Guides
LLM API cost guides and playbooks.
Original, practical writing on the mechanics behind an LLM API bill — cost optimization, prompt caching, token economics, and how to pick the best-value model for a given workload. Written and maintained by the TokenCost editorial team; see our editorial policy.
Jul 2026
Prompt Caching Explained: Save Up to 90% on Input Tokens
How prompt caching works across Anthropic, OpenAI, and Google — cache writes vs. reads, break-even hit rates, and when it doesn't help.
Jul 2026
Input vs Output Tokens: Why Output Pricing Quietly Drives Most of Your Bill
Why generation is priced several times higher than reading, and how your input:output ratio — not your total token count — determines your real cost.
Jul 2026
How to Estimate LLM Costs Before You Build: A Capacity-Planning Guide
A practical framework for translating expected traffic into a real monthly bill before you ship — with three worked scenarios.
Jul 2026
How to Cut Your LLM API Bill by 80%: The Complete Cost-Optimization Playbook
Four concrete levers — prompt caching, batch processing, model routing, and context trimming — with real math showing how much each one saves.
Jul 2026
Which Frontier Model Is Actually Cheapest for Your Workload?
A decision framework for picking the lowest-cost model based on your input/output ratio and context size — because "cheapest" changes with your workload.
Jul 2026
The Cheapest LLM for Coding: SWE-bench Score per Dollar
Why the top-ranked coding model on a leaderboard is rarely the smartest buy, and how to find the best value using cost-vs-quality analysis.