SaveMyToken organizes practical guides for model choices, agents and token costs

SaveMyToken is a learning and reference site for choosing AI models, building agent workflows and understanding usage costs. Its homepage organizes material around questions you might encounter while working: which model fits a coding task, why a knowledge chatbot misses answers, or how prompt caching changes a bill.

You can enter a question in English to find guides, lessons and practice by topic. The site presents five paths: Models, Agents, RAG, Save Tokens and Free Interview Q&A. This is guidance you apply to your own setup; the captured page does not describe a service that intercepts requests and reduces token usage for you.

Compare models against the work you need done

The model section covers task fit, deployment and documented evidence. It includes explanations of benchmark scores and comparison pages for language, video-generation and decision models.

SaveMyToken advises checking the exact model version and benchmark method before trying representative tasks on your shortlist. That is useful when a leaderboard result comes from a different evaluation than your daily work. A price comparison also needs the billing conditions for the particular model and provider.

The agent section separates coding and office workflows. Its migration guidance covers working preferences, project rules, memory, skills, MCP connections and automations, with verification after the move.

Diagnose retrieval and measure complete-task cost

The RAG path includes building a small knowledge base from one file, diagnosing missing evidence and evaluating changes between configurations. The described evaluation records answers, human reviews, latency and measured costs alongside retrieval evidence.

For token savings, the site covers trimming repeated history and oversized tool output, prompt caching and controlling generated output. It advises measuring cached input, uncached input, output, retries and accepted results together. A smaller input count can be a poor saving if the change causes more attempts or worse accepted answers.

An AI Token Cost Calculator estimates input, output and caching costs from usage and provider prices. Treat its output as an estimate using those assumptions, then compare with your recorded bill.

Practice concepts before committing to a setup

The homepage advertises 520 free interview questions with matched source answers and original question numbers, without sign-up. Topic areas include prompts, vector databases and evaluation. Runnable JavaScript lessons cover practical tasks such as rejecting malformed tool calls and testing whether document chunks retain a rule with its exception.

The site also labels a coding-plan preview as coming soon and says purchases are not open. Keep that preview separate from the currently described guides and practice. For a useful visit, choose one question, follow its sources and test the proposed change on a small example before applying it to an expensive workflow.