How to *Dramatically* Reduce Your Token Costs

How To Guides
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I just watched Nate drop a masterclass on why your AI bills are about to explode—and how to stop it cold. With Claude Mythos and the next GPT/Gemini drops coming in the next month or two, frontier models are getting way more expensive. Trained on those beastly GB300 chips.

The smart play? Stop burning tokens and blaming the model.

Here’s the 8–10x Difference

A sloppy pipeline can eat $8–10 per session. Clean it up and the same work drops to about a buck. That swing scales fast across a team.

Frontier AI can be stupid cheap if you’re not stupid with tokens.

Rookie Mistake #1: Raw PDFs and Messy Files

Drag in three 1,500-word PDFs and the model sees 100k+ tokens of headers, footers, and binary junk. Convert to clean markdown first (takes 10 seconds) and you’re down to 4–6k tokens.

Pickaxe Knowledge Bases make this automatic—upload once and it indexes only the good stuff. No more token tax every time your Pickaxe queries.

Rookie Mistake #2: Conversation Sprawl

Those 30-turn marathons in one chat compress your instructions and waste tokens on every reply.

Start fresh every 10–15 turns. Do research in separate threads (Grok for X chatter, Perplexity for search), then bring the cleaned-up gold into one focused Pickaxe run. Context stays lean.

The Sneaky Plugin Tax

Unused connectors and plugins can load 50k tokens of junk before you type the first word.

In Pickaxe, only enable the Actions you actually use. Audit them weekly. Same with your Instructions—prune anything from 2025 that smarter models no longer need.

Advanced Move: Route Models Like a Pro

Use the heavy model for reasoning only. Switch to faster/cheaper ones (Sonnet or Haiku equivalents) for formatting and polish inside the same Pickaxe. One-click switching makes it easy.

Cache stable stuff—system prompts, tool definitions, reference chunks. Prompt caching is basically free money in 2026.

Try the Grok Models (I had to sorry)

They are a lot faster and smarter than you would think. And Grok 4.20 and 4.1 are really cheap.

Five Commandments for Agent Pickaxes

  1. Index references—never dump full docs.
  2. Pre-process everything (markdown, summaries, chunks).
  3. Cache the stable bits.
  4. Scope context to exactly what that agent needs.
  5. Measure every call so you actually improve.

Pickaxe already gives you the tools—auto-retrieval instead of dump-and-search, model switching per step, and clean Knowledge Base guardrails. But you still have to be intentional.

What’s the biggest token eater in your Pickaxes right now? Drop it below.

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Have you managed to lower token amount regarding user loaded documents? For me the big spender is document uploads that can have HUGE amount of text in image (scanned documents) or text overall in PDFs.

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@thomasumstattd: Thanks for sharing this :slight_smile:

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This was something I was hoping to bring up in today’s office hours. If there was a way Pickaxe could convert PDFs to .MD files before passing them to the models, that would be a massive token saver. There are a lot of packages that can do this like this one. So it is just a matter of making it an option.

It should be an option though. Sometimes the images are important for the pickaxe. I have a web page scanner that looks over a pdf screenshot. That would break if converted to a markdown file.

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