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

DeepSeek took off into the world’s awareness this past weekend. It stands apart for 3 powerful reasons:

1. It’s an AI chatbot from China, rather than the US

2. It’s open source.

3. It uses vastly less infrastructure than the huge AI tools we have actually been looking at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

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

In this article, we’re preventing politics. Instead, I’m putting both DeepSeek V3 and DeekSeek R1 through the same set of AI coding tests I’ve tossed at 10 other large language models. According to DeepSeek itself:

Choose V3 for jobs requiring depth and accuracy (e.g., fixing innovative math problems, producing complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, standard text processing).

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

The brief answer is this: outstanding, however plainly not perfect. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my very first test of ChatGPT’s programs prowess, method back in the day. My spouse needed a plugin for WordPress that would help her run an involvement device for her online group.

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Her requirements were fairly basic. It needed 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 truly have time to code it for her, so I chose to offer the AI the difficulty on a whim. To my huge surprise, it worked.

Since then, it’s been my first test for AIs when evaluating their shows abilities. It needs the AI to understand how to establish code for the WordPress framework and follow triggers plainly adequate to produce both the interface and program reasoning.

Only about half of the AIs I’ve evaluated can fully pass this test. Now, nevertheless, we can add another to the winner’s circle.

DeepSeek V3 developed both the user interface and program logic precisely as specified. As for DeepSeek R1, well that’s a fascinating case. The “reasoning” element of R1 caused the AI to spit out 4502 words of analysis before sharing the code.

The UI looked various, with much larger input locations. However, both the UI and logic worked, so R1 likewise passes this test.

Up until now, DeepSeek V3 and R1 both passed one of four tests.

Test 2: Rewriting a string function

A user complained that he was unable to get in dollars and cents into a contribution entry field. As written, my code only permitted dollars. So, the test involves providing the AI the routine that I wrote and asking it to reword it to permit both dollars and cents

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Usually, this results in the AI creating some regular expression validation code. DeepSeek did create code that works, although there is room for improvement. The code that DeepSeek V2 wrote was needlessly long and repetitive while the thinking before creating the code in R1 was also really long.

My most significant issue is that both designs of the DeepSeek validation ensures validation up to 2 decimal locations, but if a large number is entered (like 0.30000000000000004), the use of parseFloat does not have specific rounding knowledge. The R1 design also utilized JavaScript’s Number conversion without inspecting for edge case inputs. If bad data comes back from an earlier part of the regular expression or a non-string makes it into that conversion, the code would crash.

It’s odd, due to the fact that R1 did provide a very good list of tests to confirm against:

So here, we have a split decision. I’m offering the indicate DeepSeek V3 due to the fact that neither of these concerns its code produced would trigger the program to break when run by a user and would create the expected results. On the other hand, I have to offer a stop working to R1 because if something that’s not a string in some way gets into the Number function, a crash will ensue.

And that provides DeepSeek V3 two wins out of 4, but DeepSeek R1 just one win out of four up until now.

Test 3: Finding a bothersome bug

This is a test developed when I had a really frustrating bug that I had difficulty locating. Once again, I decided to see if ChatGPT could manage it, which it did.

The challenge is that the answer isn’t apparent. Actually, the challenge is that there is an obvious answer, based on the mistake message. But the obvious response is the incorrect response. This not just captured me, but it regularly catches a few of the AIs.

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Solving this bug needs understanding how particular API calls within WordPress work, having the ability to see beyond the mistake message to the code itself, and after that understanding where to discover the bug.

Both DeepSeek V3 and R1 passed this one with almost identical responses, bringing us to three out of four wins for V3 and 2 out of 4 wins for R1. That already puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.

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

Test 4: Writing a script

And another one bites the dust. This is a challenging test due to the fact that it needs the AI to comprehend the interplay in between three environments: AppleScript, the Chrome things model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unjust test since Keyboard Maestro is not a traditional programming tool. But ChatGPT handled the test easily, comprehending precisely what part of the issue is handled by each tool.

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

Unfortunately, neither DeepSeek V3 or R1 had this level of knowledge. Neither model knew that it required to divide the job in between instructions to Keyboard Maestro and Chrome. It also had relatively weak understanding of AppleScript, composing custom regimens for AppleScript that are belonging to the language.

Weirdly, the R1 design stopped working as well due to the fact that it made a lot of inaccurate presumptions. It presumed that a front window always exists, which is absolutely not the case. It also made the presumption that the currently front running program would always be Chrome, rather than explicitly examining to see if Chrome was running.

This leaves DeepSeek V3 with 3 proper tests and one fail and DeepSeek R1 with 2 right tests and two fails.

Final thoughts

I found that DeepSeek’s persistence on using a public cloud e-mail address like gmail.com (instead of my regular e-mail address with my business domain) was annoying. It also 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 does well and what it does not

I wasn’t sure I ‘d be able to compose this short article because, for many of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have just recently faced massive harmful attacks. To ensure continued service, registration is temporarily restricted to +86 phone numbers. Existing users can log in as usual. Thanks for your understanding and support.

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

DeepSeek seems to be overly chatty in terms of the code it creates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The routine expression code in Test 2 was right in V3, but it might have been written in a way that made it far more maintainable. It failed in R1.

Also: If ChatGPT produces AI-generated code for your app, who does it actually belong to?

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

That stated, for a brand-new tool working on much lower infrastructure than the other tools, this could be an AI to watch.

What do you think? Have you tried DeepSeek? Are you utilizing any AIs for programs support? Let us know in the comments below.

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