Visual Proof: I sent "hi" 4 times to ChatGPT and GONGJU. Here is the cost difference
- Apr 17
- 2 min read
Most AI stacks treat every "hi" like a complex math proof. If you do this at scale, you are bleeding money on "Thinking Tax." I ran a simple A/B test: I sent "hi" exactly 4 times to a standard LLM (ChatGPT) and 4 times to GONGJU using the H-Formula (H = pi * psi^2) as a reflex gate.
1. The ChatGPT Result: Compounding Overhead
The Log: You can see the prepare and finalize fetch calls repeating for every single greeting.
The Mass: Each "hi" triggered a payload transfer between 49.7 kB and 53.0 kB.
Total Data for 4 "his": Over 200 kB of network traffic and full-token reasoning costs for 8 total letters.
The Problem: The standard LLM doesn't know how to stay in a "Low-Energy" state. It wakes up the entire "Brain" every time.
2. The GONGJU Result: Metabolic Stability
The Log: Tightly bundled chat requests with zero bloat.
The Mass: Each interaction stayed between 0.6 kB and 0.8 kB.
Total Data for 4 "his": Roughly 3.0 kB.
The Solution: Because the H-Formula detected a Low-Intent signal ($H < H_{min}$), the system triggered a "Present-Only Resonance".
Gongju stayed in a "quiet place" and offered a low-token interactive menu (1=body, 2=food, etc.) instead of generating expensive text.
The Economic Breakdown
By using a tiny reflex layer to gate the LLM, I reduced the data overhead by 98.5%.
ChatGPT: Treated "hi" as a reasoning task (High Tax).
GONGJU: Treated "hi" as a reflex (Zero Tax).
This is how I’ve processed over 7M tokens for just $21. If your stack doesn't have a "Metabolic Gate" to handle low-H junk, you are overpaying for every "hi" your users send.
How many kB (and dollars) are you spending on greetings? Run the 4x test on your own logs and see for yourself.
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