Without Benchmarking LLMs, You're Likely Overpaying 5-10x
Summary
The article argues that benchmarking LLMs is essential to avoid overspending on API costs, sharing a real-world case where an API bill dropped by 80% after testing 100+ models. It provides a practical, step-by-step approach to create your own benchmarks for tasks like customer support, including data collection, defining expected outputs, running models via OpenRouter, and using an LLM as a judge. It also promotes Evalry as a tool to automate benchmarking across hundreds of models and highlights the Pareto frontier concept to balance quality, cost, and latency.