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

DeepSeek took off into the world’s consciousness this past weekend. It sticks out for three powerful reasons:

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

2. It’s open source.

3. It utilizes significantly less infrastructure than the huge AI tools we have actually been taking a look at.

Also: Apple researchers reveal the secret sauce behind DeepSeek AI

Given the US government’s issues over TikTok and possible Chinese federal government participation in that 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 short article Why China’s DeepSeek might break our AI bubble.

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

Choose V3 for jobs requiring depth and accuracy (e.g., fixing sophisticated mathematics problems, generating complex code).

Choose R1 for latency-sensitive, high-volume applications (e.g., customer assistance automation, fundamental 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 short answer is this: excellent, but clearly not perfect. Let’s dig in.

Test 1: Writing a WordPress plugin

This test was in fact my first test of ChatGPT’s programming expertise, method back in the day. My better half 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 utilize)

Her needs were fairly easy. It needed to take in a list of names, one name per line. It then had to sort 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 challenge on an impulse. To my big surprise, it worked.

Ever since, it’s been my very first test for AIs when examining their programming skills. It needs the AI to know how to set up code for the WordPress structure and follow prompts clearly sufficient to develop both the interface and program reasoning.

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

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

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

So far, DeepSeek V3 and R1 both passed among four 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 only permitted dollars. So, the test includes giving the AI the regular that I composed and asking it to reword it to permit both dollars and cents

Also: My preferred ChatGPT feature simply got way more effective

Usually, this results in the AI creating some routine expression recognition 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 thinking before generating the code in R1 was also very long.

My most significant issue is that both models of the DeepSeek recognition ensures recognition as much as 2 decimal places, but if a huge number is entered (like 0.30000000000000004), the use of parseFloat does not have specific rounding understanding. The R1 design also 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, because R1 did present a very good list of tests to confirm versus:

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

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

Test 3: Finding a frustrating bug

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

The challenge is that the response isn’t obvious. Actually, the challenge is that there is an apparent answer, based upon the mistake message. But the obvious response is the incorrect response. This not only captured me, but it frequently captures some of the AIs.

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

Solving this bug requires understanding how specific API calls within WordPress work, being able 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 responses, bringing us to 3 out of 4 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 home run for V3? Let’s discover.

Test 4: Writing a script

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

I would have called this an unreasonable test because Keyboard Maestro is not a shows tool. But ChatGPT dealt with the test quickly, 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 task between directions to Keyboard Maestro and Chrome. It likewise had relatively weak knowledge of AppleScript, writing custom-made routines for AppleScript that are native to the language.

Weirdly, the R1 design stopped working 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 assumption 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 right tests and one stop working and DeepSeek R1 with two correct tests and two fails.

Final thoughts

I discovered that DeepSeek’s persistence on using a public cloud email address like gmail.com (instead of my regular email address with my business domain) was irritating. It likewise had a variety of responsiveness stops working that made doing these tests take longer than I would have liked.

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

I wasn’t sure I ‘d be able to write this article due to the fact that, for the majority of the day, I got this mistake when trying to sign up:

DeepSeek’s online services have actually just recently dealt with massive harmful attacks. To make sure continued service, registration is briefly restricted to +86 phone numbers. Existing users can visit as usual. Thanks for your understanding and support.

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

DeepSeek seems to be extremely chatty in terms of the code it produces. The AppleScript code in Test 4 was both wrong and excessively long. The regular expression code in Test 2 was proper in V3, however it could have been written 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 definitely amazed that DeepSeek V3 beat out Gemini, Copilot, and Meta. But it appears to be at the old GPT-3.5 level, which implies there’s definitely space for improvement. I was dissatisfied with the results for the R1 model. Given the option, I ‘d still select ChatGPT as my shows code helper.

That said, for a new tool operating on much lower infrastructure than the other tools, this might be an AI to view.

What do you believe? Have you tried DeepSeek? Are you using any AIs for shows support? Let us know in the comments below.

You can follow my day-to-day project updates on social networks. Make certain to register for 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.