A benchmark compresses a model’s behavior into a number. That makes comparison easy—but it also hides the prompts, trade-offs, and failure modes that shape everyday use.
Start with representative work
Collect 20 to 50 tasks that reflect the real mix of easy, normal, and difficult requests. Remove sensitive information, preserve the messy details, and record the result a skilled person would accept.
Score more than correctness
A useful scorecard can include factual accuracy, instruction following, clarity, citation quality, latency, and cost. Weight each measure according to its business impact. A fast, readable answer is still a failure if it invents the source.
- Run every candidate with equivalent instructions and settings.
- Hide model names from reviewers where practical.
- Record failures by type, not only as an average score.
- Repeat borderline cases to reveal inconsistency.
- Calculate cost per accepted result.
Use public results as context
Public benchmarks help explain broad strengths and track progress over time. Use them to build a shortlist, then let a small task-specific evaluation choose the winner. The closer the test resembles production, the more useful the result.
← Return to the model comparison