Comparative Analysis of LLMs: DeepSeek-V3, Qwen2.5, Llama3.1, Claude-3.5, and GPT-4o on English, Math, Code, and Chinese Benchmarks

Definitions and Metrics

Architecture Information

1. MoE (Mixture of Experts):

• Indicates the type of model architecture. MoE models have dynamically activated subsets of parameters for specific tasks, improving efficiency.
• For example, DeepSeek-V3 uses MoE architecture with 37B activated parameters out of 671B total parameters.

1. Dense Models:

• All parameters are used for every task, leading to consistent but computationally expensive operations.
• Qwen2.5 and Llama3.1 are dense models with 72B and 405B parameters, respectively.

English Benchmarks

1. MMLU (Massive Multitask Language Understanding):

• EM (Exact Match): Measures model accuracy across various topics.
• MMLU-Redux and MMLU-Pro are modified versions for specific task subsets.

1. DROP (Discrete Reasoning Over Paragraphs):

• Evaluates reasoning over text using numerical and logical comprehension.
• Metric: 3-shot F1 (the harmonic mean of precision and recall in a few-shot setting).

1. IF-Eval (Prompt Strict):

• Tests the model’s strict adherence to input prompts and accurate task completion.

1. GPQA-Diamond:

• Focuses on general-purpose question-answering under strict criteria (Pass@1 accuracy).

1. SimpleQA:

• Evaluate the model’s ability to handle straightforward question-answering tasks.

1. FRAMES:

• Measures the model’s accuracy in generating structured responses.

1. LongBench v2:

• Focuses on long-context understanding and answering (accuracy metric).

Code Benchmarks

1. HumanEval-Mul (Pass@1):

• Assesses performance on coding problems with multiple correct outputs.

1. LiveCodeBench:

• Includes two metrics:
• 1-COT (Chain-of-Thought): Evaluates step-by-step problem-solving.
• Pass@1: Measures correctness on the first attempt.

1. Codeforces:

• Compares percentile scores on competitive programming tasks.

1. SWE Verified (Resolved):

• Evaluate the correctness of software engineering-related tasks.

1. Aider-Edit (Accuracy):

• Accuracy in editing pre-existing codebases effectively.

1. Aider-Polyglot:

• Measures performance across multiple programming languages.

Math Benchmarks

1. AIME 2024 (Pass@1):

• American Invitational Mathematics Examination-style problems.

1. MATH-500 (Exact Match):

• A set of 500 advanced mathematical problems requiring exact solutions.

1. CNMO 2024 (Pass@1):

• A benchmark for solving complex numerical and mathematical problems.

Chinese Benchmarks

1. CLUEWSC (Exact Match):

• Evaluates performance in Chinese-specific tasks requiring disambiguation.

1. C-Eval (Exact Match):

• Measures understanding of Chinese texts across various domains.

1. C-SimpleQA:

• Focuses on simple question-answering tasks in Chinese.

Final Remarks and Conclusion: Best Models from the Comparison

From the benchmarking table, several standout models excel in different domains, indicating their specialization and strengths. Here is the final analysis:

Best Overall Model: DeepSeek-V3

• Key Strengths:
• MMLU Suite: Achieves the highest score in tasks requiring massive multitask understanding (MMLU: 88.5%, MMLU-Redux: 89.1%).
• Reasoning and QA: Tops benchmarks like DROP (91.6%) and IF-Eval (86.1%).
• Math and Code: Strong performance in AIME 2024 (39.2%) and Codeforces (51.6%).
• Conclusion:
  DeepSeek-V3 demonstrates superior generalization capabilities across diverse domains, making it the most versatile model on this list. Its MoE architecture ensures efficient parameter utilization, achieving high accuracy with specialized parameter activation.

Best in English QA: Claude-3.5 (Sonnet-1022)

• Key Strengths:
• GPQA-Diamond: Achieves the highest score (65%), showcasing exceptional question-answering ability.
• IF-Eval (Prompt Strict): Leads with a score of 86.5%, demonstrating strict adherence to prompt instructions.
• Conclusion:
  Claude-3.5 shines in complex question-answering and prompt-adherence tasks, making it ideal for English language QA applications and structured response generation.

Best in Coding and Software Engineering: Qwen2.5

• Key Strengths:
• HumanEval-Mul: Achieves an impressive 77.3% accuracy in solving coding problems.
• Aider-Edit: Scores 84.2%, the highest in code editing and debugging accuracy.
• Conclusion:
  Qwen2.5 demonstrates exceptional performance in coding and software engineering tasks, making it the preferred choice for developers and automation in programming workflows.

Best in Multilingual and Chinese Tasks: DeepSeek-V3 and GPT-4o

• DeepSeek-V3:
• Dominates CLUEWSC (90.9%) and C-Eval (86.5%), showcasing exceptional performance in Chinese-specific tasks.
• GPT-4o:
• Displays competitive results in multilingual QA tasks (SimpleQA: 38.2%) and Chinese benchmarks.
• Conclusion:
  For Chinese and multilingual applications, DeepSeek-V3 stands out, while GPT-4o offers robust alternative capabilities with a balance of precision and efficiency.

Final Model Rankings Based on Domains

1. Best Generalist Model: DeepSeek-V3
2. Best for English QA: Claude-3.5
3. Best for Coding Tasks: Qwen2.5
4. Best for Math: DeepSeek-V3
5. Best for Multilingual/Chinese: DeepSeek-V3

Conclusion

While DeepSeek-V3 emerges as the best model for its versatility and dominance across multiple benchmarks, other models like Claude-3.5 and Qwen2.5 demonstrate leadership in niche areas such as English QA and coding tasks, respectively. This highlights the growing trend of specialization in LLMs, where different architectures and parameterizations excel in specific tasks.

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