Without benchmarking LLMs, you're likely overpaying 5-10x
2 months ago
- #LLM
- #Cost Optimization
- #Benchmarking
- Benchmarking LLMs on specific tasks can save significant costs, as default choices like GPT-5 may not be the most cost-effective.
- Standard benchmarks don't accurately predict performance on specific tasks, necessitating custom benchmarks based on actual prompts.
- Creating a benchmark involves collecting real examples, defining expected outputs, and scoring responses with an LLM-as-judge.
- Quality, cost, and latency must be balanced when selecting an LLM, with Pareto Efficiency helping identify optimal models.
- Using tools like Evalry can automate benchmarking across 300+ LLMs, saving time and money by identifying better models for specific use cases.