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How do I choose the right LLM for my business?

Quick Answer

Choose an LLM based on your specific use case requirements: capability level, speed, cost, data privacy needs, and deployment preferences. For complex reasoning tasks, use frontier models like GPT-4o or Claude Opus. For high-volume, simpler tasks, smaller models like Claude Haiku or GPT-4o-mini offer better cost-performance ratios. For maximum data control, consider open-source models deployed locally.

Summary

Key takeaways

  • Match model capability to task complexity rather than defaulting to the largest model
  • Smaller, faster models are more cost-effective for straightforward tasks
  • Consider data privacy requirements when choosing between cloud and local deployment
  • Test multiple models with your actual use case before committing

Key Selection Criteria

Selecting the right LLM involves balancing several factors. Capability determines whether the model can perform your task accurately. Frontier models like GPT-4o, Claude Opus, and Gemini Ultra excel at complex reasoning, nuanced analysis, and creative tasks. Mid-tier models like Claude Sonnet and GPT-4o-mini handle most business tasks well at lower cost. Speed matters for user-facing applications where latency affects experience. Smaller models respond faster, which is critical for real-time interactions. Cost scales with model size and usage volume. For processing thousands of documents daily, the difference between a large and small model can be substantial. Data privacy requirements may rule out cloud-based models for sensitive data, pointing you towards open-source options like Llama or Mistral that can run on your own infrastructure.

How to Evaluate Models Practically

Do not rely on benchmark scores alone. Build a test set of 50 to 100 representative examples from your actual use case, including edge cases and difficult scenarios. Run each candidate model against this test set and evaluate the outputs manually. Measure accuracy, consistency, format adherence, and handling of ambiguous inputs. Track costs per request and response latency. Test at the volume you expect in production, as some models degrade under load. Consider running the same evaluation quarterly, as model capabilities and pricing change frequently. Many organisations find that different models work best for different use cases within the same system, using a capable model for complex tasks and a faster, cheaper model for simpler ones.

Related

Related questions

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FAQ

Frequently asked questions

No. Using the most powerful model for every task is like using a lorry for every journey. Match model capability to task complexity. Many business tasks work excellently with mid-tier or small models at a fraction of the cost.

Review quarterly. The AI landscape moves fast, with new models and pricing changes regularly. Build your systems with abstraction layers that allow model swapping without major rework.

Yes, and this is increasingly common. Route simple tasks to fast, cheap models and complex tasks to more capable ones. This model routing approach optimises both cost and performance.

Create a test dataset of 50 to 100 representative examples with expected outputs. Run each candidate model against this dataset and score results on accuracy, format adherence, and relevance. Track latency and cost per request. Use human evaluation for quality assessment.

For user-facing applications where response time affects experience, speed matters more for simple tasks. For backend processing where accuracy drives business value, quality takes priority. Many systems use both: fast models for real-time interaction and capable models for complex analysis.

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