OpenAI API Pricing Calculator

Convert token counts into real dollar costs for OpenAI models: GPT-4o, GPT-4o mini, o1, embeddings. Input/output split, monthly volume projection. Free, in-browser.

API pricing looks simple until you multiply it out: per-million-token rates, different input and output prices, cached-input discounts, and embeddings billed separately. This calculator takes a sample of your prompt and response text and turns it into a concrete dollar figure per request, per thousand requests, or per month.

It also compares equivalent workloads across models — often GPT-4o mini or a smaller model handles a task at 5–10% of flagship cost, and seeing the actual difference in dollars makes the choice obvious.

100% in-browser · no upload · no signup · nothing you paste or drop here leaves your device

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Developer/LLM Token & Cost Calculator

LLM Token & Cost Calculator

Estimate token counts and API costs for OpenAI, Anthropic, Google, Meta & DeepSeek models.

18 MODELS10 modelsAPI pricingToken estimateContext usage
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The CalculatorPage 1
0 chars · 0 words
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Paste text and select models to see token estimates
Data Source & Legal Disclaimer
Effective: Pricing as of August 2026 (subject to change)Last updated: 2026-08-17Update: Manual review

LLM API prices are per 1M tokens as of June 2026 and may change without notice. Token estimates use model-specific chars-per-token ratios and may vary ±10-20% from actual counts.

How it worksPage 2

How tokenizers chop text and price API calls — illustrated

LLMs don't read characters — they read tokens. A tokenizer (BPE or SentencePiece, e.g. GPT's cl100k_base/o200k_base, Claude's tokenizer, Gemini's SentencePiece) splits text into subword units, and English averages roughly 4 characters per token (CJK and dense code can differ). This tool estimates input tokens as ceil(chars ÷ charsPerToken) for each model, then computes cost as tokens ÷ 1,000,000 × per-1M price for input and output and sums them. It also shows what fraction of the model's context window the call would consume.

Text → subword tokens → tokens ÷ 1M × price
The quick brown fox jumps over the lazy dogThequickbrownfox+ 5 more≈ 9 tokens · English averages ≈ 4 chars/tokencost = (tokens ÷ 1M) × price-per-1Minput cost + output cost, summedMODELSgpt-4oBPE · o200k_baseclaude-4-sonnetClaude tokenizergemini-2.5-proSentencePieceeach: own c/t + $/1Mcontext % = (input + output) ÷ context window × 100

A token is often a whole common word, or a subword chunk of a longer word. Because each model ships its own tokenizer and its own per-1M prices, the same prompt yields slightly different token counts and costs across providers.

Pricing a prompt before you ship the API call

Ken, an app developer, is about to call an LLM endpoint with a 1,024-character prompt and wants to know which model is cheapest before the invoice lands.

  1. Paste the prompt:1,024 chars ÷ ~4 chars/token ≈ 256 input tokens (English text)
  2. Set output budget:500 output tokens via the slider — the completion allowance
  3. Compare models:Rows sort by total cost; input and output priced separately per model
  4. Watch context %:(input + output) ÷ context window × 100 — flagged red past 80%

About this OpenAI API pricing calculator

This page covers openai api cost estimator, gpt token pricing calculator, azure openai pricing calculator, llm api cost calculator — all the same underlying task as OpenAI API pricing calculator. The tool above is FreeToolHub's token calculator embedded in full: every feature works right here, and nothing you process is uploaded to any server.

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Frequently asked questions

Which OpenAI models does the calculator cover?

Current-generation chat models (GPT-4o, GPT-4o mini, o1-family), plus common embedding models. The same calculator also covers Claude, Gemini, Llama, and Mistral pricing, so you can compare across providers in one place.

How do I estimate tokens without pasting text?

As a rule of thumb, one token is about four English characters or three-quarters of a word. Paste a real sample for exact counts — system prompts, tool definitions, and conversation history all count as input tokens on every request, which is the most common source of bill shock.

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