An LLM token counter shows how many tokens your text uses across different AI models like GPT-4, Claude, and Gemini. This free tool compares every major LLM tokenizer side-by-side and estimates the API cost for each model in real-time as you type.
Step 1
Used to estimate output cost per request. Default 500 tokens (~375 words).
Step 2
OpenAI counts (GPT-4o, GPT-4o mini, o1, o1-mini, GPT-4 Turbo) are exact - the official tiktoken-compatible BPE encoders run in your browser.
Anthropic, Google, Meta, Mistral, and DeepSeek do not publish JavaScript tokenizers, so those counts are estimated from documented characters-per-token ratios (Claude ~3.5, Gemini ~4.0, Llama ~3.8, Mistral ~3.7, DeepSeek ~3.5). Estimates are usually within 10% of the real count, but for production budgeting confirm against the provider's API once you have a representative sample of prompts.
Pick the right model
Compare features beyond pricing - context window, multimodal support, function calling, latency, and benchmarks - on the AI Wins side-by-side comparison chart.
FAQ
A token is the basic unit a language model reads and generates. It is roughly a word fragment - common English words are usually one token, but longer or rarer words can be split into several. Punctuation, spaces, and code symbols also count. As a rough rule, one token equals about 4 characters of English text, so 1,000 tokens is approximately 750 words.
Each provider trains its own tokenizer on different data, so the same paragraph splits into a different number of tokens for each model. OpenAI uses BPE tokenizers like cl100k_base and o200k_base, Anthropic and Google use proprietary tokenizers, and Llama uses SentencePiece. Counts can differ by 10-30% between providers, which is why a side-by-side comparison is useful when budgeting.
Token counts for OpenAI models are exact - this tool runs the official tiktoken-compatible BPE in the browser. Counts for Claude, Gemini, Llama, Mistral, and DeepSeek are estimated from documented characters-per-token ratios because those tokenizers are not publicly available in JavaScript. Estimates are typically within 10% of the real count, but for production budgeting always confirm against the provider's API once you have a representative sample of prompts.
For pure cost per million input tokens, Gemini 1.5 Flash, Llama 3.1 8B, and DeepSeek V3 are typically cheapest. If your prompt is genuinely long (hundreds of thousands of tokens), Gemini 1.5 Pro is often the best fit because it has a 2M-token context window and competitive pricing. Paste your real prompt above and the comparison table will sort by total cost so you can see the answer for your exact text.
Yes. Code typically tokenizes more densely than prose - punctuation, indentation, and identifiers each consume tokens. Click the 'Code snippet' example above to see how a 500-line file looks across each model. For repository-scale prompts, GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro all handle long codebases well, but their pricing and context window tradeoffs differ a lot.
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