Comparative Analysis of Chinese and American Large Language Models: Comprehensive Evaluation of LLaMA, Qwen, DeepSeek, and Gemini

This article conducts a multi-dimensional comparative evaluation of mainstream Chinese and American large language models (LLaMA, Qwen, DeepSeek, Gemini), covering core metrics such as BLEU scores, perplexity, and inference time, aiming to provide technical selection references for developers and researchers. The original study was published by NaviAbhi on GitHub with the title 《Comparative-Analysis-of-USA-vs-China-Large-Language-Models》 on May 30, 2026.

With the rapid development of artificial intelligence technology, large language models (LLMs) have become core technologies in the field of natural language processing. Currently, the technical routes of China and the United States are advancing side by side: the U.S. is represented by Meta's LLaMA, Google's Gemini, etc., while China is benchmarked by Alibaba's Qwen, DeepSeek, etc. Understanding the performance characteristics and applicable scenarios of different models is of great practical significance for technical selection.

This evaluation covers four representative models:

1. LLaMA Series (Meta)

An open-source model by Meta, known for its efficient architecture and excellent open-source ecosystem, achieving outstanding performance with a relatively small parameter scale.

2. Qwen Series (Alibaba)

Optimized for both Chinese and English bilingual tasks, with outstanding performance in Chinese understanding and generation, and support for multimodal capabilities.

3. DeepSeek

Developed by DeepSeek, it excels in reasoning ability and code generation, with strong competitiveness in mathematical reasoning and logical analysis.

4. Gemini (Google)

A multimodal model that supports text, image, and audio inputs, with significant advantages in cross-modal understanding and generation.

Multi-dimensional metrics are used to ensure objectivity:

BLEU Score Evaluation

Quantifies text generation quality, assessing fluency and accuracy through n-gram overlap.

Perplexity Analysis

Measures the predictive ability of language models; lower values indicate better language understanding and generation capabilities.

Inference Time Testing

Evaluates inference efficiency under different hardware conditions, which affects actual deployment costs and user experience.

Trade-off Between Performance and Efficiency

Some models have excellent performance but long inference times, while others strike a balance between performance and speed; selection should be based on scenario requirements.

Differences in Chinese and English Capabilities

Chinese models (Qwen, DeepSeek) have local advantages in Chinese tasks, while American models (LLaMA, Gemini) are more balanced in English and cross-language tasks.

Open-source vs. Closed-source Comparison

As an open-source model, LLaMA demonstrates the ability to compete with closed-source models, promoting technological democratization.

Chinese Content Generation Scenarios

Prioritize Chinese-optimized models like Qwen to leverage advantages in semantics and cultural context.

Multilingual Mixed Scenarios

Gemini and LLaMA are more adaptable, with stable performance in cross-language transfer and code generation.

Real-time Interaction Scenarios

Need to balance model accuracy and response speed, with a focus on inference time.

1. Specialized Division of Labor: Models form differentiated advantages in specific fields, emphasizing both generality and specialization
2. Efficiency Optimization: Model compression and quantization technologies mature, making edge deployment possible
3. Multimodal Fusion: Multimodal capabilities such as text, image, and audio become standard for next-generation models
4. Prosperity of Open-source Ecosystem: Performance of open-source models improves, lowering the technical entry barrier

The competition and cooperation between Chinese and American large language models drive global AI progress. This analysis provides reference data, but selection should consider factors such as deployment costs, data privacy, and compliance requirements. We look forward to more efficient and intelligent models bringing changes to various industries.