AI Token Counter & Context Window Calculator
Paste code or text and instantly see token counts across 5 top LLMs. Compare how much of each model's context window your input consumes, with estimated API costs per model.
GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, Llama 4, DeepSeek R1.
Your text never leaves your browser. No API calls, no data collection.
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How it works
- Paste your code or text. Supports any language or format up to 100K characters.
- See token counts instantly. Exact counts for GPT-5.2, calibrated estimates for Claude, Gemini, Llama, and DeepSeek.
- Compare context window usage. Visual bars show % capacity consumed per model.
Last updated: February 2026
Count Your Tokens
Paste or type text below. Results update in real time.
Token Counts by Model
Detailed breakdown across 5 top models. GPT-5.2 uses exact tokenization via tiktoken; other models show calibrated estimates.
| Model | Tokens | Context Window | % Used | Est. Input Cost | Method | AI Tools |
|---|---|---|---|---|---|---|
| GPT-5.2 OpenAI |
0 | 400K | 0.0% | <$0.001 | Exact | Cursor, GitHub Copilot |
| Claude Opus 4.6 Anthropic |
0 | 200K | 0.0% | <$0.001 | Estimated | Claude Code, Cursor |
| Gemini 3 Pro |
0 | 1.0M | 0.0% | <$0.001 | Estimated | Gemini Code Assist |
| Llama 4 Maverick Meta (open-weight) |
0 | 1.0M | 0.0% | <$0.001 | Estimated | Continue, Tabby |
| DeepSeek R1 DeepSeek (open-weight) |
0 | 128K | 0.0% | <$0.001 | Estimated | Cline |
What Are Tokens in AI?
Tokens are the basic units that large language models (LLMs) use to process text. A token is not always a whole word — it can be a word, a subword, a single character, or even a punctuation mark. For English text, one token averages about 4 characters or 0.75 words. Code tends to produce more tokens per word than prose because of syntax characters, operators, and indentation.
Token Examples
| Text | Approximate Tokens | Note |
|---|---|---|
| "Hello, world!" | 4 tokens | Comma and exclamation are separate tokens |
| 100 words of English | ~130 tokens | Natural language averages 1.3 tokens per word |
| 100 lines of Python | ~600-900 tokens | Indentation, operators, and syntax add tokens |
| 1 page of documentation | ~400-500 tokens | Roughly 300 words per page |
How Does Tokenization Differ Across Models?
Each model family uses a different tokenizer, which means the same text can produce different token counts. OpenAI's GPT-5.2 uses tiktoken with the o200k_base encoding (200K-token vocabulary). Claude Opus 4.6 uses a custom BPE tokenizer with ~65K tokens. Gemini 3 Pro uses a SentencePiece BPE tokenizer with ~262K tokens. Llama 4 Maverick uses tiktoken-based BPE (~200K vocab). DeepSeek R1 uses a custom BPE tokenizer. In practice, token counts across models typically vary by 5-15% for the same input.
What Is a Context Window?
A context window is the maximum number of tokens an LLM can process in a single request, including both the input (your prompt, code, and system instructions) and the output (the model's response). If your input exceeds the context window, the model cannot process it at all. Larger context windows let you include more code files, longer conversation history, or bigger documents in a single prompt.
Context Window Sizes by Model (February 2026)
| Model | Context Window | Approx. Words | Key AI Tools |
|---|---|---|---|
| Gemini 3 Pro | 1,000,000 tokens | ~750,000 | Gemini Code Assist |
| Llama 4 Maverick | 1,000,000 tokens | ~750,000 | Continue, Tabby |
| GPT-5.2 | 400,000 tokens | ~300,000 | Cursor, GitHub Copilot |
| Claude Opus 4.6 | 200,000 tokens | ~150,000 | Claude Code, Cursor |
| DeepSeek R1 | 128,000 tokens | ~96,000 | Cline |
How to Reduce Token Count in Your Code
When working with AI coding tools, reducing token count lets you fit more relevant context into the model's window. Here are practical strategies:
- Remove comments and docstrings before pasting code into prompts — the model doesn't need them to understand the code.
- Minify whitespace — excessive indentation and blank lines add tokens. Reduce to 2-space indentation.
- Include only relevant files — don't paste your entire codebase. Select the specific functions and files the model needs.
- Summarize long documents — ask the model to summarize a document first, then use the summary in subsequent prompts.
- Use references instead of repetition — say "the function above" instead of re-pasting it.
Frequently Asked Questions
How many tokens is 1,000 words?
1,000 words is approximately 1,300-1,500 tokens for most LLMs. The exact count depends on the model's tokenizer and the content type. Code typically uses more tokens per word than natural language because of syntax characters and indentation. Paste your text above to get exact counts.
What is a context window in AI?
A context window is the maximum number of tokens an AI model can process in a single conversation. It includes both your input and the model's response. Gemini 3 Pro and Llama 4 Maverick support 1 million tokens. GPT-5.2 supports 400K. Claude Opus 4.6 supports 200K. DeepSeek R1 supports 128K. Larger windows let you include more code and conversation history.
Do different AI models tokenize text differently?
Yes. GPT-5.2 uses tiktoken (o200k_base, ~200K vocabulary), Claude Opus 4.6 uses a custom BPE tokenizer (~65K vocabulary), Gemini 3 Pro uses SentencePiece (~262K vocabulary), Llama 4 Maverick uses tiktoken-based BPE, and DeepSeek R1 uses a custom BPE tokenizer. The same text typically varies by 5-15% in token count across models.
What is the GPT-5.2 context window size?
GPT-5.2 has a 400,000 token context window with a 128,000 token output limit. It uses the o200k_base encoding with a 200,000-token vocabulary. This is enough for roughly 300,000 words of input. GPT-5.2 is OpenAI's latest flagship model at $1.75 per million input tokens.
What is the largest context window available in 2026?
As of February 2026, Gemini 3 Pro and Llama 4 Maverick offer 1M token context windows. GPT-5.2 supports 400K tokens. Claude Opus 4.6 supports 200K tokens. DeepSeek R1 supports 128K tokens.
How can I reduce my token count?
Remove comments and docstrings, minify whitespace, include only relevant code files, summarize long documents, and use references instead of repetition. Code minification can reduce tokens by 20-40%.
Is this token counter accurate?
For GPT-5.2, we use the official tiktoken tokenizer with the o200k_base encoding and provide exact counts. For Claude Opus 4.6, Gemini 3 Pro, Llama 4 Maverick, and DeepSeek R1, we use empirically-calibrated ratio estimates that are typically within 5% of actual counts. All estimates are clearly labeled.
Is my text sent to any server?
No. This tool runs entirely in your browser using client-side JavaScript. Your text is never sent to any server or API. The tokenizer library is loaded once and processes everything locally. Your data stays on your device.