Evaluate AI models beyond the leaderboard

Two models sit close together on a leaderboard. One may suit your project because it respects an output format; another may investigate a difficult error better. An overall position does not identify the capability missing from your work.

A benchmark helps form a testable hypothesis. To become a product decision, that hypothesis needs tasks, tools and criteria representing your operation.

Read the measurement unit first

SWE-bench presents different variants and results based on resolved tasks. A score only makes sense alongside the dataset and environment used.

Find out whether the system received a prompt alone or access to files, a terminal and a browser. Check time limits, attempts, model version and rules for choosing the best answer. Several attempts should not be silently compared with one response.

Distinguish model evaluation from evaluation of a complete agent. The same model can behave differently with other instructions, context retrieval and tools. If these change together, the result describes the system rather than just the model.

Create cases that represent decisions

For a coding assistant, include bug fixes, small extensions, code reading and tasks that should stop for missing context or permissions. Include subtle failure cases, such as querying the wrong customer account.

Write the expected outcome before running the test. For a report export, check filters, ordering, columns, empty data and authorization. “The file was generated” is insufficient if it contains another customer’s information.