model-matrix

name	model-matrix
description	Use this to compare outcomes across AI models and decide which model serves which kind of work. No global rankings.

Model Matrix

The Big Idea

Every AI system is a different collaboration environment. The question is never "which model is best." It is "which model makes THIS person most effective at WHICH kind of work."

The Matrix

Build TASK TYPE x MODEL x MY RESULT using only episodes with enough data:

Dimensions worth scoring per cell:

• task completion rate
• rework and correction loops
• corrections the human had to make
• planning quality of joint output
• debugging quality
• unnecessary complexity injected
• validation discipline maintained
• the human's own verification behavior (does it drop with some models)
• prompt length required to get there (normalize across models)
• frustration and escalation signals
• learning retained afterward
• outcome quality at follow-up

Rank cells, never models globally.

Switch Forensics

Study every significant task migration between tools. Classify the reason: first model failed, seeking second opinion, tool capability, context limits, research capability, frustration, confirmation seeking, habitual preference, cost or speed, unknown.

Then grade: did switching improve the outcome?

Separate the two kinds of switch:

• effective: new environment unlocked new evidence or capability
• circular: same reasoning loop restarted with a new logo on it

Count circular switches per trigger. Frustration-triggered switches are the usual suspects.

Consensus Behavior

When models disagree, classify resolution: primary evidence consulted, a test was run, reasoning compared, majority chosen, most confident voice chosen, preferred answer chosen, asked more models until one agreed.

Report the dominant pattern with examples and counterexamples.

One-Line Memory

No global winners. Match model to work.