OpenAI

from deepeval.metrics import AnswerRelevancyMetric
from deepeval.models import OpenAIModel

model = OpenAIModel(
    model="gpt-4.1",
    temperature=0,
    cost_per_input_token=0.000002,
    cost_per_output_token=0.000008
)
answer_relevancy = AnswerRelevancyMetric(model=model)

There are ZERO mandatory and SEVEN optional parameters when creating an OpenAIModel:

• [Optional] model: A string specifying the name of the GPT model to use. Defaulted to OPENAI_MODEL_NAME if not set; falls back to gpt-5.4.
• [Optional] api_key: A string specifying the OpenAI API key for authentication. Defaults to OPENAI_API_KEY if not passed; raises an error at runtime if unset.
• [Optional] base_url: A string specifying your OpenAI URL.
• [Optional] temperature: A float specifying the model temperature. Defaults to TEMPERATURE if not passed; falls back to 0.0 if unset.
• [Optional] cost_per_input_token: A float specifying the cost for each input token for the provided model. Defaults to OPENAI_COST_PER_INPUT_TOKEN if available in deepeval's model cost registry, else None.
• [Optional] cost_per_output_token: A float specifying the cost for each output token for the provided model. Defaults to OPENAI_COST_PER_OUTPUT_TOKEN if available in deepeval's model cost registry, else None.
• [Optional] generation_kwargs: A dictionary of additional generation parameters forwarded to the OpenAI chat.completions.create(...) and beta.chat.completions.parse(...) calls.

deepeval by default uses OpenAI models for evaluations, you can simply pass the name of your desired model in metric initialization and set the OPENAI_API_KEY to use OpenAI models:

from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy = AnswerRelevancyMetric(
    model="gpt-4.1",
)