There is no single best model. The right choice depends on your task, constraints and budget.
Start From the Task
Classification and extraction often work well with smaller, cheaper models. Complex reasoning, long documents, coding and agentic tasks tend to benefit from larger, more capable models.
Factors to Weigh
- Quality on your task: measured on your own examples, not just public benchmarks.
- Cost: priced per input and output token; estimate at your real volume.
- Latency: time to first token and total response time matter for interactive use.
- Context window: how much text it can consider at once.
- Modalities: text only, or images, audio and documents too.
- Tool use and structured output support.
- Data handling: where data is processed, retention, and whether it's used for training.
- Hosting: API, cloud provider, or self-hosted open-weight model.
- Licence: open-weight models vary in what they permit.
Build a Small Evaluation
Collect 50–100 representative inputs with good answers or grading criteria. Run candidate models on them and compare quality, cost and speed side by side. Revisit as new models are released.
Use More Than One Model
Many systems route simple requests to a small model and hard ones to a larger model, balancing cost and quality.
Plan for Change
Models are updated and retired. Keep prompts and evaluations versioned so you can re-test quickly when switching.