Name: LLM API Pricing Comparison
Creator: llmprice.fyi
License: https://creativecommons.org/licenses/by/4.0/

Question 1

How is LLM API pricing calculated?

Accepted Answer

Most providers charge per token — small chunks of text (roughly 4 characters). Input tokens (your prompt) and output tokens (the model response) are priced separately. llmprice.fyi shows both prices per million tokens so you can easily compare costs across providers.

Question 2

What is a context window?

Accepted Answer

A context window is the maximum number of tokens a model can process in a single request — including both your input and the model's response. Larger context windows (128k, 200k, 1M+ tokens) allow longer documents and conversations but often cost more per token.

Question 3

Which LLM is the cheapest?

Accepted Answer

The cheapest model depends on your use case. For simple tasks, smaller models like Gemini Flash or Llama 3 are very affordable. For complex reasoning, larger models like GPT-4o or Claude may be more cost-effective despite higher per-token prices because they produce better results in fewer attempts. Use our calculator to estimate costs for your specific workload.

Question 4

How often is pricing data updated?

Accepted Answer

Pricing data is sourced from the OpenRouter API and updated daily. When providers change their prices, the changes typically appear on llmprice.fyi within 24 hours.

Question 5

What is the difference between input and output pricing?

Accepted Answer

Input pricing applies to the text you send to the model (your prompt, context, instructions). Output pricing applies to the text the model generates. Output tokens are typically 2-4x more expensive than input tokens because generating text requires more compute than processing it.

Question 6

Do prices include fine-tuning or training costs?

Accepted Answer

No. llmprice.fyi only compares inference pricing — the cost of sending requests to hosted models via API. Fine-tuning, training, and hosting your own models have separate pricing not covered here.

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