What should I do if Claude Code consumes too many Tokens? 3 tips to save you more than 50% of the cost

Claude Code’s AI Token will burn quickly, usually not because you ask too many questions, but because the context is too long, the model selection is too expensive, and the task is obviously small but the effort is too high.

If you do not first manage the cost difference between slash command, model selection, input token and output token, and the repo structure itself is not clean enough, Claude Code can easily drive up the cost of AI Tokens before you even notice. The truly effective way to save is not to simply type less, but to control the context, select the right model, adjust effort, and reduce unnecessary reading, reasoning and modification of AI.

This is also what many people most often overlook when using AI Coding tools. On the surface it looks like it's just asking Claude Code to help change a few files, but in fact it may reread the previous conversation, the current repo content, the relevant file context, and your current round of new commands every time. The longer the previous task is delayed, the more complicated the chat, the higher-level the model, and the longer the output, the higher the AI Token cost will be. Therefore, the key to saving Tokens in Claude Code is actually closer to a set of workflow management than to adjusting general chat habits.

Why Claude Code is so easy to burn AI Tokens

The biggest difference between Claude Code and general chat AI is that it does not just reply with a single answer, but will process the entire task background, development environment, file context and current instructions together. This means that the cost of AI Token does not just come from the few words you enter at the moment, but from the entire workflow.

Claude Code will always read the previous context

If a lot of discussion content has been accumulated previously, Claude Code may continue to read these content together in each subsequent round. This will directly pull up the input token. Many people think that they are just asking a small question, but for Claude Code, it is not just that sentence that really enters the model, but the entire context.

Claude Code will read the repo and file contents

When you ask it to modify the program, find bugs, understand the structure or refactor the function, it will not only look at the prompt, but also look at the files, settings and related code in the repo. The larger the repo, the less clear the task, and the more confusing the context, the more content it has to read, and the higher the AI Token cost will naturally be.

Claude Code’s output token may also be expensive

Many people only pay attention to the input token, but ignore that Claude Code often outputs a long section of code, explanations, diffs, correction steps, and suggestions at once. These output tokens are also charged, and the unit price of output tokens is usually higher than input tokens. So if you let it continue to generate very long content, the cost of AI Token will of course go up.

The first step: first learn to use slash command to manage context

The most important point of this video is not to tell you to ask less questions, but to use Claude Code's slash command first. Because in AI Coding tools like Claude Code, context management is almost directly equivalent to AI Token management.

/clear: Clear the old context after the task is completed

/clear has a very direct function, which is to clear the previously accumulated context. When a task has been completed, or you are ready to start new functions, new files, and new problems, you should no longer let Claude Code run with the entire previous task.

This command is best used when the previous task has ended, the previous discussion is no longer important, or you do not want old requirements to continue to interfere with the new task. What really saves AI Tokens is not to clean up the picture, but to not let the AI reread a bunch of old contexts that are no longer needed every time.

/compact: When you want to continue working on the same project, compress the previous text into a summary

/compact is different from /clear. It does not clear everything, but compresses the previous content to the key points, leaving only important information.

If you want to continue working on the same project, but cannot forget the specifications, restrictions, formats, and naming methods discussed earlier, /clear is not suitable at this time, but /compact is more suitable. The core of it is this: keep the important context, but don't let Claude Code carry on with all the details.

This approach is suitable for medium and large projects because it allows the AI to remember key requirements while reducing input token pressure.

/btw: Don’t pollute the main task with side problems

When working at Claude Code, the most common thing that happens is not that the main task goes wrong, but that side problems keep being inserted midway. You are modifying the website, and suddenly want to ask what a certain package is; you are fixing a bug, and suddenly want to confirm whether a certain API is charged; you are working on a piece of code, and you want to ask, "Why should this behavior be written like this?"

If these questions are mixed directly into the main task, Claude Code is likely to treat them as formal context, making the entire session fatter and fatter. /btw The value is here: you can ask side questions, but don't let it pollute the session memory of the main task.

This not only makes the workflow cleaner, but also prevents irrelevant content from remaining in the context, causing subsequent AI Token consumption to continue to increase.

Top 2: Don’t use high effort for small tasks, and don’t use the most expensive models for all tasks

Claude Code It’s easy for people to misunderstand that “the stronger, the better”, but what the video wants to say is actually the opposite. Not every task deserves intensive inference, and not every task requires expensive models. The real way to save AI Tokens is to adjust effort and model according to the difficulty of the task.

/effort low: Don’t over-reason for simple tasks

If you just change a logo, modify a small CSS, adjust a piece of copywriting, or change a column name, such tasks do not require deep reasoning by AI. At this time, using /effort low allows Claude Code to process it in a relatively lightweight way, without wasting AI Token on unnecessary thinking costs.

Many people will put a high effort on such small tasks, and the result is like a half-hour meeting just to change the color of a button. The problem is not that it can’t be done, but that the cost is not cost-effective.

/effort high: Only complex tasks are worthy of high intensity

If you are doing architecture design, system planning, multi-file reconstruction, complex bug tracking or large-scale feature development, this kind of task really requires Claude Code to enter a deeper reasoning mode. It makes sense to use /effort high at this time, because the task itself is worth spending more AI Tokens in exchange for more complete thinking.

What you should really remember is not that high is more powerful, but that effort must match the difficulty of the task.

/model: Don’t use the most expensive model for every task

/model in Claude Code is also the core of cost control. The video mentions that models can be selected based on the task, rather than just using the strongest, most expensive, and largest context model.

If a simple task can be completed with a cheap model, there is no need to use an expensive model from the beginning. Quick modifications, small-scale adjustments, and daily routine work are usually more suitable for cheap models; daily coding and general functional modifications are suitable for mid-level models; large repos, long contexts, and difficult tasks are worthy of higher-level models or larger contexts.

The really important concept is: the stronger the model is not necessarily the more suitable, because the cost of AI Token will also increase together.

The third step: first understand how to burn input token and output token together

This video is very suitable to explain something that many novices will ignore: saving AI Token does not just depend on how many words you type, but also how much Claude Code is read and generated.

Input Token: AI actually reads more content than you think

In Claude Code, the input token is not just the sentence you typed, but also includes the previous dialogue, repo content, file context, settings, and task background. This is why many people misjudge the cost of AI Token, thinking that they are just filling in a small problem, but why the cost has increased so much.

The real reason is usually: Claude Code does not only read your sentence, but also counts the entire context.

Output Token: The more content AI generates, the higher the cost

When Claude Code outputs a long code, explanation, diff, reconstruction results and suggestions at one time, these output tokens will also be charged, and the output tokens of many models are usually more expensive than the input tokens.

So when saving AI Tokens, you can’t just look at “less input”, but also whether Claude Code keeps outputting results that are too long, too many, too complex, or exceed the needs of the task.

What you really need to look at is the cost of the complete workflow

What you really need to look at is not how many words you type, but:

How much did Claude Code read? How much did Claude Code read? How much repo did Claude Code do? How much inference did Claude Code produce? How much output did Claude Code produce? Did you use a high-priced model for simple tasks? Did you let small tasks enter a high-effort state?||This is the core of Claude Code AI Token cost control.

If the Repo and rules are not clean, the AI Token will continue to be wasted

Another key point is mentioned later in the video: Claude Code can easily make things too complicated, or even change them randomly. If it's not given clear rules, repo files, skills, or coding guidelines, it can over-engineer simple tasks.

Why over-modification wastes AI Tokens

When Claude Code makes simple things complicated, it will read more files, change more places, and generate more output. In the end, you will have to spend several rounds to fix it back. These back and forths will increase input tokens and output tokens, and eventually become rework costs.

A cleaner repo equals a lower AI Token cost

If the repo structure is clearer, the rules are clearer, and the skill files are more complete, Claude Code will be less likely to go off topic and over-design simple requirements. This not only makes the code quality more stable, but also makes AI Token consumption more controllable.

Claude Code’s complete logic of saving AI Token

This video is not a single technique, but a three-layer approach.

The first level is the management context. Use /clear to clear old tasks, use /compact to compress the previous text, and use /btw to deal with side branch problems. This layer mainly saves input tokens.

The second layer is to control inference and model costs.用 /effort low 处理小任务、用 /effort high 处理复杂任务、用 /model 选合适模型，这一层主要是在控制 AI Token 单价与推理成本。

The third layer is to make the repo and instructions cleaner. Don’t let Claude Code change randomly, over-design, or do too many things smoothly. This layer is mainly to reduce rework and prevent you from using more AI Tokens to fix additional problems caused by the AI itself.

Claude Code consumes too much AI Token. It is usually not unsolvable, nor can it be saved by typing less. The truly effective approach is to first manage the context, select the right model, adjust the effort, and avoid letting the AI read too much, think too deeply, and make too many changes. As long as the main tasks and side problems are mixed together, the tasks are small but high effort is used, simple modifications are made but high-priced models are used, and there are no clear rules for repo, AI Token costs can easily get out of control.

如果是用 Claude Code 这类 AI Coding 工具，比较实际的省法通常是：任务做完就 clear，同专案但对话过长就 compact，旁支问题用 btw，小任务用 low effort，复杂任务才开 high effort，模型依任务分流，不要全部都用最贵的。 This not only saves AI Tokens, but also makes the overall development process cleaner.

If you want to understand the basic concept of AI Token first, you can also go back to what is AI Token? Novices can understand why AI keeps mentioning Token.

Why is Claude Code so easy to burn AI Token?

因为它不只读你当下输入的文字，还可能一起读前面的 context、repo 内容、档案上下文与任务背景，所以 input token 通常比一般聊天工具更容易膨胀。

What is the difference between /clear and /compact?

/clear 是把前面的 context 清掉，适合新任务重新开始；/compact 是把前面内容压成摘要，适合同一个专案还要继续做，但不想再背整段冗长对话。

/btw Why can I save AI Token?

Because it can isolate side problems, prevent irrelevant content from remaining in the session memory of the main task, and prevent the context from becoming fat.

Do small tasks really need to adjust effort?

Required. If high effort is used for simple tasks, it will essentially allow AI to do unnecessary in-depth reasoning, and the cost of AI Token will naturally be wasted.

Claude Code Why is model selection so important?

Claude Code 選模型為什麼這麼重要？

Because the input token and output token unit prices and inference costs of different models are different. Using cheap models when the task is simple is usually more efficient than using expensive models for everything.

What is the difference between this article and general AI Token teaching?

This article is not a general discussion of what AI Token is, but is focused on the use case of Claude Code, AI Coding tools, Slash Command, Context management and model offloading, with the focus on actually saving Tokens.

Data source and credibility statement

This article summarizes the common AI Token cost issues in actual use of Claude Code, focusing on slash command, context management, model selection, and the impact of the Input Token / Output Token cost difference on the user experience.

For the function of Claude Code slash command in this article, please mainly refer to Anthropic's official Claude Code Slash Commands file, which can confirm the use of built-in commands such as /clear, /compact, /model; for model input token, output token and cache price, please refer to Anthropic's official Claude Pricing page.

In addition, if you want to further compare the price, speed and capabilities of different large language models, you can also refer to Artificial Analysis. The focus of this article is not to transcribe the video verbatim, but to organize the Claude Code Token logic mentioned in the video into practical teaching content that is easier to understand and more in line with the purpose of AI Token search.

This article belongs to the category "AI Token Usage Tutorial"

This category mainly organizes the actual use of AI Token, cost optimization, model division of labor and workflow design. It is suitable for readers who no longer just want to know what AI Token is, but are ready to actually use AI in programming, customer service, content, automation or team workflow.

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AI Token organizes the basic concepts, calculation methods, API fees and model comparisons of AI Token (word elements), and covers common models such as ChatGPT, Gemini, and Claude to help you establish clear understanding and judgment faster.

What should I do if Claude Code consumes too many Tokens? 3 tips to save you more than 50% of the cost