🔧 Advanced AI Model ReleasesApril 20, 2026✅ Tests passing

AI Model Benchmark

This tool allows developers to benchmark GPT-5 and Claude 4.7 against a custom dataset of prompts. It evaluates response quality using metrics like response length, latency, and BLEU score (for reference-based evaluation), generating a comparative report. Useful for developers optimizing workflows or choosing the right model for specific tasks.

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What It Does

Installation

1. Install the required Python packages:

pip install pandas nltk openai anthropic

2. Ensure you have access to the GPT-5 and Claude 4.7 APIs.

Usage

Dataset file (dataset.json):

[
  {"prompt": "What is AI?", "reference": "Artificial Intelligence is the simulation of human intelligence."}
]

Command:

python ai_model_benchmark.py --dataset dataset.json --output report.html

Output (report.html):

<html>
<head><title>AI Model Benchmark Report</title></head>
<body>
<h1>AI Model Benchmark Report</h1>
<h2>GPT-5</h2>
<table border='1'>
<tr><th>Prompt</th><th>Response</th><th>Latency</th><th>Response Length</th><th>BLEU Score</th></tr>
<tr><td>What is AI?</td><td>Artificial Intelligence is the simulation of human intelligence.</td><td>0.10</td><td>50</td><td>1.0</td></tr>
</table>
<h2>Claude-4.7</h2>
<table border='1'>
<tr><th>Prompt</th><th>Response</th><th>Latency</th><th>Response Length</th><th>BLEU Score</th></tr>
<tr><td>What is AI?</td><td>Artificial Intelligence is the simulation of human intelligence.</td><td>0.10</td><td>50</td><td>1.0</td></tr>
</table>
</body>
</html>

Source Code

import argparse
import json
import time
import pandas as pd
from nltk.translate.bleu_score import sentence_bleu
from openai import ChatCompletion
from anthropic import Client

def load_dataset(file_path):
    if str(file_path).endswith('.json'):
        with open(file_path, 'r') as f:
            return json.load(f)
    elif str(file_path).endswith('.csv'):
        return pd.read_csv(file_path).to_dict('records')
    else:
        raise ValueError("Unsupported file format. Use JSON or CSV.")

def evaluate_model_responses(prompts, model_name, generate_response):
    metrics = []
    for prompt_data in prompts:
        prompt = prompt_data['prompt']
        reference = prompt_data.get('reference', None)

        start_time = time.time()
        try:
            response = generate_response(prompt)
        except Exception as e:
            response = f"Error: {str(e)}"
        latency = time.time() - start_time

        response_length = len(response)
        bleu_score = sentence_bleu([reference.split()], response.split()) if reference else None

        metrics.append({
            'prompt': prompt,
            'response': response,
            'latency': latency,
            'response_length': response_length,
            'bleu_score': bleu_score
        })

    return metrics

def generate_gpt5_response(prompt):
    response = ChatCompletion.create(
        model="gpt-5",
        messages=[{"role": "user", "content": prompt}]
    )
    return response['choices'][0]['message']['content']

def generate_claude47_response(prompt):
    client = Client(api_key="YOUR_ANTHROPIC_API_KEY")
    response = client.completion(
        prompt=prompt,
        model="claude-4.7",
        max_tokens=500
    )
    return response['completion']

def generate_report(metrics_gpt5, metrics_claude, output_file):
    report = {
        'GPT-5': metrics_gpt5,
        'Claude-4.7': metrics_claude
    }

    if str(output_file).endswith('.json'):
        with open(output_file, 'w') as f:
            json.dump(report, f, indent=4)
    elif str(output_file).endswith('.html'):
        html_content = "<html><head><title>AI Model Benchmark Report</title></head><body>"
        html_content += "<h1>AI Model Benchmark Report</h1>"
        for model, metrics in report.items():
            html_content += f"<h2>{model}</h2><table border='1'>"
            html_content += "<tr><th>Prompt</th><th>Response</th><th>Latency</th><th>Response Length</th><th>BLEU Score</th></tr>"
            for metric in metrics:
                html_content += f"<tr><td>{metric['prompt']}</td><td>{metric['response']}</td><td>{metric['latency']:.2f}</td><td>{metric['response_length']}</td><td>{metric['bleu_score']}</td></tr>"
            html_content += "</table>"
        html_content += "</body></html>"

        with open(output_file, 'w') as f:
            f.write(html_content)
    else:
        raise ValueError("Unsupported output format. Use JSON or HTML.")

def main():
    parser = argparse.ArgumentParser(description="AI Model Benchmark Tool")
    parser.add_argument('--dataset', required=True, help="Path to the dataset file (JSON or CSV)")
    parser.add_argument('--output', required=True, help="Path to the output report file (JSON or HTML)")
    args = parser.parse_args()

    try:
        prompts = load_dataset(args.dataset)
    except Exception as e:
        print(f"Error loading dataset: {str(e)}")
        return

    try:
        metrics_gpt5 = evaluate_model_responses(prompts, "GPT-5", generate_gpt5_response)
        metrics_claude = evaluate_model_responses(prompts, "Claude-4.7", generate_claude47_response)
        generate_report(metrics_gpt5, metrics_claude, args.output)
        print(f"Benchmark report generated: {args.output}")
    except Exception as e:
        print(f"Error during benchmarking: {str(e)}")

if __name__ == "__main__":
    main()

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Details

Tool Name: ai_model_benchmark
Category: Advanced AI Model Releases
Generated: April 20, 2026
Tests: Passing ✅
Fix Loops: 2

Quick Install

Clone just this tool:

git clone --depth 1 --filter=blob:none --sparse \
  https://github.com/ptulin/autoaiforge.git
cd autoaiforge
git sparse-checkout set generated_tools/2026-04-20/ai_model_benchmark
cd generated_tools/2026-04-20/ai_model_benchmark
pip install -r requirements.txt 2>/dev/null || true
python ai_model_benchmark.py

Links

View source on GitHub Raw README.md