Choosing between LLM providers and models requires objective evaluation beyond marketing claims. Public benchmarks provide baseline comparisons, while custom evaluations assess performance on your specific use cases. Combining multiple evaluation approaches builds confidence in model selection.
Public Benchmarks
MMLU tests broad knowledge across academic subjects. HumanEval measures code generation capability. MT-Bench evaluates multi-turn conversation quality. While useful for initial filtering, benchmark performance doesn't guarantee suitability for specific applications.
- Use benchmarks for initial model shortlisting, not final selection
- Test models on your actual use cases with representative examples
- Measure latency and throughput alongside quality metrics
- Evaluate cost per request for realistic usage patterns
- Consider consistency—run tests multiple times to assess variance
Custom Evaluation Frameworks
Build evaluation datasets representing your actual queries and expected outputs. Automated metrics like BLEU, ROUGE, and semantic similarity provide scalable measurement. Human evaluation remains essential for subjective quality assessment. Continuous evaluation catches model degradation over time.