Choose the right model and keep up without wasting time

What you'll see in this lesson

1. Each benchmark measures one thing, not how “smart” it is

   A benchmark is a standardized exam: before trusting a number, check what tasks it contains and whether they resemble yours.

2. Contamination and saturation: when the exam stops working

   If a test's questions are on the internet, they can end up in training data and the model passes from memory.

3. The fine print: effort, attempts and cost

   The same score changes depending on the reasoning level, the tools and the number of attempts the model is allowed.

4. Arenas: thousands of people voting blind

   On Arena (formerly LMArena) you write a prompt, two anonymous models answer and you vote for the better one; millions of votes produce a ranking.

5. Artificial Analysis: intelligence, price and speed

   Artificial Analysis runs its own tests on models and publishes an index alongside cost per task and real-world speed.

6. Your own exam: twenty real cases

   The most reliable thing is a mini evaluation set with real tasks from your work and what you expect from each one.

7. Run the exam on several models at once

   With an evaluation tool you run the same cases against several models and compare accuracy, cost and time.

8. A 30-minute weekly routine

   You don't need to read everything: a fixed slot each week and a decision log are enough to keep up.

9. Summary: choose with data, not headlines

   Rankings help you build a shortlist; your own exam with real cases makes the decision.