Prompt Token Counter & LLM Cost Estimator
Count prompt tokens in real time and calculate API call costs across GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, DeepSeek, and Llama 3 models.
Model Comparison & Budget Forecast
| Model Provider & Tier | Input / 1M | Output / 1M | Cost per Call | Total Forecast |
|---|
💡 Architecture & Cost-Saving Recommendations
Switching from GPT-4o to GPT-4o mini or Gemini 1.5 Flash yields over 90% cost savings. For repetitive long-context workflows, enabling prompt caching drops cached input token costs by up to 90%.
About this tool
How to use it
- Paste your prompt, context chunk, or system instructions into the text editor, or choose one of the pre-loaded templates (RAG, Chatbot, Code).
- Specify your anticipated output completion token length (e.g. 500 tokens for brief answers, 2,000 for code generation).
- Set your monthly API request volume and optional prompt caching hit rate percentage.
- Click 'Calculate API Costs' to compare per-call pricing and monthly cloud budget forecasts across all 8 major frontier models.
Why it matters
LLM providers charge per million tokens on an asymmetric model (output tokens are typically 3x to 5x more expensive than input tokens). Knowing your exact token usage prevents unexpected billing shocks and helps developers select the right balance of reasoning capability and operational cost.
When to use it
Use this estimator prior to architecting RAG pipelines, when setting up LLM agent loops, during monthly API budget forecasting, or when benchmarking prompt compression techniques.
Good to know
Tokenization uses Byte Pair Encoding (BPE) heuristics aligned with standard OpenAI (cl100k) and Anthropic tokenizer distributions (averaging 3.8 to 4 characters per token in English). Exact token counts vary slightly by tokenizer vocabulary. Pricing reflects standard publicly published API card rates.
Common questions
What is the difference between a word and a token?
In natural language models, words are segmented into sub-word chunks called tokens. In standard English, 1 token is roughly 4 characters or 0.75 words (meaning 1,000 words is approximately 1,333 tokens). Code, numbers, and non-Latin scripts frequently have higher token-to-word ratios.
Why are LLM output tokens so much more expensive than input tokens?
Input tokens can be processed in parallel across GPU memory using attention KV caching, whereas output tokens must be generated sequentially one token at a time (autoregressively). This makes generation computationally heavier, leading providers to price output tokens 3x to 5x higher.
How does LLM prompt caching reduce API costs?
Providers like Anthropic, OpenAI, and Google support prompt caching. When identical prefixes (such as system instructions or RAG documents) are reused across requests, the provider reads them directly from cached GPU memory, reducing input token costs by up to 80% to 90%.
Which model offers the best balance of cost and capability?
For routine extraction, classification, and conversational support, lightweight models such as GPT-4o mini, Claude 3.5 Haiku, and Gemini 1.5 Flash cost less than $0.50 per million tokens while matching or exceeding the capabilities of previous-generation frontier models.
Does this tool transmit my prompt data to external servers?
No. All text parsing, tokenization heuristics, and pricing mathematics run 100% locally in your browser. No prompt content or API keys are ever stored or sent across a network.