---
# id: deepseek
title: DeepSeek
sidebar_label: DeepSeek
---

`deepeval` allows you to use `deepseek-chat` and `deepseek-reasoner` directly from DeepSeek to run all of `deepeval`'s metrics, which can be set through the CLI or in python.

### Command Line

To configure your DeepSeek model through the CLI, run the following command:

```bash
deepeval set-deepseek --model=deepseek-chat \
    --temperature=0
```

The CLI command above sets `deepseek-chat` as the default model for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset DeepSeek:

```bash
deepeval unset-deepseek
```

:::tip[Persisting settings]
You can persist CLI settings with the optional `--save` flag.
See [Flags and Configs -> Persisting CLI settings](/docs/evaluation-flags-and-configs#persisting-cli-settings-with---save).
:::

### Python

You can also specify your model directly in code using `DeepSeekModel`.

<Tabs items={["Python", "ENV"]}>
<Tab value="Python">

```python
from deepeval.models import DeepSeekModel
from deepeval.metrics import AnswerRelevancyMetric

model = DeepSeekModel(
    model="deepseek-chat",
    api_key="your-api-key",
    temperature=0
)

answer_relevancy = AnswerRelevancyMetric(model=model)
```

</Tab>
<Tab value="ENV">

To use any DeepSeek model directly in `deepeval`, set the `USE_DEEPSEEK_MODEL=1` in your `env` and simply pass the name of your desired model in your metric initialization:

```python
from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy = AnswerRelevancyMetric(
    model="deepseek-chat",
)
```

You should also set the other necessary vars like `DEEPSEEK_API_KEY` to be able to use the Deepseek models as shown above.

</Tab>
</Tabs>

There are **ZERO** mandatory and **SIX** optional parameters when creating a `DeepSeekModel`:

- [Optional] `model`: A string specifying the name of the DeepSeek model to use. Defaults to `DEEPSEEK_MODEL_NAME` if not passed; raises an error at runtime if unset.
- [Optional] `api_key`: A string specifying your DeepSeek API key for authentication. Defaults to `DEEPSEEK_API_KEY` if not passed; raises an error at runtime if unset.
- [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 `DEEPSEEK_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 `DEEPSEEK_COST_PER_OUTPUT_TOKEN` if available in `deepeval`'s model cost registry, else `None`.
- [Optional] `generation_kwargs`: A dictionary of additional generation forwarded to the OpenAI `chat.completions.create(...)` call.

Parameters may be explicitly passed to the model at initialization time, or configured with optional settings. The **mandatory** parameters are required at runtime, but you can provide them either explicitly as constructor arguments, **or** via `deepeval` settings / environment variables (constructor args take precedence). See [Environment variables and settings](/docs/evaluation-flags-and-configs#model-settings-deep-seek) for the DeepSeek-related environment variables.

:::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](https://api-docs.deepseek.com/api/create-chat-completion#request).
:::

### Available DeepSeek Models

Below is the comprehensive list of available DeepSeek models in `deepeval`:

- `deepseek-chat`
- `deepseek-v3.2`
- `deepseek-v3.2-exp`
- `deepseek-v3.1`
- `deepseek-v3`
- `deepseek-reasoner`
- `deepseek-r1`
- `deepseek-r1-lite`
- `deepseek-v2.5`
- `deepseek-coder`
- `deepseek-coder-6.7b`
- `deepseek-coder-33b`