The Deepseek team has quietly launched a new version of their flagship language model, the Deepseek v3 0324, also known as the Deepseek v3.1 model. This massive 700GB open-source model, released under the MIT license, is already making waves in the AI community.

According to internal benchmarks and user reports, the Deepseek v3.1 model is outperforming the proprietary GPT-3.5 and GPT-3.7 models in various tasks, particularly in the areas of coding, math, and reasoning. The model's enhanced performance in frontend development, where it can quickly and efficiently generate code, is especially noteworthy. Users have reported the model building an entire website with 800 lines of flawless code in a single shot.

The Deepseek v3.1 model is also excelling in mathematical problem-solving, showcasing its ability to tackle complex equations and provide multiple valid solutions. Its reading comprehension and logical reasoning capabilities have also been praised, with the model demonstrating strong memory recall and problem-solving skills.

Deepseek has made the API for this new model available, allowing users to access it through their platform or the open-source Openrouter. This accessibility, combined with the model's impressive performance, makes it a compelling alternative to the more expensive proprietary models.

As the Deepseek team prepares for the launch of their Deepseek R2 model in April, the Deepseek v3.1 stands as a significant milestone for open-source language models, proving that they can rival and even outperform their proprietary counterparts in various domains.

The Deep Seek 3.1 model has showcased its impressive capabilities in developing a comprehensive finance tracking application. With just a simple prompt, the model was able to generate a well-structured app that allows users to easily manage their monthly income, expenses, and overall financial balance.

The application features a clean and intuitive user interface, enabling users to input their transactions seamlessly. The model has effectively implemented the necessary functionality to display a monthly summary, including total income, expenses, and the resulting balance. Additionally, the app provides a visual representation of the data, allowing users to gain valuable insights into their financial trends.

The model's ability to quickly and efficiently code out this finance tracking application, writing over 800 lines of flawless code, is a testament to its exceptional performance in front-end development. This open-source model has the potential to be a game-changer for developers seeking a high-speed, open-source solution for their coding needs.

The Deep Seek 3.1 model has demonstrated its exceptional capabilities in implementing complex logic and optimizing large-scale simulations, as evidenced by its successful generation of the classic "Game of Life" in Python.

The Game of Life is a cellular automaton, a mathematical model that simulates the evolution of a two-dimensional grid of cells over time. The model follows a set of simple rules that govern the birth, survival, and death of cells, resulting in complex and often unpredictable patterns.

The Deep Seek 3.1 model was able to generate a fully functional implementation of the Game of Life, complete with the necessary logic to simulate the evolution of the grid and the visualization of the resulting patterns. The model's ability to handle the intricate calculations and optimizations required for large-scale simulations is a testament to its impressive problem-solving skills and understanding of complex algorithms.

This achievement showcases the Deep Seek 3.1 model's versatility and its potential to be a valuable tool for developers and researchers working on a wide range of computational problems, from simulations and modeling to optimization and algorithm design.

The Deep Seek 3.1 model has showcased its impressive capabilities in generating a symmetrical SVG representation of a butterfly. This is a prompt that often challenges many language models, but the Deep Seek 3.1 model has risen to the occasion.

The generated SVG code displays a beautifully crafted butterfly with symmetrical wings and simple, yet elegant styling. The model has not only accurately captured the overall shape and structure of the butterfly but has also added intricate details such as the antenna, further enhancing the visual appeal of the final output.

This achievement is particularly noteworthy as only a handful of models have been able to successfully generate such a symmetrical and visually appealing SVG representation of a butterfly. The Deep Seek 3.1 model's ability to tackle this challenge with ease is a testament to its advanced capabilities in the realm of visual generation and creative problem-solving.

The model demonstrated its strong mathematical capabilities by successfully solving a quadratic equation. It utilized the standard quadratic formula to find the two solutions, x = 3 and x = 1, which are the correct answers. This showcases the model's ability to handle complex mathematical problems with accuracy and efficiency. The step-by-step approach it took to solve the equation highlights its understanding of the underlying principles and its aptitude for applying them correctly. This performance on the quadratic equation prompt is a testament to the model's robust mathematical reasoning skills, making it a valuable tool for users who require precise solutions to such problems.

A train leaves City A at 8:00 a.m. traveling at 60 km/h. Another train leaves City B at 9:30 a.m. traveling at 90 km/h towards City A. The distance between the cities is 300 km. At what time do the two trains meet?

To solve this problem, we need to find the time when the distance between the two trains is equal to the distance between the cities.

Let's define the variables:

• Distance between the cities: 300 km
• Speed of the first train: 60 km/h
• Speed of the second train: 90 km/h
• Time difference between the departure of the two trains: 1.5 hours (9:30 a.m. - 8:00 a.m.)

We can use the formula: Distance = Speed × Time to find the time when the two trains meet.

The first train travels for t hours and covers a distance of 60t km. The second train travels for (t - 1.5) hours and covers a distance of 90(t - 1.5) km.

The total distance covered by the two trains is equal to the distance between the cities: 60t + 90(t - 1.5) = 300 150t - 135 = 300 150t = 435 t = 2.9 hours

Therefore, the two trains meet at 10:54 a.m.

The model demonstrated its proficiency in debugging Python code by successfully identifying and fixing the bug in the provided code snippet. The bug was that the function was supposed to return the sum of even numbers, but it was returning an odd number instead. The model correctly identified the issue and provided the fixed code, replacing the 1 with 0 to ensure the function returns the sum of even numbers. Additionally, the model offered an alternative fix, showcasing its versatility in problem-solving. This ability to efficiently debug code and provide clear explanations highlights the model's expertise in Python programming and its potential to be a valuable asset for developers in identifying and resolving coding issues.

The model was able to provide multiple valid combinations of product purchases that add up to a total of $500. Here are the key details:

• Product A costs $15 per unit
• Product B costs $25 per unit
• Product C costs $40 per unit
• The customer has a budget of exactly $500 to spend on a mix of these products

The model generated the following valid combinations:

1. 0 units of Product A, 4 units of Product B, 10 units of Product C ($500 total)
2. 5 units of Product A, 2 units of Product B, 8 units of Product C ($500 total)
3. 10 units of Product A, 0 units of Product B, 7 units of Product C ($500 total)
4. 0 units of Product A, 20 units of Product B, 0 units of Product C ($500 total)

This demonstrates the model's strong capabilities in solving multi-variable math problems and finding optimal solutions within a given set of constraints. The ability to provide multiple valid combinations is particularly impressive and showcases the model's problem-solving skills.

The model demonstrated impressive reading comprehension and memory recall abilities. When presented with a passage and asked a specific question about it, the model was able to quickly retrieve the correct answer without rereading the passage. This showcases the model's strong understanding of the text and its ability to recall relevant information effectively.

The model's performance on this task highlights its potential for applications that require efficient information processing and retrieval, such as question-answering systems, summarization tools, and knowledge-based assistants. Its ability to comprehend and remember details from textual input can be valuable in various domains, from educational support to customer service.

Overall, the model's reading comprehension and memory recall capabilities are a testament to its robust natural language understanding and reasoning skills, making it a valuable asset for tasks that involve processing and understanding written information.