I have a DGX Spark at home and more ideas than time to test them. The question I keep coming back to is not which model is generally smarter. It is which model can do the particular job I need done. I think of that job as a seat: planner, retriever, implementer or critic. If a model cannot hold the seat, I want to know why before I decide what to change.
That gives me three things to ask of a benchmark.
- What fails, and when? A model may defer to authority, bury the important finding, fail to re-derive a result or drift over a long context. I want those failure modes made visible, not folded into a single score.
- Which model fits the job? Planning, retrieval, execution and review need different strengths. An overall leaderboard does not tell me which model I should put in each seat. A role-specific comparison needs evidence from the work that role will actually do.
- Where does the remedy belong? Fine-tuning, harness engineering, prompt and context distillation, and OPRO or promptbreeding-style search address different problems. Treating them as interchangeable is an expensive way to avoid diagnosing the failure.
What I want the result to tell me
- Whether the model passed the task it was actually given, rather than where it landed on a composite score.
- Which failure occurred, and the evidence that distinguishes it from the alternatives.
- What the result suggests changing next. The remedy should follow the observed failure, not the intervention I happened to want to try.