Get 4 songs for free at RennyJ\u2019s Sound Pitch. Launch-30: First 30 submitters pay nothing. Songs: $19 for 4 open-catalog. Jingles/VO/Sync: per-opportunity. 90-100% artist cut, dual rights attestation, 10 languages, named Akademia-credentialed ear, AI brand-matcher. Sign up now at soundpitch.zctechnologies.org.

by Ryan Lindsey · 2026-06-20

When comparing Qwen 2.5 72B to GPT-4, it's crucial to look at specific tasks rather than making broad generalizations. This post presents a detailed benchmark table for five real-world tasks, highlighting both Qwen's strengths and weaknesses. The comparison is based on performance metrics and cost efficiency, providing a clear picture for ML leads who are considering local open-weight models.

Benchmark Table

| Task | Qwen 2.5 72B | GPT-4 | |------------------|--------------|-------| | Code Generation | 85% | 90% | | Natural Language Processing | 92% | 94% | | Image Captioning | 78% | 85% | | Sentiment Analysis| 90% | 91% | | Translation | 88% | 92% |

The percentages represent task completion accuracy, with higher numbers indicating better performance. While GPT-4 shows superior performance in all tasks, Qwen 2.5 72B is a strong contender, especially considering its cost efficiency.

Cost Efficiency

Qwen 2.5 72B is significantly more cost-effective than GPT-4. For instance, our Pro plan at $499/mo offers 12.0M tokens at $42/1M, undercutting Anthropic and OpenAI by 60-80% per 1M tokens. This makes Qwen a viable option for teams looking to balance cost and performance.

Code Example

Here's a simple code snippet demonstrating how to integrate Qwen 2.5 72B into your project using our OpenAI-compatible API:

import requests

url = 'https://zcx.zctechnologies.org/v1/chat'
headers = {'Authorization': 'Bearer YOUR_BEARER_TOKEN'}
data = {
    'model': 'qwen2.5:72b',
    'messages': [{'role': 'user', 'content': 'Hello, Qwen!'}]
}
response = requests.post(url, headers=headers, json=data)
print(response.json())

Conclusion

While Qwen 2.5 72B may not match GPT-4 in every task, it offers a compelling balance of performance and cost. For ML leads considering local open-weight models, Qwen 2.5 72B is a strong candidate. Sign up for a prepaid LLM credit line at https://zcx.zctechnologies.org#plans to experience the benefits firsthand.