> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-3/pick-a-model.md).

# Recipe: pick a model for a task

Recipe — pick the right model for a task. Pointer to the canonical SDK quickstart + filtering sample.

The canonical pattern lives in the SDK:

* [`samples/core/quickstart.py`](https://github.com/layerlens/stratix-python/blob/main/samples/core/quickstart.py) — your first end-to-end evaluation in <30 lines
* [`samples/core/public_catalog.py`](https://github.com/layerlens/stratix-python/blob/main/samples/core/public_catalog.py) — browse and filter the public catalog
* [`samples/core/benchmark_evaluation.py`](https://github.com/layerlens/stratix-python/blob/main/samples/core/benchmark_evaluation.py) — run model vs. benchmark and retrieve results

## Stratix pattern

1. Browse the [Stratix Public catalog](/5.-select-pick-the-model/models-catalog.md) to shortlist 3-5 candidates.
2. Use `client.public.models.get(query=, name=, key=, categories=, companies=, regions=, licenses=)` to filter programmatically. Discover available filter values via `response.categories`, `response.companies`, etc.
3. Run a private evaluation against your dataset using `client.evaluations.create(model=, benchmark=)`.
4. Read the per-dimension result deltas, not just headline accuracy.

## Where to next

* [Use case: Model evaluation](/4.1-general-use-cases/model-evaluation.md)
* [Tutorial 1: First evaluation](/8.-evaluate-score-the-outputs/01-first-evaluation.md)
* [Compare models (Stratix Public)](/5.-select-pick-the-model/compare-models.md)