AI Token Usage Calculator

How token estimation works

Large language models process text as tokens -- chunks of characters that typically represent about 4 characters or 0.75 words in English. This calculator uses a character-based heuristic (characters / 4) which closely approximates the output of production tokenizers like tiktoken and the Anthropic tokenizer for English text.

Actual token counts may vary slightly depending on the specific model, language, and content type. Code and non-English text often use more tokens per character than plain English.

Tips for reducing token usage

• Be concise in prompts. Remove filler words, redundant instructions, and unnecessary context. A shorter prompt that says the same thing costs less.
• Use system prompts wisely. System prompts are sent with every request. Keep them short and focused on instructions the model actually needs.
• Limit output length. Use max_tokens to cap responses. If you only need a yes/no answer, set max_tokens to 10 instead of the default 4096.
• Cache repeated context. Many providers offer prompt caching. If you send the same system prompt or context repeatedly, caching can cut input costs by 50-90%.
• Choose the right model. Use smaller, cheaper models for simple tasks. Reserve large models like GPT-4o and Claude Opus for complex reasoning.
• Batch similar requests. Combine multiple small questions into a single prompt when possible. One request with 5 questions costs less than 5 separate requests.

Best times to use LLMs

Off-peak hours

API response times are often faster during off-peak hours (late night and early morning US time). While pricing does not change, faster responses mean your workflows complete sooner and you can iterate more efficiently.

Batch processing

Several providers offer batch APIs at a 50% discount. If your workload is not time-sensitive -- such as processing product descriptions, generating reports, or analyzing historical data -- batch processing can cut costs in half.

When to use real-time vs. batch

Use real-time APIs for customer-facing features where latency matters: chatbots, live repricing, instant analysis. Use batch APIs for background tasks: catalog enrichment, bulk content generation, weekly report preparation.