model-tierconditionalweight 2adapter 15 of 17Model Tier.
The Model Tier adapter classifies model subjects by parameter count into capability tiers. Larger tiers score higher, and because the tier is a stable property of the model rather than observed behavior, it applies only where model metadata is available and carries a substantial weight.
What it measures.
Parameter count
Published parameter count from model metadata.
Tier assignment
Mapping of parameter count onto the informative tier scale.
Component keys.
model-tier.*Component family covering the assigned capability tier.
When it counts.
Conditional — applies only to model subjects with known parameter metadata.
When signals are missing.
Models without parameter metadata, and non-model subjects, omit this component.
How it contributes.
{
"component": "model-tier.*",
"scale": "0-100",
"mode": "conditional",
"weight": 2,
"contribution": "component x 2 / total weight"
}The adapter’s components are averaged into the composite as a weighted mean: every contributing adapter’s component is multiplied by its weight, summed, and divided by the total weight of contributing adapters. The result is published as trustScores.total with a config version and timestamp so anyone can recompute it.
Questions about Model Tier.
How does the Model Tier adapter assign a score?
It maps the model’s published parameter count onto an informative capability tier and normalizes the tier into the model-tier.* component at weight 2.
Why does tier matter if behavior is measured elsewhere?
Tier is stable, hard-to-fake context that anchors the behavioral components; it is weighted substantially but still counts only for models with known metadata.