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Most token waste does not come from one long answer. It comes from repeated context, messy conversations, overpowered models, unnecessary reasoning and invisible tool overhead.
Every time you paste the same brief, document, codebase summary or client context again, you spend tokens again. Use projects, memory, files, reusable instructions or cached context whenever possible.
A chat that keeps growing becomes expensive and harder to steer. When the topic changes, start fresh or summarize the useful state before continuing.
Thinking modes are powerful, but they are not free. Use deeper reasoning for architecture, strategy, debugging and synthesis. Do not use it for rewriting a short email, formatting a list or naming files.
Coding agents, MCP servers, plugins, files, test logs and tool outputs can add hidden context. If an AI coding tool feels expensive, inspect what it keeps in context before blaming the model.
Many users slowly start using the strongest model for everything. That is usually the fastest way to burn through limits. Keep your best model for the work where quality actually matters.
Claude is strong for long-form work, research, writing, analysis and project-based workflows. The main risk is simple: you keep repeating context Claude could have reused.
Use Claude Projects as your "context home." If you work on the same client, product, document set or research topic every week, do not rebuild the context from scratch.
A good Claude Project should include:
Before answering, use only the context that is still relevant to this task.
Ignore old details from this conversation if they are not needed.
Give me the shortest useful answer first, then ask if I want a deeper version.Start fresh when:
Do not use one endless conversation as your workspace. Use Projects for stable context, new chats for new tasks, and summaries for handoffs.
Claude Code can burn usage quickly because it does not only read your prompt. It may also keep files, logs, tool results, previous commands, project instructions and MCP context in the session.
/usage to see what the session is consuming./clear when switching tasks./compact before a long session becomes messy.CLAUDE.md short and focused.CLAUDE.md leanYour CLAUDE.md should not be a full internal wiki. It should be a map.
Good CLAUDE.md content:
Bad CLAUDE.md content:
Before editing, inspect only the files needed for this task.
Do not scan the whole repository unless you can explain why.
Keep test output compact and summarize failures instead of pasting long logs./clear aggressivelyThe easiest Claude Code mistake is continuing a new task inside an old session. If you just finished a bug fix and now want to refactor a different area, clear the session first.
Use this pattern:
/clear.The best way to save usage in Claude Code is not to write shorter prompts. It is to control what the agent is allowed to carry into the next step.
ChatGPT is often used as a general-purpose workspace: writing, analysis, coding, research, brainstorming and planning. That makes it easy to waste usage by repeating your preferences, files and context across chats.
Use three layers:
Give me the useful answer directly.
Do not over-explain unless I ask.
If this task does not require deep reasoning, keep the response compact.Do not use a Thinking model for:
Use Thinking for:
Stop re-briefing ChatGPT manually. Put stable context in Projects or custom instructions, then keep each chat focused on one job.
Codex is not only a chat model. It is an agent working inside a code context. That means your usage depends heavily on task scope, repository size, instructions, tools and how much output the agent needs to inspect.
AGENTS.md short.Weak prompt:
Improve this app.Better prompt:
In the billing settings page, fix the empty state when a user has no invoices.
Only inspect the billing route, invoice components and related tests unless you find a direct dependency.
Return a short summary and the files changed.AGENTS.md useful, not hugeA strong AGENTS.md should answer:
It should not include every product detail, every process document or every example you have ever written.
Keep the context narrow.
Before reading extra files, explain why they are needed.
Prefer a small targeted change over a broad refactor.
Summarize long outputs instead of repeating them.Codex gets expensive when the task is vague. The tighter the scope, the less context it needs and the better the result usually is.
Gemini is often used for large context, multimodal work, research and app/API workflows. The main trap is assuming a huge context window means you should always fill it.
A large context window is useful when the model truly needs the whole document set. But many tasks only require:
Before sending a huge file, ask yourself: "Does Gemini need all of this to answer well?"
Use only the parts of the uploaded material that are relevant to this question.
If the full file is not needed, ignore the rest.
Give me a compact answer and list any missing context only if it blocks the task.Images, video and audio can consume a lot of tokens. If your task does not require small visual details, use lower resolution, shorter clips or a screenshot of the exact part you want analyzed.
Do not treat the large context window as a dumping ground. Treat it as a workspace, and only bring in the material the model needs for the next decision.
Before paying for a higher plan, identify what you are actually running out of.
You probably have a context-management problem. Try:
You probably have an agent-context problem. Try:
You may need stronger limits, but first check whether every step really needs a premium reasoning model. Try:
This is where Tokenkarma is useful. Each tool has different limits, reset windows and usage rules. Without visibility, you only discover the limit when your work is already interrupted.
Tokenkarma is built for people who pay for several AI tools and want to know what they have left before starting work.
Join Founders ListUse this before starting a heavy session.
It means reducing the amount of unnecessary context, reasoning, files, tool output and repeated instructions your AI tool needs to process. For subscription users, it also means avoiding wasteful usage that pushes you into limits faster.
No. A short vague prompt can waste more usage than a clear detailed prompt. The goal is not to write the fewest words. The goal is to provide only the context the model needs.
No. Use faster models for routine work and stronger models for high-value reasoning. The mistake is using your strongest model for everything by default.
Because they often inspect files, read instructions, run commands, process logs, call tools and carry previous steps forward. The prompt is only one part of the total context.
Tokenkarma is designed to help you see your AI usage limits before you hit them. The tips on this page help you change your workflow. Together, visibility and better habits make it easier to avoid wasted sessions.
Tokenkarma is for people who use multiple AI tools every day and pay for more than one subscription: consultants, writers, marketers, developers, translators, researchers, analysts and operators.
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