In this comprehensive analysis, I'll examine the key differences between two state-of-the-art language models: DeepSeek-V3 and Qwen 2.5. These models represent different approaches to large language model development, each bringing unique innovations and architectural decisions to the field.

DeepSeek-V3 leverages a sophisticated Mixture-of-Experts (MoE) architecture with 671B total parameters and 37B activated parameters per token, while Qwen 2.5 offers both dense models for open-source use and MoE models for API services. Their training data differs significantly, with DeepSeek-V3 utilizing 14.8T tokens and Qwen 2.5 expanding to 18T tokens, focusing particularly on knowledge, coding, and mathematics.

Let's explore their distinct characteristics, from their benchmark performances and specialized capabilities to their training methodologies and practical applications, understanding how each model contributes uniquely to the current AI landscape. Their contrasting approaches to tasks like mathematical reasoning, coding, and multilingual processing offer valuable insights into the diverse strategies in modern AI development.

How to read lengthy and complex technical papers faster and more in-depth? This blog uses rflow.ai to help with analysis.

The ResearchFLow digested version of the DeepSeek-R1 paper is here.

The original paper link is here.

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DeepSeek-V3 vs Qwen 2.5: Performance Comparison

This node compares the performance of DeepSeek-V3 and Qwen 2.5 across various benchmarks and tasks, highlighting their strengths and differences.

Model Architectures

DeepSeek-V3

• Mixture-of-Experts (MoE) architecture
• 671B total parameters
• 37B activated parameters for each token

Qwen 2.5

• Dense models for open-source (0.5B to 72B parameters)
• MoE models for API service (Qwen2.5-Turbo and Qwen2.5-Plus)

Pre-training Data

DeepSeek-V3

• 14.8T high-quality and diverse tokens

Qwen 2.5

• 18T tokens, increased from 7T in previous versions
• Focus on knowledge, coding, and mathematics

General Task Performance

MMLU (5-shot)

• DeepSeek-V3: 88.5
• Qwen2.5-72B: 86.1

MMLU-Pro (5-shot)

• DeepSeek-V3: 75.9
• Qwen2.5-72B: 58.1

BBH (3-shot)

• DeepSeek-V3: 87.5
• Qwen2.5-72B: 86.3

DeepSeek-V3 shows slightly better performance on these general tasks.

Mathematics and Science Tasks

MATH (4-shot)

• DeepSeek-V3: 61.6
• Qwen2.5-72B: 62.1

GSM8K (8-shot)

• DeepSeek-V3: 89.3
• Qwen2.5-72B: 91.5

Qwen 2.5 shows a slight edge in mathematical reasoning tasks.

Coding Tasks

HumanEval (0-shot)

• DeepSeek-V3: 65.2
• Qwen2.5-72B: 59.1

MBPP (3-shot)

• DeepSeek-V3: 75.4
• Qwen2.5-72B: 84.7

Mixed results, with DeepSeek-V3 performing better on HumanEval and Qwen 2.5 excelling in MBPP.

Multilingual Performance

C-Eval (5-shot)

• DeepSeek-V3: 90.1
• Qwen2.5-72B: 90.1

CMMLU (5-shot)

• DeepSeek-V3: 88.8
• Qwen2.5-72B: 88.8

Both models show comparable performance on Chinese language tasks.

Long Context Capabilities

DeepSeek-V3

• Context length up to 128K tokens
• Qwen2.5-Turbo supports up to 1 million tokens

Qwen 2.5 demonstrates superior long context handling, especially with Qwen2.5-Turbo.

Instruction Following and Alignment

IFEval (Prompt Strict)

• DeepSeek-V3: 86.1
• Qwen2.5-72B: 84.1

Arena-Hard

• DeepSeek-V3: 85.5
• Qwen2.5-72B: 81.2

DeepSeek-V3 shows slightly better performance in instruction following and human preference alignment.

Training Efficiency

DeepSeek-V3

• 2.788M H800 GPU hours for full training

Qwen 2.5

• Specific training hours not provided, but emphasizes cost-effectiveness

Both models prioritize efficient training, with DeepSeek-V3 providing specific metrics.

Conclusion

Both DeepSeek-V3 and Qwen 2.5 demonstrate strong performance across various tasks. DeepSeek-V3 shows slight advantages in general tasks and instruction following, while Qwen 2.5 excels in mathematical reasoning and long context handling. The choice between the two may depend on specific use cases and requirements, such as multilingual needs or long context processing.