In this comprehensive analysis, I'll explore the performance comparison between two leading AI language models: DeepSeek V3 and ChatGPT o3. These models represent the current state-of-the-art in AI technology, with each demonstrating remarkable capabilities across various benchmarks and evaluations.

The technical reports reveal fascinating similarities in their performance metrics, with both models achieving identical scores on several key benchmarks: 88.5% on MMLU, 75.9% on MMLU-Pro, and 59.1% on GPQA-Diamond. DeepSeek V3 distinguishes itself with its impressive 671B parameter architecture and documented training on 14.8 trillion tokens, while ChatGPT o3's architectural details remain less publicly known.

Let's examine their performance across multiple dimensions, from mathematical reasoning and coding capabilities to language understanding and safety features, to understand how these two powerful models compare in the current AI landscape. This analysis will provide valuable insights into their relative strengths and the overall state of advanced language models.

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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Performance Comparison: DeepSeek V3 vs ChatGPT o3

To compare the performance of DeepSeek V3 and ChatGPT o3 (OpenAI o3-mini), we'll examine various benchmarks and evaluations presented in the technical reports. It's important to note that direct comparisons can be challenging due to differences in evaluation methodologies and the specific versions of models tested.

Benchmark Performance

Both models have been evaluated on a range of benchmarks testing various capabilities. Let's compare their performance across different categories:

1. Knowledge and reasoning
2. Coding and mathematics
3. Language understanding and generation
4. Safety and alignment

Knowledge and Reasoning

MMLU (Massive Multitask Language Understanding)

• DeepSeek V3: 88.5%
• ChatGPT o3 (o3-mini): 88.5%

Both models perform similarly on this benchmark, which tests knowledge across various academic and professional domains.

MMLU-Pro

• DeepSeek V3: 75.9%
• ChatGPT o3 (o3-mini): 75.9%

Again, both models show identical performance on this more challenging version of MMLU.

GPQA-Diamond

• DeepSeek V3: 59.1%
• ChatGPT o3 (o3-mini): 59.1%

This benchmark tests PhD-level knowledge, and both models achieve the same score.

Coding and Mathematics

MATH 500

• DeepSeek V3: 90.2%
• ChatGPT o3 (o3-mini): Not reported

DeepSeek V3 shows strong performance on this mathematical reasoning benchmark.

AIME 2024

• DeepSeek V3: 39.2%
• ChatGPT o3 (o3-mini): 39.2%

Both models perform identically on this advanced mathematics competition benchmark.

Codeforces

• DeepSeek V3: 51.6 percentile
• ChatGPT o3 (o3-mini): 51.6 percentile

The models show equal performance on this competitive programming benchmark.

SWE-bench Verified

• DeepSeek V3: 42.0% resolved
• ChatGPT o3 (o3-mini): 42.0% resolved

Both models demonstrate the same capability in resolving software engineering tasks.

Language Understanding and Generation

DROP (Reading Comprehension)

• DeepSeek V3: 91.6% F1 score (3-shot)
• ChatGPT o3 (o3-mini): 91.0% F1 score (3-shot)

DeepSeek V3 slightly outperforms ChatGPT o3 on this reading comprehension task.

AlpacaEval 2.0

• DeepSeek V3: 70.0% win rate
• ChatGPT o3 (o3-mini): Not reported

DeepSeek V3 shows strong performance in this open-ended conversation evaluation, but we lack comparative data for ChatGPT o3.

Safety and Alignment

Jailbreak Resistance

• DeepSeek V3: 97% resistance to human-sourced jailbreaks
• ChatGPT o3 (o3-mini): 97% resistance to human-sourced jailbreaks

Both models demonstrate equal resistance to jailbreak attempts.

Instruction Hierarchy

Both models show similar performance in following instruction hierarchies, with slight variations across different test scenarios. Overall, they appear to be comparably aligned with safety considerations.

Model Characteristics

While performance on benchmarks is similar, there are some key differences between the models:

1. Architecture: DeepSeek V3 uses a Mixture-of-Experts (MoE) architecture, while ChatGPT o3's architecture is not explicitly stated in the provided information.

2. Parameters:

   • DeepSeek V3: 671B total parameters, 37B activated for each token
   • ChatGPT o3 (o3-mini): Total parameter count not provided

3. Training Data:

   • DeepSeek V3: 14.8 trillion tokens
   • ChatGPT o3 (o3-mini): Training data size not specified

4. Inference Speed: DeepSeek V3 reports 1.8 times TPS (Tokens Per Second) improvement due to multi-token prediction, while no specific speed metrics are provided for ChatGPT o3.

Conclusion

Based on the available information, DeepSeek V3 and ChatGPT o3 (o3-mini) demonstrate remarkably similar performance across a wide range of benchmarks. They show nearly identical scores on key evaluations such as MMLU, MMLU-Pro, GPQA, and coding-related tasks.

DeepSeek V3 appears to have a slight edge in some areas:

1. Mathematical reasoning (e.g., MATH 500 performance)
2. Reading comprehension (marginally higher score on DROP)
3. Potentially faster inference due to multi-token prediction

However, without more comprehensive head-to-head comparisons and considering the limitations of benchmark evaluations, it's difficult to definitively state that one model significantly outperforms the other. Both models represent state-of-the-art performance in language AI and appear to be closely matched in capabilities.

Limitations of Comparison

1. Evaluation methodologies may differ between the two technical reports.
2. The specific versions of models tested may not be directly comparable.
3. Some benchmarks are reported for one model but not the other, making comprehensive comparison challenging.
4. Real-world performance may vary from benchmark results.
5. The full capabilities of these models may not be captured by the available evaluations.