> ## Documentation Index
> Fetch the complete documentation index at: https://emartai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini

> Configure evalflow to run evals against Google Gemini models.

Use Gemini when you want Google's latest models with a free tier and generous rate limits during development. It is a natural fit for teams already on Google Cloud, and the free tier is sufficient for routine CI eval runs.

## Configure evalflow\.yaml

Add a `gemini` block under `providers` and set `default_provider` to `gemini`:

```yaml theme={null}
providers:
  gemini:
    api_key_env: "GEMINI_API_KEY"
    default_model: "gemini-1.5-flash"

eval:
  default_provider: "gemini"
```

`api_key_env` is the name of the environment variable that holds your key — evalflow reads the variable at runtime and never stores the key itself.

## Set your API key

Get a free key at [aistudio.google.com](https://aistudio.google.com), then export it:

```bash theme={null}
export GEMINI_API_KEY="your-key-here"
```

<Tip>Add this line to your shell profile (`~/.bashrc`, `~/.zshrc`, etc.) or a `.env` file so you do not have to re-export it each session. Never commit your `.env` file to version control.</Tip>

## Verify the connection

Run `evalflow doctor` to confirm evalflow can see the key before running any evals:

```bash theme={null}
evalflow doctor
```

```text theme={null}
✓ GEMINI_API_KEY set
```

## Run evals

```bash theme={null}
evalflow eval --provider gemini
```

```text theme={null}
Running test cases against gemini-1.5-flash...
Quality Gate: PASS
```

If `eval.default_provider` is already set to `gemini` in your `evalflow.yaml`, you can omit the `--provider` flag:

```bash theme={null}
evalflow eval
```

## Available models

Set `default_model` in your `evalflow.yaml` to any of these model names:

```text theme={null}
gemini-1.5-flash   — fast, low cost, good for CI
gemini-1.5-pro     — higher quality, slower
gemini-2.0-flash   — latest generation
```

## Provider notes

* **Default model:** `gemini-1.5-flash`. This model is optimized for speed and is well-suited for high-volume CI runs.
* **Free tier:** Google AI Studio provides a free tier with rate limits that cover most routine eval pipelines.
* **Judge model:** By default, evalflow uses Groq as the LLM judge. If you want Gemini to serve as both the model under test and the judge, update the `judge` block in `evalflow.yaml`:

```yaml theme={null}
judge:
  provider: "gemini"
  model: "gemini-1.5-flash"
```

## Best for

* Teams already using Google Cloud infrastructure
* High-volume eval runs on the free tier
* Experimenting with Google's latest model generations