> 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/8.-evaluate-score-the-outputs/01-first-evaluation.md).

# Tutorial 1: First evaluation in 10 minutes

**Time:** \~10 minutes **Level:** Beginner **You'll build:** A complete evaluation against a public benchmark, programmatically and via the UI.

## What you'll learn

* How to run a Stratix evaluation from the SDK
* How to read evaluation results
* How to compare two models on the same benchmark

## Prerequisites

* [ ] Stratix Premium account ([sign up](/2.-get-started/sign-up.md))
* [ ] Python 3.9+
* [ ] `pip install layerlens --extra-index-url https://sdk.layerlens.ai/package`
* [ ] `LAYERLENS_STRATIX_API_KEY` exported

## Step 1: Verify the SDK

```bash
python -c "import layerlens; print(layerlens.__version__)"
```

**Checkpoint:** `1.3.0` (or later).

## Step 2: Pick a model and a benchmark

```python
from layerlens import Stratix

client = Stratix()

model = client.models.get_by_key("openai/gpt-4o")
benchmark = client.benchmarks.get_by_key("arc-agi-2")

print(model.name, "→", benchmark.name)
```

## Step 3: Create the evaluation

```python
evaluation = client.evaluations.create(
 model=model,
 benchmark=benchmark,
)
print("Started:", evaluation.id)
```

## Step 4: Wait for completion

```python
result = client.evaluations.wait_for_completion(evaluation)
print(f"Accuracy: {result.accuracy}")
```

**Checkpoint:** You should see an accuracy score.

## Step 5: Inspect in the UI

The same evaluation appears in **Premium → Evaluations**. Click any failing row to see input, output, expected, verdict.

## Step 6: Compare two models

```python
model_a = client.models.get_by_key("openai/gpt-4o")
model_b = client.models.get_by_key("anthropic/claude-opus-4-7")

eval_a = client.evaluations.create(model=model_a, benchmark=benchmark)
eval_b = client.evaluations.create(model=model_b, benchmark=benchmark)

result_a = client.evaluations.wait_for_completion(eval_a)
result_b = client.evaluations.wait_for_completion(eval_b)

print(f"GPT-4o: {result_a.accuracy}")
print(f"Claude Opus 4: {result_b.accuracy}")
```

## What's next

* [Tutorial 2: First judge](/8.-evaluate-score-the-outputs/02-first-judge.md) — score subjective dimensions
* [Cookbook](/2.-get-started/all-cookbook-recipes.md) — pick a recipe matching your task
* [Concept: Evaluations](/8.-evaluate-score-the-outputs/evaluations-1.md)


---

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