2024-01-15
Watch the full analysis:
Introduction & Features
- Version: DeepSeek V3
- Performance: 3x faster than V2
- APA Compatibility: Complete
- Open Source Model: On par with Claude 3.5 Sonnet, surpassing Claude 30 Sonnet
- Model Scale: 67.1B Mixture of Experts model, 37B active parameters
- Training Data: 14 trillion high-quality tokens
- Cost-effectiveness: One of the lowest costs, especially before February 8th
Performance Comparison
- Math benchmark: DeepSeek scores 90, surpassing GPT-40's 74.6
- Language Understanding: DeepSeek excels in multiple benchmark tests
Architecture & Technology
- Base Architecture: Transformer blocks, Mixture of Experts (MoE)
- Attention Mechanism: Multi-head latent attention, supporting 128,000 tokens
- Memory Capability: Able to remember every bit of information in long sequences
Programming Tests
- Python Tests: Challenging problems including unit matrix generation, LCM, Faray sequence, and ECG sequence
- JavaScript Tests: Advanced challenges like the Josephus problem
- Results: DeepSeek performs excellently in expert-level tests, resolving errors and passing most challenges
Logic & Reasoning Tests
- Logic Problems: Such as counting the number of "O"s in "strawberry"
- Reasoning Ability: Successfully solves a series of logical problems
Autonomous Behavior Tests
- Agent Behavior: Tested using the Praise AI package
- Task Example: Creating a movie script about a lost cat
- Results: Agents work collaboratively, utilizing search tools and completing tasks
Misdirection Tests
- Scenario Test: Runway trolley problem
- Results: DeepSeek shows limitations in handling moral judgments
Summary
- DeepSeek V3 matches Claude 3.5 Sonnet, outperforming in certain benchmarks
- Open source, cost-effective, and excels in expert-level programming and logical reasoning tests
- Good autonomous behavior capabilities but faces challenges in misdirection tests
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