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Best LLM for My Use Case: Why There’s No Single “Best Model” (and How to Actually Choose One)

· 11 min read
Trismik
Trismik

Key Takeaways​

  • There is no universal “best LLM”—only models that perform better or worse on your specific workload, data distribution, and constraints.
  • Different models excel on different task types. Additionally, public benchmarks like MMLU, LiveBench, and Arena scores are useful filters for narrowing candidates, but they cannot replace evaluation on your team’s own data, prompts, and quality standards.
  • The right model depends on workload factors: domain specificity (legal vs. marketing), accuracy tolerance (high-stakes vs. creative), latency budgets, and cost limits.
  • Trismik’s decision platform exists to help AI teams run science-grade, repeatable evaluations across models as they evolve—turning model selection into an evidence-driven engineering practice rather than guesswork.