LLM Cost vs. Accuracy

This notebook fetches benchmark data from the DRB public API and computes Pareto frontiers locally.

pip install futuresearch requests pandas

import requests
import pandas as pd

url = "https://rguraxphqescakvvzmju.supabase.co/rest/v1/rpc/get_average_scores_by_model"
PUBLIC_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."

headers = {
    "apikey": PUBLIC_API_KEY,
    "authorization": f"Bearer {PUBLIC_API_KEY}",
    "content-type": "application/json",
}

response = requests.post(url, headers=headers, json={"min_num_of_distinct_instances": 150})
df = pd.DataFrame(response.json())

To override FutureSearch's default model selection:

from futuresearch.ops import agent_map
from futuresearch.task import LLM

result = await agent_map(
    task="Find each company's latest funding round",
    input=companies_df,
    effort_level=None,
    llm=LLM.CLAUDE_4_6_OPUS_HIGH,
    iteration_budget=10,
    include_research=True,
)