AI Model Selection
Process of choosing the right AI model for a specific task based on capability, cost, latency, and deployment constraints.
Category: AI
Tags: ai, decisions, strategies, models
Explanation
AI Model Selection is the process of 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**: frontier models can be 100x more expensive than small language models. Cost management is inseparable from model selection
- **Latency**: real-time applications need fast inference. Smaller models and local deployment win here
- **Privacy**: sensitive data may require 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
- **Frontier models**: best capabilities, highest cost, API-only
- **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
- **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. Evaluating models before committing avoids expensive mistakes.
The model landscape changes rapidly. A model that was state-of-the-art six months ago may now be outperformed by something cheaper and faster. Regular re-evaluation of model choices is essential.
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