Question 1

How are AI API token costs calculated?

Accepted Answer

Token costs are calculated by multiplying input tokens by input price per million tokens, plus output tokens by output price per million tokens. Example: GPT-5.4 ($2.5 in / $15 out per 1M). A request with 1,000 input and 500 output tokens costs (1000/1,000,000*2.5) + (500/1,000,000*15) = $0.01.

Question 2

What is the difference between input and output token pricing?

Accepted Answer

Input tokens (prompt) are usually cheaper than output tokens (generation). Example tiers: GPT-5.4 input $2.5 vs output $15 per 1M; Claude Sonnet 4.6 input $3 vs output $15 per 1M. The input/output multiplier differs by model family, so estimate both sides separately.

Question 3

How many tokens does a typical AI request use?

Accepted Answer

Simple chat messages use 50-200 tokens. Document analysis uses 500-5,000 input tokens. Code generation typically uses 1,000-3,000 output tokens. RAG queries with context use 2,000-10,000 input tokens. Agent workflows can use 10,000-50,000 tokens per session across multiple calls.

Question 4

How can I reduce my AI API costs?

Accepted Answer

Key strategies: use smaller models for simple tasks, cache frequent prompts, limit output token length, implement prompt compression, use batching for multiple requests, choose region-optimized pricing, monitor usage with budgets, and route complex queries to premium models only when necessary.

Question 5

What is context caching and how does it save costs?

Accepted Answer

Context caching stores frequently used prompts (like system instructions or large documents) server-side, reducing input token costs on subsequent requests. Claude offers 90% discount on cached tokens. Gemini offers free caching for 1 hour. Useful for RAG, multi-turn conversations, and repeated system prompts.

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