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

# Quickstart

> Run your first benchmark evaluation in 3 easy steps.

## Your First Evaluation

Let's run a complete benchmark evaluation from start to finish. We'll evaluate a model on MMLU, one of the most popular benchmarks for testing general knowledge.

### Step 1: [Install openbench](/installation)

<CodeGroup>
  ```bash uv (recommended) theme={null}
  # Install UV if you haven't already
  curl -LsSf https://astral.sh/uv/install.sh | sh

  # Create environment and install openbench

  uv venv
  source .venv/bin/activate
  uv pip install openbench

  ```

  ```bash pip theme={null}
  python -m venv .venv
  source .venv/bin/activate
  pip install openbench
  ```
</CodeGroup>

### Step 2: Set Your API Key

Set environment variables:

<Tabs>
  <Tab title="Groq">
    ```bash theme={null}
    export GROQ_API_KEY="gsk_..."
    ```
  </Tab>

  <Tab title="OpenAI">
    ```bash theme={null}
    export OPENAI_API_KEY="sk-..."
    ```
  </Tab>

  <Tab title="Anthropic">
    ```bash theme={null}
    export ANTHROPIC_API_KEY="sk-ant-..."
    ```
  </Tab>

  <Tab title="OpenRouter">
    ```bash theme={null}
    export OPENROUTER_API_KEY="sk-or-..."
    ```
  </Tab>
</Tabs>

Or create a `.env` file in your project directory:

```bash .env theme={null}
# Model Provider API Keys
GROQ_API_KEY="gsk_..."
OPENAI_API_KEY="sk_..."
ANTHROPIC_API_KEY="sk-ant-..."
```

See [supported providers](/providers).

### Step 3: Run Your First Benchmark

```bash theme={null}
# Quick test with 10 questions
bench eval mmlu --model groq/llama-3.3-70b-versatile --limit 10
```

<Info>
  The `--limit 10` flag runs only 10 questions for a quick test. Remove it to run the full benchmark (14,042 questions).
  Learn more about [configuration flags](/configuration#model-configuration).
</Info>

## Understanding the Output

When you run a benchmark, you'll see:

1. **Progress Bar**: Shows evaluation progress in real-time
2. **Live Metrics**: Accuracy updates as questions are answered
3. **Final Results**: Overall accuracy and benchmark-specific metrics
4. **Log Location**: Where detailed results are saved

Example Results:

```
╭────────────────────────────────────────────────────────────╮
│mmlu (30 samples): openai/gpt-4.1                                                              │
╰────────────────────────────────────────────────────────────╯
timeout: 10000, max_connections: 10, temperature: 0.5, log_buffer: 10, dataset: mmlu_simple_eval                             
                                                                                                                             
total time:                0:00:11                                                                                           
openai/gpt-4.1             12,220 tokens [I: 3,242, CW: 0, CR: 0, O: 8,978, R: 0]                                            
                                                                                                                             
mcq_scorer                                                                                                                   
accuracy       0.900                                                                                                         
stderr         0.056                                                                                                         
std            0.305                                                                                                         
stem_accuracy  0.900                                                                                                         
stem_stderr    0.056                                                                                                         
stem_std       0.305                                                                                                         
                                                                                                                             
Log: logs/2025-09-12T18-44-17-04-00_mmlu_KnXAbsNXcoQapiDi6PndUR.eval                                                         
                                                                                                                             
Evaluation complete!
```

## Viewing and Analyzing Results

### Interactive Viewer

Launch the web-based viewer for detailed sample-by-sample results with:

```bash theme={null}
bench view
```

### Log Files

Results are saved as .eval files in `./logs/`. The exact file path is denoted as part of the eval output:

```
Log: logs/2025-09-17T21-39-21-04-00_mmlu_kikHACevKMYey8r5iWEG96.eval  
```