Pick a model by testing candidates on your own task against real cost and latency budgets, instead of trusting benchmarks or defaulting to the largest. Use when choosing an LLM, cutting inference cost, or considering a switch.
--- name: choose-a-model-for-the-job category: ai-agents description: >- Pick a model by testing candidates on your own task against real cost and latency budgets, instead of trusting benchmarks or defaulting to the largest. Use when choosing an LLM, cutting inference cost, or considering a switch. --- # Choose a Model for the Job Two failure modes, equally common: sending everything to the largest available model, and picking whatever topped a leaderboard last month. Both skip the only question that matters — **how does it do on your task, at your budget?** ## Start from the constraints Write these down before looking at any model: - **Quality bar.** What does "good enough" mean, and how would you measure it? - **Latency budget.** Interactive chat needs a fast first token. A nightly batch job does not care. - **Cost ceiling.** Per request, and at your expected volume. - **Privacy.** Can this data leave your infrastructure at all? - **Context needs.** Realistically, how much input? Constraints usually eliminate most of the field before you test anything, which is exactly the point.
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