> 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/5.-select-pick-the-model/workflow.md).

# Select (workflow)

Stage 1 — Select. Review leaderboards and benchmarks, optionally benchmark candidates side-by-side, pick the model to build with.

Stage 1. Before you instrument or evaluate anything, **select** the model you'll build with. The choice should be evidence-based — leaderboards, benchmark coverage, side-by-side scores against your target capabilities.

## The question this stage answers

*"Which model should I use to build this feature?"*

## What to do

1. **Browse the leaderboard.** Start at the [public leaderboard](/5.-select-pick-the-model/leaderboard.md) for an overview of how frontier and cost-optimized models are ranked across capabilities.
2. **Review the benchmarks catalog.** Open the [benchmarks catalog](/5.-select-pick-the-model/benchmarks-catalog.md) and pick 3–5 benchmarks that match your task — code, math, reasoning, multimodal, multilingual, instruction-following. More benchmarks doesn't mean more signal.
3. **Look at per-benchmark scores.** Each benchmark's page shows every model's score, a top-N leaderboard, and the score-history-over-time. Don't trust a single headline number — look at the dimensions that matter to your task.
4. **Optionally, run a side-by-side comparison.** Use [Compare models](/5.-select-pick-the-model/compare-models.md) to put two or three candidates head-to-head across the benchmarks you care about. Public-catalog scores narrow the shortlist; private evaluations later in the workflow will decide.
5. **Match capability to constraint.** Frontier vs cost-optimized; latency budget; context-window need; licensing constraint; vendor-lock-in tolerance.

## What you'll have at the end

* A shortlist of 1–3 candidate models for the feature
* Documented basis for the choice (which benchmarks, which scores, which trade-offs)
* A baseline expectation of how the candidates should perform on your actual data — useful later when you compare against your private evaluations

## Public-catalog selection vs custom benchmarks

The Stratix Public catalog covers the broad capability surface. **For domain-specific selection** — pricing in your tariff, citing your jurisdiction, summarizing your documents — public benchmarks narrow the field but they won't decide. Premium tier lets you author **custom benchmarks** built from your own data and run the same candidates against them. The selection narrowed by Public gets confirmed by a domain-aligned private benchmark.

## Common patterns

* **Frontier vs cost-optimized** — pick the smallest model that meets your bar on the dimensions that matter
* **Provider against provider** — knowing your second-best protects against vendor lock-in
* **New generation drop** — when a model family upgrades, re-run the same selection exercise to confirm or replace your incumbent
* **Capability-specialized choice** — code task → HumanEval / MBPP weight heavier; long-context task → needle-in-haystack benchmarks weight heavier

## Where to next

* [Build →](/5.-select-pick-the-model/workflow.md)
* [Stratix Public — Leaderboard](/5.-select-pick-the-model/leaderboard.md)
* [Stratix Public — Benchmarks catalog](/5.-select-pick-the-model/benchmarks-catalog.md)
* [Stratix Public — Compare models](/5.-select-pick-the-model/compare-models.md)
* [Stratix Premium — Benchmarks (custom)](/5.-select-pick-the-model/benchmarks.md)
* [Use case: Model evaluation](/4.1-general-use-cases/model-evaluation.md)