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

# LLM Roles

> Designate different LLMs for different responsibilities

You can customize the Magnitude agent to use different LLMs for each of the three primary operations: `act`, `extract`, `query`.

By default when a single LLM is provided, all responsibilites will be handled by that LLM. However, by specifying different LLMs for certain roles you may be able to save on cost and speed.

Example:

```typescript theme={null}
import { startBrowserAgent } from 'magnitude-core';
import z from 'zod';

async function main() {
    const agent = await startBrowserAgent({
        url: 'https://magnitasks.com/tasks',
        narrate: true,
        llm: [
            {
                provider: 'claude-code',
                options: {
                    model: 'claude-sonnet-4-20250514'
                }
            },
            {
                roles: ['extract'],
                provider: 'google-ai',
                options: {
                    model: 'gemini-2.5-flash-lite-preview-06-17',//'gemini-2.5-flash'
                }
            },
            {
                roles: ['query'],
                provider: 'google-ai',
                options: {
                    // Balance intelligent querying and cheap tokens
                    model: 'gemini-2.5-flash'
                }
            }
        ]
    });
    
    const tasks = await agent.extract(
        'Extract all tasks in To Do column',
        z.array(z.object({ title: z.string(), desc: z.string() }))
    ); // ^ this will use gemini-2.5-flash-lite-preview-06-17

    await agent.act('Move each to in progress', { data: tasks });
    // ^ this will use Claude

    const numTodosMoved = await agent.query(
        'How many todos were moved?',
        z.number()
    ); // ^ this will use gemini-2.5-flash

    console.log(numTodosMoved);

    await agent.stop();
}

main();
```

One great use case for this is to reduce the cost of extracting data. While `act` requires an intelligent and [visually grounded model](/core-concepts/compatible-llms), `extract` and `query` do not require grounded models, and can often work fine with less intelligent models.

General recommendations:

* `act`: MUST use an [intelligent, visually grounded model](/core-concepts/compatible-llms)
* `extract`: Can use a fast and cheap model, like `gemini-2.5-flash` or even `gemini-2.5-flash-lite`
* `query`: Can use any model that's reasonably intelligent but fast, depending on the complexity of the queries you plan to ask. `gemini-2.5-flash` might be a good option.