asked Questions
How many tokens is 1,000 words?
Approximately 1,300-1,500 tokens for typical English text. Technical writing or code may use more. A rough formula: words x 1.3 = tokens.
How many tokens fit in a page?
A standard page (~500 words) is approximately 650-700 tokens. A full book (80,000 words) is roughly 100,000-110,000 tokens.
Why do different AI models count tokens differently?
Each provider uses a different tokenizer. OpenAI uses tiktoken (o200k_base for GPT-5/4o), Anthropic uses their proprietary tokenizer, Google uses SentencePiece, and Meta uses SentencePiece BPE. The same text can have slightly different token counts across models.
Are input and output tokens priced the same?
No. Output tokens typically cost 2-8x more than input tokens. For example, GPT-5.1 charges $1.25/1M for input but $10.00/1M for output. Always budget for both when estimating API costs.
What is the largest context window available?
As of February 2026: Gemini 3 Pro and 2.5 Pro offer 2M tokens. GPT-4.1 supports 1M tokens. Claude Opus 4.6 has 200K standard (1M in beta). Llama 4 Scout supports 128K natively with 10M extended context.
How can I reduce token usage?
- Be concise -- remove filler words from prompts
- Avoid repetition -- don't restate the same thing
- Summarize context -- compress long documents before sending
- Use cheaper models -- use Flash/Mini/Haiku for simple tasks
- Use shorter system prompts -- system tokens count toward context
Is this token counter accurate?
This tool provides estimates based on character analysis with adjustments for code content and non-ASCII text. Estimates are typically within 10-15% of actual counts for English text. For exact counts, use the official tokenizers:
- OpenAI Tokenizer (official web tool)
- tiktoken (Python library for OpenAI)
- Anthropic count-tokens API
- Google CountTokens API
Pricing data as of February 7, 2026. Prices change frequently -- always verify with the official provider documentation: OpenAI | Anthropic | Google Gemini | Groq | Together AI
Privacy & Limitations
- All calculations run entirely in your browser -- nothing is sent to any server.
- Results are estimates and may vary based on actual conditions.
Related Tools
- OpenAI Cost Calculator -- Estimate API cost from token counts
- OpenAI Token Counter -- Count tokens and estimate costs for GPT-5.x, GPT-4o, and other OpenAI models
- Claude Token Counter -- Count tokens and estimate costs for Claude Opus 4.6, Sonnet, and Anthropic
- Gemini Token Counter -- Count tokens and estimate costs for Google Gemini 3 Pro, 2.5 Pro and Flash
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AI Token Counter FAQ
How many tokens is 1000 words?
Approximately 1,300-1,500 tokens for English text. The exact count depends on word complexity and the specific tokenizer used by each model.
How are AI tokens calculated?
AI models use tokenizers that split text into subword units. Common words become single tokens, while rare or complex words split into multiple tokens. Most tokenizers use Byte Pair Encoding (BPE) or similar algorithms.
Why do different AI models count tokens differently?
Each AI provider uses different tokenizers. OpenAI uses tiktoken (o200k_base), Anthropic uses their proprietary tokenizer, Google uses SentencePiece, and Meta uses SentencePiece BPE. The same text can have different token counts across models.
How much does 1 million tokens cost?
Costs vary widely. GPT-5.1 charges $1.25/1M input tokens. Claude 4.5 Sonnet charges $3.00/1M. Gemini 2.5 Pro charges $1.25/1M. Llama models on Groq start at $0.05/1M. Output tokens typically cost 2-8x more than input.
What is the largest context window available?
Gemini 3 Pro and 2.5 Pro offer 2 million tokens. GPT-4.1 supports 1 million tokens. Claude Opus 4.6 has 200K standard with 1M in beta. Llama 4 Scout supports 128K natively with 10M extended context.
How can I reduce token usage?
Be concise in prompts, remove filler words, avoid repetition, summarize long context before sending, and use cheaper models for simple tasks. Code uses more tokens per character than prose due to special characters.
Is this token counter accurate?
This tool provides estimates based on character analysis with adjustments for code and non-ASCII text. Estimates are typically within 10-15% of actual counts. For exact counts, use each provider's official tokenizer or API.