12 Comparing and Selecting LLM Models
This chapter covers
- The four major open-weight model families: Llama, Gemma, Qwen, and Mistral
- How model size affects quality, speed, and memory requirements
- Which models work best for different languages
- Switching models in your Streamlit chatbot with a single line of code
- The February 2026 revolution: frontier open models that match proprietary systems
When you first visit the Ollama model library, the sheer number of available models can feel overwhelming. Dozens of names, cryptic version numbers, parameter counts ranging from half a billion to hundreds of billions. How do you choose?
This chapter gives you a practical framework for understanding and selecting LLM models. By the end, you will know the major model families, understand the trade-offs between size and quality, and be able to switch between models in your chatbot with ease.
Note
Model names and rankings change quickly. Treat the tables in this chapter as a snapshot and learn the model-checking workflow too: run ollama list to see what you have, run ollama pull <model> to download a model, and check the Ollama model library for the latest tags before choosing one.