By Hitarth Koshiya 
With the AI landscape changing rapidly, open-source coding models are getting popular because they provide fast access to offline developer tools so code can be written without an internet connection. We will be diving into the powers of the three nown: DeepSea Coder V2, YCoder 9B, and Quen 2.5 Coder 7B. By including a detailed description of the analyses – through different coding challenges, the blog post will give the reader ideas about which model is dominant.
Meet the Contenders
There is a trio of models captured within the ring, all of them which are impressive in terms of their capabilities:
- DeepSea Coder V2:A versital model that has been acclaimed for its robustness.
- YCoder 9B: Efficient, small, and fast model constructed basically for performance.
- Quen 2.5 Coder 7B: The top performer holding the record of the smallest mass row behind to the outstanding performance track record.
Both all specs of all the three given models are run on Dell Precision 5860, which expertly has two NVIDIA RTX A6000 GPUs with a total of 96 GB of VRAM. This arrangement was the case when the three models were loaded at the same time, making a level playing field for the comparison to be done.
Testing Methodology
In order to determine which model works the best, we will first run through a series of coding challenges that begin with a simple task, i.e., creating a game of Snake in Python. Then we will, based on the time efficiency of the code writing and the correcting of the code, evaluate each model.
Round 1: The Snake Game
The first one is the task of writing the code for the Snake game.
DeepSea Coder V2
DeepSea Coder V2 did the coding in no time at all, making use of about 30 tokens per second. The Tkinter library was used for the game interface, a special decision in the clear choice. But the first code failed to be operational when tried.
YCoder 9B
Next, YCoder 9B created codes that come close to 50 tokens per second, relying on the Turtle library. Even though there were some problems with pixel alignment in the game, this version was far better than the previous one.
Quen 2.5 Coder 7B
The winner turned out to be Quen 2.5, which produced code at an incredible 70 tokens per second. A library called Pygame was also employed by the Quen, and the gameplay was smooth as it did not mismanage the game mechanisms as well.
Round 2: Tetris Challenge
Next, we challenged the models to create Tetris.
DeepSea Coder V2
It was followed by a code generation step, but no links to Pygame were given. Consequently, the game could not be played.
YCoder 9B
It again coded faulty output the same way as it did in the implementation. The game froze upon testing.
Quen 2.5 Coder 7B
Albeit it created new code that was more complete, it yet suffered from issues in execution. The problem was that all the models could not come up with a working Tetris game.
Round 3: Code Wars Challenges
The models were trained using coding challenges from Code Wars, with a very simple task of moving letters in a word forward by ten positions in the alphabet as the first exercise.
DeepSea Coder V2
A highly skilled programming algorithm or function came into existence with the challenge. The function that was created turned out to be the correct one after some minor corrections were done.
YCoder 9B
Besides, the group came up with a working solution that is equivalent to DeepSea Coder in terms of performance.
Quen 2.5 Coder 7B
Quen exhibited the fast body and the mental acuity to come up with the correct answer and then completing the task with ease yet again.
More Complex Challenges
We hurled a harder task by a more complicated challenge which was the production of a prime number using a function. Each model’s performance was as follows:
DeepSea Coder V2
The task remained incomplete as it essayed its alacrity in producing a result upon execution only to time out in the end.
YCoder 9B
Similar results were obtained, the model was not able to meet the time limits and failed to complete the task.
Quen 2.5 Coder 7B
Although it did come up with a solution, it also timed out, which manifested that the problem was too hard for all three models.
Final Round: Median of Two Sorted Arrays
For one last test, we gave the models the task of finding the medium of two sorted arrays and at the same time make sure that the solution met specific runtime complexity constraints.
DeepSea Coder V2
Through some adjustments, the previously generated model gave rise to a well-functioning solution.
YCoder 9B
Besides, the system’s ability to incorporate the correct implementation of toggle switch indicates its high performance.
Quen 2.5 Coder 7B
One more time, Quen has made the code execution quick and precise, which validated its title of the best performer in this series of challenges.
Conclusion: The Winner
After several tests on multiple tasks, Quen 2.5 Coder 7B came out as the highest open-source coding model for local usage. It continually displayed a level of speed and correctness higher than the others, especially in the field of creating functional games and solving coding challenges.
Even though every model has its strengths, it is clear that Quen is preferred by developers who want a reliable, powerful coding assistant that runs completely locally.
If you wish to try out these models, you can use high-performance Dell Precision 5860 workstation, one of the powerful models, to your great success.
