How to Choose the Right LLM for Your AI Agent

Before evaluating any model, write down exactly what the model needs to do. Is it classifying text into categories? Generating natural language responses? Calling functions and tools? Processing long documents? Extracting structured data from unstructured input? Each task type has different model requirements.

Classification and extraction tasks can use smaller, cheaper models. Complex reasoning and multi-step planning need larger models. Long-context tasks (processing entire documents or conversation histories) need models with large context windows. Match the model capability to the actual task, not to the most impressive demo on the vendor's website.

Never trust benchmarks alone. Build an evaluation set of 50-100 examples from your actual business data. Run each candidate model against the same examples and score the results. Compare accuracy, formatting consistency, tool calling reliability, and response quality.

I typically evaluate 3-4 models per project: one premium (Claude Opus or GPT-4), one mid-tier (Claude Sonnet or GPT-4o), and one budget (Claude Haiku or GPT-4o-mini). The mid-tier model wins about 70% of the time — good enough quality at reasonable cost. The premium model is only justified when the evaluation shows a meaningful accuracy gap.

Multiply the average tokens per request by your expected daily volume, then by the model's per-token price. Include both input and output tokens — output tokens typically cost 3-5x more than input tokens. A model that seems cheap per token can get expensive when your agent generates verbose responses.

Project costs at 1x, 5x, and 10x your current volume. The model that's affordable today might blow your budget when traffic grows. Consider whether caching, shorter prompts, or a smaller model for routine tasks could reduce costs without affecting quality.

Run 100 concurrent requests and measure p50, p95, and p99 latency. A model that responds in 2 seconds on a quiet API can spike to 15 seconds during peak hours. For real-time customer-facing agents, the p95 latency is what your users actually experience.

Test reliability over a 24-hour period. Track error rates, timeout rates, and response quality degradation. Some providers have significant quality variance during high-load periods. Your agent needs to work well during your busiest hours, not just during off-peak testing.

Build your agent architecture so the model is a configuration parameter, not a hardcoded dependency. Frameworks like LangChain and Vercel AI SDK support model switching with a single line change. This lets you upgrade to a better model when one launches, downgrade to a cheaper model when costs need trimming, or route different task types to different models.

Multi-model routing is increasingly common in production. Simple classification tasks go to a fast, cheap model. Complex reasoning tasks go to a premium model. The routing logic adds minimal latency and can cut costs by 40-60% compared to running everything on the expensive model.