About Token Counter

Estimate token count for OpenAI and Anthropic models. Part of the DevTools Surf developer suite. Browse more tools in the AI / Modern Dev collection.

Use Cases

• Estimate API costs before sending large prompts
• Optimize system prompts to stay under token limits
• Compare token efficiency across different LLM providers
• Budget context window usage for RAG applications

Tips

• Compare token counts across GPT-4 and Claude models side by side
• Paste your full system prompt to estimate per-request cost
• Check if your context fits within the model's token limit

Fun Facts

• GPT-4's tiktoken BPE tokenizer uses roughly 100,000 tokens in its vocabulary, compared to about 50,000 in GPT-2's tokenizer.
• The word 'tokenization' in NLP was borrowed from compiler theory, where lexical analysis breaks source code into tokens.
• Claude's tokenizer can represent common English words as single tokens, but a single emoji may consume 2-4 tokens.

FAQ

Which model tokenizers?

OpenAI (tiktoken for GPT-3.5, GPT-4, GPT-4o), Anthropic (Claude), and a generic BPE for approximation. Pick the target model.

Is the count exact?

For OpenAI yes — uses the actual tiktoken library in WASM. For Anthropic, close approximation — Anthropic doesn't publish the exact tokenizer; the estimate is typically within 5%.

Why does token count matter?

Cost and context limits. GPT-4o: $2.50/M input tokens. Claude Sonnet 4.5: $3/M. Knowing exact counts before sending matters for high-volume apps.

What about 1 token = ~4 chars?

Rule of thumb for English. Other languages (Japanese, Chinese) often use ~1 token per character. Code uses even more tokens per char due to syntax.