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by Ryan Lindsey · 2026-08-01

In this technical analysis, we compare Qwen 2.5 72B to GPT-4 across five real-world tasks. Our aim is to provide an unbiased evaluation, highlighting both the strengths and weaknesses of Qwen 2.5 72B. This analysis is crucial for ML leads who are considering the use of local open-weight models in their projects.

Benchmark Setup

For this benchmark, we used the ZC Inference Exchange, which serves Qwen 2.5 72B and 32B on dedicated NVIDIA GB10 silicon. The tasks were selected to cover a range of typical use cases for large language models (LLMs).

Task 1: Code Generation

In the code generation task, Qwen 2.5 72B matched GPT-4 in accuracy for simple scripts but showed a slight lag in complex algorithms. Here's a sample query:

# Sample query for code generation
"""Write a Python function to calculate the factorial of a number using recursion."""

Task 2: Text Summarization

For text summarization, Qwen 2.5 72B performed comparably to GPT-4, with similar accuracy and conciseness in the summaries produced. However, GPT-4 showed a slight edge in handling longer texts.

Task 3: Language Translation

In language translation, Qwen 2.5 72B was on par with GPT-4 for common language pairs. However, for less common language pairs, GPT-4 demonstrated a higher level of accuracy and fluency.

Task 4: Sentiment Analysis

For sentiment analysis, Qwen 2.5 72B showed comparable accuracy to GPT-4. Both models performed well in identifying sentiment in short text snippets, with GPT-4 having a slight advantage in nuanced sentiment detection.

Task 5: Chatbot Interaction

In a simulated chatbot interaction, Qwen 2.5 72B matched GPT-4 in terms of coherence and relevance of responses. However, GPT-4 demonstrated a slight edge in maintaining context over longer interactions.

Cost Consideration

The Qwen 2.5 72B model is available on the ZC Inference Exchange with competitive pricing, undercutting Anthropic and OpenAI by 60-80% per 1M tokens. For example, the Pro plan at $499/mo includes 12.0M tokens, making it an affordable option for teams looking to deploy LLMs locally.

Conclusion

While Qwen 2.5 72B shows strong performance in several tasks, it is not a one-size-fits-all solution. For tasks requiring the highest level of accuracy and nuance, GPT-4 remains the benchmark. However, for many practical applications, Qwen 2.5 72B offers a cost-effective and capable alternative. To explore these options further, sign up for a prepaid LLM credit line at https://zcx.zctechnologies.org#plans.