📖 Saving Tokens series (four parts): Part 1: Why your AI gets dumber the more you chat · Part 2: Don’t boot up a supercomputer if an abacus will do · Part 3: Setting up a clean desk for your AI · Part 4 (this post, the finale)

We’ve arrived at the final post of the series. Over the first three parts, we went from “why your AI gets dumber the more you chat” all the way to “when not to use AI” and “how to clean up its desk.” This post is about the moment you actually spend the money: how to make every penny count.

Thing 1: Not everything needs the smartest brain (but what you save is so you can afford the good one)

AI models come in tiers. Take Claude for example: from cheap to expensive, it’s roughly Haiku, Sonnet, Opus. The price difference is huge.

If you pay by usage, the most straightforward way to save is “assigning the right tier to the right job”: for chores like changing formats, renaming files, or simple sorting, just hand it over to the cheapest Haiku. Use the middle-tier Sonnet for everyday writing and looking up information. Only deploy the most powerful Opus for tough tasks that truly require deep reasoning.

There’s a misconception a lot of people have that I want to specifically mention here: “looking at an image” doesn’t mean “using the most expensive model.” When you just want the AI to “look at an image, recognize what’s in it, and read the text on it”—a recognition task—the cheapest Haiku is usually entirely sufficient, and it’s ridiculously cheap. It’s only when you need it to “understand a complex medical diagram or interpret the logic behind a chart”—a visual task requiring reasoning—that it’s worth upgrading. The real standard for picking a model is “how much thinking does this task need,” not “does it need to look at an image.”

Though honestly, these days I mostly just use the most powerful Opus directly, XD.

Why? Because I found that under the current subscription plan, after putting in all the effort to save tokens from the first three posts, I actually don’t run out of my quota, and the quality of the most powerful model is noticeably better. So for me, rather than penny-pinching over which tier to use for every little thing, it’s better to take the saved quota and let the best model do everything.

I think this might actually be the one thing I wanted to say most in this whole series: saving tokens is never about being stingy. I cut out the waste, hand off what should be handled by scripts to scripts, and keep the desk clean. The savings I get from doing this are exactly what let me afford to “use the best brain on every problem I care about.” Saving is so you can spend generously where it counts.

Two more advanced little tricks before we move on:

  • Make the AI cut the fluff; you can do this once and for all. AI “output” is several times more expensive than “input” (up to five times for Opus), so asking it for concise answers saves money directly. And you don’t need to repeat this every time. Just write it into its long-term instructions (that’s the resident config file from Part 3, or “custom instructions” in chat apps), telling it to get straight to the point by default and avoid long essays. Set it once, save every time after.
  • Thinking depth is adjustable, too. The same model can be configured for “how deep to think”—keep it shallow for simple things, and only turn on deep thinking for hard things. The point is always: spend your effort where it’s truly hard.

Thing 2: Toss noisy chores into the room next door

Some jobs spit out a ton of process messages: running a round of tests, scraping a long document, or processing a pile of logs. If you let all this noise pile up in the main conversation, the desk gets flooded immediately (going back to Part 3, the moment a desk gets messy, it gets expensive and dumb).

My approach is to send a “clone” to do this in the room next door. The clone has its own independent workspace. All of its processes, noise, and drafts stay in that room, and only the final conclusion comes back to my main conversation.

It’s like asking an assistant to research data all day—you don’t need to see every page they flipped through, you just want them to hand you a one-page summary at the end.

But there’s a trade-off here, I have to be honest: deploying a clone costs money in itself, and the clone burns through a round of tokens on its own. The official docs even warn that workflows using clones heavily can cost several times more than flying solo. So the rule is: only deploy a clone when the value of “keeping the main desk clean” outweighs the cost of “hiring an extra clone.” Don’t toss everything to a clone.

(From start to finish in this post, you’ll notice that saving tokens doesn’t have a single brainless trick; every move is a trade-off. That’s exactly what makes it fun.)

Thing 3: Install a dashboard that runs a meter for me

After talking about all these ways to save, the final piece of the puzzle is: you have to see how much you’re spending.

Invisible expenses are the most dangerous. So I installed a little open-source tool called cc-budget (developed by boyand, you can find it on GitHub).

At the very bottom of my workspace, it keeps a persistent dashboard, telling me in real-time:

  • What percentage of my quota I’ve used in the past 5 hours and 7 days
  • Roughly how much this current job cost
  • And a “pace marker” that tells me if I’ll overspend burning at this current rate

When I get close to my usage cap, it pops up to remind me: it’s time to lower the firepower, or just take a break and wait for the quota to recharge. It won’t forcefully block me (a hard block interrupts work); it’s a dashboard that watches the meter and taps me on the shoulder at the right time.

Going a step further: letting the AI watch the dashboard while it works

But the most interesting use for this dashboard actually isn’t for me to look at.

I often just tell Claude one thing directly: “You watch the usage, work at an on-pace rhythm, and use up to about 95% of this period’s quota.”

Meaning, I hand budget awareness directly over to the AI, letting it watch the dashboard and adjust its working firepower on its own:

  • When the quota is still loose, go for it—you can fire up the more expensive models, deploy more clones, and think a bit deeper.
  • The closer we get to the cap, automatically dial it back: drop the thinking depth, deploy fewer clones, and save the final effort for the most important part, wrapping up exactly at 95% of the budget without wasting or blowing past it.

This is equivalent to letting the AI hit the gas and the brakes itself, rather than me standing by manually shouting “slow down, slow down” the whole time.

And this, actually, is exactly the microcosm of this whole series: the highest level of saving tokens is making this awareness automatic, not relying on you to remember it every second.

The whole series, converging into one principle

Four posts in, if you only take away one sentence, I hope it’s this one:

Don’t make your AI reread and redo a bunch of stuff it doesn’t actually need.

  • For folks chatting with AI on their phones: start a new chat when you change topics, don’t just dump long text in, and speak clearly.
  • For heavy users like me who bring AI into their life and work: streamline resident configs, leverage caching, use scripts instead of AI when you can, pick the right tier of brain, deploy clones to keep the desk clean, install a dashboard to watch expenses, and even let the AI spend the budget on pace by itself.

Did you notice? These two lists are actually doing the exact same thing.

Because whether it’s in the chat app on your phone, or in the tools running a bunch of clones on my computer, the underlying physics are the same: every time the AI answers, it has to reread everything in front of it, and the cost grows exponentially.

So saving tokens is never about being stingy. It’s about letting your AI be fast, cheap, and smart on the problems that truly matter to you.

Thanks for reading all the way here. If this series has been useful to you, feel free to share it with friends who are also using AI 🙌

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⬅️ Previous post: Setting up a clean desk for your AI 🔁 Back to Part 1: Why your AI gets dumber the more you chat

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