I Tested DeepSeek’s R1 and V3 Coding Skills – and we’re not All Doomed (Yet).

DeepSeek took off into the world’s consciousness this previous weekend. It stands apart for 3 effective factors:

1. It’s an AI chatbot from China, instead of the US

2. It’s open source.

3. It uses vastly less infrastructure than the big AI tools we’ve been looking at.

Also: Apple researchers expose the secret sauce behind DeepSeek AI

Given the US federal government’s issues over TikTok and possible Chinese government involvement in that code, a brand-new AI emerging from China is bound to generate attention. ZDNET’s Radhika Rajkumar did a deep dive into those issues in her post Why China’s DeepSeek might burst our AI bubble.

In this post, we’re avoiding politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the very same set of AI coding tests I have actually thrown at 10 other big language designs. According to DeepSeek itself:

Choose V3 for jobs requiring depth and accuracy (e.g., solving sophisticated mathematics problems, producing complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., consumer assistance automation, fundamental text processing).

You can select in between R1 and V3 by clicking the little button in the chat interface. If the button is blue, you’re using R1.

The short answer is this: impressive, however plainly not ideal. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my first test of ChatGPT’s programs prowess, way back in the day. My better half needed a plugin for WordPress that would help her run a participation gadget for her online group.

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Her needs were relatively simple. It required to take in a list of names, one name per line. It then had to arrange the names, and if there were replicate names, separate them so they weren’t noted side-by-side.

I didn’t actually have time to code it for her, so I decided to provide the AI the challenge on an impulse. To my huge surprise, it worked.

Since then, it’s been my first test for AIs when evaluating their programs skills. It needs the AI to understand how to set up code for the WordPress structure and follow triggers plainly enough to create both the interface and program logic.

Only about half of the AIs I have actually tested can totally pass this test. Now, nevertheless, we can add another to the winner’s circle.

DeepSeek V3 created both the user interface and program reasoning precisely as defined. When It Comes To DeepSeek R1, well that’s an interesting case. The “thinking” element of R1 caused the AI to spit out 4502 words of analysis before sharing the code.

The UI looked different, with much wider input areas. However, both the UI and reasoning worked, so R1 likewise passes this test.

So far, DeepSeek V3 and R1 both passed one of 4 tests.

Test 2: Rewriting a string function

A user complained that he was unable to get in dollars and cents into a donation entry field. As composed, my code just allowed dollars. So, the test involves providing the AI the routine that I wrote and asking it to rewrite it to permit both dollars and cents

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Usually, this results in the AI producing some regular expression validation code. DeepSeek did generate code that works, although there is space for enhancement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the thinking before generating the code in R1 was also very long.

My greatest concern is that both models of the DeepSeek recognition makes sure validation approximately 2 decimal places, but if a huge number is entered (like 0.30000000000000004), using parseFloat doesn’t have specific rounding understanding. The R1 design likewise used JavaScript’s Number conversion without looking for edge case inputs. If bad data returns from an earlier part of the regular expression or a non-string makes it into that conversion, the code would crash.

It’s odd, because R1 did provide an extremely good list of tests to validate against:

So here, we have a split decision. I’m giving the point to DeepSeek V3 since neither of these problems its code produced would trigger the program to break when run by a user and would produce the anticipated outcomes. On the other hand, I have to a fail to R1 since if something that’s not a string in some way gets into the Number function, a crash will occur.

Which offers DeepSeek V3 two triumphes of 4, but DeepSeek R1 only one triumph of four up until now.

Test 3: Finding an annoying bug

This is a test produced when I had a really irritating bug that I had difficulty locating. Once once again, I decided to see if ChatGPT might manage it, which it did.

The obstacle is that the response isn’t apparent. Actually, the challenge is that there is an obvious response, based on the error message. But the obvious response is the incorrect answer. This not just caught me, however it routinely captures some of the AIs.

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Solving this bug needs understanding how specific API calls within WordPress work, being able to see beyond the error message to the code itself, and after that understanding where to find the bug.

Both DeepSeek V3 and R1 passed this one with nearly identical answers, bringing us to 3 out of four wins for V3 and two out of four wins for R1. That already puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.

Will DeepSeek score a home run for V3? Let’s learn.

Test 4: Writing a script

And another one bites the dust. This is a challenging test because it requires the AI to understand the interplay between 3 environments: AppleScript, the Chrome item design, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test due to the fact that Keyboard Maestro is not a mainstream programming tool. But ChatGPT dealt with the test quickly, understanding precisely what part of the issue is handled by each tool.

Also: How ChatGPT scanned 170k lines of code in seconds, saving me hours of work

Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither model understood that it required to divide the job in between directions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing customized routines for AppleScript that are native to the language.

Weirdly, the R1 design failed as well since it made a lot of inaccurate assumptions. It assumed that a front window always exists, which is certainly not the case. It also made the assumption that the presently front running program would constantly be Chrome, instead of explicitly checking to see if Chrome was running.

This leaves DeepSeek V3 with three correct tests and one stop working and DeepSeek R1 with 2 appropriate tests and two stops working.

Final thoughts

I found that DeepSeek’s insistence on using a public cloud e-mail address like gmail.com (rather than my regular email address with my business domain) was irritating. It likewise had a number of responsiveness stops working that made doing these tests take longer than I would have liked.

Also: How to utilize ChatGPT to write code: What it succeeds and what it doesn’t

I wasn’t sure I ‘d have the ability to compose this short article due to the fact that, for most of the day, I got this mistake when attempting to register:

DeepSeek’s online services have actually just recently dealt with large-scale malicious attacks. To make sure continued service, registration is briefly limited to +86 contact number. Existing users can visit as normal. Thanks for your understanding and support.

Then, I got in and had the ability to run the tests.

DeepSeek seems to be overly loquacious in regards to the code it produces. The AppleScript code in Test 4 was both wrong and excessively long. The routine expression code in Test 2 was proper in V3, but it might have been written in a method that made it a lot more maintainable. It stopped working in R1.

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I’m absolutely satisfied that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which means there’s absolutely room for enhancement. I was dissatisfied with the results for the R1 design. Given the choice, I ‘d still pick ChatGPT as my programming code assistant.

That said, for a brand-new tool operating on much lower facilities than the other tools, this could be an AI to enjoy.

What do you believe? Have you tried DeepSeek? Are you utilizing any AIs for programming assistance? Let us know in the remarks listed below.

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