LLM Leaderboard
We evaluate leading AI models against real EscapeLife platform tasks. Every score reflects performance on our actual workflows, not generic benchmarks.
| Model | AI Voice | Yield Engine | Itinerary Building | Messaging | API Integration | Knowledge Synthesis | Avg |
|---|---|---|---|---|---|---|---|
1 Meridian 1.0 NativeEscapeLife | 92% | 91% | 93% | 91% | 89% | 92% | 91% |
2 Claude 4.6 Anthropic | 86% | 84% | 87% | 85% | 83% | 86% | 85% |
3 GPT-5.4 OpenAI | 85% | 87% | 84% | 83% | 88% | 84% | 85% |
4 Claude 3.7 Sonnet Anthropic | 81% | 79% | 83% | 80% | 78% | 82% | 81% |
5 GPT-4.1 OpenAI | 79% | 81% | 78% | 77% | 83% | 79% | 80% |
6 Gemini 2.5 Pro | 76% | 75% | 78% | 74% | 73% | 77% | 76% |
7 GPT-4o OpenAI | 73% | 72% | 75% | 74% | 76% | 73% | 74% |
8 Claude 3.5 Sonnet Anthropic | 70% | 68% | 72% | 71% | 67% | 71% | 70% |
9 Grok 3 xAI | 66% | 64% | 68% | 67% | 63% | 68% | 66% |
10 Gemini 2.0 Flash | 64% | 63% | 66% | 65% | 61% | 67% | 64% |
11 MiniMax 2.6 MiniMax | 62% | 61% | 64% | 63% | 60% | 64% | 62% |
12 Gemini 1.5 Pro | 59% | 58% | 62% | 60% | 57% | 63% | 60% |
13 Mistral Large 2 Mistral | 56% | 57% | 58% | 55% | 54% | 59% | 57% |
14 Llama 4 Scout Meta | 54% | 52% | 56% | 55% | 51% | 57% | 54% |
15 Llama 3.3 70B Meta | 51% | 49% | 54% | 53% | 48% | 55% | 52% |
16 Mistral Large Mistral | 48% | 50% | 51% | 49% | 47% | 52% | 50% |
Task Categories
AI Voice
Elara EVI persona calibration, voice configuration, conversation flow design and emotional tone matching.
Yield Engine
Dynamic pricing rule generation, demand-based rate logic, and revenue optimization recommendations.
Itinerary Building
Personalized guest itinerary generation from property context, local activity curation, and experience sequencing.
Messaging
Guest communication templates, sentiment-matched tone, escalation intent detection, and multi-channel adaptation.
API Integration
EscapeLife API usage accuracy, webhook implementation, SDK code quality, and error handling completeness.
Knowledge Synthesis
Property knowledge extraction, policy summarization, FAQ generation, and RAG retrieval quality.
Methodology
Models are tested on identical prompts across all six categories with no fine-tuning or system prompt optimization. Scores reflect 50 tasks per category using automated test suites plus spot-checked human review.
Task success rate
Did the model produce a working, correct output?
Hallucination rate
Did the model invent API endpoints or fields that don't exist?
Output quality
Scored by rubric for tone, structure, and completeness.
Context adherence
Does the model correctly use property knowledge via RAG?