Context Window Visualizer & Token Counter

Count tokens in your text and visualize how much of each AI model's context window you are using

~/context-window
0 chars0 words0 lines~0 tokens
4.0 c/t

Paste text above to see how it fits in different AI model context windows.

Compares token usage across models from OpenAI, Anthropic, Google, Mistral, and more. All counting happens in your browser.

What is a Context Window Visualizer?

A context window visualizer shows you exactly how much of each AI model's context window your text consumes. Context windows — the maximum amount of text a model can process in a single request — vary dramatically across models, from 65K tokens (DeepSeek) to over 1 million tokens (Gemini 2.5).

Understanding context limits is critical for building AI applications. If your prompt plus expected response exceeds the context window, the model will either truncate your input or fail entirely. This tool helps you plan by showing a visual comparison across all major models.

The built-in token counter estimates tokens in real time as you type or paste. While not a perfect tokenizer (which would require loading large WASM libraries), the character-to-token ratio provides a practical estimate accurate to within 10-20% for planning purposes. All processing happens in your browser.

How to Use This Tool

Counting tokens and comparing context windows is straightforward:

  1. Paste your text into the input area — this can be a prompt, a document, or any text you plan to send to an AI model.
  2. View the real-time counters: characters, words, lines, and estimated token count update as you type.
  3. Adjust the chars-per-token ratio slider if needed. The default of 4.0 works well for English. Use 2.5-3.0 for CJK languages (Chinese, Japanese, Korean) or code-heavy content.
  4. Filter models by provider (OpenAI, Anthropic, Google, etc.) or tier (Flagship, Mid-tier, Budget) to focus on relevant options.
  5. Compare the visual bars — green means plenty of room, yellow means you're using over half, red means you're near the limit.
  6. Copy the full comparison summary to share with your team or include in documentation.

Understanding Token Estimation

Tokens are the fundamental units that large language models process. A token roughly corresponds to 4 characters or about three-quarters of an English word. The word 'hamburger' is split into 'ham', 'bur', and 'ger' — three tokens. Common words like 'the' or 'is' are single tokens.

Why Approximate?

Each AI provider uses a different tokenizer. OpenAI uses tiktoken (based on BPE), Anthropic has its own tokenizer, and Google uses SentencePiece. A real tokenizer for even one provider weighs 800KB+ as a WASM module. Since this is a comparison tool across all providers, we use a lightweight ratio-based estimate that works across all of them.

Language Differences

English averages about 4 characters per token. CJK languages (Chinese, Japanese, Korean) are closer to 2-3 characters per token because each character carries more information. Code tends to use 3-4 characters per token. Use the ratio slider to adjust for your content type.

Context Window Comparison by Provider

Context windows have grown rapidly. In 2023, 4K-8K tokens was standard. By 2025, most flagship models offer 128K-200K tokens, and Google's Gemini models support over 1 million tokens. Larger context windows enable use cases like analyzing entire codebases, processing long documents, and maintaining extended conversations.

  • OpenAI GPT-5 series: 128K tokens across all tiers, with o3/o4-mini models offering 200K
  • Anthropic Claude 4.x: 200K tokens across all tiers, currently the largest standard window
  • Google Gemini 2.x: 1M+ tokens, by far the largest context windows available
  • Mistral: 128K-256K tokens depending on model
  • xAI Grok: 131K tokens
  • DeepSeek: 65K tokens, more compact but budget-friendly pricing

Frequently Asked Questions

How accurate is the token count?

The estimate is typically within 10-20% of the actual count for English text. It uses a character-to-token ratio (default 4.0 for English), which is a well-established approximation. For exact counts, use the provider's official tokenizer. This tool is designed for quick planning, not precise billing calculations.

What's the difference between context window and max output?

The context window is the total limit for input + output combined. Max output is the maximum number of tokens the model will generate in its response. For example, Claude Opus 4.6 has a 200K context window but a 32K max output. Your prompt can use up to 168K tokens, leaving room for the response.

Why does my text use a different percentage in each model?

Because each model has a different total context window size. The same text that uses 0.2% of Gemini's 1M context might use 1.5% of a 65K context model. This is exactly why the comparison view is useful — it shows you which models can comfortably handle your content.

Should I adjust the ratio slider?

For most English text, the default of 4.0 works well. If your content is primarily code, try 3.5. For Chinese, Japanese, or Korean text, use 2.5. For mixed content, leave it at 4.0 — the margin of error is acceptable for planning purposes.

What happens when text exceeds a model's context window?

The model's API will return an error or silently truncate your input, depending on the provider. OpenAI returns a clear error message. Some providers auto-truncate from the beginning of the conversation. It's best to stay under 80% usage to leave room for the model's response.

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