While traditional IQ tests are designed to measure human intelligence across reasoning, problem-solving, and verbal skills, this approach fundamentally fails when applied to AI systems. LLMs function as "sophisticated pattern recognition systems trained on vast amounts of data to predict language sequences," not as thinking entities with genuine reasoning capabilities.
The Chatbot Arena Leaderboard
There's an evaluation platform where users compare LLM outputs in head-to-head competitions. Users input prompts, receive responses from two models, and select the better answer. The Elo rating system aggregates results.
Current top performers include:
- ChatGPT-4o-latest: 1338 Elo
- Gemini-1.5-Pro-002: 1304 Elo
- Meta-Llama-3.1-405b: 1257 Elo
- Mixtral-8x22b: 1148 Elo
Understanding Elo Differences
The formula used to calculate win probability is:
Win probability = 1 / (1 + 10^(rating difference / 400))
Key thresholds to understand:
- 70-point difference = 60% win probability
- 100-point difference = 64% win probability
- 200-point difference = 76% win probability
SynapseDX's Perspective
At SynapseDX, we focus on operational efficiency and decision routing, deliberately avoiding creative content generation and code debugging applications. We prioritize identifying cost-effective LLMs for specific tasks, noting our conclusions may not apply universally to all use cases.
Key Findings
Elo Evolution Within Models
Models improve over time through updates:
- ChatGPT-4o: 74-point increase over time
- ChatGPT-4: 88-point gain
Impact of Prompt Complexity
Our internal testing revealed something crucial: asking five questions individually versus simultaneously showed a performance difference exceeding 200 Elo points.
Academic research from Subbarao Kambhapati's team confirms that LLM performance degrades with complexity:
- Five-level depth problems: 100-150 Elo point decrease
- Ten-level depth problems: 400+ Elo point decrease
Recommendations
Based on our findings, we advocate for:
- Maintaining single focus per prompt - One question, one answer
- Targeting precision - Be specific about what you need
- Avoiding ambiguity - Clear prompts yield better results
Practical Implication
Precise prompting could enable downscaling from ChatGPT-4o (1338 Elo) to self-hosted Mixtral-8x22b (1148 Elo) while maintaining performance. This approach also improves auditability and error detection.
Conclusion
The real stakes are beyond the 34-point difference between ChatGPT and Gemini. In the end, the human impact remains predominant.
What matters isn't which model ranks highest on a leaderboard - it's how you use these tools. A well-prompted "lesser" model can outperform a poorly-prompted "superior" one. Focus on prompt engineering, task decomposition, and appropriate model selection for your specific use case.
For business applications, the 200-point Elo gap between focused and complex prompts is far more significant than the gaps between competing models. Master your prompting strategy, and model choice becomes secondary.