Cost Per Token
The unit economics of LLM APIs.
/ quick answer
Providers price separately for input and output tokens, usually per million. Output is typically 3-5x more expensive than input, so long context is cheap but long answers are not. The unit economics of LLM APIs.
/ frequently asked
What is Cost Per Token?
Providers price separately for input and output tokens, usually per million. Output is typically 3-5x more expensive than input, so long context is cheap but long answers are not.
What is an example of Cost Per Token?
GPT-4o mini: $0.15/M input, $0.60/M output — a $0.001 support answer at 2k in + 500 out.
Why does Cost Per Token matter?
The unit economics of LLM APIs.
What should I learn after Cost Per Token?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Token Budgeting
Token budgeting is the strategic allocation and management of token usage within large language model (LLM) operations to control costs and optimize performance.
- →AI Cost Control
AI cost control is the practice of monitoring, analyzing, and managing the financial expenditures associated with developing, deploying, and operating artificial intelligence systems.
- →Token Budget
A token budget is a predefined limit on the number of tokens an AI application or specific request can consume within a given period or for a single interaction. It is a critical mechanism for controlling costs and managing resource allocation for Large Language Model (LLM) usage.
- →AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
Related workflows
Turn this into a repeatable process.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
- →Optimize AI Agent Token Costs
This workflow outlines steps to systematically analyze, reduce, and manage token consumption for AI agents, ensuring cost-effective operation.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
Related tool stacks
The tools that run it in production.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
- →Lovable vs Cursor
Prompt-to-app builder vs AI-assisted code editor — which one should you reach for?