LLM Pricing Explained: Understanding the True Cost of AI

How LLM Pricing Works

Most LLM providers charge based on tokens — the fundamental units that models process. A token is roughly 3/4 of a word in English, so 1,000 tokens is about 750 words.

Pricing is quoted per million tokens (1M), with separate rates for:

• Input tokens — the text you send to the model (prompts, context, instructions)
• Output tokens — the text the model generates back

Output tokens are typically 3-5x more expensive than input tokens because generation requires more computation than reading.

Understanding Blended Price

Comparing models with different input/output ratios is confusing. That's why we use blended price — a single number that assumes a typical 3:1 input-to-output ratio:

blended = (3 × input_price + 1 × output_price) / 4

This gives you a realistic per-million-token cost for most applications.

Real-World Cost Examples

Let's say you're running a customer support chatbot that handles 1,000 conversations per day, each averaging 500 input tokens and 200 output tokens:

The difference between cheapest and most expensive is 34x — and the cheapest option might be perfectly adequate for simple support queries.

Hidden Cost Factors

Context Window Usage

Longer conversations consume more input tokens per message as the conversation history grows. A 10-turn conversation might use 5x more tokens than the first message alone.

Reasoning Models

Models like o3 and DeepSeek R1 use "thinking tokens" during reasoning that may not appear in the output but still cost money. Their effective cost per useful output token can be significantly higher than the listed price.

Rate Limits

Most providers impose rate limits (requests per minute, tokens per minute). If your application exceeds these, you'll need to implement queuing or use multiple API keys.

Estimate Your Costs

Use our Cost Calculator to model your specific usage pattern and compare costs across models, including growth projections over 12 months.