🔬 AINFT’s Billing Transparency — When AI Usage Becomes Verifiable, Not Guesswork
Most AI platforms hide the most important layer:
How you are actually charged.
You only see:
➜ “$20/month”
➜ “credits deducted”
➜ or a vague usage number
But you never see:
➜ how many tokens were consumed
➜ how much reasoning happened
➜ whether cache was used
➜ what you truly paid for
That’s Web2 logic.
Opaque. Black-box. Trust us.
AINFT takes the opposite approach.
It treats billing like on-chain data — fully observable, fully auditable.
And that changes user psychology completely.
Let’s break down why this design feels so different 👇
1️⃣ Deep Thinking — Reasoning Is Separately Metered 🧠
➜ Deep Thinking: 124 tokens
➜ Text Output: only 9 tokens
This detail is extremely important.
It proves the system is using:
➜ chain-of-thought
➜ reasoning steps
➜ internal inference cycles
Similar to:
➜ GPT reasoning models
➜ Claude advanced reasoning
➜ Gemini Pro logic passes
In most platforms, this creates a frustrating experience:
➠ “Why did I pay so much when the answer is only 1 sentence?”
Because the thinking cost is hidden.
AINFT does something rare:
It explicitly shows:
➜ how much the model thought
➜ not just what it said
So the cost becomes:
compute-based, not output-length-based
This is honest engineering.
You’re paying for brainpower, not word count.
2️⃣ Cache Status — Enterprise-Level Cost Logic ⚙️
➜ Uncached Input: 57 tokens
This means:
➜ the request did NOT hit cache
➜ full computation was required
➜ therefore full cost applies
But the implication is bigger.
If future queries:
➜ reuse context
➜ repeat prompts
➜ reference the same long document
Then:
➠ cache hits
➠ lower compute
➠ lower cost
This is exactly how:
➜ cloud computing
➜ database systems
➜ enterprise APIs
optimize expenses.
AINFT is applying infrastructure-grade billing logic, not consumer flat pricing.
That’s a very Web3 + engineering mindset.
#TRONEcoStar
#AINFT @Justin Sun孙宇晨