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OpenAI

By default, DeepEval uses gpt-4.1 to power all of its evaluation metrics. To enable this, you’ll need to set up your OpenAI API key. DeepEval also supports all other OpenAI models, which can be configured directly in Python.

Setting Up Your API Key​

DeepEval autoloads .env.local then .env at import time (process env -> .env.local -> .env).

Recommended (local dev):

# .env.local
OPENAI_API_KEY=<your-openai-api-key>

Alternative (Shell/CI)

export OPENAI_API_KEY=<your-openai-api-key>

Alternative (notebook)

If you're working in a notebook environment (Jupyter or Colab), set your OPENAI_API_KEY in a cell:

%env OPENAI_API_KEY=<your-openai-api-key>

Command Line​

Run the following command in your CLI to specify an OpenAI model to power all metrics.

deepeval set-openai \
    --model=gpt-4.1
    --cost_per_input_token=0.000002
    --cost_per_output_token=0.000008
info

The CLI command above sets gpt-4.1 as the default model for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset the current settings:

deepeval unset-openai
Persisting settings

You can persist CLI settings with the optional --save flag. See Flags and Configs -> Persisting CLI settings.

Python​

You may use OpenAI models other than gpt-4.1, which can be configured directly in python code through DeepEval's GPTModel.

info

You may want to use stronger reasoning models like gpt-4.1 for metrics that require a high level of reasoning — for example, a custom GEval for mathematical correctness.

from deepeval.models import GPTModel
from deepeval.metrics import AnswerRelevancyMetric

model = GPTModel(
    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 ONE mandatory and ONE optional parameters when creating a GPTModel:

  • model: A string specifying the name of the GPT model to use. Defaulted to gpt-4o.
  • [Optional] temperature: A float specifying the model temperature. Defaulted to 0.
  • [Optional] cost_per_input_token: A float specifying the cost for each input token for the provided model.
  • [Optional] cost_per_output_token: A float specifying the cost for each output token for the provided model.
  • [Optional] generation_kwargs: A dictionary of additional generation parameters supported by your model provider.
tip

Any **kwargs you would like to use for your model can be passed through the generation_kwargs parameter. However, we request you to double check the params supported by the model and your model provider in their official docs.

Available OpenAI Models​

note

This list only displays some of the available models. For a comprehensive list, refer to the OpenAI's official documentation.

Below is a list of commonly used OpenAI models:

  • gpt-5
  • gpt-5-mini
  • gpt-5-nano
  • gpt-4.1
  • gpt-4.5-preview
  • gpt-4o
  • gpt-4o-mini
  • o1
  • o1-pro
  • o1-mini
  • o3-mini
  • gpt-4-turbo
  • gpt-4
  • gpt-4-32k
  • gpt-3.5-turbo
  • gpt-3.5-turbo-instruct
  • gpt-3.5-turbo-16k-0613
  • davinci-002
  • babbage-002