Tutorial•2026

ModelScout SDK Tutorial: Benchmark LLMs for Free Using NexaAPI (Python Guide 2026)

ModelScout just launched a Python SDK for LLM benchmarking. Use NexaAPI as your inference backend at $0.003/call — run 1000 evaluations for just $3.

March 27, 2026•NexaAPI Team

⚡ TL;DR

  • • ModelScout SDK just launched on PyPI for LLM benchmarking
  • • NexaAPI is the cheapest inference backend at $0.003/call
  • • Run 1000 benchmark evaluations for just $3 (vs $15-50 with direct APIs)
  • • Free tier: 100 free calls at rapidapi.com/user/nexaquency

What Is ModelScout SDK?

ModelScout is a Python SDK for LLM benchmarking and evaluation. It lets you compare LLMs side-by-side on your own data — measuring quality scores, cost analysis, latency metrics, and statistical significance.

from modelscout import Benchmark, ModelConfig

results = Benchmark().run(
    pack="trial",
    prompts=["Write a SQL query to find active users", "Explain quantum computing"],
    models=[
        ModelConfig(provider="openai", model="gpt-5-mini"),
        ModelConfig(provider="anthropic", model="claude-haiku-4-5-20251001"),
    ],
)
print(results.best_model_for("quality"))
print(results.best_model_for("cost"))

Why NexaAPI Is the Best Inference Backend

When running 1000+ evaluation calls, pricing matters. NexaAPI offers:

  • $0.003/call — 5-10x cheaper than direct API access
  • 50+ models — GPT-4o, Claude, Gemini, and more
  • No rate limits — perfect for batch benchmarking
  • Free tier — 100 free calls at rapidapi.com/user/nexaquency

Cost Comparison: 1000 LLM Evaluation Calls

ProviderCost per 1000 callsMonthly (10K calls)
NexaAPI ✅~$3~$30
OpenAI direct$15-50$150-500
Other inference APIs$10-30$100-300

Installation

pip install nexaapi modelscout-sdk
npm install nexaapi

Python Tutorial: ModelScout + NexaAPI

# pip install nexaapi modelscout-sdk
from nexaapi import NexaAPI

# Get your free API key at: https://rapidapi.com/user/nexaquency
client = NexaAPI(api_key='YOUR_NEXAAPI_KEY')

def run_benchmark_prompt(prompt: str, model: str = 'gpt-4o') -> str:
    """Use NexaAPI as inference backend for ModelScout evaluations"""
    response = client.chat.completions.create(
        model=model,
        messages=[{'role': 'user', 'content': prompt}]
    )
    return response.choices[0].message.content

benchmark_prompts = [
    'Explain quantum computing in simple terms.',
    'Write a Python function to reverse a linked list.',
    'What is the capital of France? Explain its history.',
]

results = []
for prompt in benchmark_prompts:
    result = run_benchmark_prompt(prompt)
    results.append({'prompt': prompt, 'response': result})
    print(f'Evaluated: {prompt[:50]}...')

total_cost = len(benchmark_prompts) * 0.003
print(f'Total: {len(results)} prompts | Cost: ${total_cost:.3f}')
print('Sign up free: https://rapidapi.com/user/nexaquency')

JavaScript Tutorial

// npm install nexaapi
import NexaAPI from 'nexaapi';

const client = new NexaAPI({ apiKey: 'YOUR_NEXAAPI_KEY' });

async function runBenchmarkBatch(prompts) {
  const results = [];
  for (const prompt of prompts) {
    const response = await client.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: prompt }]
    });
    results.push({ prompt, response: response.choices[0].message.content });
    console.log(`Evaluated: ${prompt.substring(0, 50)}...`);
  }
  console.log(`Cost: ${results.length * 0.003} (at $0.003/call)`);
  return results;
}

const prompts = ['Explain quantum computing', 'Write a sort function'];
runBenchmarkBatch(prompts);

Resources