IBEN DIGITAL / AI Efficiency Engine
How much does AI intelligence cost?
Token Lab turns tokens into a measurable working unit: cost, context, compute, energy and value. The calculator works locally; uncertain market data stays visibly uncertain.
Core doctrine
Send the smallest useful context to the model, then measure what the task actually created.
INDEX FIRST — RAG SECONDSend the smallest useful context to the model, then measure what the task actually created.
01 / Signal board
Token dashboard.
Only derived values from the calculator or verified source records should become numbers. The rest is deliberately marked as unknown.
02 / Measure
Token calculator.
Enter the values from a real model call or an internal estimate. Prices are expressed per one million tokens.
Derived cost
Waiting for inputs.
Cost / request—Input + output.
Tokens / request—Input + output.
Cost / day—Normalized from frequency.
Cost / month—30-day normalization.
Cost / year—365-day normalization.
Weighted / 1M—Based on the input/output mix.
Input share—Share of total tokens.
Output share—Share of total tokens.
03 / Meaning
Token economy.
There is a useful economic analogy between compute credits and scarce resources. There is no physical or legal equivalence between an AI token and a crypto token.
AI token
≠ crypto token
An AI token is a unit used by a model to process input or produce output. A crypto token is a digital asset recorded on a distributed ledger or another token system. One measures language-model workload; the other represents ownership, access, value or rights according to its design.
A service can denominate access to compute without pretending that a language token is money.
GPU, TPU, NPU, memory, networking and energy turn inference into an operating cost.
Moving inference closer to the user changes the cost structure, latency, privacy and hardware burden.
Future systems may tokenize access to compute or capacity. That remains a separate economic layer.
ENERGYElectricity and cooling.
HARDWAREGPU · TPU · NPU · memory.
COMPUTEMatrix operations and inference.
TOKENSModel processing units.
AI OUTPUTText, code, decision support.
VALUETime, revenue or capability.
04 / Experiment
Value per token.
IBEN DIGITAL metric under experimentation. It becomes meaningful only when task value, token usage and the comparison baseline are declared.
Value / token
A compact measure for comparing tasks with a declared value model.
task value ÷ total tokens consumed
Experimental · not an official standardToken ROI
1
Measure AI costUse a dated price record or label the value as an estimate.
2
Estimate human time savedDeclare the baseline task, hourly value and quality threshold.
3
Compare with cautionROI is a decision aid, not a universal property of a token.
05 / Reduce waste
Context efficiency.
INDEX FIRST — RAG SECOND: identify the relevant documents in a compact index before retrieving and sending full context to a model.
Context selection
Use token counts from an actual retrieval step. This local view never claims that a smaller context is automatically better.
Selected context / total base
Total base —Selected —
Reduction—
Efficiency—
Docs—
06 / Compare
OPEN MODEL JSON ↗Model comparison.
The interface reads model records from a separate JSON registry. No provider or price is hardcoded into the architecture.
| Model | Input / 1M | Output / 1M | Context window | Source status |
|---|---|---|---|---|
| Loading verified records… | ||||
07 / Hardware frontier
Photon → token.
Photonics may change how computation moves through hardware. It does not turn a photon into a language token.
One photon is not one token.
Physical layer: photons, electrons, semiconductor materials, optical interconnects and energy transfer.
Compute layer: matrix multiplication, memory movement, accelerator architecture and inference.
Language layer: tokenization, model context and generated output.
The relationship is a possible efficiency chain — not a one-to-one physical equivalence. “Photon → token” is an economic and systems lens, not a unit conversion.
ElectricityPowering chips, memory, networking and cooling.
PhotonicsPotentially reducing data-movement or computation costs in some architectures.
AcceleratorHardware executes the operations required for inference.
InferenceThe model converts context into probabilities and output tokens.
08 / Integration
OPEN CONTRACT JSON ↗Prompt Lab contract.
Ready for the next layer.
Prompt Lab will send optimization metrics here. The contract is deliberately small so Octopus IA can route it without loading a deep knowledge base.
{
"original_tokens": 0,
"optimized_tokens": 0,
"token_reduction": null,
"estimated_cost": null,
"model": null,
"prompt_efficiency_score": null
}Source policy.
Verified priceMust include source URL and date.
Energy estimateMust expose workload and measurement method.
Experimental valueMust identify baseline and assumptions.
Pricing documentation ↗External source · verify before loading.Energy & AI reference ↗Context source · not a per-token meter.