📊 Token & Cost Estimator

AI Model

Sample Prompt / System Prompt

Average Output Tokens per Call

500 tokens

API Calls Per Day

1,000 calls

📈 Cost Estimate

Estimated Monthly Cost

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Input Tokens (prompt)

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Output Tokens (est.)

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Cost Per Call

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Daily Cost

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Annual Cost

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Cost per 1K calls

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📊 Model Price Comparison (same volume)

Input vs Output Cost

Model Comparison

⚠️ Prices shown are estimates based on publicly available pricing as of mid-2025. Verify current rates on each provider's pricing page. Token counts are approximations (~4 chars/token for English text).

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Select a model and enter your usage details

About

Understanding AI Token Pricing

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What is a Token?

Tokens are chunks of text — typically 3-4 characters or about 0.75 words in English. "Hello world!" = 3 tokens. Code and non-English text may tokenize differently. Models use tiktoken (OpenAI) or SentencePiece tokenization, which differ slightly.

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Input vs Output Pricing

Most providers charge separately for input tokens (your prompt + context) and output tokens (the model's response). Output tokens typically cost 3-5× more than input tokens. Minimizing output length (e.g., using structured JSON, bullet points) reduces costs significantly.

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Cost Optimization Tips

Use smaller models for simple tasks (GPT-4o mini, Claude Haiku). Cache repeated system prompts where supported. Use streaming to detect early completion. Compress context with summarization. Monitor actual token usage with provider dashboards.

FAQ

Frequently Asked Questions

Common questions about AI Token calculations

How do I count tokens accurately?

Use the official tokenizer for each model. OpenAI provides tiktoken (pip install tiktoken). Anthropic's Claude uses similar tokenization — roughly 1 token per 3-4 English characters. For production systems, always use the provider's token counting API before billing rather than estimating.

What is context window size?

Context window is the maximum tokens a model can process in one request (input + output combined). GPT-4o: 128K tokens, Claude 3.5 Sonnet: 200K tokens, Gemini 1.5 Pro: 1M tokens. Larger contexts enable longer documents and conversations but increase costs. Only include relevant context to control costs.

How can I reduce my AI API costs?

Key strategies: (1) Use cheaper models for simple tasks — GPT-4o mini is 20× cheaper than GPT-4o; (2) Implement prompt caching (Anthropic's cache tokens are 90% cheaper); (3) Batch non-urgent requests; (4) Fine-tune a smaller model for your specific use case; (5) Compress system prompts; (6) Use streaming to detect natural stopping points.

What is prompt caching and how does it save money?

Prompt caching stores repeated prefixes (system prompts, documents) so they're not re-processed each call. Anthropic charges cached tokens at 10% of regular input price. If your system prompt is 2K tokens and you make 10K calls/day, caching saves ~90% of those input costs. OpenAI also offers automatic prompt caching for qualifying requests.

🤖 AI Token Calculator