1. What is a benchmark?
A benchmark is a standardised test where many models solve the same set of questions or tasks. The goal is to compare them under similar conditions.
For example, a reasoning benchmark might ask multiple-choice questions and measure how often a model picks the correct answer.
2. Accuracy is not the whole story
When you see “94% accuracy” vs “91% accuracy”, the higher score is usually better. But the difference might be:
- Very visible in some tasks.
- Almost invisible in casual day-to-day usage.
Benchmarks also focus on a specific type of problem. A model can be strong on one benchmark and weaker on another one.
3. What about “reasoning” benchmarks?
Reasoning benchmarks try to measure how well a model follows logic, solves puzzles, or answers multi-step questions. They are useful, but still limited:
- Real conversations are messier than benchmark questions.
- Prompting technique can change results a lot.
- Benchmarks sometimes lag behind new capabilities.
4. How to use scores in practice
You can think of benchmark scores as:
- A signal of general capability level.
- One input into your decision, not the only one.
- More useful for technical teams than casual users.
For most people, it is more important to test a model on their own real tasks (emails, documents, code, workflows) than to focus only on benchmark tables.