Paste your text to instantly see estimated token counts and API costs across GPT-4o, Claude, Gemini, Llama, and Mistral models.
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Input Cost
$0.00
0 tokens × $2.5/1M
Output Cost (2× tokens)
$0.00
0 tokens × $10/1M
Total Estimated Cost
$0.00
Tokens are the fundamental units that large language models use to process text. A token can be as short as a single character or as long as an entire word. In English, one token is roughly ¾ of a word — or about 4 characters. The word "hamburger" might be split into "ham", "bur", and "ger" (3 tokens), while "the" is typically a single token.
Each model uses a tokenizer (like BPE or SentencePiece) to break text into sub-word pieces. Different models have different tokenizers, so the exact token count varies slightly between GPT-4o, Claude, and Gemini. This tool provides a reliable estimate using the widely-accepted heuristic of ~1.3 tokens per English word.
Most providers charge differently for input and output tokens. Output tokens are typically 2-5× more expensive because they require more computation (autoregressive generation). This tool estimates output tokens at 2× your input to give a realistic cost picture for conversational or content-generation use cases.
Use concise system prompts, avoid unnecessary examples in few-shot prompts, leverage prompt caching (available on OpenAI and Anthropic), and consider smaller models like GPT-4o-mini or Gemini Flash for simpler tasks. Model routing — sending easy queries to cheap models and hard queries to premium ones — can cut costs by 60-80%.
The ~1.3 tokens per English word heuristic is accurate within 5–10% for typical English text. Actual token counts vary slightly between models because each uses a different tokenizer (BPE, SentencePiece). Code, non-English text, and special characters may tokenize differently. For exact counts, use the provider's tokenizer library.
Pricing reflects model capability, infrastructure cost, and competitive positioning. Larger models like GPT-4o and Claude Opus require more compute per token. Smaller models like GPT-4o Mini and Gemini Flash are cheaper but may produce lower-quality output for complex tasks. Match the model to the task difficulty for the best cost-quality tradeoff.
Write concise system prompts, cache repeated instructions with providers that support prompt caching, use model routing to send simple queries to cheaper models, and limit output with the max_tokens parameter. Our AI development team builds optimized LLM pipelines that typically cut costs by 60–80% while maintaining quality.