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 previous weekend. It stands apart for 3 effective factors:

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 big AI tools we’ve been looking at.

Also: Apple scientists expose the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese government participation in that 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 rupture our AI bubble.

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

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

Choose R1 for latency-sensitive, high-volume applications (e.g., customer support automation, fundamental 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 using R1.

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

Test 1: Writing a WordPress plugin

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

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

Her requirements were fairly simple. 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 actually have time to code it for her, so I chose to offer the AI the obstacle on an impulse. 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 understand how to set up code for the WordPress structure and follow triggers clearly adequate to develop both the user interface and program reasoning.

Only about half of the AIs I’ve can totally pass this test. Now, however, we can add one more 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 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 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 unable to go into dollars and cents into a contribution entry field. As written, my code only allowed dollars. So, the test includes offering the AI the routine that I wrote and asking it to reword it to permit both dollars and cents

Also: My preferred ChatGPT feature just got method more effective

Usually, this results in the AI creating some routine expression validation code. DeepSeek did create code that works, although there is space for improvement. The code that DeepSeek V2 composed was unnecessarily long and repetitive while the thinking before producing the code in R1 was also long.

My most significant issue is that both models of the DeepSeek validation ensures recognition approximately 2 decimal places, however if a large number is gotten in (like 0.30000000000000004), using parseFloat doesn’t have explicit rounding knowledge. The R1 design likewise used JavaScript’s Number conversion without inspecting 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 provide a really good list of tests to verify against:

So here, we have a split choice. I’m providing the indicate DeepSeek V3 because neither of these concerns its code produced would trigger the program to break when run by a user and would produce the expected results. On the other hand, I have to offer a fail to R1 since if something that’s not a string in some way enters into the Number function, a crash will ensue.

And that gives DeepSeek V3 two triumphes of 4, however DeepSeek R1 just one win out of four up until now.

Test 3: Finding an annoying bug

This is a test produced when I had a very annoying bug that I had problem tracking down. Once once again, I decided to see if ChatGPT could 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 upon the mistake message. But the obvious answer is the incorrect response. This not only caught me, but it routinely captures some of the AIs.

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

Solving this bug requires comprehending how particular 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 four wins for V3 and two out of 4 wins for R1. That already puts DeepSeek ahead of Gemini, Copilot, Claude, and Meta.

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

Test 4: Writing a script

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

I would have called this an unfair test because Keyboard Maestro is not a mainstream programming tool. But ChatGPT dealt with the test quickly, comprehending precisely what part of the problem 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 design knew that it needed to split the task in between directions to Keyboard Maestro and Chrome. It likewise had fairly weak knowledge of AppleScript, composing custom regimens for AppleScript that are native 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 certainly not the case. It likewise 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 3 appropriate tests and one fail and DeepSeek R1 with 2 proper tests and two stops working.

Final ideas

I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (rather than my normal e-mail address with my corporate domain) was frustrating. 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 succeeds and what it doesn’t

I wasn’t sure I ‘d be able to compose this post due to the fact that, for most of the day, I got this error when attempting to register:

DeepSeek’s online services have recently dealt with large-scale harmful attacks. To ensure continued service, registration is temporarily restricted to +86 telephone number. Existing users can log in as normal. Thanks for your understanding and support.

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

DeepSeek appears to be overly loquacious in regards to the code it produces. The AppleScript code in Test 4 was both incorrect and excessively long. The regular expression code in Test 2 was appropriate in V3, but it could have been composed in a way that made it a lot more maintainable. It failed in R1.

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

I’m certainly amazed that DeepSeek V3 vanquished Gemini, Copilot, and Meta. But it seems at the old GPT-3.5 level, which indicates there’s certainly space for improvement. 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 new tool operating on much lower facilities than the other tools, this might be an AI to view.

What do you believe? Have you attempted DeepSeek? Are you utilizing any AIs for programs assistance? Let us understand in the comments listed below.

You can follow my daily project updates on social media. Be sure to subscribe to 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.