> 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-6/score-code.md).

# Recipe: evaluate code-generation models

**When to use:** model returns code; correctness is measured by passing tests.

Evaluate against the relevant code benchmark from the [public catalog](/5.-select-pick-the-model/benchmarks-catalog.md):

```python
from layerlens import Stratix
client = Stratix()

# Pick a code benchmark from the public catalog
benchmark = client.benchmarks.get_by_key("humaneval") # or mbpp, swe-bench, etc.
model = client.models.get_by_key("openai/gpt-4o")

evaluation = client.evaluations.create(model=model, benchmark=benchmark)
evaluation = client.evaluations.wait_for_completion(evaluation, timeout_seconds=1800)

print(f"Accuracy: {evaluation.accuracy}")
```

For your own code-evaluation set with custom test harnesses, upload a JSONL of `{"input": "...", "truth": "..."}` rows via `client.benchmarks.create_custom(...)` — see [SDK reference: models-benchmarks](/more-in-this-section-9/models-benchmarks.md).

For domain-specific code review (security, style, correctness against your codebase conventions), use the [Cowork code-review pattern](https://github.com/layerlens/stratix-python/blob/main/samples/cowork/code_review.py) — Instrumentor uploads code traces, Reviewer evaluates with focused judges.

## See also

* [Concept: Models and benchmarks](/5.-select-pick-the-model/models-and-benchmarks.md)
* [Stratix Public — Benchmarks catalog](/5.-select-pick-the-model/benchmarks-catalog.md)
* [SDK sample: code-review cowork](https://github.com/layerlens/stratix-python/blob/main/samples/cowork/code_review.py)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.layerlens.ai/more-in-this-section-6/score-code.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.