Question 1

How much does 1 million tokens cost?

Accepted Answer

It depends on the provider, model family, and whether tokens are input, output, or cached. Popular models from OpenAI, Google, Anthropic, and others all have different per-million prices, and output tokens are usually billed higher than input.

Question 2

Why are output tokens more expensive?

Accepted Answer

Most providers charge a premium for generated tokens because they use more computation and extra safety or reasoning steps. In many public price sheets, output ends up several times more expensive than input per million tokens.

Question 3

How accurate is this LLM cost calculator?

Accepted Answer

This tool uses the latest public pricing from major providers, but it cannot reflect every discount, region, or enterprise deal. Treat the result as a planning estimate, not an official invoice.

Question 4

Why might my real bill differ from the estimate?

Accepted Answer

Actual bills can differ because of tokenizer quirks, hidden characters, retries, streaming, prompt caching, and batch APIs that change your effective rate. Always cross-check critical workloads against your provider’s dashboard.

Question 5

How often do LLM prices change?

Accepted Answer

Vendors occasionally drop prices or launch new tiers, especially for smaller or "flash/mini" models. For long-term projects, it’s worth rechecking official pricing pages every few months.

Question 6

How many words are in 1,000 tokens?

Accepted Answer

A useful rule of thumb is 1,000 tokens ≈ 700–800 English words, depending on language and formatting. For code, 1,000 tokens is often in the range of 80–120 lines.

Question 7

What affects my token usage the most?

Accepted Answer

The biggest drivers are context length (how much you send), response length, and how often you repeat large prompts like long docs or codebases. RAG, batching, and prompt caching help reduce repeated tokens.

Question 8

What is prompt or context caching?

Accepted Answer

Prompt/context caching lets the API remember a shared prefix so follow-up requests pay a cheaper cached-read price instead of full input cost. It is especially effective when many requests reuse the same long context.

Question 9

When should I care about caching vs. using a smaller model?

Accepted Answer

If you reuse the same large context across many calls, caching usually wins. If each call is short and unique, switching to a cheaper mini/flash model or capping max output tokens typically matters more.

Question 10

Which types of models are best for RAG?

Accepted Answer

RAG workloads are read-heavy, so models with low input prices and long context windows tend to be best. Many teams pair a cheap high-context model for retrieval with a stronger model only for final polishing.

Question 11

Does this include vector DB or hosting costs?

Accepted Answer

No. This calculator focuses on raw LLM API costs only. You still need to budget separately for vector databases, storage, orchestration, and app hosting.

Question 12

Why doesn’t this replace official pricing docs?

Accepted Answer

This tool is designed for fast budgeting and scenario planning. For exact numbers and billing rules, always rely on your provider’s official pricing documentation and account dashboard.

LLM Cost Calculator

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