> 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/models-and-benchmarks.md).

# Models and benchmarks

Models and benchmarks — the building blocks of the Stratix catalog.

The two primitives that anchor every Stratix evaluation.

## Model

A specific LLM identified by `provider/model-name` (e.g., `openai/gpt-4o`, `anthropic/claude-opus-4-7`). In Stratix:

* Public models live in the global catalog (200+)
* Private models are BYOK custom models in your org

Each model has metadata: provider, capabilities, context window, modalities, licensing, pricing, score history.

### Why it matters

The model is the substrate. Picking the wrong substrate is the most expensive AI mistake.

## Benchmark

A standardized dataset and grading procedure. In Stratix:

* Public benchmarks live in the catalog (52+, e.g., MMLU, HumanEval, GSM8K)
* Private benchmarks are datasets you upload to your org

Each benchmark has metadata: capability tag, sample size, methodology, license, score history.

### Why it matters

Benchmarks are how you make "is the model good?" measurable. Pick benchmarks whose distribution matches your task; ignore the ones that don't.

## How they relate

Every public evaluation is `(model, benchmark, scoring config)`. The leaderboard is "all public evaluations of `benchmark X`, sorted by score."

## Capability tags

Both models and benchmarks carry capability tags:

* `chat` — general dialog
* `code` — code generation
* `reasoning` — chain-of-thought, math, logic
* `multilingual`
* `vision`
* `multi-turn`
* `tool-use`

When picking, match the model's strongest tag against the benchmark's tag.

## Score history

The catalog preserves history. A model evaluated against MMLU last quarter gets a score; if it's re-evaluated against an updated MMLU, both scores remain visible with methodology versions.

## Common confusions

* **A benchmark is not a "test set" in the ML sense.** It's a public, curated, citable evaluation harness. Your private dataset is your "test set."
* **A model's score on benchmark X doesn't predict its score on your data.** Public scores are a leading indicator; your private evaluation is the verdict.

## Where to next

* [Evaluations](/8.-evaluate-score-the-outputs/evaluations-1.md)
* [Stratix Public — Models catalog](/5.-select-pick-the-model/models-catalog.md)
* [Stratix Public — Benchmarks catalog](/5.-select-pick-the-model/benchmarks-catalog.md)
* [BYOK models](/5.-select-pick-the-model/byok-models.md)