Term
Output Token
Tokens an AI model produces as its response. Billed separately and usually three to five times more expensive than input tokens because the model has to actively generate them.
Output token — in more detail
Output tokens are everything the model produces as a response — generated token by token via billions of probability calculations. That active generation step is what makes output more expensive than input: for every single output token the model has to push the entire context through its layers again. Typical price ranges are $0.60 to $15 per million output tokens — several times the input price.
Example / practical context
With Claude Opus at $75/MTok output, a 2,000-token answer costs $0.15 — fine for a short reply, but it adds up quickly for longer ones. Reasoning models additionally produce “invisible” output tokens (thinking tokens) that are not displayed but still fully billed — a single reasoning answer can easily consume 5,000–20,000 output tokens. To keep costs in check, set max_tokens as a hard cap per request.
Distinction from related concepts
Input tokens are what gets billed before generation — cheaper because they are only read, not produced. Streaming does not change the price; it just delivers the output tokens piece by piece instead of all at once. Cached output tokens are not offered by most providers — caching only works on the input side.
Entdecke mehr
Headless without an API bill — how do you reach the best AI models for automation in 2026?
Provider comparison mid-2026: who has a headless mode, whose subscription still covers it — and why BYOK is the most stable foundation.
Glossar$/MTok (Cost per Million Tokens)
Standard pricing unit for AI APIs — cost in US dollars per one million processed tokens. Listed separately for input, output and sometimes cache.
LexikonFunction Calling / Tool Use
How an LLM uses tools: define a tool as a schema, the model picks the function and arguments, the result returns to the chat — the basis of every agent.