ModelScout SDK Just Launched — Here's How to Benchmark 56+ AI Models via NexaAPI

#api #tutorial #llm #python

ModelScout just dropped a Python SDK for LLM benchmarking. I combined it with NexaAPI (the cheapest inference API at $0.003/call) to run 1000 benchmark evaluations for just $3.

What Is ModelScout SDK?

ModelScout benchmarks LLMs side-by-side on your data — quality scores, cost analysis, latency metrics.

pip install modelscout-sdk nexaapi

Why NexaAPI for Benchmarking?

Running 1000 eval calls costs:

NexaAPI: ~$3 (at $0.003/call)
OpenAI direct: $15-50
Other APIs: $10-30

NexaAPI saves 70-90% — perfect for large-scale benchmarking.

Get your free API key (100 free calls): rapidapi.com/user/nexaquency

Python Example

from nexaapi import NexaAPI

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?',
]

for prompt in benchmark_prompts:
    result = run_benchmark_prompt(prompt)
    print(f'✅ {prompt[:40]}: {result[:80]}...')

print('Cost: ~$0.003/call | 1000 evals = ~$3')
print('Sign up: https://rapidapi.com/user/nexaquency')

JavaScript Example

import NexaAPI from 'nexaapi'; // npm install 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 });
  }
  return results;
}