How to Compare AI Models

Learning Objectives Evaluate AI models using a structured comparison framework Understand what benchmarks actually measure (and what they do not) Choose the right model for different tasks based on practical criteria The Comparison Problem Every AI company claims their model is "state of the art." Benchmark tables show tiny percentage differences. Marketing materials cherry-pick metrics where their model wins. How do you cut through this and make an informed choice? The answer: ignore the marketing and focus on what matters for your specific use cases. A model that scores 2% higher on a coding benchmark is irrelevant if you never write code. A model with a massive context window does not help if your longest document is 3 pages. The Six Dimensions That Matter Intelligence and Capability What the model can actually do — how well it reasons, writes, codes, and handles complex instructions. How it is measured: Benchmarks like MMLU (general knowledge), HumanEval (coding), SWE-bench (real-world software engineering), and various reasoning tests. What to actually care about: Try the model yourself with your actual tasks. Benchmarks are useful for broad strokes but do not capture whether a model works well for writing marketing copy, summarizing legal documents, or explaining concepts to your specific audience. Current leaders (mid-2026): General reasoning: Claude Opus 4.7, GPT-5.5, Gemini Ultra Agentic coding and computer use: GPT-5.5 (Terminal-Bench 2.0 82.7%, OSWorld 78.7%) Hardest end-to-end software engineering: Claude Opus 4.7 (SWE-bench Pro 64.3% vs. GPT-5.5's 58.6%) Creative writing: Claude models tend to be more natural; GPT…