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

DeepSeek blew up into the world’s consciousness this previous weekend. It sticks out for 3 effective factors:

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

2. It’s open source.

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

Also: Apple scientists expose the secret sauce behind DeepSeek AI

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

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

Choose V3 for tasks requiring depth and precision (e.g., solving advanced math problems, creating intricate code).

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

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

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

Test 1: Writing a WordPress plugin

This test was actually my very first test of ChatGPT’s programming expertise, way back in the day. My partner required a plugin for WordPress that would assist her run an involvement device for her online group.

Also: The very best AI for coding in 2025 (and what not to use)

Her requirements were relatively basic. 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 really have time to code it for her, so I decided to give the AI the challenge on an impulse. To my big surprise, it worked.

Since then, it’s been my first test for AIs when assessing their programs skills. It requires the AI to know how to establish code for the WordPress structure and follow triggers plainly sufficient to develop both the interface and program reasoning.

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

DeepSeek V3 produced both the interface and program logic exactly as defined. As for DeepSeek R1, well that’s an intriguing case. The “thinking” aspect of R1 triggered the AI to spit out 4502 words of analysis before sharing the code.

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

Up until now, DeepSeek V3 and R1 both passed one of 4 tests.

Test 2: Rewriting a string function

A user complained that he was not able to get in dollars and cents into a contribution entry field. As composed, my code just permitted dollars. So, the test includes providing the AI the regular that I wrote and asking it to rewrite it to allow for both dollars and cents

Also: My preferred ChatGPT feature simply got way more effective

Usually, this leads to the AI generating some routine expression validation code. DeepSeek did create code that works, although there is room for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the reasoning before producing the code in R1 was also extremely long.

My biggest issue is that both designs of the DeepSeek validation guarantees recognition approximately 2 decimal locations, but if a huge number is gone into (like 0.30000000000000004), using parseFloat doesn’t have specific rounding understanding. The R1 model likewise used JavaScript’s Number conversion without looking for edge case inputs. If bad information 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, since R1 did present an extremely nice list of tests to confirm versus:

So here, we have a split decision. I’m offering the point to DeepSeek V3 due to the fact that neither of these concerns its code produced would cause the program to break when run by a user and would produce the anticipated results. On the other hand, I need to offer a fail to R1 since if something that’s not a string in some way gets into the Number function, a crash will take place.

Which gives DeepSeek V3 two triumphes of 4, but R1 only one win out of 4 up until now.

Test 3: Finding a bothersome bug

This is a test produced when I had a very frustrating bug that I had difficulty tracking down. Once once again, I chose to see if ChatGPT could handle it, which it did.

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

Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the complimentary version

Solving this bug requires comprehending how specific API calls within WordPress work, having the ability to see beyond the error message to the code itself, and after that knowing where to find the bug.

Both DeepSeek V3 and R1 passed this one with almost identical answers, bringing us to 3 out of four wins for V3 and 2 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 discover out.

Test 4: Writing a script

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

I would have called this an unfair test since Keyboard Maestro is not a mainstream programs tool. But ChatGPT handled the test easily, comprehending exactly 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 needed to divide the task between instructions to Keyboard Maestro and Chrome. It also had fairly weak understanding of AppleScript, writing customized routines for AppleScript that are belonging to the language.

Weirdly, the R1 design failed too due to the fact that it made a bunch of incorrect presumptions. It presumed that a front window constantly exists, which is certainly not the case. It also made the presumption that the currently front running program would constantly be Chrome, instead of explicitly inspecting to see if Chrome was running.

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

Final thoughts

I found that DeepSeek’s persistence on utilizing a public cloud email address like gmail.com (rather than my normal e-mail address with my business domain) was irritating. It likewise had a variety of responsiveness fails that made doing these tests take longer than I would have liked.

Also: How to use ChatGPT to write code: What it does well and what it does not

I wasn’t sure I ‘d have the ability to compose this short article because, 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 temporarily restricted to +86 phone numbers. Existing users can log in as normal. Thanks for your understanding and assistance.

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

DeepSeek seems to be extremely loquacious in regards to the code it creates. The AppleScript code in Test 4 was both incorrect and exceedingly long. The regular expression code in Test 2 was correct in V3, but it could have been composed in a method that made it a lot more maintainable. It stopped working in R1.

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

I’m absolutely pleased that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which means there’s absolutely space for improvement. I was dissatisfied with the outcomes for the R1 model. Given the choice, I ‘d still choose ChatGPT as my programming code assistant.

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

What do you think? Have you attempted DeepSeek? Are you using any AIs for programming support? Let us know in the comments listed below.

You can follow my daily job updates on social networks. Make certain to sign up for my weekly upgrade newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/ DavidGewirtz, on Instagram at Instagram.com/ DavidGewirtz, on Bluesky at @DavidGewirtz. com, and on YouTube at YouTube.com/ DavidGewirtzTV.