Count tokens for GPT-5.6, Claude, Gemini, DeepSeek, and GLM. See context window usage and estimated API cost instantly.
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Every call to a language model API has a cost measured in tokens, not words. Understanding token counts is essential for three things. It helps you stay within context window limits. It helps you budget API spend. It helps you optimize prompt efficiency. A prompt that uses 90 percent of a model's context window leaves little room for the response and may cause errors or truncation. A system prompt that could be trimmed by 1,000 tokens saves real money at scale. This tool gives you an instant count across every major provider side by side. Select GPT-5.6 Sol, Terra, or Luna from OpenAI, Claude Opus 5, Sonnet 5, or Haiku 4.5 from Anthropic, Gemini 3 Pro or Flash from Google, DeepSeek V4 Pro or Flash, or Z.ai's GLM-5.2, and the count updates as you type. The context usage bar turns amber above 70 percent and red above 90 percent. Estimated input cost is shown per call. This helps when you are building batch processing pipelines. It also helps when you are evaluating which model makes the most economic sense for your use case.
Language models do not read text character by character. They split text into pieces called tokens, which are roughly 3 to 4 characters of English text on average. A single word can be one or multiple tokens depending on how common it is. Tokens matter because API pricing is based on tokens, not characters or words. Context windows (how much a model can "remember") are also measured in tokens.
Different providers use different tokenizers. OpenAI's GPT-5.6 family uses an o200k-derived tokenizer, which this tool counts exactly. Claude, Gemini, DeepSeek, and GLM use proprietary tokenizers that are not publicly available, so this tool uses OpenAI tokenization as a close approximation for those models.
Remove redundant instructions and boilerplate. Use concise, direct language. For structured data, consider using TOON format (our JSON to TOON converter) which reduces token count by around 40 percent for tabular data. Avoid repeating context that the model already has. Break long tasks into smaller calls.
The context window is the maximum number of tokens a model can process in a single call, including both the input (prompt plus conversation history) and the output. GPT-5.6, Claude Opus 5, Claude Sonnet 5, Gemini 3, DeepSeek V4, and GLM-5.2 all support context windows around 1,000,000 tokens. Claude Haiku 4.5 supports 200,000. Exceeding the context window causes an error or truncates the input.
The estimates are based on publicly listed input pricing per million tokens as of Q3 2026 and are approximate. Output tokens are priced differently (usually higher) but not shown here since output length is not known in advance. Check each provider's pricing page for current rates before budgeting API usage.
Word Counter
Real-time words, characters, sentences, paragraphs, reading time, and keyword density analysis.
JSON ↔ TOON
Convert JSON to TOON (Token-Oriented Object Notation) to reduce LLM prompt token usage by ~40%. Lossless and bidirectional.
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Autosaving plain-text editor with word count, Find & Replace, and .txt download. Nothing leaves your browser.
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