In this comprehensive analysis, I'll examine the distinctive features and capabilities of two prominent AI language models: DeepSeek-V3 and Claude-haiku. These models represent different philosophies in AI development, each bringing unique advantages to the table in terms of architecture, performance, and practical applications.

DeepSeek-V3 showcases impressive capabilities with its sophisticated Mixture-of-Experts (MoE) architecture, utilizing 671B total parameters while activating only 37B for each token. In contrast, Claude-haiku positions itself as the most efficient member of the Claude 3 family, prioritizing speed and cost-effectiveness while maintaining robust performance across various tasks.

Let's explore their comparative strengths across multiple dimensions, from their performance in reasoning and mathematical tasks to their capabilities in coding and multilingual processing. This analysis will help understand how these models serve different needs in the current AI landscape, particularly considering DeepSeek-V3's superior performance in traditional benchmarks versus Claude-haiku's efficiency and multimodal strengths.

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 Claude-haiku: Performance Comparison

This node compares the performance of DeepSeek-V3 and Claude-haiku across various benchmarks and capabilities to determine which model works better in different areas.

Model Architecture

DeepSeek-V3:

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

Claude-haiku:

• Specific architecture details not provided
• Described as the fastest and least expensive model in the Claude 3 family

General Reasoning and Knowledge

MMLU (Massive Multitask Language Understanding)

• DeepSeek-V3: 88.5% (5-shot)
• Claude-haiku: 75.2% (5-shot)

BBH (BIG-Bench Hard)

• DeepSeek-V3: 87.5% (3-shot CoT)
• Claude-haiku: 73.7% (3-shot CoT)

DeepSeek-V3 outperforms Claude-haiku in general reasoning and knowledge tasks.

Mathematical Problem Solving

MATH

• DeepSeek-V3: 61% (4-shot)
• Claude-haiku: 40.9% (4-shot)

GSM8K

• DeepSeek-V3: 95.0% (0-shot CoT)
• Claude-haiku: 88.9% (0-shot CoT)

DeepSeek-V3 demonstrates superior performance in mathematical problem-solving tasks.

Coding Capabilities

HumanEval

• DeepSeek-V3: 84.9% (0-shot)
• Claude-haiku: 75.9% (0-shot)

MBPP (Mostly Basic Python Programming)

• DeepSeek-V3: 86.4% (Pass@1)
• Claude-haiku: 80.4% (Pass@1)

DeepSeek-V3 shows better performance in coding tasks compared to Claude-haiku.

Multimodal Capabilities

AI2D (Science Diagrams)

• DeepSeek-V3: Not available
• Claude-haiku: 86.7% (0-shot)

DocVQA (Document Visual Question Answering)

• DeepSeek-V3: Not available
• Claude-haiku: 88.8% (ANLS score)

Claude-haiku demonstrates strong multimodal capabilities, while DeepSeek-V3's performance in this area is not reported.

Long Context Performance

QuALITY (Question Answering with Long Input Texts)

• DeepSeek-V3: Not available
• Claude-haiku: 79.4% (0-shot), 80.2% (1-shot)

Context Window

• DeepSeek-V3: Up to 128K tokens
• Claude-haiku: Up to 200K tokens (with potential for 1M tokens)

Claude-haiku shows strong performance in long context tasks and has a larger context window.

Multilingual Capabilities

MGSM (Multilingual Grade School Math)

• DeepSeek-V3: 90.7% (0-shot)
• Claude-haiku: 75.1% (0-shot)

Multilingual MMLU

• DeepSeek-V3: Not available
• Claude-haiku: 65.2% (5-shot)

DeepSeek-V3 shows superior performance in multilingual math tasks, while Claude-haiku demonstrates capabilities across various languages.

Factual Accuracy and Honesty

Claude-haiku is designed with a focus on being helpful, harmless, and honest. It has specific training in factual accuracy and avoiding false assertions.

DeepSeek-V3's specific training in this area is not detailed in the provided information.

Deployment and Efficiency

• Claude-haiku is described as the fastest and least expensive model in the Claude 3 family, optimized for efficiency.
• DeepSeek-V3 uses various techniques for efficient training and inference, such as Multi-head Latent Attention (MLA) and DeepSeekMoE architectures.

Both models prioritize efficiency, but Claude-haiku is specifically designed for speed and cost-effectiveness.

Conclusion

DeepSeek-V3 generally outperforms Claude-haiku in tasks related to general reasoning, mathematical problem-solving, and coding. However, Claude-haiku shows strengths in multimodal capabilities, long context performance, and is optimized for speed and cost-effectiveness. The choice between the two depends on the specific use case and priorities (e.g., raw performance vs. efficiency and multimodal capabilities).