Write tests that are pytest-native, high-signal, and behavior-focused.

Apply these rules:

Example (high-signal header behavior):

def test_auth_headers_set_before_request() -> None:
    client = OpenAI(base_url=base_url, api_key=lambda: "test_bearer_token")
    # Access/trigger the behavior you actually rely on at request time
    client.auth_headers  # should force/ensure correct Authorization value
    assert client.auth_headers["Authorization"] == "Bearer test_bearer_token"

Example (parametrize endpoints):

@pytest.mark.parametrize("path", ["/images/generations", "/chat/completions"])
def test_model_stripped_from_body(client: Client, path: str) -> None:
    req = client._build_request(
        FinalRequestOptions.construct(method="post", url=path, json_data={"model": "gpt-image-1-5", "prompt": "sunset"})
    )
    assert "model" not in req.json

Example (inline snapshot idea):

def test_schema_snapshot() -> None:
    schema = to_strict_json_schema(MyModel)
    assert schema == {"type": "object", "additionalProperties": False, "properties": {...}, "required": [...]}
    # or use an inline snapshot if your codebase supports it

Example (lazy import regression assertion):

import importlib
import openai
m1 = openai.types
m2 = importlib.reload(openai.types)
assert m1 is m2