# AI Model Selection Choosing the right model for a given task. The model landscape is broad and the right choice depends on the use case, not the hype cycle. ## Selection criteria - **Capability**: can the model actually do the task? Coding, reasoning, creative writing, and classification require different strengths. - **Cost**: [[AI Frontier Model|frontier models]] can be 100x more expensive than [[Small Language Models (SLMs)]]. See [[AI Cost Management]]. - **Latency**: real-time applications need fast inference. Smaller models and local deployment win here. - **Privacy**: sensitive data may require [[Running AI Models Locally|local models]] or private deployments. - **[[Context Window]]**: long documents need models with large context windows. - **Tool support**: agentic workflows need models with strong function calling capabilities. ## The model landscape - **[[AI Frontier Model|Frontier models]]**: best capabilities, highest cost, API-only. - **[[AI Open Weight Models|Open weight models]]**: strong and improving fast, can self-host, full control. - **[[Small Language Models (SLMs)]]**: fast, cheap, good enough for many tasks. Often the right answer. - **[[Running AI Models Locally|Local models]]**: maximum privacy and zero marginal cost after setup. ## Matching model to use case Simple classification, extraction, and formatting tasks do not need frontier models. [[Model routing]] enables dynamic selection: route each request to the cheapest model that can handle it. [[AI Evaluation]] before committing to a model avoids expensive mistakes. ## References ## Related - [[AI Frontier Model]] - [[AI Open Weight Models]] - [[Small Language Models (SLMs)]] - [[Running AI Models Locally]] - [[Model routing]] - [[AI Cost Management]] - [[AI Evaluation]] - [[Context Window]]