How accurate is the token count?

The tool uses a heuristic of 1 token ≈ 4 characters, which matches the OpenAI rule of thumb for English text. Exact counts depend on the model's tokenizer (BPE for GPT, SentencePiece for Gemini). For production budgeting, run your actual text through the model's official tokenizer; this tool is accurate within ~10–15% for typical English prompts.

Are the prices up to date?

Prices are hardcoded to the values published at build time (mid-2025). LLM pricing changes frequently — verify current rates on the provider's pricing page before making financial decisions.

What is an input token vs an output token?

Input tokens are the tokens in your prompt (system message + user message). Output tokens are the tokens in the model's response (completion). Most models charge different rates for each; output tokens are usually 3–5× more expensive.

Why does the cost differ so much between models?

Pricing reflects model size, compute cost, and market positioning. Frontier models like GPT-4o and Claude 3 Opus cost 20–50× more per token than lightweight models like GPT-4o mini or Haiku, which are designed for high-volume, cost-sensitive workloads.

Does context window size affect cost?

Yes — every token in your context window (including conversation history and system prompts) is billed as input tokens on each call. Long conversations accumulate cost quickly; techniques like summarization or sliding windows help manage this.

LLM Token Cost Estimator

Paste your prompt and completion text to see token counts and API cost estimates across popular LLM models.

Prompt (Input Tokens)

Estimated input tokens: 0 0 chars

Completion (Output Tokens)

Estimated output tokens: 0 0 chars

Volume Projection

calls / month

Cost Comparison

Prices as of mid-2025

Input tokens

Output tokens

Total tokens

Monthly Cost at 1,000 calls/mo

Summary

Paste your prompt and completion text to see token counts and API cost estimates across popular LLM models.

How it works

Paste your prompt text into the input field and any expected completion text into the output field.
The tool uses the GPT-2/GPT-3 tokenization rule of thumb (1 token ≈ 4 characters, or ~0.75 words) to estimate token counts.
Select the models you want to compare from the model list.
Cost estimates update instantly based on current published API pricing (input and output token rates).
Use the monthly volume field to project costs at scale.

Use cases

Budget LLM API spend before committing to a production deployment.
Compare GPT-4o vs Claude 3.5 Sonnet vs Gemini 1.5 Pro for a specific use case.
Estimate monthly costs when running thousands of API calls per day.
Quickly check whether a long system prompt meaningfully increases per-call cost.
Evaluate cheaper models (GPT-4o mini, Haiku) as drop-in alternatives.
Prototype prompt cost before wiring up an API key.

Frequently Asked Questions

Last updated: 2026-09-19 · Reviewed by Nham Vu