Token Usage Estimator
Not sure how many tokens you need? Pick a use case, adjust the numbers, and see which AI models fit your budget.
Choose Your Use Case
Select a preset to auto-fill typical token usage, or pick Custom to enter your own values.
Understanding AI Tokens and API Costs
Tokens are the fundamental unit of text that large language models process. When you send a prompt to an AI API, your text is first broken down into tokens by the model's tokenizer. For English text, one token typically represents about four characters or roughly three-quarters of a word. A 1,000-word blog post, for example, translates to approximately 1,300 tokens. Non-Latin scripts, code, and structured data often tokenize less efficiently, meaning the same amount of content may consume more tokens.
AI API providers charge based on the number of tokens processed, split into two categories: input tokens (the prompt, system instructions, and any context you supply) and output tokens (the response the model generates). Output tokens are almost always more expensive than input tokens because generation requires more computation. Some providers also offer batch processing at a discount and prompt caching to reduce repeated context costs.
Several factors affect your monthly API spend. The model you choose matters most: flagship reasoning models like GPT-4o, Claude Opus, and Gemini 2.5 Pro cost significantly more than budget alternatives like GPT-4.1 Nano, Claude 3 Haiku, or Gemini 2.0 Flash. The length of your prompts is another major driver. Including large context windows, retrieval-augmented generation chunks, or multi-turn conversation history inflates input token counts quickly. Similarly, tasks that require long-form output (such as content generation or code completion) will drive up output token usage.
To keep costs under control, consider these strategies: use the smallest model that meets your quality requirements, keep system prompts concise, leverage prompt caching where available, and use batch APIs for non-latency-sensitive workloads. Monitoring your actual token usage against estimates is essential. This estimator tool helps you project costs before you commit, so you can choose the right model and plan your budget with confidence.
Frequently Asked Questions
What exactly is a token?
A token is a chunk of text that a language model processes as a single unit. Depending on the tokenizer, a token can be as short as a single character or as long as a full word. Common English words like “the” or “and” are usually one token, while longer or less common words may be split into multiple tokens. On average, one token equals roughly four characters of English text.
Why are output tokens more expensive than input tokens?
Generating output requires the model to run its full inference pipeline for every single token produced, predicting the next token one at a time. Processing input tokens can be done in parallel and is computationally cheaper. This asymmetry is reflected in pricing: output tokens typically cost two to five times more than input tokens depending on the provider.
How accurate is the token estimate from pasting text?
The paste-to-estimate feature uses a simple heuristic of roughly one token per four characters. This is a reasonable approximation for standard English prose but may differ from the actual token count produced by a specific model's tokenizer. For precise counts, use the official tokenizer tool from your provider (e.g., OpenAI's tiktoken or Anthropic's token counter).
What is batch pricing and when should I use it?
Batch pricing lets you submit multiple requests as a batch job that is processed within a longer time window (usually up to 24 hours) at a 50% discount. It is ideal for workloads that do not require real-time responses, such as bulk data extraction, document classification, or offline content generation. Not all providers offer batch pricing.
How can I reduce my AI API costs?
Start with the most affordable model that meets your quality bar, and only upgrade if needed. Keep system prompts short and avoid sending unnecessary context. Use prompt caching to avoid re-processing the same instructions across requests. Take advantage of batch APIs for non-urgent tasks. Finally, monitor usage closely and set spending alerts with your provider to avoid surprises.