Latest Benchmark Comparison Analysis of Major AI Language Models

In recent years, the evolution of AI language models has been remarkable, and their performance evaluation has become an important indicator for companies and developers. This article provides a detailed comparative analysis of five major AI language models (DeepSeek V3, Claude-3.5, GPT-4o, Qwen2.5, and Llama3.1) from various perspectives.

The following evaluation results show the highest scores in each category and the models that achieved them.

Architectural Features

Modern AI language models are primarily divided into two architectural approaches:

1. Mixture of Experts (MoE) Method

   • Adopted by DeepSeek V3 and DeepSeek V2.5
   • Achieves efficient processing through a combination of expert models specialized for specific tasks

2. Dense Method

   • Adopted by Qwen2.5 and Llama3.1
   • Maintains a conventional densely connected neural network structure

It should be noted that the architectural details of Claude-3.5 and GPT-4o are not publicly disclosed.

Detailed Analysis of English Processing Capability

Basic English Proficiency Evaluation
- MMLU (Accuracy rate on English multiple-choice questions testing knowledge across multiple fields)
  DeepSeek V3 (88.5%)

- MMLU-Redux (Evaluation of basic English comprehension using a simplified version of MMLU)
  DeepSeek V3 (89.1%)

- MMLU-Pro (Accuracy rate on English problems requiring more advanced specialized knowledge)
  Claude-3.5 (78.0%)

Advanced Language Processing Capability
- DROP (Comprehensive evaluation combining reading comprehension and numerical reasoning ability)
  DeepSeek V3 (91.6%)

- IF-Eval (Evaluation of understanding and execution accuracy of detailed instructions)
  Claude-3.5 (86.5%)

- GPQA-Diamond (Accuracy of question answering requiring advanced specialized knowledge)
  Claude-3.5 (65.0%)

Practical Task Processing
- SimpleQA (Accuracy evaluation of basic question answering)
  GPT-4o (38.2%)

- FRAMES (Response accuracy of complex dialogue systems)
  GPT-4o (80.5%)

- LongBench v2 (Evaluation of long text comprehension and processing ability)
  DeepSeek V3 (48.7%)

Programming Capability Evaluation

Code Generation Capability
- HumanEval-Mul (Accuracy of generating programs containing multiple functions)
  DeepSeek V3 (82.6%)

- LiveCodeBench-COT (Evaluation of code generation including step-by-step thinking processes)
  DeepSeek V3 (40.5%)

- LiveCodeBench (Evaluation of real-time code generation capability)
  DeepSeek V3 (37.6%)

- Codeforces (Problem-solving ability in competitive programming format)
  DeepSeek V3 (51.6%)

Code Editing and Management Capability
- SWE Verified (Resolution rate of software engineering tasks)
  Claude-3.5 (50.8%)

- Aider-Edit (Accuracy of editing and correcting existing code)
  Claude-3.5 (84.2%)

- Aider-Polyglot (Editing capability for multiple programming languages)
  Claude-3.5 (45.3%)

Mathematical Processing Capability Evaluation

Mathematics Field Evaluation
- AIME 2024 (Problem-solving ability at the American Mathematics Olympiad intermediate level)
  DeepSeek V3 (39.2%)

- MATH-500 (Accuracy rate on 500 university-level mathematics problems)
  DeepSeek V3 (90.2%)

- CNMO 2024 (Problem-solving ability at the Chinese Mathematics Olympiad level)
  DeepSeek V3 (43.2%)

Chinese Language Processing Capability

Chinese Language Capability Evaluation
- CLUEWSC (Evaluation of Chinese context understanding and ambiguity resolution ability)
  Qwen2.5 (91.4%)

- C-Eval (Accuracy rate on multiple-choice questions testing knowledge across multiple fields in Chinese)
  DeepSeek V3 (86.5%)

- C-SimpleQA (Accuracy evaluation of basic Chinese question answering)
  DeepSeek V3 (64.1%)

Overall Evaluation and Conclusion

When comprehensively evaluating the characteristics of each model, the following features emerge:

1. DeepSeek V3

   • Overall high performance in English, code generation, and mathematics fields
   • Shows particular advantage in basic language understanding and code generation

2. Claude-3.5

   • Outstanding performance in code editing and specialized knowledge fields
   • Particularly excellent in instruction understanding

3. GPT-4o

   • High performance in practical tasks and dialogue systems
   • Has an advantage in basic question answering

4. Qwen2.5

   • Shows excellent performance in Chinese language processing
   • Recorded the highest score particularly in CLUEWSC

These benchmark results indicate that each model has different strengths. Selecting the appropriate model according to the use case will lead to effective utilization.