Stop guessing which model to use
There are dozens of capable models and the “best” one depends entirely on your constraints. This picker turns that into five questions and gives you a concrete recommendation plus the reasoning behind it — so you can move on and build.
How it works
The tool evaluates your answers as a decision tree, applying the most binding constraints first:
- Privacy is checked before anything else. If data must stay on-premise, hosted APIs are off the table and the tool recommends self-hosted open-weight models.
- Task type comes next — hard reasoning points to dedicated reasoning models, code to Claude 3.5 Sonnet, and very long documents to Gemini’s huge context window.
- Output type routes image generation and vision-input tasks to multimodal models.
- Budget and volume then break ties between similarly capable options, favouring cheaper small-flagship models when cost or scale dominate.
Each recommendation comes with a one-paragraph rationale so you understand the trade-off rather than blindly trusting an answer.
Tips for using the recommendation
- Treat the result as a strong starting point, not gospel — the runner-up named in the rationale is often nearly as good and may suit your existing stack.
- If you are cost-sensitive and high-volume, the small-flagship models (GPT-4o mini, Gemini 1.5 Flash) almost always win; only escalate to premium models for the requests that genuinely need them.
- Validate the cost implication with the LLM Pricing Calculator before committing to a model at production volume.