How do I evaluate an AI model's reasoning ability?
What to test
Reasoning ability is about how well a model can draw conclusions, solve problems, and make decisions based on given information. To evaluate it, use tasks that require logical inference, such as math word problems, syllogisms, or planning tasks. Also test with ambiguous or incomplete information to see if the model asks clarifying questions or makes reasonable assumptions.
It's important to use tasks that are not likely in the model's training data, to avoid testing memorization instead of reasoning. For example, create novel puzzles or use recent events.
How to assess
Don't just look at final answers—ask the model to show its work. A good reasoning model will provide a step-by-step explanation that you can follow. Check for logical consistency: does it contradict itself? Does it handle edge cases?
Also consider robustness: does the model's reasoning hold up when you rephrase the question or add irrelevant details? And test for bias: does it make assumptions based on stereotypes? Finally, compare multiple models on the same set of tasks to see relative strengths.
- Use novel problems, not textbook examples
- Ask for step-by-step reasoning
- Check for consistency across variations
- Test with ambiguous inputs
- Compare multiple models side-by-side
Common mistakes
- Confusing fluency with reasoning—a model can sound confident but be wrong.
- Relying solely on benchmark scores, which can be gamed or outdated.
- Ignoring the model's ability to say 'I don't know' when it lacks information.
