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: 
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, 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
  • 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.