Choosing an LLM isn't about finding the "best" model—it's about finding the best model for your specific use case. A chatbot needs different characteristics than a code completion tool or a document summarizer.
Start With Your Constraints
Before comparing models, define your requirements:
- Latency tolerance: Real-time chat needs sub-200ms time-to-first-token. Batch processing can wait.
- Budget: A hobby project and an enterprise product have different cost sensitivities.
- Context needs: Processing long documents requires 100k+ context. Simple Q&A works with 8k.
- Accuracy requirements: Medical or legal applications need higher accuracy than creative writing.
The Decision Framework
For Real-Time Applications (Chat, Autocomplete)
Prioritize latency and throughput. Users notice delays over 300ms. Look for models with sub-100ms latency and high tokens-per-second throughput. Smaller, faster models often beat larger ones for UX.
For Batch Processing (Analysis, Summarization)
Prioritize cost and accuracy. Latency doesn't matter when processing overnight. Optimize for the lowest cost-per-token that meets your quality bar.
For Code Generation
Prioritize code-specific training. Models trained on code repositories outperform general models. Look for models with "code" or "coder" in the name—they're optimized for programming tasks.
For Vision Tasks
Filter to multimodal models. Not all LLMs can process images. You need models that explicitly support image input.
Compare models by your priorities
Filter by speed, latency, price, and capabilities to find models that match your requirements.
Browse ModelsPractical Tips
- Test with real data. Benchmarks don't tell the whole story. Run your actual prompts through candidate models.
- Consider provider reliability. Some providers have better uptime than others. Check if a model is available from multiple providers for redundancy.
- Start cheap, upgrade as needed. Begin with a budget model. Only upgrade if quality is genuinely insufficient—you might be surprised.
- Watch for context limits. A model's stated context length is the max. Actual useful context is often lower due to attention degradation.