DeepSeek-V3 is a notable advancement in the landscape of open-source language models, particularly due to its innovative architecture and impressive performance metrics. Here’s a comprehensive comparison of DeepSeek-V3 with other leading open-source models.
Overview of DeepSeek-V3
DeepSeek-V3 is a Mixture-of-Experts (MoE) model featuring:
- Total Parameters: 671 billion
- Activated Parameters: 37 billion per token during inference
- Context Length: Up to 128,000 tokens
- Training Dataset: 14.8 trillion tokens
- Inference Speed: Approximately 60 tokens per second, which is three times faster than its predecessor, DeepSeek-V2.5.
Key Innovations
- Multi-head Latent Attention (MLA): Reduces memory usage while maintaining performance.
- Auxiliary-loss-free Load Balancing: Enhances specialization among experts without degrading performance.
- FP8 Mixed Precision Training: Allows for efficient resource utilization, requiring only 2.788 million GPU hours for training.
Performance Highlights
DeepSeek-V3 has demonstrated superior performance across various benchmarks:
- Mathematical Reasoning: Scored 90.2% on MATH-500, outperforming many competitors.
- Coding Tasks: Achieved 51.6% on Codeforces and excelled in other coding benchmarks.
- Multilingual Capabilities: Strong performance in Chinese evaluations (e.g., 90.9% on CLUEWSC) and competitive scores in English tasks like MMLU (88.5%) and GPQA (59.1%).
Comparison with Other Open-Source Models
| Benchmark (Metric) | DeepSeek V3 | DeepSeek V2.5 | Qwen2.5 | Llama3.1 | Claude-3.5 | GPT-4o |
| Architecture | MoE | MoE | Dense | Dense | – | – |
| # Activated Params | 37B | 21B | 72B | 405B | – | – |
| # Total Params | 671B | 236B | 72B | 405B | – | – |
| English | ||||||
| MMLU (EM) | 88.5 | 80.6 | 85.3 | 88.6 | 88.3 | 87.2 |
| MMLU-Redux (EM) | 89.1 | 80.3 | 85.6 | 86.2 | 88.9 | 88.0 |
| MMLU-Pro (EM) | 75.9 | 66.2 | 71.6 | 73.3 | 78.0 | 72.6 |
| DROP (3-shot F1) | 91.6 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 |
| IF-Eval (Prompt Strict) | 86.1 | 80.6 | 84.1 | 86.0 | 86.5 | 84.3 |
| GPQA-Diamond (Pass@1) | 59.1 | 41.3 | 49.0 | 51.1 | 65.0 | 49.9 |
| SimpleQA (Correct) | 24.9 | 10.2 | 9.1 | 17.1 | 28.4 | 38.2 |
| FRAMES (Acc.) | 73.3 | 65.4 | 69.8 | 70.0 | 72.5 | 80.5 |
| LongBench v2 (Acc.) | 48.7 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 |
| Code | ||||||
| HumanEval-Mul (Pass@1) | 82.6 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 |
| LiveCodeBench (Pass@1-COT) | 40.5 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 |
| LiveCodeBench (Pass@1) | 37.6 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 |
| Codeforces (Percentile) | 51.6 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 |
| SWE Verified (Resolved) | 42.0 | 22.6 | 23.8 | 24.5 | 50.8 | 38.8 |
| Aider-Edit (Acc.) | 79.7 | 71.6 | 65.4 | 63.9 | 84.2 | 72.9 |
| Aider-Polyglot (Acc.) | 49.6 | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 |
| Math | ||||||
| AIME 2024 (Pass@1) | 39.2 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 |
| MATH-500 (EM) | 90.2 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 |
| CNMO 2024 (Pass@1) | 43.2 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 |
| Chinese | ||||||
| CLUEWSC (EM) | 90.9 | 90.4 | 91.4 | 84.7 | 85.4 | 87.9 |
| C-Eval (EM) | 86.5 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 |
| C-SimpleQA (Correct) | 64.1 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 |
Conclusion
DeepSeek-V3 sets a new standard in the open-source language model arena with its advanced architecture and exceptional performance metrics. While it competes favorably against both open-source and closed-source models like GPT-4 and Claude 3.5, considerations regarding context length and response consistency remain important factors for potential users. Its cost-effective nature and robust capabilities make it an attractive option for developers and researchers alike looking to leverage cutting-edge AI technology.
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