Deepseek v3 0324: Powerful New Opensource LLM! BEATS 3.7 Sonnet! (Fully Tested)

DeepSeek V3.1 (30324) Model Release Summary

Key Concepts:

• DeepSeek V3.1 (30324) Model: A new 700GB open-source model released by DeepSeek, built upon the DeepSeek V3 model.
• MIT License: The open-source license under which the model is released.
• Mixture of Experts (MoE): The architecture expected for the upcoming DeepSeek R2 model.
• Coding Performance: The model's ability to generate and debug code in various languages.
• Mathematical Reasoning: The model's ability to solve mathematical problems and equations.
• Open Router: A platform providing free API access to the DeepSeek V3.1 model.
• API Access: Paid access to the model through DeepSeek's API platform.
• Benchmarking: The process of evaluating the model's performance against other models.

Overview of DeepSeek V3.1 Model

The DeepSeek team has launched the DeepSeek V3.1 (30324) model, a 700GB open-source model under the MIT license. This model is built upon the DeepSeek V3 model and is reported to have enhanced performance in math, coding, and reasoning. The DeepSeek team is also gearing up for the R2 launch in April, which will be a mixture of experts model.

Performance Claims and Benchmarks

Internal benchmarks suggest that the DeepSeek V3.1 model outperforms Claude 3.5 and 3.7 in coding-related tasks. Some users claim it to be the best non-reasoning model currently available. The model excels in front-end development, generating code quickly and efficiently. It also performs well in mathematical tasks.

Accessing the Model

• DeepSeek API: Users can access the model via the DeepSeek API by creating an account, linking a credit card, and using the chatbot interface.
• Open Router: Free API access is available through the Open Router platform.

Testing and Evaluation

The model was assessed on various prompts, including:

• Front-end Development: Building an app to track monthly incomes and expenses.
  • Result: The model successfully generated a functional finance app with income/expense tracking, a monthly summary, data visualization, and transaction history. Pass.
• Python Coding: Generating The Game of Life in Python.
  • Result: The model successfully generated the Game of Life simulation. Pass.
• SVG Generation: Creating an SVG representation of a butterfly with symmetrical wings.
  • Result: The model successfully generated an SVG image of a symmetrical butterfly, a task that many models fail at. Pass.
• Mathematical Problem Solving: Solving a quadratic equation.
  • Result: The model correctly solved the quadratic equation using the quadratic formula, arriving at the correct answers of 3 and 1. Pass.
• Logical Reasoning: Solving a train meeting problem.
  • Result: The model correctly solved the problem, using the distance equals speed times time equation and arriving at the correct meeting time of 10:54 AM. Pass.
• Debugging: Fixing a Python bug.
  • Result: The model correctly identified and fixed the bug in the Python code, providing both a corrected code snippet and an explanation of the fix. Pass.
• Combinatorial Math: Determining valid combinations of products to reach a specific total cost.
  • Result: The model provided multiple valid combinations of product quantities that totaled $500, demonstrating its ability to solve equations with multiple variables. Pass.
• Reading Comprehension: Answering a question based on a provided passage.
  • Result: The model correctly recalled the number of kittens Sofia saw (three) from the passage, demonstrating good reading comprehension and memory recall. Pass.

Conclusion

The DeepSeek V3.1 model is a remarkable open-source model with strong capabilities in coding and math. It is accessible through both DeepSeek's API and the Open Router platform. The model's performance in various tasks, including front-end development, Python coding, SVG generation, mathematical problem-solving, and debugging, is impressive. The model is a cost-effective alternative to models like Claude 3.5 and 3.7. Further benchmark results and coding-focused videos are anticipated.