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Claude vs GPT token counts: why the same prompt uses different tokens on each model

Claude and GPT models use different tokenizers, so the same text rarely produces the same token count. Claude's tokenizer is not publicly available, so all Claude token counts from third-party tools are estimates based on character-to-token ratios. For the same English text, Claude's token count is typically within 5-15% of GPT's count, which is close enough for cost estimation.

Knowledge area
Model Comparison
Tokenization across model families
Topic focus
Claude vs GPT tokens
claude-vs-gpt

How this is calculated

Anthropic reveals the token count in the API response header (x-should-return-tokens), so you always know the exact count after a request. Before sending, the rule of thumb is characters ÷ 3.6 for Claude Sonnet and Opus. The exact count matters most when you're approaching the context window limit or optimizing prompts for cost. For general use, the estimate is good enough. For production applications with tight cost constraints, build a small calibration set: send 10 representative prompts to each model, record the actual token counts, and use the ratio to calibrate your estimates.

Verdict

Don't stress about exact token counts across different models. The estimate (chars ÷ 3.5-4) is close enough for planning. Use the API response headers for exact post-hoc counts. The real cost difference between models comes from per-token pricing, not tokenization efficiency.

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Frequently asked questions

What is a token in an LLM?
A token is a chunk of text that a language model reads as a single unit. It is usually a common word, part of a longer word, or a piece of punctuation rather than a whole word or a single character. As a rough rule of thumb, one token is about four characters of English text, and 100 tokens is roughly 75 words.
How accurate is this token counter?
For OpenAI and Llama models the count is exact, because it uses the same Byte Pair Encoding tokenizers those models ship (o200k_base for GPT-5 and GPT-4o, cl100k_base for GPT-4 and GPT-3.5, and the Llama 3 tokenizer for Llama 3 and 4). For Claude and Gemini the count is a labelled estimate, since those tokenizers are not publicly available to run in the browser. Estimates are typically within about 10% of the real value.
Why do different models report different token counts?
Each model family is trained with its own tokenizer and vocabulary, so the same sentence can split into a different number of tokens depending on the model. Newer vocabularies like OpenAI's o200k_base are generally more efficient, packing more characters into each token, which lowers the count compared to older tokenizers.
Is my text sent to a server?
No. All tokenization and counting happens locally in your browser using a tokenizer that loads on the page. Nothing you type or paste is uploaded, logged, or stored, which makes the tool safe to use for private prompts and confidential text.

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