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

DeepSeek exploded into the world’s consciousness this past weekend. It stands out for 3 powerful reasons:

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

2. It’s open source.

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

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

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

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

Choose V3 for tasks needing depth and precision (e.g., solving advanced mathematics issues, creating complicated code).

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

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

The brief answer is this: remarkable, however clearly not ideal. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was actually my first test of ChatGPT’s shows expertise, method back in the day. My partner required a plugin for WordPress that would help her run an involvement gadget for her online group.

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Her requirements were relatively basic. It required to take in a list of names, one name per line. It then needed to sort the names, and if there were duplicate names, separate them so they weren’t noted side-by-side.

I didn’t really have time to code it for her, so I decided to give the AI the challenge on a whim. To my huge surprise, it worked.

Ever since, it’s been my first test for AIs when examining their programs abilities. It requires the AI to know how to set up code for the WordPress framework and follow triggers clearly adequate to produce both the user interface and program logic.

Only about half of the AIs I have actually checked can totally pass this test. Now, nevertheless, we can include one more to the winner’s circle.

DeepSeek V3 developed both the interface and program logic exactly as defined. 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 different, with much areas. However, both the UI and logic worked, so R1 also passes this test.

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

Test 2: Rewriting a string function

A user grumbled that he was not able to get in dollars and cents into a contribution entry field. As composed, my code just allowed dollars. So, the test includes offering the AI the regular that I composed and asking it to reword it to enable 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 space for enhancement. The code that DeepSeek V2 wrote was unnecessarily long and repetitive while the thinking before creating the code in R1 was likewise long.

My most significant concern is that both models of the DeepSeek recognition guarantees validation as much as 2 decimal places, but if a huge number is gotten in (like 0.30000000000000004), making use of parseFloat does not have specific rounding knowledge. The R1 design likewise used 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 present a very great list of tests to verify against:

So here, we have a split choice. I’m offering 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 generate the expected results. On the other hand, I need to give a stop working to R1 due to the fact that if something that’s not a string somehow enters the Number function, a crash will ensue.

And that offers DeepSeek V3 two triumphes of 4, but DeepSeek R1 only one win out of 4 so far.

Test 3: Finding an irritating bug

This is a test produced when I had an extremely bothersome bug that I had problem locating. Once again, I chose to see if ChatGPT might manage it, which it did.

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

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

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

Will DeepSeek score a crowning achievement for V3? Let’s learn.

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 3 environments: AppleScript, the Chrome item model, and a Mac scripting tool called Keyboard Maestro.

I would have called this an unreasonable test because Keyboard Maestro is not a mainstream programming tool. But ChatGPT dealt with the test easily, understanding precisely what part of the issue is dealt with 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 understanding. Neither design knew that it needed to divide the job in between guidelines to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, composing custom-made regimens for AppleScript that are native to the language.

Weirdly, the R1 design failed too due to the fact that it made a lot of incorrect presumptions. It assumed that a front window always exists, which is certainly not the case. It also made the presumption that the presently front running program would always be Chrome, instead of explicitly examining to see if Chrome was running.

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

Final thoughts

I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (instead of my normal 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 doesn’t

I wasn’t sure I ‘d have the ability to write this article since, for the majority of the day, I got this error when trying to sign up:

DeepSeek’s online services have just recently faced large-scale harmful attacks. To make sure continued service, registration is momentarily limited to +86 phone numbers. Existing users can log in as typical. Thanks for your understanding and assistance.

Then, I got in and was able to run the tests.

DeepSeek appears to be overly loquacious in regards to the code it creates. The AppleScript code in Test 4 was both wrong and exceedingly long. The regular expression code in Test 2 was appropriate in V3, however it might have been composed in a method that made it far more maintainable. It stopped working in R1.

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

I’m definitely impressed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which suggests there’s certainly space for improvement. I was disappointed with the results for the R1 model. Given the option, I ‘d still select ChatGPT as my programming code assistant.

That said, for a new tool working on much lower infrastructure than the other tools, this could be an AI to enjoy.

What do you think? Have you attempted DeepSeek? Are you utilizing any AIs for shows assistance? Let us know in the remarks below.

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