DeepSeek V3 is HERE! (They Just Beat EVERYONE)

1. DeepSeek V3 achieves impressive benchmark scores.

🥇95 00:05

DeepSeek V3 outperforms competitors in various benchmarks, showcasing its capabilities in math and coding tasks.

• It dominates benchmarks like MMLU and GPTQA, with significant scores in math.
• The only benchmark it doesn't lead in is Swebench Verified.
• Its performance is more than double that of Cloud 3 5 Sonnet in Code Forces.

2. DeepSeek V3 is an advanced mixture of experts model.

🥇92 01:06

This model utilizes a mixture of experts approach, activating only a subset of its parameters during prompts.

• It has 671 billion total parameters, with 37 billion activated for efficiency.
• This design allows for high performance without requiring excessive local resources.
• The model is optimized for both inference and pre-training efficiency.

3. Training DeepSeek V3 was cost-effective and efficient.

🥇90 02:33

The total training cost for DeepSeek V3 was approximately $5.5 million, highlighting its efficiency.

• It required only 2.7 million H800 GPU hours for full training.
• The training process was stable, avoiding significant loss spikes.
• Costs were calculated based on rental prices, excluding infrastructure and employee expenses.

4. Reinforcement learning enhances DeepSeek V3's reliability.

🥇93 04:49

DeepSeek V3 employs both rule-based and model-based reward systems in its reinforcement learning.

• Rule-based rewards validate deterministic answers, improving reliability for math and coding tasks.
• Model-based rewards are used for creative tasks without definitive answers.
• This dual approach mitigates risks of reward manipulation.

5. DeepSeek V3 is open source and widely accessible.

🥈88 06:46

The model is open source, allowing for broad distribution and use across various platforms.

• Users can access it through DeepSeek, although data privacy concerns exist.
• The open-source nature encourages community contributions and improvements.
• It supports a range of functionalities, including file processing and web search.

6. DeepSeek V3 demonstrates superior coding capabilities.

🥇95 14:50

DeepSeek V3 outperforms other models like Claude 3.7 Max in coding tasks, particularly in creating complex simulations.

• It successfully generated a fully interactive Rubik's cube simulation with dynamic size adjustments.
• Despite some issues, it serves as a benchmark for evaluating AI coding models.
• Other models struggled to replicate the same level of functionality.

7. DeepSeek V3's performance in game development is impressive.

🥇92 18:48

The model created a visually stunning version of the classic Snake game with unique enhancements and dynamic effects.

• It included features like glowing trails and particle explosions for food consumption.
• The game mechanics were complex, showcasing the model's advanced capabilities.
• This highlights DeepSeek V3's potential in game development applications.

8. DeepSeek V3 can simulate realistic ant farm behavior.

🥇90 19:48

The model generated an interactive ant farm simulation, mimicking real ant behaviors and environmental interactions.

• It included features like dynamic tunnel digging and food foraging.
• While some aspects required manual adjustments, the output was still impressive.
• This further establishes DeepSeek V3 as a versatile tool for simulation tasks.