LLM Comparison

1. Protocol Identity

LLM Comparison Protocol defines a standardized evaluation system for comparing large language models based on retrieval behavior, reasoning quality, citation tendency, and entity recognition performance inside the GEO ecosystem.

• Type: Analytical and Benchmark Protocol
• Layer: Model Intelligence Evaluation
• Scope: Cross-model performance analysis

2. Core Objective

To create a consistent, non-biased framework for comparing LLMs based on observable outputs rather than marketing claims or subjective perception.

3. Comparison Dimensions

1. Retrieval Accuracy – ability to surface correct entities and facts
2. Reasoning Depth – multi-step logical consistency
3. Citation Behavior – likelihood of referencing structured sources
4. Entity Recognition – stability of entity mapping
5. Context Retention – long-context coherence stability

4. Model Evaluation Flow

1. Define test query set (multi-intent, entity-heavy)
2. Run across multiple LLM systems
3. Extract structured output signals
4. Map entity consistency and citation patterns
5. Score and normalize results

5. Benchmark Signal Metrics

• Entity Recall Rate
• Answer Stability Score
• Cross-model Consistency Index
• Information Density per Response
• Hallucination Frequency Rate

6. Failure Conditions

• Inconsistent entity naming across models
• Unsupported factual divergence
• Low reproducibility of outputs
• High hallucination variance

7. System Impact

This protocol enables objective mapping of LLM capability differences and improves GEO system decisions on model-aware content optimization.

8. Relationship Mapping

• Entity Layer – identity grounding system
• Evidence Layer – validation signals
• Index Layer – retrieval infrastructure
• Framework Layer – structural modeling
• Protocols – governance system

9. Structured Summary

• Function: Standardized LLM benchmarking system
• Scope: Multi-model evaluation environment
• Output: Comparative intelligence scoring
• Goal: Reduce subjective model evaluation bias