Model Benchmarks is a comprehensive course designed to help you understand how AI models are evaluated and how to choose the right model for your engineering needs.

You’ll begin with the foundations of model benchmarking: why benchmarks matter, how datasets, run configurations, scoring methods, and aggregation shape a benchmark score, and how to apply a practical seven-step evaluation framework. You’ll also follow a live, code-based benchmark from start to finish.

The course explores benchmarks across knowledge, reasoning, mathematics, coding, and instruction following. You’ll learn what each domain measures, how to distinguish memorized answers from genuine problem-solving, and how to evaluate reasoning processes, executable code, structured outputs, and multi-turn instruction-following. You’ll also examine issues such as stale answers, data contamination, test-time compute, verifiers, and tool-assisted performance.

You’ll then move into long-context, agentic AI, multimodal, and safety benchmarks. Learn how to assess a model’s ability to retrieve and reason over long documents, use tools across multiple steps, understand images, video, and audio, and handle harmful requests safely without over-refusing legitimate ones.

Finally, the course covers benchmark leaderboards and model selection. You’ll learn how to interpret rankings, choose relevant benchmark families, audit evaluation results, and weigh performance against cost, latency, context length, privacy, deployment options, and tool support.

By the end of the course, you’ll have a practical framework for evaluating AI models and selecting one that fits your real-world requirements.

Course Duration: 19+ hours
Refund: 4 Days