AI Model Leaderboard: Intelligence, Speed, and Price Comparison

Artificial Analysis evaluates hundreds of AI models using a proprietary Intelligence Index and various performance metrics like speed and latency. Claude Opus 5 and GPT-5.6 Sol lead in intelligence, while models like Mercury 2 and Gemini Flash-Lite offer superior speed and low latency. The platform helps developers choose models by visualizing the trade-offs between cost, quality, and technical capabilities.

Key Points

• Claude Opus 5 and GPT-5.6 Sol are the top-performing models in terms of intelligence and complex reasoning.
• Mercury 2 and Gemini 3.5 Flash-Lite are the industry leaders for output speed and low-latency performance.
• The Intelligence Index v4.1 uses a multi-faceted evaluation approach including agentic work, coding, and scientific reasoning.
• Open-weights models are competitive, with GLM-5.2 (max) currently ranked as the most intelligent non-proprietary model.
• Pricing and cost-per-task vary significantly based on token types, including input, output, and prompt caching discounts.

Sentiment

Skeptical and frustrated; while users acknowledge the technical progress, they are increasingly annoyed by restrictive safety filters and the gap between benchmark scores and real-world reliability.

In Agreement

• Opus 5 represents a generational leap in intelligence for creative tasks like game development.
• OpenAI's Sol (GPT-5.6) is impressively cost-effective and token-efficient compared to Anthropic's models.
• The AA-Omniscience Index provides a valuable measure of knowledge reliability and hallucination rates.
• Gemini 3.1 Pro is highly competitive for general knowledge and image analysis tasks.

Opposed

• Single-metric leaderboards are becoming meaningless for end-users because model performance is highly task-dependent.
• Anthropic's aggressive censorship and safety guardrails make their models 'compromised' and unreliable for professional scientific or technical work.
• Benchmarks do not accurately reflect real-world 'sloppiness' or the inability of models like Sol to follow instructions in complex codebases.
• The high cost of frontier models is only justifiable if they eliminate the need for manual architecture and planning, which they often fail to do.