How To Cut MCP Token Costs? Save Up To 92% At Scale With Code Mode 💎

Today, new artificial intelligence models are released every six months, such as GPT 5.6, Fable 5, Kimi K3 and many others.

And now the most active discussion in the technical community is the concept of tokens, which allows us to use LLM in our favorite IDEs such as Cursor.

Plans are quite expensive today and every request is important to the business. In this article, I would like to tell you how you can save your money using LLM using one tool.

Well, let's get started!

🖥️ Real example

Instead of words, let's look at one small example that I prepared for you:

Request 1 (2,350 tokens): "Fetch the data from the endpoint..."

↓ (Model reasons about what to do)

Request 2 (3,100 tokens): "Here's the response. Now transform it..."

↓ (Model reasons again)

Request 3 (2,900 tokens): "Now check these projects..."

↓ (Model reasons about results)

Request 4 (2,500 tokens): "Generate a summary based on..."

↓

Total: 10,850 tokens = $0.32 per execution

Now let's imagine that our entire business is built on these requests. The site can have as many users as you like, and each such request to LLM spends our money. Considering that the plans of providers like Claude, although not so expensive, are still a serious budget expenditure. You will have to think through a business model so that it makes a profit.

👀 What do I want to suggest in this situation?

If you have a small business and want to cut down on the costs of using tokens, I suggest you try

Code Mode is a transformative approach to using MCP that solves a critical problem at scale:

The Problem: When you connect 8-10 MCP servers (150+ tools), every single request includes all tool definitions in the context. The LLM spends most of its budget reading tool catalogs instead of doing actual work.

Instead of exposing 150 tools directly, Code Mode exposes just four generic tools. The LLM uses those tools to write Starlark that orchestrates everything else in a sandbox.

📊 Comparison

Since I have written many articles on the topic of Bifrost, you already know how to work with it. Let's try to apply what I wanted. First of all, here's what I want to do:

Default Mode:

Request 1: "Use the database tool to fetch data"

  → Model reasons, calls tool

  → Response: 2,450 tokens



Request 2: "Transform the result..."

  → Model reasons, calls tool

  → Response: 3,100 tokens



Total Requests: 4+ API calls

Total Tokens: 10,850+

Now working with the solution:

Code Mode:

Request 1: "Here's what I need. Write Python to do it."

  → Model writes code (single request)

  → Python executes all tools in sequence

  → Results fed back in one batch



Total Requests: 1 API call

Total Tokens: 850

If you think this is some kind of deception, then no. The reason here is very simple. Language models are good at writing code. Bad at reasoning step-by-step through tool chains.

Thanks for reading this article! ❤️

I'd love to hear your thoughts on this mode in the comments!