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

DeepSeek blew up into the world’s awareness 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 utilizes vastly less facilities than the big AI tools we have actually been looking at.

Also: Apple scientists expose the secret sauce behind DeepSeek AI

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

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

Choose V3 for tasks needing depth and accuracy (e.g., resolving innovative math issues, generating complicated code).

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

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

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

Test 1: Writing a WordPress plugin

This test was in fact my very first test of ChatGPT’s programs prowess, way back in the day. My partner required a plugin for WordPress that would help her run an involvement gadget for her online group.

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

Her needs were fairly simple. 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, different 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 obstacle on a whim. To my substantial surprise, it worked.

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

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

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

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

So far, DeepSeek V3 and R1 both passed among four tests.

Test 2: Rewriting a string function

A user grumbled that he was not able to enter dollars and cents into a donation entry field. As composed, my code just permitted dollars. So, the test involves offering the AI the regular that I composed and asking it to rewrite it to permit both dollars and cents

Also: My favorite ChatGPT function simply got way more effective

Usually, this leads to the AI generating some regular expression recognition code. DeepSeek did generate code that works, although there is space for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitious while the thinking before creating the code in R1 was also long.

My greatest concern is that both models of the DeepSeek recognition ensures validation as much as 2 decimal locations, however if a huge number is gotten in (like 0.30000000000000004), the use of parseFloat doesn’t have explicit rounding understanding. The R1 model also used JavaScript’s Number conversion without inspecting for edge case inputs. If bad information returns from an earlier part of the routine expression or a non-string makes it into that conversion, the code would crash.

It’s odd, because R1 did present a very nice list of tests to confirm against:

So here, we have a split decision. I’m giving the indicate 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 create the expected outcomes. On the other hand, I need to give a fail to R1 because if something that’s not a string somehow enters into the Number function, a crash will take place.

And that provides DeepSeek V3 2 triumphes of 4, however DeepSeek R1 only one win out of 4 so far.

Test 3: Finding an irritating bug

This is a test developed when I had a very bothersome bug that I had trouble tracking down. Once again, I chose to see if ChatGPT could handle it, which it did.

The difficulty is that the answer isn’t apparent. Actually, the difficulty is that there is an apparent response, based upon the mistake message. But the apparent response is the wrong response. This not only caught me, however it frequently catches a few of the AIs.

Also: Are ChatGPT Plus or Pro worth it? Here’s how they compare to the totally free variation

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

Both DeepSeek V3 and R1 passed this one with almost identical answers, bringing us to three 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 crowning achievement for V3? Let’s learn.

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 interaction between three environments: AppleScript, the Chrome object design, 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 managed the test easily, comprehending precisely what part of the issue is dealt with 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 understood that it required to divide the task between directions to Keyboard Maestro and Chrome. It likewise had relatively weak understanding of AppleScript, writing custom regimens for AppleScript that are native to the language.

Weirdly, the R1 model failed too since it made a bunch of incorrect presumptions. It assumed that a front window always exists, which is certainly not the case. It also made the presumption that the currently front running program would always be Chrome, rather than clearly checking to see if Chrome was running.

This leaves DeepSeek V3 with 3 proper tests and one fail and DeepSeek R1 with two correct tests and 2 stops working.

Final ideas

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

Also: How to use ChatGPT to compose code: What it does well and what it doesn’t

I wasn’t sure I ‘d have the ability to compose this article because, for the majority of the day, I got this mistake when trying to register:

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

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

DeepSeek appears to be excessively chatty 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 right in V3, however it could have been composed in a manner in which made it a lot more maintainable. It stopped working in R1.

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

I’m certainly impressed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which implies there’s certainly room for enhancement. I was disappointed with the results for the R1 design. Given the option, I ‘d still choose ChatGPT as my shows code assistant.

That said, for a new tool working on much lower facilities than the other tools, this might be an AI to see.

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

You can follow my daily job updates on social media. Be sure to subscribe to my weekly update 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.